Train early or late self-correction auxiliary driving method, device, equipment and storage medium

Abstract

The present invention relates to the field of rail transit technology, and discloses a train early or late self-correction assisted driving method, device, equipment and storage medium. By comparing the running track planning information and real-time running information of the target train, it is judged whether the early or late self-correction triggering condition is met, and a train driving status list for each stop node in the running track is established. Considering the mutual influence between the target train and other associated trains at the same stop node, based on the principle of meeting the early or late deviation range of each train and the proportion of stop nodes on the way for the target train to perform self-correction, a train early or late self-correction auxiliary strategy is generated. Then, according to the speed limit requirements of the running track section and the acceleration and deceleration requirements of the train, a train early or late self-correction execution instruction is generated. The train early or late self-correction execution instruction is visualized and provided with driving assistance guidance using a human-computer interaction interface, thereby improving the accuracy of train early or late correction operation, judgment speed and passenger experience.

Description

Technical Field

[0001] The present invention relates to the field of rail transit technology, and in particular to a method, device, equipment and storage medium for self-correcting early or late train driving assistance. Background Art

[0002] With the rapid development of the railway passenger transport industry, the punctuality and efficiency of train operations have become important indicators for measuring the quality of railway operation services. During the operation of trains, due to various unforeseen factors such as weather, passenger volume, equipment failure, etc., trains often run early or late. When early or late, train drivers need to adjust their operating strategies in a timely manner to meet the requirements of the established timetable. Existing self-correction assisted driving methods for trains have the following limitations:

[0003] (1) Traditional train operation correction mainly relies on the driver's driving experience. It is difficult to make effective corrections in a short time based on manual experience. The driver needs to make continuous adjustments based on the actual situation, which makes it difficult for the train to meet the timetable requirements in time, reduces operating efficiency, and leads to poor passenger experience.

[0004] (2) When the running track has multiple stop nodes, the self-correction of the train's early or late time at a certain stop node does not take into account the influence of the associated trains at other stop nodes on the entire running track, resulting in contradictions in the early or late self-correction auxiliary driving strategies between the target train and the associated trains, which reduces the efficiency and effect of the self-correction.

[0005] Therefore, how to improve the accuracy and efficiency of the train's self-correction of early and late running, improve the accuracy of the train's early and late running correction operation, the judgment speed, and the experience of passengers and waiting passengers is a technical problem that needs to be solved urgently. Summary of the Invention

[0006] The present invention provides a method, device, equipment and storage medium for self-correction of train early or late running assisted driving, aiming to solve at least one of the above-mentioned technical problems.

[0007] To achieve the above object, the present invention provides a train self-correction assisted driving method, comprising the following steps:

[0008] Acquiring running track planning information of the target train; wherein the running track planning information includes the running track and the stop information of each stop node in the running track;

[0009] Determine whether the target train meets the early or late self-correction triggering condition based on the real-time operation information of the target train and the operation trajectory planning information, and if so, establish a train operation status list for each stop node in the operation trajectory;

[0010] Extract the associated train running status information recorded in each self-correction cycle from the train running status list of each stop node. Based on the associated train running status information and the real-time operation information of the target vehicle, the train early or late self-correction auxiliary strategy is generated based on the principle of meeting the early or late deviation range of each train and the proportion of stop nodes on the way for the target train to perform self-correction.

[0011] The train early or late self-correction auxiliary strategy is sent to the auxiliary driving terminal of the target train, driving the auxiliary driving terminal to generate a train early or late self-correction execution instruction based on the speed limit requirements of the running track section and the train acceleration and deceleration requirements, so that the driver of the target train can perform the auxiliary driving action of the target train according to the train early or late self-correction execution instruction generated by the auxiliary driving terminal.

[0012] Optionally, the step of obtaining the target vehicle's trajectory planning information specifically includes:

[0013] Accessing a train operation planning database; wherein the train operation planning database stores a plurality of train operation schedules and a train identifier to which each train operation schedule belongs;

[0014] Obtaining a target train identifier of a target train, matching a target train schedule of the target train in the train operation planning database based on the target train identifier, and extracting a run start time, a run arrival time, and a number of en route stop nodes recorded in the target train schedule;

[0015] According to the time interval between two adjacent stop nodes and the length of stay at each stop node of trains with the same historical train schedule, the running track of the target train schedule and the stay information of each stop node in the running track are determined to generate the running track planning information.

[0016] Optionally, based on the real-time operation information of the target train and the operation trajectory planning information, it is determined whether the target train meets the early or late self-correction triggering condition. If so, a step of establishing a train operation status list for each stop node in the operation trajectory specifically includes:

[0017] Acquiring real-time operation information of the target train; wherein the real-time operation information includes time parameters, position parameters and speed parameters of the target train;

[0018] Predicting the actual arrival time of the target train at each stop in the running trajectory based on the time parameter, the position parameter, and the speed parameter; determining whether the difference between the actual arrival time and the estimated arrival time of the stop information in the running trajectory planning information exceeds a preset value, and if so, determining that the target train meets the early or late self-correction triggering condition;

[0019] When the target train meets the early or late self-correction triggering condition, a train running status list is established for each stop node passed through in the running track.

