A train stopping control method and device, electronic equipment and storage medium

Abstract

The application discloses a train stopping control method and device, electronic equipment and storage medium. Each first deviation value of train stopping in a historical time period is obtained, the first deviation value refers to the position deviation value of the actual stopping point of the train on the platform and the preset target stopping point; the deviation average value and the deviation standard deviation are determined according to each first deviation value; the predicted deviation value of the train stopping at the next platform is determined according to the deviation average value, the deviation standard deviation and the normal distribution relationship; in the process that the train drives to the next platform, for each preset period, the first distance between the train and the target stopping point of the next platform is obtained in the period; the second distance is determined according to the first distance and the predicted deviation value; the first speed of the train in the period is determined according to the second distance; the train is controlled according to the first speed in the period, and the accuracy of train stopping is improved.

Description

Technical Field

[0001] This application relates to the field of urban rail transit technology, and in particular to a train stopping control method, device, electronic equipment and storage medium. Background Technology

[0002] In urban rail transit, after a train comes to a complete stop at a station, the train doors and platform screen doors are prepared to open for passenger transfers. Due to factors such as track resistance and wear and tear on the train's electrical equipment, the actual stopping position may deviate from the planned stopping position, resulting in poor accuracy in stopping at stations. If the actual stopping position deviates significantly from the planned stopping position, the train doors and platform screen doors cannot be perfectly aligned in the forward and backward direction, affecting the speed of passenger boarding and alighting and impacting the passenger experience.

[0003] Therefore, how to reduce the deviation between the actual stopping position and the planned stopping point of the train and improve the accuracy of train stopping is a technical problem that needs to be solved. Summary of the Invention

[0004] This application provides a train stopping control method, device, electronic equipment, and storage medium to solve the problem of poor train stopping accuracy in the prior art.

[0005] In a first aspect, this application provides a train stopping control method, the method comprising:

[0006] Obtain each first deviation value of the train stopping at the station within a historical time period, wherein the first deviation value refers to the positional deviation value between the actual stopping point of the train at the platform and the preset target stopping point;

[0007] The average deviation and standard deviation are determined based on the first deviation values; the predicted deviation value for the train to stop at the next platform is determined based on the average deviation, the standard deviation, and the normal distribution formula.

[0008] During the process of the train moving towards the next station, for each preset cycle, a first distance between the train and the target stopping point of the next station is obtained in the cycle; a second distance is determined based on the first distance and the predicted deviation value; a first speed of the train in the cycle is determined based on the second distance; and the train is controlled based on the first speed in the cycle.

[0009] The above technical solution has the following advantages or beneficial effects:

[0010] In this application, based on the first deviation values ​​of train stops within a historical time period and the normal distribution formula, the predicted deviation value of the train stopping at the next platform is determined. During the train's journey to the next platform, for each preset cycle, a first distance between the train and the target stopping point at the next platform is obtained within that cycle. A second distance is determined based on the first distance and the predicted deviation value, and then a first speed of the train for that cycle is determined based on the second distance. The train is then controlled according to the first speed within that cycle. This application first predicts the stopping deviation of the train at the next platform based on a normal distribution. During the train's journey to the next platform, the first distance between the train's actual position and the target stopping point is adjusted based on the predicted stopping deviation to obtain a second distance. Then, the train's speed is determined based on the second distance, and the train is controlled using this speed, thereby reducing the deviation value of the train stopping at the next platform. This improves the accuracy of train stops and enhances the speed of passenger boarding and alighting, as well as the passenger travel experience.

[0011] In one optional implementation, determining the predicted deviation value for the train stopping at the next platform based on the average deviation, the standard deviation of the deviation, and the normal distribution formula includes:

[0012] Based on the preset normal distribution probability parameters, the average deviation, the standard deviation of the deviation, and the normal distribution formula, the predicted deviation value of the train stopping at the next station is determined; wherein, if the average deviation is negative, the preset normal distribution probability parameter is negative; if the average deviation is positive, the preset normal distribution probability parameter is positive.

[0013] The above technical solution has the following advantages or beneficial effects:

[0014] In this application, a normal distribution probability parameter is determined based on the average deviation. If the average deviation is negative, the preset normal distribution probability parameter is negative; if the average deviation is positive, the preset normal distribution probability parameter is positive. Then, based on the preset normal distribution probability parameter, the average deviation, the standard deviation of the deviation, and the normal distribution formula, the predicted deviation value for the train stopping at the next platform is determined. This improves the accuracy of determining the predicted deviation value for the train stopping at the next platform.

[0015] In one optional implementation, determining the predicted deviation value of the train stopping at the next platform based on preset normal distribution probability parameters, the average deviation, the standard deviation of the deviation, and the normal distribution formula includes:

[0016] Obtain the second deviation value of the train stopping at the previous platform of the next platform, and determine the adjustment value of the normal distribution probability parameter based on the second deviation value;

[0017] The adjusted normal distribution probability parameter is determined based on the sum of the adjusted value of the normal distribution probability parameter and the preset normal distribution probability parameter; the predicted deviation value of the train stopping at the next station is determined based on the adjusted normal distribution probability parameter, the average deviation, the standard deviation of the deviation, and the normal distribution formula.

[0018] The above technical solution has the following advantages or beneficial effects:

[0019] To further improve the accuracy of predicting the train's stop at the next platform, a second deviation value for the train's stop at the previous platform is first obtained. Based on this second deviation value, an adjustment value for the normal distribution probability parameter is determined. Then, the preset normal distribution probability parameter is adjusted according to this adjustment value, thus ensuring its accuracy. Finally, based on the adjusted normal distribution probability parameter, the mean deviation, the standard deviation, and the normal distribution formula, the predicted deviation value for the train's stop at the next platform is determined. This further improves the accuracy of predicting the train's stop at the next platform.

