Accurate parking method and device, electronic equipment and readable medium

Abstract

The invention relates to a precise parking method and device, electronic equipment and a readable medium, and the method comprises the steps: obtaining the state and route information of a train, and selecting an EB0 mode or a non-EB0 mode according to the current state information and route information of the train; constructing a corresponding agent model according to the selected mode; and according to the selected mode, the theoretical model and the proxy model, a target operation curve is planned, and the target operation curve is divided into a conventional operation stage, a precise parking preorder stage and a precise parking stage. In the precise parking stage, the agent model is used for planning a first target operation curve; in the precise parking preorder stage, planning a second target operation curve by using the theoretical model; and in a conventional operation stage, planning a third target operation curve by utilizing the theoretical model, integrating the three target operation curves, and controlling the train to stop by utilizing a speed following control module. According to the method, the precise parking function compatible with an EB0 (electric braking to zero speed) mode and a non-EB0 mode (electric-pneumatic hybrid braking to zero speed) is achieved.

Description

Technical Field

[0001] This invention belongs to the field of automatic driving of urban rail transit, specifically relating to a precise parking method, device, electronic equipment, and readable medium. Background Technology

[0002] As a crucial component of urban transportation, the automation level of urban rail transit systems directly impacts the efficiency and safety of urban traffic. Autonomous trains face numerous technical challenges in achieving efficient and precise stopping. Precise train stopping technology is particularly important in the field of autonomous driving, as it involves the precise stopping of the train at designated station locations, directly affecting passenger safety and comfort.

[0003] According to the "Specification for CBTC Signaling System - ATO Subsystem of Urban Rail Transit", precise stopping is a technical requirement to ensure that the train's stopping position error is controlled within an extremely small range. The specification states that when the train's stopping position error is within 0.3 meters, an accuracy rate of 99.99% must be guaranteed; and when the error is within 0.5 meters, the accuracy rate must reach 99.9998%.

[0004] During the stopping phase, the train faces various changes in braking modes and forces, as shown in the attached diagram. Figure 1 Appendix Figure 2 As shown, it specifically includes:

[0005] EB0 mode and non-EB0 mode: In EB0 mode, the train mainly relies on electric braking to stop; while in non-EB0 mode, due to insufficient electric braking force or traction failure, air braking is required to supplement braking force, resulting in delayed braking force response time and inaccurate force control, increasing the difficulty of stopping precision control.

[0006] Braking characteristics at low speeds: When approaching zero speed, the train needs to apply a holding brake to prevent runaway. At this time, the nonlinear change of braking force and the withdrawal of electric braking force increase the complexity of control. Summary of the Invention

[0007] This invention provides a precise parking method, device, electronic equipment, and readable medium, the purpose of which is to solve the problem that the complex characteristics of nonlinear changes in braking force during the parking phase affect parking accuracy.

[0008] To achieve the above objectives, a first aspect of the present invention provides a precise parking method, the precise parking method comprising:

[0009] Obtain current train status information and route information;

[0010] The stopping mode is selected based on the current train status information and line information. The stopping mode includes EB0 mode or non-EB0 mode.

[0011] Constructing a proxy model for the corresponding parking mode, wherein the method for constructing the proxy model for the parking mode includes:

[0012] Select the initial stage and initial velocity;

[0013] A single test is conducted and the braking time and braking distance of the single test are obtained, the single test being conducted based on a predetermined EB0 mode or a non-EB0 mode;

[0014] Based on the results of a single test, a proxy model is constructed in either EB0 mode or non-EB0 mode. This proxy model includes four parameters: initial level, initial velocity, braking time, and braking distance.

[0015] Based on the selected parking mode, the constructed agent model, and the theoretical model, a target operation curve from the current location to the predetermined parking point is planned. This target operation curve includes three stages: the normal operation stage, the precise parking preparatory stage, and the precise parking stage.