[0020] Optionally, when the target train meets the early or late self-correction triggering condition, the step of establishing a train running status list for each stop node passed in the running trajectory specifically includes:

[0021] Accessing a train operation planning database, matching a plurality of associated train operation schedules stored in the train operation planning database whose operation trajectory passes through any of the stop nodes;

[0022] Acquire the real-time operation information of the associated train corresponding to each of the associated train operation shifts, and determine the associated arrival time of each associated train at the stop node along the way based on the real-time operation information;

[0023] The associated arrival time of the associated train at each en route stop node and the actual arrival time of the target train are used as the train running status, and a train running status list for each en route stop node in the running track is constructed.

[0024] Optionally, the associated train running status information recorded in each self-correction cycle in the train running status list of each stop node is extracted. Based on the associated train running status information and the real-time operation information of the target vehicle, the train early or late self-correction auxiliary strategy steps are generated based on the principle of meeting the early or late deviation range of each train and the proportion of stop nodes on the way for the target train to perform self-correction. Specifically, the steps include:

[0025] Extracting train arrival time information stored in the train travel status list of each stop node along the way; wherein the train arrival time information includes the associated arrival time of the associated train at each stop node along the way and the actual arrival time of the target train;

[0026] The first constraint condition is that the time difference between the adjusted arrival time and the expected arrival time at each stop node after the target train performs self-correction for early or late trains is within the train early or late deviation range corresponding to each stop node. The second constraint condition is that the time difference between the associated arrival time of the associated train at each stop node and the adjusted arrival time of the target train at the stop node after the target train performs self-correction for early or late trains is greater than the train arrival safety interval corresponding to each stop node. With the goal of minimizing the proportion of stop nodes where the target train performs self-correction, the adjusted arrival time of the target train at each stop node on the running trajectory is optimized.

[0027] According to the adjusted arrival time of the target train at each stop node along the running track, a self-correction auxiliary strategy for the train is generated.

[0028] Optionally, the train early or late self-correction auxiliary strategy is sent to the auxiliary driving terminal of the target train, driving the auxiliary driving terminal to generate a train early or late self-correction execution instruction based on the speed limit requirements of the running track section and the train acceleration and deceleration requirements, so that the driver of the target train executes the auxiliary driving action steps of the target train according to the train early or late self-correction execution instruction generated by the auxiliary driving terminal, specifically including:

[0029] The train early or late self-correction auxiliary strategy is sent to the auxiliary driving terminal of the target train; wherein the train early or late self-correction auxiliary strategy includes the adjusted arrival time of each stop node along the running track of the target train;

[0030] Obtaining a track section speed limit range for each track point in the running track of the running track planning information of the target train, taking the running speed of each track point in the remaining section of the running track of the target train within the track section speed limit range of the corresponding track point as a first constraint condition, taking the running speed of each track point in the remaining section of the running track of the target train meeting the train acceleration and deceleration requirements as a second constraint condition, and taking the time difference between the execution arrival time and the adjusted arrival time of each stop node passed by the target train on the running track as the goal, generating a train early or late self-correction execution instruction including the running speed of each track point;

[0031] The driver of the target train executes the assisted driving action of the target train according to the train early or late self-correction execution instruction generated by the assisted driving terminal.

[0032] Optionally, the driver of the target train executes the assisted driving action steps of the target train according to the train early or late self-correction execution instruction generated by the assisted driving terminal, specifically including:

[0033] The assisted driving terminal constructs an assisted driving speed curve for the target train based on the generated train early or late self-correction execution instructions, displays the assisted driving speed curve through a visual human-computer interaction interface, displays the real-time operating speed and the corrected operating speed, and outputs assisted operation instructions;

[0034] The driver of the target train performs the assisted driving action of the target train according to the assisted driving speed curve and the assisted operation instruction generated by the assisted driving terminal.

[0035] In order to achieve the above-mentioned object, the present invention further provides a train early or late self-correction auxiliary driving device, comprising:

[0036] An acquisition module is used to acquire the running track planning information of the target train; wherein the running track planning information includes the running track and the stop information of each stop node in the running track;

[0037] a judgment module, configured to judge whether the target train meets the early or late self-correction triggering condition based on the real-time operation information of the target train and the operation trajectory planning information, and if so, to establish a train operation status list for each stop node in the operation trajectory;

[0038] A generation module is used to extract the associated train running status information recorded in each self-correction cycle in the train running status list of each stop node, and generate a train early or late self-correction auxiliary strategy based on the associated train running status information and the real-time operation information of the target vehicle, in accordance with the principle of meeting the early or late deviation range of each train and the proportion of stop nodes on the way for the target train to perform self-correction;

[0039] The execution module is used to send the train early or late self-correction auxiliary strategy to the auxiliary driving terminal of the target train, drive the auxiliary driving terminal to generate a train early or late self-correction execution instruction based on the speed limit requirements of the running track section and the train acceleration and deceleration requirements, so that the driver of the target train can execute the auxiliary driving action of the target train according to the train early or late self-correction execution instruction generated by the auxiliary driving terminal.