[0020] In one optional implementation, determining the adjustment value of the normal distribution probability parameter based on the second deviation value includes:

[0021] If the second deviation value is within the preset deviation value range, the adjustment value of the normal distribution probability parameter is determined to be 0;

[0022] If the second deviation value is not within the preset deviation value range, if the second deviation value is positive, the adjustment value of the normal distribution probability parameter is determined to be negative; if the second deviation value is negative, the adjustment value of the normal distribution probability parameter is determined to be positive.

[0023] The second deviation value is the difference between the position of the target stopping point on the previous platform and the position of the actual stopping point of the train on the previous platform.

[0024] The above technical solution has the following advantages or beneficial effects:

[0025] To improve the accuracy of determining the adjustment value of the normal distribution probability parameter, this application first determines whether the second deviation value is within a preset deviation value range. If it is, it indicates that the parking at the previous station was relatively accurate, and the adjustment value of the normal distribution probability parameter is determined to be 0, meaning no adjustment is made. If the second deviation value is not within the preset deviation value range and is positive, the adjustment value of the normal distribution probability parameter is determined to be negative; if the second deviation value is not within the preset deviation value range and is negative, the adjustment value is determined to be positive. Then, based on the sum of the adjustment value and the preset normal distribution probability parameter, the adjusted normal distribution probability parameter is determined. This ensures the accuracy of determining the adjustment value of the normal distribution probability parameter and further improves the accuracy of determining the adjusted normal distribution probability parameter.

[0026] In one optional implementation, determining the second distance based on the first distance and the prediction deviation value includes:

[0027] The second distance is determined based on the sum of the first distance, the predicted deviation value, and the preset distance fine-tuning value.

[0028] The above technical solution has the following advantages or beneficial effects:

[0029] To improve the accuracy of determining the second distance, and thus the accuracy of train stopping control, this application determines the second distance based on the sum of three factors: the first distance, the predicted deviation value, and a preset distance fine-tuning value. The preset deviation value can be set according to factors such as the wear and tear of the train's electrical equipment; the preset deviation value can be the same or different for trains with different levels of wear. This improves the accuracy of the determined second distance, thereby improving the accuracy of determining the train's first speed; thus, it makes train control based on the first speed more accurate.

[0030] In an optional implementation, for each preset cycle, before acquiring the first distance between the train and the target stopping point at the next station in that cycle, the method further includes:

[0031] Determine whether the train has entered the station parking phase. If so, perform the step of obtaining the first distance between the train and the target parking point of the next station in each preset cycle.

[0032] The above technical solution has the following advantages or beneficial effects:

[0033] To reduce the energy consumption of train stop control, this application first determines whether the train has entered the station parking phase before executing train stop control. If so, the train stop control process is executed, which involves performing preset cycles, obtaining the first distance between the train and the target stopping point at the next platform within each cycle, and subsequent processes. If it is determined that the train has not entered the station parking phase, the train operation is controlled according to the normal train operation configuration, and the train stop control process is not performed. This reduces the energy consumption of train stop control.

[0034] In one optional implementation, determining whether the train has entered the station parking phase includes:

[0035] During the process of the train moving to the next station, the pre-configured target speed of the train at each moment is obtained;

[0036] For each of the aforementioned times, the braking rate at that time is determined based on the target speed at that time and a pre-configured braking rate coefficient; a third distance between the train and the target stopping point at the next station at that time is obtained; and a second speed at that time is determined based on the third distance and the braking rate.

[0037] For each of the aforementioned times, if the target speed at that time is greater than the second speed at that time, it is determined that the train has entered the station parking phase.

[0038] The above technical solution has the following advantages or beneficial effects:

[0039] To accurately determine when a train is entering the station's stopping phase and thus perform train stopping control, this application describes a process where, as the train travels to the next platform, pre-configured target speeds for the train at various times are acquired. For each time moment, the braking rate is determined, and a second speed is calculated based on the braking rate and the third distance between the train and the target stopping point at the next platform. The relationship between the target speed and the second speed at that moment is used to determine whether the train is entering the station's stopping phase. Specifically, if the target speed is greater than the second speed, the train is confirmed to be entering the station's stopping phase. This allows for accurate determination of when a train is entering the station's stopping phase.

[0040] Secondly, this application provides a train stopping control device, the device comprising:

[0041] The acquisition module is used to acquire each first deviation value of the train stopping at the station within a historical time period, wherein the first deviation value refers to the positional deviation value between the actual stopping point of the train at the platform and the preset target stopping point.

[0042] The determination module is used to determine the average deviation and standard deviation of the deviation based on the first deviation values; and to determine the predicted deviation value of the train stopping at the next station based on the average deviation, the standard deviation of the deviation, and the normal distribution formula.

[0043] The control module is configured to, during the process of the train moving towards the next station, acquire a first distance between the train and the target stopping point of the next station in each preset cycle; determine a second distance based on the first distance and the predicted deviation value; determine a first speed of the train in the cycle based on the second distance; and control the train based on the first speed in the cycle.

[0044] Thirdly, this application provides an electronic device, including a processor, a communication interface, a memory, and a communication bus, wherein the processor, the communication interface, and the memory communicate with each other through the communication bus;

[0045] Memory, used to store computer programs;

[0046] A processor, used to execute a program stored in memory, implements the method described.