[0016] During the precise parking phase, a proxy model is used to plan the first target running curve;

[0017] In the preliminary stage of precise parking, a theoretical model is used to plan the second target operation curve;

[0018] During the normal operation phase, the operating curve of the third objective is planned using theoretical models;

[0019] The third target operation curve, the second target operation curve, and the first target operation curve of the three stages are integrated to form a complete target operation curve. The complete target operation curve is sent to the speed following control module to control the train to stop. In the precise stopping pre-stage, the train is made to run at a fixed level.

[0020] Furthermore, the method for selecting EB0 mode or non-EB0 mode based on the current status information and line information of the train includes: obtaining the current status information and line information of the train from the train control and management system, the train on-board unit and local files; analyzing the train traction characteristics, train attributes and predicted maximum braking force demand; and calculating the ratio of the train's maximum available electric braking force to the maximum braking force demand. If the ratio is less than a predetermined threshold value, the non-EB0 mode is selected; otherwise, the EB0 mode is selected.

[0021] Furthermore, in the construction step of the proxy model, the selected initial stage is smaller than the stage corresponding to the train's common braking deceleration, and the initial speed is greater than the electric braking force exit threshold speed.

[0022] Furthermore, in the construction of the proxy model, a single test is repeated multiple times, with each single test set according to the same initial conditions and parking mode; the average braking time and average braking distance of multiple single tests are calculated.

[0023] Furthermore, the steps for planning the target travel curve from the current location to the predetermined parking point include:

[0024] Determine the planned starting point Ps and planned ending point Pe of the train, and set the planned initial speed Vt0;

[0025] During the precise stopping phase, based on the selected agent model and the actual train operation status, a first target running curve from Pe-Sd to Pe is planned, where the starting speed is set to Vd and the ending speed is 0, so that the precise stopping phase is completed within the time Td.

[0026] In the precise parking pre-stage, a theoretical model is used to plan the second target running curve from Pe-St-Sd to Pe-Sd, so that the train runs at a fixed level Rd and meets the duration Th of the precise parking pre-stage.

[0027] During the regular operation phase, a third target operation curve is planned from Ps to Pe-St-Sd to ensure that the train speed and class during the regular operation phase are consistent with the starting point of the precise stopping phase.

[0028] By using the target operating curve planned above, the train can be controlled to stop precisely from its current position to the predetermined stopping point.

[0029] Where Sd is the braking distance during the precise stopping phase, and St is the train displacement under a fixed level Rd and a duration Th.

[0030] Furthermore, the speed following control module periodically receives the train's current status information and the target running curve.

[0031] Furthermore, the steps of sending the complete target running curve to the speed following control module to control the train to stop include:

[0032] Obtain the target running curve for the current cycle, and obtain the current train status information and track information;

[0033] Determine if the precise parking phase has been entered:

[0034] If it enters, a fixed-level position will be output to stop the vehicle;

[0035] If not entered, extract the third or second target running curve segment from the current position to the stopping station; obtain the current cycle train operating condition, traction braking force, and train speed; based on the obtained current cycle train operating condition, traction braking force, and train speed, with the impact rate as a constraint, and with the third or second target running curve as the tracking target, calculate the traction braking level and send it to the DCU / BCU.

[0036] Repeat the above steps periodically until parking is complete.

[0037] To achieve the above objectives, a second aspect of the present invention provides an apparatus for implementing a precise parking method, comprising the following modules:

[0038] Information acquisition module: used to acquire current train status information and track information;

[0039] Mode selection module: used to select the parking mode based on the current status information of the train and the line information, wherein the parking mode includes EB0 mode or non-EB0 mode;

[0040] Model building module: used to build a proxy model for the corresponding parking mode. The method for building the proxy model includes: selecting an initial level and initial speed; conducting a single test and obtaining the braking time and braking distance of the single test, which is based on a predetermined EB0 mode or non-EB0 mode; and building a proxy model in the EB0 mode or non-EB0 mode based on the results of the single test. The proxy model includes four parameters: initial level, initial speed, braking time, and braking distance.