[0040] In addition, in order to achieve the above-mentioned purpose, the present invention also provides a train early or late self-correction assisted driving device, which includes: a memory, a processor, and a train early or late self-correction assisted driving program stored on the memory and runnable on the processor. When the train early or late self-correction assisted driving program is executed by the processor, the steps of the train early or late self-correction assisted driving method described above are implemented.

[0041] In addition, in order to achieve the above-mentioned purpose, the present invention also provides a storage medium, on which a train early or late self-correction assisted driving program is stored. When the train early or late self-correction assisted driving program is executed by a processor, the steps of the above-mentioned train early or late self-correction assisted driving method are implemented.

[0042] The beneficial effects of the present invention are as follows: a method, device, equipment and storage medium for assisting driving of self-correction of train early or late are proposed, by obtaining the running track planning information of the target train, judging whether the target train meets the triggering condition of self-correction of early or late according to the running track planning information and real-time running information of the target train, after which a list of train driving conditions for each stop node in the running track is established, taking into account the mutual influence between the target train and other associated trains at the same stop node, and generating an assisting strategy for self-correction of train early or late based on the principle of satisfying the early or late deviation range of each train and the proportion of stop nodes at which the target train performs self-correction, and then generating an execution instruction for self-correction of train early or late according to the speed limit requirements of the running track section and the acceleration and deceleration requirements of the train, and using a human-computer interaction interface to visualize the execution instruction for self-correction of train early or late and provide driving assistance guidance, thereby improving the accuracy and efficiency of self-correction of train early or late, and improving the accuracy of operation, judgment speed and experience of passengers and waiting passengers in correcting early or late train operation. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] Figure 1 A schematic diagram of the device structure of the hardware operating environment involved in the embodiment of the present invention;

[0044] Figure 2 This is a flow chart of an embodiment of a method for self-correcting early or late train driving assistance according to the present invention;

[0045] Figure 3 The present invention is a structural block diagram of a train self-correction auxiliary driving device according to an embodiment of the present invention.

[0046] The purpose, features and advantages of the present invention will be further described with reference to the accompanying drawings and in conjunction with the embodiments. DETAILED DESCRIPTION

[0047] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0048] like Figure 1 As shown, Figure 1 It is a schematic diagram of the device structure of the hardware operating environment involved in the embodiment of the present invention.

[0049] like Figure 1As shown, the device may include: a processor 1001, such as a CPU, a communication bus 1002, a user interface 1003, a network interface 1004, and a memory 1005. Among them, the communication bus 1002 is used to realize the connection and communication between these components. The user interface 1003 may include a display screen (Display), an input unit such as a keyboard (Keyboard), and the optional user interface 1003 may also include a standard wired interface and a wireless interface. The network interface 1004 may optionally include a standard wired interface and a wireless interface (such as a WI-FI interface). The memory 1005 may be a high-speed RAM memory or a stable memory (non-volatile memory), such as a disk memory. The memory 1005 may optionally be a storage device independent of the aforementioned processor 1001.

[0050] Those skilled in the art will understand that Figure 1 The structure of the device shown in the figure does not constitute a limitation of the device, and the device may include more or fewer components than shown in the figure, or combine certain components, or arrange the components differently.

[0051] like Figure 1 As shown, the memory 1005 as a computer storage medium may include an operating system, a network communication module, a user interface module, and a train early or late self-correction auxiliary driving program.

[0052] exist Figure 1 In the terminal shown, the network interface 1004 is mainly used to connect to the backend server and communicate data with the backend server; the user interface 1003 is mainly used to connect to the client (user end) and communicate data with the client; and the processor 1001 can be used to call the train early or late self-correction auxiliary driving program stored in the memory 1005 and perform the following operations:

[0053] Acquiring running track planning information of the target train; wherein the running track planning information includes the running track and the stop information of each stop node in the running track;

[0054] Determine whether the target train meets the early or late self-correction triggering condition based on the real-time operation information of the target train and the operation trajectory planning information, and if so, establish a train operation status list for each stop node in the operation trajectory;

[0055] Extract the associated train running status information recorded in each self-correction cycle from the train running status list of each stop node. Based on the associated train running status information and the real-time operation information of the target vehicle, the train early or late self-correction auxiliary strategy is generated based on the principle of meeting the early or late deviation range of each train and the proportion of stop nodes on the way for the target train to perform self-correction.

[0056] The train early or late self-correction auxiliary strategy is sent to the auxiliary driving terminal of the target train, driving the auxiliary driving terminal to generate a train early or late self-correction execution instruction based on the speed limit requirements of the running track section and the train acceleration and deceleration requirements, so that the driver of the target train can perform the auxiliary driving action of the target train according to the train early or late self-correction execution instruction generated by the auxiliary driving terminal.