[0047] Fourthly, this application provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the method described herein. Attached Figure Description

[0048] To more clearly illustrate the technical solutions in the embodiments of this application, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0049] Figure 1 This application provides a schematic diagram of the first type of train stopping control process.

[0050] Figure 2 This is a schematic diagram of the second type of train stopping control process provided in this application;

[0051] Figure 3 A schematic diagram illustrating the process of determining the predicted deviation value of a train stopping at the next platform, as provided in this application;

[0052] Figure 4 This application provides a schematic diagram of the third type of train stopping control process.

[0053] Figure 5 A schematic diagram illustrating the process of determining whether a train has entered the station parking phase, provided in this application;

[0054] Figure 6 This is a schematic diagram of the train stopping control scenario provided in this application;

[0055] Figure 7 The detailed flowchart of train stopping control provided in this application;

[0056] Figure 8 The flowchart for determining the prediction deviation value provided in this application;

[0057] Figure 9 This is a schematic diagram of the train stopping control device provided in this application;

[0058] Figure 10 A schematic diagram of the electronic device structure provided in this application. Detailed Implementation

[0059] To make the objectives and implementation methods of this application clearer, the exemplary implementation methods of this application will be clearly and completely described below with reference to the accompanying drawings of the exemplary embodiments of this application. Obviously, the exemplary embodiments described are only some embodiments of this application, and not all embodiments.

[0060] It should be noted that the brief descriptions of terms in this application are only for the convenience of understanding the embodiments described below, and are not intended to limit the embodiments of this application. Unless otherwise stated, these terms should be understood in their ordinary and common meaning.

[0061] The terms "first," "second," "third," etc., used in the specification, claims, and accompanying drawings of this application are used to distinguish similar or related objects or entities, and do not necessarily imply a specific order or sequence, unless otherwise specified. It should be understood that such terms are interchangeable where appropriate.

[0062] The terms “comprising” and “having”, and any variations thereof, are intended to cover but not exclude inclusion, for example, a product or device that includes a range of components is not necessarily limited to all of the components that are clearly listed, but may include other components that are not clearly listed or that are inherent to such product or device.

[0063] The term "module" refers to any known or subsequently developed hardware, software, firmware, artificial intelligence, fuzzy logic, or combination of hardware and / or software code that is capable of performing the functions associated with that element.

[0064] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features therein. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of this application.

[0065] For ease of explanation, the above description has been provided in conjunction with specific embodiments. However, the above exemplary discussion is not intended to be exhaustive or to limit the embodiments to the specific forms disclosed above. Various modifications and variations can be obtained based on the above teachings. The selection and description of the above embodiments are for the purpose of better explaining the principles and practical applications, thereby enabling those skilled in the art to better utilize the described embodiments and various different variations of embodiments suitable for specific use considerations.

[0066] Figure 1 The first train stopping control process provided in this application includes the following steps:

[0067] S101: Obtain each first deviation value of the train stopping at the station within the historical time period, wherein the first deviation value refers to the positional deviation value between the actual stopping point of the train at the platform and the preset target stopping point.

[0068] S102: Determine the average deviation and standard deviation of the deviation based on the first deviation values; determine the predicted deviation value of the train stopping at the next station based on the average deviation, the standard deviation of the deviation, and the normal distribution formula;

[0069] S103: During the process of the train moving towards the next station, for each preset cycle, a first distance between the train and the target stopping point of the next station is obtained in the cycle; a second distance is determined based on the first distance and the predicted deviation value; a first speed of the train in the cycle is determined based on the second distance; and the train is controlled based on the first speed in the cycle.

[0070] The train stopping control method provided in this application is applied to electronic devices, such as PCs, computers, servers, etc.

[0071] The electronic equipment first acquires the first deviation values ​​for each train stop within a historical time period, such as one month or two months. Within this period, after each train stop, the deviation between the actual stopping point and the preset target stopping point is calculated and used as the first deviation value for that stop. If the train's actual stopping point at the platform does not reach the target stopping point, it is determined to be in a state of under-targeting, and the first deviation value is positive. If the train's actual stopping point exceeds the target stopping point, it is determined to be in a state of over-targeting, and the first deviation value is negative. After acquiring the first deviation values ​​for each train stop within the historical time period, the average deviation and standard deviation are determined based on these values. Then, based on the average deviation, standard deviation, and normal distribution formula, the predicted deviation value for the train stopping at the next platform is determined.

[0072] Optionally, the normal distribution relationship is: In the formula, μ is the average deviation, σ is the standard deviation of the deviation, and u is a preset normal distribution probability parameter, where u can be a preset fixed value. Substituting the average deviation and the standard deviation of the deviation into the above normal distribution formula, the predicted deviation value X of the train stopping at the next station can be determined.

[0073] As the train travels to the next station, a first distance between the train and the target stopping point at the next station is acquired within each preset cycle. These preset cycles are, for example, every 50 milliseconds or every 80 milliseconds. For each cycle, the first distance between the train and the target stopping point at the next station is acquired based on a timer. The train's current position information is obtained through its positioning device. Additionally, the location information of the target stopping point at each station is pre-set. The distance between the train's current position and the target stopping point at the next station is defined as the first distance. A second distance is determined based on the first distance and a prediction deviation value. Optionally, the sum of the first distance and the prediction deviation value is used as the second distance. Then, the first speed of the train for the cycle is determined based on the second distance; the train is controlled based on the first speed during the cycle. Optionally, after determining the second distance, the first speed can be determined by substituting the second distance into the corresponding formula for the train's distance and speed. The corresponding formula for the train's distance and speed can be a formula from existing technologies, which will not be elaborated here.