[0041] The target operation curve planning module is used to plan the target operation curve from the current location to the predetermined parking point based on the selected parking mode, the constructed agent model, and the theoretical model. The target operation curve includes three stages: the normal operation stage, the precise parking preparatory stage, and the precise parking stage.

[0042] Speed ​​following control module: Used to periodically receive the train's current status information and the complete target running curve, and perform the following operations:

[0043] Obtain the target running curve at the current moment, and obtain the current train status information and line information;

[0044] Determine if the precise parking phase has been entered:

[0045] If it enters, a fixed-level position will be output to stop the vehicle;

[0046] If the target running curve from the current position to the stop is not entered, the train operating condition, traction braking force, and train speed at the current moment are obtained. Based on the obtained train operating condition, traction braking force, and train speed at the current moment, with the impact rate as a constraint, and with the target running curve from the current position to the stop as the tracking target, the traction braking level is calculated and sent to the DCU / BCU.

[0047] Repeat the above steps periodically until parking is complete.

[0048] To achieve the above objectives, a third aspect of the present invention provides an electronic device, comprising: at least one memory and at least one processor;

[0049] The at least one memory is used to store a machine-readable program;

[0050] The at least one processor is used to invoke the machine-readable program to execute the precise parking method.

[0051] To achieve the above objectives, a third aspect of the present invention provides a computer-readable medium storing computer instructions that, when executed by a processor, cause the processor to perform the precise parking method.

[0052] The beneficial effects of this invention are that the precise stopping method provided by this invention effectively solves the complexity problem of nonlinear changes in braking force during the stopping phase by constructing and optimizing a surrogate model. First, by acquiring the train's current state information and track information, it determines whether to select EB0 mode or non-EB0 mode, ensuring adaptability to the train's braking mode. Then, by conducting a single test in a predetermined EB0 or ​​non-EB0 mode to obtain key braking parameters (including braking time and distance), a corresponding surrogate model is constructed. This surrogate model can reflect the specific braking characteristics under different modes. This surrogate model includes initial stage, initial speed, braking time, and braking distance, thus allowing for more precise control in the subsequent precise stopping phase. Finally, by precisely controlling the train's speed and position at different stages through the target running curve planned by the theoretical model and the surrogate model, high-precision stopping control is achieved. The application of this method not only improves the accuracy of stopping but also ensures seamless switching between different braking modes, optimizing the safety and efficiency of train operation. Attached Figure Description

[0053] Figure 1 This is a diagram showing the changes in braking force in non-EB0 mode.

[0054] Figure 2 This is a diagram showing the change in braking force under EB0 mode.

[0055] Figure 3A flowchart of a precise parking method provided by the present invention.

[0056] Figure 4 The flowchart of a method for constructing a precise parking stage agent model provided by the present invention is shown.

[0057] Figure 5 This invention provides a target operation curve diagram.

[0058] Figure 6 This invention provides a flowchart for generating a target running curve.

[0059] Figure 7 The present invention provides a flowchart of a speed following control program execution.

[0060] Figure 8 This invention provides a diagram showing the changes in train dynamics during train operation.

[0061] Figure 9 This invention provides a functional module execution flowchart.

[0062] Figure 10 This is a schematic diagram of a train position and speed curve provided by the present invention.

[0063] Figure 11 A structural diagram of a device provided by the present invention.

[0064] Reference numerals: 10, storage; 20, processor. Detailed Implementation

[0065] To address two challenges in precise parking of urban rail trains: (1) the difficulty in constructing an accurate theoretical model for the parking phase; and (2) the incompatibility of existing algorithms with both EB0 and non-EB0 modes, this invention proposes a precise parking technology compatible with both EB0 and non-EB0 modes. This technology separates the EB0 and non-EB0 mode scenarios, constructs proxy models for each mode, and selects the EB0 / non-EB0 mode at the initial stage of program execution based on train traction characteristics, train attributes, and the train's predicted maximum braking force requirement. This ensures that the technology is compatible with both EB0 and non-EB0 modes. To address the issue of insufficient accuracy in theoretical models, this invention solves this problem by constructing proxy models. The specific solution is as follows:

[0066] like Figure 3 As shown, the present invention provides a precise parking method, comprising the following steps:

[0067] Step S100: Obtain train status information and line information;

[0068] In this step, the Train Control System (TCMS) and the Onboard Computing Unit (CCU) actively obtain detailed data on the train's operating status from the TCMS, CCU, and local files, such as the train's speed, position, braking system status, and traction status. Simultaneously, the system also obtains information about the track, including speed limits, gradients, curve radii, and the location of upcoming signal points.