[0057] The specific embodiments of the present invention applied to the device are basically the same as the embodiments of the following train early or late self-correction assisted driving method, and will not be described in detail here.

[0058] The embodiment of the present invention provides a method for self-correcting the early or late running of a train by assisting driving. Figure 2 , Figure 2 The figure is a flow chart of an embodiment of the method for self-correcting early or late train driving according to the present invention.

[0059] In this embodiment, the train self-correction assisted driving method includes the following steps:

[0060] S100: Acquire running track planning information of a target train; wherein the running track planning information includes the running track and the stop information of each stop node in the running track;

[0061] S200: Determine whether the target train meets the early or late self-correction triggering condition based on the real-time operation information of the target train and the operation trajectory planning information; if so, establish a train operation status list for each stop node in the operation trajectory;

[0062] S300: Extracting the associated train running status information recorded in each self-correction cycle in the train running status list of each stop node, generating a train early or late self-correction auxiliary strategy based on the associated train running status information and the real-time operation information of the target vehicle, and satisfying the early or late deviation range of each train and the proportion of stop nodes on the way for the target train to perform self-correction;

[0063] S400: Send the train early or late self-correction auxiliary strategy to the auxiliary driving terminal of the target train, drive the auxiliary driving terminal to generate a train early or late self-correction execution instruction based on the speed limit requirements of the running track section and the train acceleration and deceleration requirements, so that the driver of the target train can perform the auxiliary driving action of the target train according to the train early or late self-correction execution instruction generated by the auxiliary driving terminal.

[0064] It should be noted that the existing train early or late self-correction assisted driving method has the following limitations: (1) Traditional early or late correction operations for train operation mainly rely on the driver's driving experience. It is difficult to make effective corrections in a short period of time based on manual experience. The driver needs to make continuous adjustments based on the actual situation, which makes it difficult for the train to meet the established requirements of the timetable in a timely manner, reduces operating efficiency, and provides a poor passenger experience. (2) When the running track has multiple stop nodes, the early or late self-correction of a certain stop node does not take into account the influence of the associated trains of other stop nodes on the entire running track, resulting in contradictions in the early or late self-correction assisted driving strategies between the target train and the associated trains, reducing the efficiency and effect of self-correction.

[0065] In order to solve the above problems, this embodiment obtains the running trajectory planning information of the target train, and determines whether the target train meets the early or late self-correction triggering conditions based on the running trajectory planning information and real-time running information of the target train. After that, a train driving status list for each stop node in the running trajectory is established, and the mutual influence between the target train and other associated trains at the same stop node is considered. The train early or late self-correction auxiliary strategy is generated based on the principle of meeting the early or late deviation range of each train and the proportion of stop nodes where the target train performs self-correction. Then, according to the speed limit requirements of the running trajectory section and the train acceleration and deceleration requirements, the train early or late self-correction execution instructions are generated, and the train early or late self-correction execution instructions are visualized and provided with driving assistance guidance using a human-computer interaction interface, thereby improving the accuracy and efficiency of the train early or late self-correction, and improving the accuracy of the train operation early or late correction, the judgment speed, and the experience of passengers and waiting passengers.

[0066] In a preferred embodiment, the step of obtaining the target vehicle's trajectory planning information specifically includes:

[0067] S110: Accessing a train operation planning database; wherein the train operation planning database stores a plurality of train operation schedules and a train identifier to which each train operation schedule belongs;

[0068] S120: Obtain a target train identifier of a target train, match a target train schedule of the target train in the train operation planning database based on the target train identifier, and extract the run start time, run arrival time, and a number of stop nodes along the way recorded in the target train schedule;

[0069] S130: According to the time interval between two adjacent stop nodes and the duration of stay at each stop node of the trains with the same historical train schedule, the running track of the target train schedule and the stay information of each stop node in the running track are determined to generate running track planning information.

[0070] In this embodiment, by accessing the train operation planning database, matching the target train operation schedule in the train operation planning database according to the identification of the target train, extracting the target train's operation start time, operation arrival time and several stop nodes along the way, and then calculating the arrival time and departure time of the current target train at each stop node along the operation trajectory (for example, the average time under non-abnormal circumstances) according to the time when the same train operation schedule passes through each stop node in history, and calculating the stop information to generate the final operation trajectory planning information.

[0071] In a preferred embodiment, based on the real-time operation information of the target train and the operation trajectory planning information, it is determined whether the target train meets the early or late self-correction triggering condition. If so, a train operation status list for each stop node in the operation trajectory is established, specifically comprising:

[0072] S210: Acquire real-time operation information of the target train; wherein the real-time operation information includes time parameters, position parameters, and speed parameters of the target train;

[0073] S220: Predicting the actual arrival time of the target train at each stop in the running trajectory based on the time parameter, the position parameter, and the speed parameter; determining whether the difference between the actual arrival time and the estimated arrival time of the stop information in the running trajectory planning information exceeds a preset value; if so, determining that the target train meets the early or late self-correction triggering condition;

[0074] S230: When the target train meets the early or late self-correction triggering condition, a train running status list is established for each stop node passed through in the running trajectory.