[0074] It should be noted that the historical time period can change as trains continue to run. For example, if the current date is October 1st, the historical time period could be from August 1st to September 30th. As time progresses, for example, if the current date is December 1st, the historical time period can be adjusted to October 1st to November 30th. After the historical time period is adjusted, the first deviation values ​​for each train stop within the historical time period are recalculated for subsequent train stop control processes.

[0075] In this application, based on the first deviation values ​​of train stops within a historical time period and the normal distribution formula, the predicted deviation value of the train stopping at the next platform is determined. During the train's journey to the next platform, for each preset cycle, a first distance between the train and the target stopping point at the next platform is obtained within that cycle. A second distance is determined based on the first distance and the predicted deviation value, and then a first speed of the train for that cycle is determined based on the second distance. The train is then controlled according to the first speed within that cycle. This application first predicts the stopping deviation of the train at the next platform based on a normal distribution. During the train's journey to the next platform, the first distance between the train's actual position and the target stopping point is adjusted based on the predicted stopping deviation to obtain a second distance. Then, the train's speed is determined based on the second distance, and the train is controlled using this speed, thereby reducing the deviation value of the train stopping at the next platform. This improves the accuracy of train stops and enhances the speed of passenger boarding and alighting, as well as the passenger travel experience.

[0076] Figure 2 The second train stopping control process provided in this application includes the following steps:

[0077] S201: Obtain each first deviation value of the train stopping at the station within the historical time period, wherein the first deviation value refers to the positional deviation value between the actual stopping point of the train at the platform and the preset target stopping point.

[0078] S202: Determine the average deviation and standard deviation of the deviation based on each first deviation value; determine the predicted deviation value of the train stopping at the next platform based on the preset normal distribution probability parameter, the average deviation, the standard deviation of the deviation, and the normal distribution formula; wherein, if the average deviation is negative, the preset normal distribution probability parameter is negative; if the average deviation is positive, the preset normal distribution probability parameter is positive.

[0079] S203: During the process of the train moving towards the next station, for each preset cycle, a first distance between the train and the target stopping point of the next station is obtained in the cycle; a second distance is determined based on the first distance and the predicted deviation value; a first speed of the train in the cycle is determined based on the second distance; and the train is controlled based on the first speed in the cycle.

[0080] The normal distribution relationship is: By substituting the preset normal distribution probability parameter u, the average deviation μ, and the standard deviation σ into the above normal distribution formula, the predicted deviation value X of the train stopping at the next platform is determined. Wherein, if the average deviation is negative, the preset normal distribution probability parameter is, for example, -1.96, that is, u = -1.96; if the average deviation is positive, the preset normal distribution probability parameter is, for example, 1.96, that is, u = 1.96.

[0081] In this application, a normal distribution probability parameter is determined based on the average deviation. If the average deviation is negative, the preset normal distribution probability parameter is negative; if the average deviation is positive, the preset normal distribution probability parameter is positive. Then, based on the preset normal distribution probability parameter, the average deviation, the standard deviation of the deviation, and the normal distribution formula, the predicted deviation value for the train stopping at the next platform is determined. This improves the accuracy of determining the predicted deviation value for the train stopping at the next platform.

[0082] Figure 3 The process diagram provided for determining the predicted deviation value of a train stopping at the next platform, as provided in this application, includes the following steps:

[0083] S301: Obtain the second deviation value of the train stopping at the previous platform of the next platform, and determine the adjustment value of the normal distribution probability parameter based on the second deviation value;

[0084] S302: Determine the adjusted normal distribution probability parameter based on the sum of the adjusted value of the normal distribution probability parameter and the preset normal distribution probability parameter; determine the predicted deviation value of the train stopping at the next station based on the adjusted normal distribution probability parameter, the average deviation, the standard deviation of the deviation, and the normal distribution formula.

[0085] The second deviation value of the train's stop at the previous platform from the next platform is obtained. If the train's actual stopping point at the previous platform has not reached the target stopping point, the train is determined to be in a state of underestimation, and the second deviation value is positive. If the train's actual stopping point at the previous platform has exceeded the target stopping point, the train is determined to be in a state of overestimation, and the second deviation value is negative. When determining the adjustment value of the normal distribution probability parameter based on the second deviation value, different deviation value ranges and their corresponding adjustment values ​​can be preset. After determining the second deviation value, the adjustment value is determined according to this correspondence. Then, the sum of the adjusted value of the normal distribution probability parameter and the preset normal distribution probability parameter is determined as the adjusted normal distribution probability parameter. Finally, the adjusted normal distribution probability parameter, the deviation mean, and the deviation standard deviation are substituted into the normal distribution formula to determine the predicted deviation value of the train's stop at the next platform.

[0086] In this application, to further improve the accuracy of predicting the train's stop at the next platform, a second deviation value for the train's stop at the previous platform is first obtained. Based on this second deviation value, an adjustment value for the normal distribution probability parameter is determined. Then, the preset normal distribution probability parameter is adjusted according to this adjustment value, thereby ensuring the accuracy of the normal distribution probability parameter. Finally, based on the adjusted normal distribution probability parameter, the deviation average, the deviation standard deviation, and the normal distribution formula, the predicted deviation value for the train's stop at the next platform is determined. This further improves the accuracy of predicting the train's stop at the next platform.