[0069] Step S200: Select a parking mode based on the current train status information and line information. The parking mode includes EB0 mode or non-EB0 mode.

[0070] After collecting the current status of the train and the information on the line, this step analyzes this data to determine the train's operating mode—EB0 mode (electric braking to zero speed) or non-EB0 mode (electric-pneumatic hybrid braking to zero speed).

[0071] Step S300: Construct a proxy model corresponding to the parking mode, wherein the construction of the proxy model includes:

[0072] Step S301: Select the initial stage and initial velocity;

[0073] Step S302: Perform a single test and obtain the braking time and braking distance of the single test. The single test is performed based on a predetermined EB0 mode or a non-EB0 mode.

[0074] Step S303: Based on the results of a single test, construct a proxy model in EB0 mode or non-EB0 mode. The proxy model includes four parameters: initial level, initial speed, braking time, and braking distance.

[0075] Step S400: Based on the selected parking mode, the constructed agent model and the theoretical model, plan the target operation curve from the current location to the predetermined parking point. The target operation curve includes three stages: the normal operation stage, the precise parking preparatory stage and the precise parking stage.

[0076] The stages are explained below:

[0077] Step S500: In the precise parking stage, use a proxy model to plan the first target running curve.

[0078] This stage is used for precise parking. In this stage, curve fitting is used to plan the target curve, and the speed follower control module outputs a fixed level until the car stops.

[0079] Step S600: In the pre-precision parking stage, the second target running curve is planned using a theoretical model;

[0080] This stage is primarily used for the precise stopping transition phase, aiming to ensure the train enters the precise stopping phase according to the expected parameters (initial speed, initial level, braking distance, and braking time specified for precise stopping). In this stage, a theoretical model is used for target curve planning, and the speed following control module employs the LQR control algorithm for train control.

[0081] Step S700: During the normal operation phase, the third target operation curve is planned using the theoretical model;

[0082] During normal operation, the train operates according to the normal schedule, mainly focusing on maintaining the train at the predetermined speed and timetable, and controlling the train using the LQR control algorithm through the speed following control module.

[0083] Step S800: Integrate the third target running curve, the second target running curve and the first target running curve of the three stages to form a complete target running curve. Send the complete target running curve to the speed following control module to control the train to stop. In the precise stopping pre-stage, the train runs at a fixed level.

[0084] It should be noted that the theoretical models are based on train dynamics and control theory, and aim to describe the behavior and response of trains through mathematical equations. These models include physical and engineering parameters of the train such as acceleration, speed, traction, and braking force, as well as environmental factors related to these parameters, such as gradient, curvature, and track conditions.

[0085] In the target operation curve planning module, the theoretical model is used for speed and trajectory planning during the regular operation phase and the precise stopping phase to ensure that the train operates safely and efficiently while meeting the timetable and speed limits.

[0086] In practice, the theoretical model is used to plan the speed configuration for the entire journey when the train begins its trip. When the train approaches a stopping station and requires more precise stopping control, the surrogate model comes into play, using previously collected experimental data to ensure the train stops at the designated location, thus meeting safety and accuracy requirements. This combined approach of using theoretical and surrogate models effectively balances prediction accuracy and computational efficiency, optimizing the overall operational performance of the urban rail transit system.