[0075] Furthermore, when the target train meets the early or late self-correction triggering condition, a train running status list step is established for each stop node in the running trajectory, specifically including:

[0076] S231: Accessing a train operation planning database, matching a plurality of associated train operation schedules stored in the train operation planning database, wherein the operation trajectory passes through any of the stop nodes;

[0077] S232: Acquire real-time operation information of the associated train corresponding to each associated train operation schedule, and determine the associated arrival time of each associated train at the stop node along the way based on the real-time operation information;

[0078] S233: Taking the associated arrival time of the associated train at each stop node along the way and the actual arrival time of the target train as the train running status, a train running status list for each stop node along the running track is constructed.

[0079] In this embodiment, after obtaining the running trajectory planning information of the target train, according to the real-time running information of the target train and the running trajectory planning information, the early or late judgment is made based on whether the difference between the actual arrival time and the predicted arrival time (the predicted arrival time is the arrival time obtained by inferring the real-time running information of the train according to the current running speed change curve) of all the way stop nodes in the running trajectory exceeds the preset value. If there is such a situation for at least one way stop node, it indicates that the target train is early or late. At this time, by obtaining the real-time running information of other associated trains of the remaining way stop nodes that the target vehicle has not passed in the running trajectory, a train travel status list for each way stop node in the running trajectory is constructed. The train travel status list stores the actual arrival time of the target train corresponding to the way stop node and the associated arrival time of the associated train, which is used for the subsequent execution of the early or late self-correction action of the target train.

[0080] In a preferred embodiment, the associated train running status information recorded in each self-correction cycle in the train running status list of each stop node is extracted. Based on the associated train running status information and the real-time operation information of the target vehicle, the train early or late self-correction auxiliary strategy steps are generated based on the principle of meeting the early or late deviation range of each train and the proportion of stop nodes on the way for the target train to perform self-correction. Specifically, the steps include:

[0081] S310: Extracting train arrival time information stored in the train travel status list of each stop node along the way; wherein the train arrival time information includes the associated arrival time of the associated train at each stop node along the way and the actual arrival time of the target train;

[0082] S320: The first constraint condition is that the time difference between the adjusted arrival time and the expected arrival time at each stop node after the target train performs self-correction for early or late trains is within the train early or late deviation range corresponding to each stop node; the second constraint condition is that the time difference between the associated arrival time of the associated train at each stop node and the adjusted arrival time of the target train at the stop node after the target train performs self-correction for early or late trains is greater than the train arrival safety interval corresponding to each stop node; with the goal of minimizing the proportion of stop nodes for which the target train performs self-correction, the adjusted arrival time of the target train at each stop node on the running trajectory is optimized;

[0083] S330: Generate a train early or late self-correction auxiliary strategy based on the adjusted arrival time of each stop node along the running track of the target train.

[0084] In this embodiment, after establishing a list of train travel conditions for each stop node in the running trajectory, the mutual influence between the target train and other associated trains at the same stop node is taken into consideration, and the train early or late self-correction auxiliary strategy is generated based on the principle of meeting the early or late deviation range of each train and the proportion of stop nodes at which the target train performs self-correction. The early or late self-correction auxiliary strategy provides the adjusted arrival time of the target train at each stop node. By running according to the adjusted arrival time, the early or late self-correction requirements of the target train can be met.

[0085] On this basis, the train early or late self-correction auxiliary strategy is sent to the auxiliary driving terminal of the target train, driving the auxiliary driving terminal to generate a train early or late self-correction execution instruction based on the speed limit requirements of the running track section and the train acceleration and deceleration requirements, so that the driver of the target train executes the auxiliary driving action steps of the target train according to the train early or late self-correction execution instruction generated by the auxiliary driving terminal, specifically including:

[0086] S410: Sending the train early or late self-correction auxiliary strategy to the auxiliary driving terminal of the target train; wherein the train early or late self-correction auxiliary strategy includes an adjusted arrival time of the target train at each stop node along the running track;

[0087] S420: obtaining a track section speed limit range for each track point in the running track of the running track planning information of the target train, taking the running speed of each track point in the remaining section of the running track of the target train within the track section speed limit range of the corresponding track point as a first constraint condition, taking the running speed of each track point in the remaining section of the running track of the target train meeting the train acceleration and deceleration requirements as a second constraint condition, taking the time difference between the execution arrival time and the adjusted arrival time of each stop node passed by the target train on the running track as the minimum as the goal, and generating a train early or late self-correction execution instruction including the running speed of each track point;

[0088] S430: The driver of the target train executes the assisted driving action of the target train according to the train early or late self-correction execution instruction generated by the assisted driving terminal.