[0087] In one optional implementation, determining the adjustment value of the normal distribution probability parameter based on the second deviation value includes:

[0088] If the second deviation value is within the preset deviation value range, the adjustment value of the normal distribution probability parameter is determined to be 0;

[0089] If the second deviation value is not within the preset deviation value range, if the second deviation value is positive, the adjustment value of the normal distribution probability parameter is determined to be negative; if the second deviation value is negative, the adjustment value of the normal distribution probability parameter is determined to be positive.

[0090] The second deviation value is the difference between the position of the target stopping point on the previous platform and the position of the actual stopping point of the train on the previous platform.

[0091] Optionally, the preset deviation range can be, for example, [-5 cm, 10 cm] or [-5 cm, 5 cm]. Let's take a preset deviation range of [-5 cm, 5 cm] as an example. If the second deviation is 3 cm, meaning it's within the preset range, the adjustment value for the normal distribution probability parameter is set to 0. If the second deviation is 10 cm, meaning it's outside the preset range and positive, the adjustment value for the normal distribution probability parameter is set to negative. This negative value can be a preset value, such as -0.01 or -0.015. If the second deviation is -8 cm, meaning it's outside the preset range and negative, the adjustment value for the normal distribution probability parameter is set to positive. This positive value can be a preset value, such as 0.01 or 0.015.

[0092] To improve the accuracy of determining the adjustment value of the normal distribution probability parameter, this application first determines whether the second deviation value is within a preset deviation value range. If it is, it indicates that the parking at the previous station was relatively accurate, and the adjustment value of the normal distribution probability parameter is determined to be 0, meaning no adjustment is made. If the second deviation value is not within the preset deviation value range and is positive, the adjustment value of the normal distribution probability parameter is determined to be negative; if the second deviation value is not within the preset deviation value range and is negative, the adjustment value is determined to be positive. Then, based on the sum of the adjustment value and the preset normal distribution probability parameter, the adjusted normal distribution probability parameter is determined. This ensures the accuracy of determining the adjustment value of the normal distribution probability parameter and further improves the accuracy of determining the adjusted normal distribution probability parameter.

[0093] In one optional implementation, determining the second distance based on the first distance and the prediction deviation value includes:

[0094] The second distance is determined based on the sum of the first distance, the predicted deviation value, and the preset distance fine-tuning value.

[0095] The predicted deviation value is, for example, 30 cm, 35 cm, etc. To improve the accuracy of determining the second distance, and thus the accuracy of train stopping control, this application determines the second distance based on the sum of the first distance, the predicted deviation value, and a preset distance fine-tuning value. The preset deviation value can be set according to factors such as the wear and tear of the train's electrical equipment; for trains with different levels of wear, the preset deviation value can be the same or different. This improves the accuracy of the determined second distance, thereby improving the accuracy of determining the train's first speed; thus, it makes train control based on the first speed more accurate.

[0096] In an optional implementation, for each preset cycle, before acquiring the first distance between the train and the target stopping point at the next station in that cycle, the method further includes:

[0097] Determine whether the train has entered the station parking phase. If so, perform the step of obtaining the first distance between the train and the target parking point of the next station in each preset cycle.

[0098] During train operation, relevant configuration parameters for train speed are configured at each stage. This application first determines whether the train has entered the station-entry and stopping stage. If not, the train operation is controlled according to the pre-configured train speed parameters. If the train has entered the station-entry and stopping stage, the train stopping control process provided in this application is executed. To reduce the control energy consumption of train stopping control, this application first determines whether the train has entered the station-entry and stopping stage before executing train stopping control. If so, the train stopping control process is executed, which involves performing preset cycles, obtaining the first distance between the train and the target stopping point of the next platform in each cycle, and subsequent processes. If the train has not entered the station-entry and stopping stage, the train operation is controlled according to the normal train operation configuration, and the train stopping control process is not performed. This reduces the control energy consumption of train stopping control.

[0099] Figure 4 The third type of train stopping control process provided in this application includes the following steps:

[0100] S401: Obtain each first deviation value of the train stopping at the station within the historical time period, wherein the first deviation value refers to the positional deviation value between the actual stopping point of the train at the platform and the preset target stopping point.

[0101] S402: Determine the average deviation and standard deviation of the deviation based on the first deviation values; determine the predicted deviation value of the train stopping at the next platform based on the average deviation, the standard deviation of the deviation, and the normal distribution formula;

[0102] S403: During the process of the train moving towards the next station, if it is determined that the train has entered the station parking stage, for each preset cycle, a first distance between the train and the target parking point of the next station is obtained in the cycle; a second distance is determined based on the first distance and the predicted deviation value; a first speed of the train in the cycle is determined based on the second distance; and the train is controlled based on the first speed in the cycle.

[0103] Figure 5 The schematic diagram provided in this application for determining whether a train has entered the station parking stage includes the following steps:

[0104] S501: During the process of the train moving to the next station, obtain the pre-configured target speed of the train at each moment;

[0105] S502: For each time moment, determine the braking rate at that time moment based on the target speed at that time moment and the pre-configured braking rate coefficient; obtain the third distance between the train and the target stopping point of the next station at that time moment; determine the second speed at that time moment based on the third distance and the braking rate.

[0106] S503: For each of the aforementioned times, if the target speed at that time is greater than the second speed at that time, it is determined that the train has entered the station parking phase.