[0089] Furthermore, the driver of the target train executes the assisted driving action steps of the target train according to the train early or late self-correction execution instruction generated by the assisted driving terminal, specifically including:

[0090] S431: The assisted driving terminal constructs an assisted driving speed curve for the target train based on the generated train early or late self-correction execution instruction, displays the assisted driving speed curve through a visual human-computer interaction interface, displays the real-time running speed and the corrected running speed, and outputs an assisted operation instruction;

[0091] S432: The driver of the target train performs the assisted driving action of the target train according to the assisted driving speed curve and the assisted operation instruction generated by the assisted driving terminal.

[0092] In this embodiment, after obtaining the train early or late self-correction auxiliary strategy, the train early or late self-correction execution instructions are generated according to the speed limit requirements of the running track section and the train acceleration and deceleration requirements (such as acceleration limit), and the train early or late self-correction execution instructions are visualized and provided with driving assistance guidance using a human-computer interaction interface, thereby improving the accuracy and efficiency of the train early or late self-correction, and improving the accuracy of the train operation early or late correction, the judgment speed, and the experience of passengers and waiting passengers.

[0093] like Figure 3 As shown, the train early or late self-correction auxiliary driving device proposed in the embodiment of the present invention includes:

[0094] An acquisition module 10 is configured to acquire the running track planning information of the target train; wherein the running track planning information includes the running track and the stop information of each stop node in the running track;

[0095] A judgment module 20 is used to judge whether the target train meets the early or late self-correction triggering condition based on the real-time operation information of the target train and the operation trajectory planning information, and if so, to establish a train operation status list for each stop node in the operation trajectory;

[0096] A generation module 30 is configured to extract the associated train travel status information recorded in the train travel status list of each enroute stop node during each self-correction cycle, and generate a train early or late self-correction auxiliary strategy based on the associated train travel status information and the real-time operation information of the target vehicle, in accordance with the principle of satisfying the early or late deviation range of each train and the proportion of enroute stop nodes at which the target train performs self-correction;

[0097] The execution module 40 is used to send the train early or late self-correction auxiliary strategy to the auxiliary driving terminal of the target train, drive the auxiliary driving terminal to generate a train early or late self-correction execution instruction based on the speed limit requirements of the running track section and the train acceleration and deceleration requirements, so that the driver of the target train can execute the auxiliary driving action of the target train according to the train early or late self-correction execution instruction generated by the auxiliary driving terminal.

[0098] Other embodiments or specific implementation methods of the train early or late self-correction auxiliary driving device of the present invention can refer to the above-mentioned method embodiments and will not be repeated here.

[0099] In addition, the present invention also proposes a train early or late self-correction assisted driving device, which includes: a memory, a processor, and a train early or late self-correction assisted driving program stored on the memory and runnable on the processor. When the train early or late self-correction assisted driving program is executed by the processor, the steps of the train early or late self-correction assisted driving method described above are implemented.

[0100] The specific implementation of the train early or late self-correction assisted driving equipment of the present application is basically the same as the various embodiments of the train early or late self-correction assisted driving method mentioned above, and will not be repeated here.

[0101] In addition, the present invention also proposes a readable storage medium, which includes a computer-readable storage medium on which a train early or late self-correction auxiliary driving program is stored. The readable storage medium can be Figure 1 The memory 1005 in the terminal may also be at least one of a ROM (Read-Only Memory) / RAM (Random Access Memory), a magnetic disk, and an optical disk. The readable storage medium includes a number of instructions for enabling a train early or late self-correction assisted driving device with a processor to execute the train early or late self-correction assisted driving method described in various embodiments of the present invention.

[0102] The specific implementation methods in the readable storage medium of this application are basically the same as the various embodiments of the above-mentioned train early or late self-correction assisted driving method, and will not be repeated here.

[0103] It should be understood that, in the description of this specification, reference to terms such as "one embodiment," "another embodiment," "other embodiments," or "first to Nth embodiments" means that the specific features, structures, materials, or characteristics described in conjunction with that embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials, or characteristics described may be combined in any appropriate manner in any one or more embodiments or examples.

[0104] It should be noted that, in this document, the terms "comprises," "includes," or any other variations thereof are intended to encompass non-exclusive inclusion, such that a process, method, article, or system comprising a series of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, method, article, or system. In the absence of further limitations, an element defined by the phrase "comprising a ..." does not exclude the presence of other identical elements in the process, method, article, or system comprising the element.

[0105] The serial numbers of the above embodiments of the present invention are for description only and do not represent the advantages or disadvantages of the embodiments.

[0106] Through the description of the above embodiments, those skilled in the art can clearly understand that the above-mentioned embodiment methods can be implemented by means of software plus the necessary general hardware platform, and of course can also be implemented by hardware, but in many cases the former is a better embodiment. Based on this understanding, the technical solution of the present invention is essentially or the part that contributes to the prior art can be embodied in the form of a software product, which is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) as described above, and includes a number of instructions for enabling a terminal device (which can be a mobile phone, computer, server, or network device, etc.) to execute the methods described in each embodiment of the present invention.