[0107] In this application, as the train travels to the next station, the pre-configured target speed V1 of the train at each moment is first obtained. Then, based on the target speed V1 and pre-configured braking rate coefficients a and b, the braking rate at that moment is determined. Optionally, the braking rate A is determined according to the formula A = aV1 + b. The braking rate coefficient a is, for example, 0.0144, and b is, for example, 23. The third distance S between the train and the target stopping point of the next station at that moment is obtained, and the second speed at that moment is determined based on the third distance and the braking rate. Optionally, the second speed is determined according to the formula V2. 2 =2AS, determine the second speed V2. For each time moment, if the target speed at that time is not greater than the second speed at that time, it is determined that the train has not entered the station parking phase. If the target speed at that time is greater than the second speed at that time, it is determined that the train has not entered the station parking phase.

[0108] The braking rate coefficient and the preset distance fine-tuning value are shown in Table 1 below:

[0109] Serial Number name numerical values 1 Braking rate coefficient a 0.0144 2 Braking rate coefficient b 23 3 Distance fine-tuning value 30 centimeters

[0110] Table 1

[0111] This application describes a train stopping control process to accurately determine when a train is entering the station's stopping phase. In this application, as the train travels to the next platform, pre-configured target speeds for the train at various times are acquired. For each time moment, the braking rate is determined, and a second speed is calculated based on the braking rate and the third distance between the train and the target stopping point at the next platform. The relationship between the target speed and the second speed at each moment is used to determine whether the train is entering the station's stopping phase. If the target speed is greater than the second speed, the train is confirmed to be entering the stopping phase. This allows for accurate determination of when a train is entering the station's stopping phase.

[0112] This application takes into account that during train operation, after the train stops at the operating platform, the train doors and platform screen doors are prepared to open for passenger transfer. If the actual stopping position of the train deviates significantly from the planned stopping position, the train doors and platform screen doors cannot be completely aligned in the front-to-back direction, affecting the speed of passengers getting on and off the train. In order to improve passenger comfort, it is necessary to manually count the stopping accuracy deviation of the Automatic Train Operation (ATO) device at each operating platform the train arrives at, and then adjust the ATO control parameters based on the counted deviation value to improve ATO stopping accuracy. However, manual counting is very time-consuming and labor-intensive.

[0113] This application addresses the aforementioned technical issues by having the ATO subsystem calculate the positional deviation between the train's current position and the target stopping point after the train has come to a complete stop at the operating platform. The system records the stopping deviation value for this platform. After multiple stops at the same or different operating platforms, the system statistically analyzes the positional deviation values ​​at each platform. This statistically analyzed positional deviation values ​​are then processed. Once the train enters the station parking phase, the distance between the train and the operating platform is optimized and adjusted based on the processed positional deviation values. This allows for more precise stopping of the train at the operating platform, avoiding the time-consuming and labor-intensive process of manually calculating stopping deviation values ​​and improving train operation efficiency.

[0114] Figure 6 This is a schematic diagram of the train stopping control scenario provided in this application, such as... Figure 6 As shown, line segment AC represents the operating platform, point B represents the target stopping point of the operating platform, and line segment DE represents the stopping window of the operating platform. When the train comes to a complete stop at the operating platform, if its position is within the range of line segment BD, it indicates that the train's position is in a state of underestimation; if its position is within the range of line segment BE, it indicates that the train's position is in a state of overestimation. Line segment JH represents the train's desired stopping range. When the train stops at the operating platform, the ATO subsystem calculates the distance between the train's front position and the target stopping point of the platform, i.e., the position deviation value, and records this position deviation value. When the train is in a state of underestimation, the position deviation value of the operating platform is positive; when the train is in a state of overestimation, the position deviation value of the operating platform is negative.

[0115] Figure 7 The detailed flowchart of train stopping control provided in this application is as follows: Figure 7As shown, the process involves: querying the train's current position; determining whether the train has come to a complete stop within the operating platform; if so, calculating the distance between the train's position and the target stopping point at the operating platform; recording the position deviation value at that operating platform; recording the position deviation values ​​of the train at the operating platform n times; calculating the mean of each position deviation value to obtain the average deviation; calculating the standard deviation based on the average deviation value to obtain the standard deviation; determining whether the train is in the process of entering and stopping at the station; if so, determining the predicted deviation value of the train at the next operating platform based on the normal distribution formula; dynamically adjusting the distance between the train and the target stopping point based on the predicted deviation value; calculating the train's speed based on the adjusted distance, and controlling the train using this speed.

[0116] After the ATO subsystem controls the train to complete multiple (n) stops at different operating platforms, according to the formula... The position deviation value is averaged, where: The average deviation is X1 to X. n This represents the position deviation of the train at each operating platform, and n represents the number of times the train stops at each operating platform.

[0117] Based on the average deviation, the number of times the train stops at operating platforms, and the positional deviation of the train at each operating platform, the formula is used to... Perform parking accuracy standard deviation calculation and processing. Note: S n X represents the standard deviation of the deviation. i This represents the position deviation of the train at each operating platform, and n represents the number of station processing times the train makes at each operating platform. This represents the average deviation.

[0118] After the train completes multiple (n) stops at different operating platforms, the ATO stopping accuracy deviation value X follows a mathematical expectation of μ and a variance of σ. 2 The parking accuracy deviation follows a normal distribution, where μ is the average value. σ is the standard deviation S. This is determined using the normal distribution formula. Calculate the predicted deviation X of ATO at the next operating station, where: μ is the average deviation of multiple stops, σ is the standard deviation of the deviation, and u is a preset normal distribution probability parameter, with a value ranging from -1.96 to 1.96. When μ is negative, the initial value of u is -1.96; when μ is positive, the initial value of u is 1.96.