[0107] The above are only preferred embodiments of the present invention and are not intended to limit the patent scope of the present invention. Any equivalent structure or equivalent process transformation made using the contents of the present invention description and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present invention.

Claims

1. A train self-correction assisted driving method, characterized in that: The following steps are involved: Acquiring running track planning information of the target train; wherein the running track planning information includes the running track and the stop information of each stop node in the running track; Determine whether the target train meets the early or late self-correction triggering condition based on the real-time operation information of the target train and the operation trajectory planning information, and if so, establish a train operation status list for each stop node in the operation trajectory; The step of determining whether the target train meets the early or late self-correction triggering condition specifically includes: determining whether the difference between the actual arrival time of the target train at any stop node in the running track and the estimated arrival time of the stop information in the running track planning information exceeds a preset value; if so, determining that the target train meets the early or late self-correction triggering condition; Extract the associated train running status information recorded in each self-correction cycle from the train running status list of each stop node. Based on the associated train running status information and the real-time operation information of the target vehicle, and in accordance with the principle of meeting the early or late deviation range of each train and the proportion of stop nodes where the target train performs self-correction, generate a train early or late self-correction auxiliary strategy. Specifically, it includes: Extracting train arrival time information stored in the train travel status list of each stop node along the way; wherein the train arrival time information includes the associated arrival time of the associated train at each stop node along the way and the actual arrival time of the target train; The first constraint condition is that the time difference between the adjusted arrival time and the expected arrival time at each stop node after the target train performs self-correction for early or late trains is within the train early or late deviation range corresponding to each stop node. The second constraint condition is that the time difference between the associated arrival time of the associated train at each stop node and the adjusted arrival time of the target train at the stop node after the target train performs self-correction for early or late trains is greater than the train arrival safety interval corresponding to each stop node. With the goal of minimizing the proportion of stop nodes where the target train performs self-correction, the adjusted arrival time of the target train at each stop node on the running trajectory is optimized. Generate a self-correction auxiliary strategy for train early or late arrival based on the adjusted arrival time of each stop node along the target train's trajectory; The train early or late self-correction auxiliary strategy is sent to the auxiliary driving terminal of the target train, driving the auxiliary driving terminal to generate a train early or late self-correction execution instruction based on the speed limit requirements of the running track section and the train acceleration and deceleration requirements, so that the driver of the target train executes the auxiliary driving action of the target train according to the train early or late self-correction execution instruction generated by the auxiliary driving terminal; specifically including: The train early or late self-correction auxiliary strategy is sent to the auxiliary driving terminal of the target train; wherein the train early or late self-correction auxiliary strategy includes the adjusted arrival time of each stop node along the running track of the target train; Obtaining a track section speed limit range for each track point in the running track of the running track planning information of the target train, taking the running speed of each track point in the remaining section of the running track of the target train within the track section speed limit range of the corresponding track point as a first constraint condition, taking the running speed of each track point in the remaining section of the running track of the target train meeting the train acceleration and deceleration requirements as a second constraint condition, and taking the time difference between the execution arrival time and the adjusted arrival time of each stop node passed by the target train on the running track as the goal, generating a train early or late self-correction execution instruction including the running speed of each track point; The driver of the target train executes the assisted driving action of the target train according to the train early or late self-correction execution instruction generated by the assisted driving terminal.

2. The train self-correction assisted driving method according to claim 1, characterized in that: The steps for obtaining the target vehicle's trajectory planning information include: Accessing a train operation planning database; wherein the train operation planning database stores a plurality of train operation schedules and a train identifier to which each train operation schedule belongs; Obtaining a target train identifier of a target train, matching a target train schedule of the target train in the train operation planning database based on the target train identifier, and extracting a run start time, a run arrival time, and a number of en route stop nodes recorded in the target train schedule; According to the time interval between two adjacent stop nodes and the length of stay at each stop node of trains with the same historical train schedule, the running track of the target train schedule and the stay information of each stop node in the running track are determined to generate the running track planning information.

3. The train early or late self-correction assisted driving method according to claim 2, characterized in that: According to the real-time operation information of the target train and the operation trajectory planning information, it is determined whether the target train meets the early or late self-correction triggering condition. If so, a train operation status list for each stop node in the operation trajectory is established, specifically comprising: Acquiring real-time operation information of the target train; wherein the real-time operation information includes time parameters, position parameters and speed parameters of the target train; Predicting the actual arrival time of the target train at each stop in the running trajectory based on the time parameter, the position parameter, and the speed parameter; determining whether the difference between the actual arrival time and the estimated arrival time of the stop information in the running trajectory planning information exceeds a preset value, and if so, determining that the target train meets the early or late self-correction triggering condition; When the target train meets the early or late self-correction triggering condition, a train running status list is established for each stop node passed through in the running track.