[0119] When the train enters the station parking phase and is preparing for the (n+1)th stop, the distance between the train and the operating platform is dynamically adjusted according to the formula: Distance between the train and the operating platform = (Predicted deviation value X calculated by the ATO subsystem + Pre-set distance fine-tuning value + Query the distance between the locomotive and the target stopping point of the platform). The train speed is calculated based on this distance value, and the train control algorithm is used to make the actual speed of the ATO control the train close to the calculated speed, thereby improving the stopping accuracy of the train at this operating platform.

[0120] Figure 8 The flowchart for determining the prediction deviation value provided in this application is as follows: Figure 8 As shown, the distance between the train position and the target stopping point of the operating platform is calculated; the position deviation value of the operating platform is recorded; it is determined whether the position deviation value is within the expected stopping range; if yes, the normal distribution probability parameter u remains unchanged; if no, if the stopping is in a state of under-target, the normal distribution probability parameter u is decreased; if the stopping is in a state of over-target, the normal distribution probability parameter u is increased; the prediction deviation value is recalculated based on the adjusted normal distribution probability parameter u.

[0121] Table 2 is a schematic diagram illustrating examples of train stopping points involved in this application:

[0122]

[0123]

[0124] Table 2

[0125] Figure 9 The schematic diagram of the train stopping control device provided in this application includes:

[0126] The acquisition module 91 is used to acquire each first deviation value of the train stopping at the station within a historical time period, wherein the first deviation value refers to the positional deviation value between the actual stopping point of the train at the platform and the preset target stopping point.

[0127] The determination module 92 is used to determine the average deviation and standard deviation of the deviation based on the first deviation values; and to determine the predicted deviation value of the train stopping at the next station based on the average deviation, the standard deviation of the deviation, and the normal distribution formula.

[0128] The control module 93 is configured to, during the process of the train moving toward the next station, acquire a first distance between the train and the target stopping point of the next station in each preset cycle; determine a second distance based on the first distance and the predicted deviation value; determine a first speed of the train in the cycle based on the second distance; and control the train based on the first speed in the cycle.

[0129] The determining module 92 is specifically used to determine the predicted deviation value of the train stopping at the next station based on the preset normal distribution probability parameter, the average deviation, the standard deviation of the deviation, and the normal distribution formula; wherein, if the average deviation is negative, the preset normal distribution probability parameter is negative; if the average deviation is positive, the preset normal distribution probability parameter is positive.

[0130] The determining module 92 is specifically used to obtain the second deviation value of the train stopping at the previous platform of the next platform; determine the adjustment value of the normal distribution probability parameter based on the second deviation value; determine the adjusted normal distribution probability parameter based on the sum of the adjusted normal distribution probability parameter and the preset normal distribution probability parameter; and determine the predicted deviation value of the train stopping at the next platform based on the adjusted normal distribution probability parameter, the deviation average value, the deviation standard deviation, and the normal distribution formula.

[0131] The determining module 92 is specifically used to determine the adjustment value of the normal distribution probability parameter to be 0 if the second deviation value is within a preset deviation value range; if the second deviation value is not within the preset deviation value range, and if the second deviation value is positive, determine the adjustment value of the normal distribution probability parameter to be negative; if the second deviation value is negative, determine the adjustment value of the normal distribution probability parameter to be positive; wherein, the second deviation value is the difference between the position of the target stopping point of the previous platform and the position of the actual stopping point of the train at the previous platform.

[0132] The control module 93 is specifically used to determine the second distance based on the sum of the first distance, the predicted deviation value, and the preset distance fine-tuning value.

[0133] The device further includes:

[0134] The judgment module 94 is used to determine whether the train has entered the station parking stage. If so, the control module 93 is triggered.

[0135] The judgment module 94 is specifically used to: acquire the pre-configured target speed of the train at each time point during the process of the train moving towards the next station; determine the braking rate at each time point based on the target speed and the pre-configured braking rate coefficient; acquire the third distance between the train and the target stopping point of the next station at each time point; determine the second speed at each time point based on the third distance and the braking rate; and determine that the train has entered the station parking stage if the target speed at each time point is greater than the second speed at each time point.

[0136] This application also provides an electronic device, such as Figure 10 As shown, it includes: processor 11, communication interface 12, memory 13 and communication bus 14, wherein processor 11, communication interface 12 and memory 13 communicate with each other through communication bus 14;

[0137] The memory 13 stores a computer program, which, when executed by the processor 11, causes the processor 11 to perform any of the above method steps.

[0138] The communication bus mentioned in the above electronic devices can be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus, etc. This communication bus can be divided into address bus, data bus, control bus, etc. For ease of illustration, only one thick line is used to represent it in the diagram, but this does not indicate that there is only one bus or one type of bus.

[0139] Communication interface 12 is used for communication between the above-mentioned electronic device and other devices.

[0140] The memory may include random access memory (RAM) or non-volatile memory (NVM), such as at least one disk storage device. Optionally, the memory may also be at least one storage device located remotely from the aforementioned processor.

[0141] The processors mentioned above can be general-purpose processors, including central processing units, network processors (NPs), etc.; they can also be digital signal processors (DSPs), application-specific integrated circuits, field-programmable gate arrays or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc.

[0142] This application also provides a computer-readable storage medium storing a computer program executable by an electronic device, which, when run on the electronic device, causes the electronic device to perform any of the above method steps.

[0143] Although preferred embodiments of this application have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments as well as all changes and modifications falling within the scope of this application.