4. The train early or late self-correction assisted driving method according to claim 3, characterized in that: When the target train meets the early or late self-correction triggering condition, the steps of establishing a train running status list for each stop node in the running trajectory specifically include: Accessing a train operation planning database, matching a plurality of associated train operation schedules stored in the train operation planning database whose operation trajectory passes through any of the stop nodes; Acquire the real-time operation information of the associated train corresponding to each of the associated train operation shifts, and determine the associated arrival time of each associated train at the stop node along the way based on the real-time operation information; The associated arrival time of the associated train at each en route stop node and the actual arrival time of the target train are used as the train running status, and a train running status list for each en route stop node in the running track is constructed.

5. The train early or late self-correction assisted driving method according to claim 1, characterized in that: The driver of the target train executes the assisted driving action steps of the target train according to the train early or late self-correction execution instruction generated by the assisted driving terminal, specifically including: The assisted driving terminal constructs an assisted driving speed curve for the target train based on the generated train early or late self-correction execution instructions, displays the assisted driving speed curve through a visual human-computer interaction interface, displays the real-time operating speed and the corrected operating speed, and outputs assisted operation instructions; The driver of the target train performs the assisted driving action of the target train according to the assisted driving speed curve and the assisted operation instruction generated by the assisted driving terminal.

6. A train early or late self-correction auxiliary driving device, characterized in that: include: An acquisition module is used to obtain the running track planning information of the target train; wherein the running track planning information includes the running track and the stop information of each stop node in the running track; a judgment module, configured to judge whether the target train meets the early or late self-correction triggering condition based on the real-time operation information of the target train and the operation trajectory planning information, and if so, to establish a train operation status list for each stop node in the operation trajectory; The step of determining whether the target train meets the early or late self-correction triggering condition specifically includes: determining whether the difference between the actual arrival time of the target train at any stop node in the running track and the estimated arrival time of the stop information in the running track planning information exceeds a preset value; if so, determining that the target train meets the early or late self-correction triggering condition; The generation module is used to extract the associated train running status information recorded in each self-correction cycle in the train running status list of each stop node, and generate a train early or late self-correction auxiliary strategy based on the associated train running status information and the real-time operation information of the target vehicle, in accordance with the principle of meeting the early or late deviation range of each train and the proportion of stop nodes on the way for the target train to perform self-correction; specifically, it includes: Extracting train arrival time information stored in the train travel status list of each stop node along the way; wherein the train arrival time information includes the associated arrival time of the associated train at each stop node along the way and the actual arrival time of the target train; The first constraint condition is that the time difference between the adjusted arrival time and the expected arrival time at each stop node after the target train performs self-correction for early or late trains is within the train early or late deviation range corresponding to each stop node. The second constraint condition is that the time difference between the associated arrival time of the associated train at each stop node and the adjusted arrival time of the target train at the stop node after the target train performs self-correction for early or late trains is greater than the train arrival safety interval corresponding to each stop node. With the goal of minimizing the proportion of stop nodes where the target train performs self-correction, the adjusted arrival time of the target train at each stop node on the running trajectory is optimized. Generate a self-correction auxiliary strategy for train early or late arrival based on the adjusted arrival time of each stop node along the target train's trajectory; An execution module is used to send the train early or late self-correction auxiliary strategy to the auxiliary driving terminal of the target train, drive the auxiliary driving terminal to generate a train early or late self-correction execution instruction based on the speed limit requirements of the running track section and the train acceleration and deceleration requirements, so that the driver of the target train executes the auxiliary driving action of the target train according to the train early or late self-correction execution instruction generated by the auxiliary driving terminal; specifically including: The train early or late self-correction auxiliary strategy is sent to the auxiliary driving terminal of the target train; wherein the train early or late self-correction auxiliary strategy includes the adjusted arrival time of each stop node along the running track of the target train; Obtaining a track section speed limit range for each track point in the running track of the running track planning information of the target train, taking the running speed of each track point in the remaining section of the running track of the target train within the track section speed limit range of the corresponding track point as a first constraint condition, taking the running speed of each track point in the remaining section of the running track of the target train meeting the train acceleration and deceleration requirements as a second constraint condition, and taking the time difference between the execution arrival time and the adjusted arrival time of each stop node passed by the target train on the running track as the goal, generating a train early or late self-correction execution instruction including the running speed of each track point; The driver of the target train executes the assisted driving action of the target train according to the train early or late self-correction execution instruction generated by the assisted driving terminal.

7. A train self-correction auxiliary driving device, characterized by: The train early or late self-correction assisted driving device includes: a memory, a processor, and a train early or late self-correction assisted driving program stored in the memory and runnable on the processor. When the train early or late self-correction assisted driving program is executed by the processor, the steps of the train early or late self-correction assisted driving method as described in any one of claims 1 to 5 are implemented.

8. A storage medium, characterized in that: The storage medium stores a train early or late self-correction assisted driving program, which, when executed by the processor, implements the steps of the train early or late self-correction assisted driving method as described in any one of claims 1 to 5.

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