[0144] Obviously, those skilled in the art can make various modifications and variations to this application without departing from the spirit and scope of this application. Therefore, if such modifications and variations fall within the scope of the claims of this application and their equivalents, this application also intends to include such modifications and variations.

Claims

1. A train stopping control method, characterized in that, The method includes: Obtain each first deviation value of the train stopping at the station within a historical time period, wherein the first deviation value refers to the positional deviation value between the actual stopping point of the train at the platform and the preset target stopping point; The average deviation and standard deviation are determined based on the first deviation values; the predicted deviation value for the train to stop at the next platform is determined based on the average deviation, the standard deviation, and the normal distribution formula. During the process of the train moving towards the next station, for each preset cycle, a first distance between the train and the target stopping point of the next station is obtained in the cycle; a second distance is determined based on the first distance and the predicted deviation value; a first speed of the train in the cycle is determined based on the second distance; and the train is controlled according to the first speed in the cycle. Based on the average deviation, the standard deviation of the deviation, and the normal distribution formula, the predicted deviation value for the train stopping at the next platform is determined as follows: Based on the preset normal distribution probability parameters, the average deviation, the standard deviation of the deviation, and the normal distribution formula, the predicted deviation value of the train stopping at the next station is determined; wherein, if the average deviation is negative, the preset normal distribution probability parameter is negative; if the average deviation is positive, the preset normal distribution probability parameter is positive. The step of determining the predicted deviation value of the train stopping at the next platform based on the preset normal distribution probability parameters, the average deviation, the standard deviation of the deviation, and the normal distribution formula includes: Obtain the second deviation value of the train stopping at the previous platform of the next platform, and determine the adjustment value of the normal distribution probability parameter based on the second deviation value; The adjusted normal distribution probability parameter is determined based on the sum of the adjusted value of the normal distribution probability parameter and the preset normal distribution probability parameter; the predicted deviation value of the train stopping at the next station is determined based on the adjusted normal distribution probability parameter, the average deviation, the standard deviation of the deviation, and the normal distribution formula.

2. The method as described in claim 1, characterized in that, Based on the second deviation value, the adjustment values ​​for the normal distribution probability parameters are determined as follows: If the second deviation value is within the preset deviation value range, the adjustment value of the normal distribution probability parameter is determined to be 0; If the second deviation value is not within the preset deviation value range, if the second deviation value is positive, the adjustment value of the normal distribution probability parameter is determined to be negative; if the second deviation value is negative, the adjustment value of the normal distribution probability parameter is determined to be positive. The second deviation value is the difference between the position of the target stopping point on the previous platform and the position of the actual stopping point of the train on the previous platform.

3. The method as described in claim 1, characterized in that, Determining the second distance based on the first distance and the prediction deviation value includes: The second distance is determined based on the sum of the first distance, the predicted deviation value, and the preset distance fine-tuning value.

4. The method as described in claim 1, characterized in that, For each preset cycle, before obtaining the first distance between the train and the target stopping point at the next station in the cycle, the method further includes: Determine whether the train has entered the station parking phase. If so, perform the step of obtaining the first distance between the train and the target parking point of the next station in each preset cycle.

5. The method as described in claim 4, characterized in that, Determining whether the train has entered the station parking phase includes: During the process of the train moving to the next station, the pre-configured target speed of the train at each moment is obtained; For each of the aforementioned times, the braking rate at that time is determined based on the target speed at that time and a pre-configured braking rate coefficient; a third distance between the train and the target stopping point at the next station at that time is obtained; and a second speed at that time is determined based on the third distance and the braking rate. For each of the aforementioned times, if the target speed at that time is greater than the second speed at that time, it is determined that the train has entered the station parking phase.

6. A train stopping control device, characterized in that, The device includes: The acquisition module is used to acquire each first deviation value of the train stopping at the station within a historical time period, wherein the first deviation value refers to the positional deviation value between the actual stopping point of the train at the platform and the preset target stopping point. The determination module is used to determine the average deviation and standard deviation of the deviation based on the first deviation values; and to determine the predicted deviation value of the train stopping at the next station based on the average deviation, the standard deviation of the deviation, and the normal distribution formula. The control module is configured to, during the process of the train moving towards the next station, acquire a first distance between the train and the target stopping point of the next station in each preset cycle; determine a second distance based on the first distance and the predicted deviation value; determine a first speed of the train in the cycle based on the second distance; and control the train based on the first speed in the cycle. The determination module is specifically used to determine the predicted deviation value of the train stopping at the next station based on the preset normal distribution probability parameter, the average deviation, the standard deviation of the deviation, and the normal distribution formula; wherein, if the average deviation is negative, the preset normal distribution probability parameter is negative; if the average deviation is positive, the preset normal distribution probability parameter is positive. The determination module is specifically used to obtain a second deviation value of the train stopping at the previous platform of the next platform; determine an adjustment value of the normal distribution probability parameter based on the second deviation value; determine the adjusted normal distribution probability parameter based on the sum of the adjusted normal distribution probability parameter and the preset normal distribution probability parameter; and determine the predicted deviation value of the train stopping at the next platform based on the adjusted normal distribution probability parameter, the average deviation, the standard deviation of the deviation, and the normal distribution formula.

7. An electronic device, characterized in that, It includes a processor, a communication interface, a memory, and a communication bus, wherein the processor, the communication interface, and the memory communicate with each other through the communication bus; Memory, used to store computer programs; A processor, when executing a program stored in memory, implements the method described in any one of claims 1-5.

8. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, implements the method described in any one of claims 1-5.

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