[0087] The purpose of planning a target operating curve is to ensure that the train can precisely control its operation according to a preset path and timetable, thereby achieving precise stopping. The target operating curve is a comprehensive operating profile containing multiple key parameters, primarily dynamic parameters such as speed (V), distance (S), and time (T). These parameters describe the specific speed and position the train should reach at different time points from its current position to the stopping point. This planning enables the control system to precisely control the train's operation by adjusting traction and braking forces according to this predetermined profile, ensuring that the train smoothly and accurately reaches the designated location along the optimized path and speed.

[0088] The theoretical model is constructed as follows: It is a train dynamics theoretical model, which includes: a basic train resistance model, a train gradient resistance model, and a train kinematic model. Under this train dynamics theoretical model, the acceleration at the corresponding position at the corresponding time is calculated based on the traction and braking forces, and then the train speed and position are further calculated. The model input is the traction force, and the output is the train position and train speed. The descriptions of each model are as follows:

[0089] a) Train resistance model:

[0090] F r =mg(a+bv+cv) 2 )

[0091] Where a, b, and c are the basic drag coefficients, which can be obtained from train test data. v is the train speed, m is the train weight, and g is the proportionality coefficient.

[0092] b) Train gradient resistance model:

[0093]

[0094] Where m is the train weight, L is the train length, and the train covers a total of n ramps at its current position, θ i Let l be the slope value of the i-th ramp. i Let be the length of the i-th ramp covered by the train. To calculate the average slope.

[0095] c) Train kinematic model:

[0096] acc = (F m -F r -F g ) / m

[0097] v'=v+acc·dt

[0098] s'=s+(v+v') / 2·dt

[0099] In the formula, Fm dt is the traction braking force, s is the calculation period, s is the train position, s' is the train position in the next period, v is the train speed in the current period, and v' is the train speed in the next period.

[0100] The specific construction process of the proxy model is as follows: Figure 4 As shown.

[0101] Figure 4 The intermediate steps are explained as follows:

[0102] (1) Step 1: Select initial position and initial velocity.

[0103] The selected initial braking level should be lower than the level corresponding to the train's normal braking deceleration, and the selected initial speed should be greater than the electric braking force exit threshold speed.

[0104] (2) Step 2: Conduct a single test and obtain the braking time t and braking distance s for that test.

[0105] 1) Step 2.1: The target running curve planning module plans the target running curve according to EB0 mode / non-EB0 mode.

[0106] Based on the {initial level, initial speed, braking distance, braking time} given in step 1 and the set EB0 / non-EB0 mode, the target running curve planning module generates the target running curve.

[0107] 2) Step 2.2: The speed following control module controls the train to stop according to the target running curve.

[0108] Based on the target running curve generated in step 2.1, the speed follower control module controls the train until it stops at the station.

[0109] 3) Step 2.3: Review the test record data and extract the braking time t and braking distance s of the train from the initial speed to zero speed.

[0110] (3) Step 3: Repeat the experiment multiple times and calculate the average value.

[0111] (4) Step 4: Obtain the proxy model under EB0 mode - [Ve,Re,Se,Te], and obtain the proxy model under non-EB0 mode - [Va,Ra,Sa,Ta].

[0112] In this embodiment, the method for determining whether the train is in EB0 mode or not includes: based on the train's traction characteristics, train attributes, and predicted maximum braking force demand, determining whether the train is currently in EB0 mode or not; if the EB0 mode conditions are met, the BCU is set to EB0 mode, and the EB0 mode proxy model parameters are assigned to the precise stopping proxy model parameters; if the EB0 mode conditions are not met, the BCU is set to non-EB0 mode, and the non-EB0 mode proxy model parameters are assigned to the precise stopping proxy model parameters. Specifically, the system evaluates the train's traction performance, current speed, braking force demand, and track conditions, such as gradient and upcoming stations, to determine whether it can stop safely and accurately using only electric braking. If the train conditions and track conditions allow for EB0 mode, the system sets the brake control unit (BCU) to EB0 mode and loads the proxy model parameters applicable to EB0 into the precise stopping control module for subsequent precise braking control. If the conditions are not met in EB0 mode, the system will switch to non-EB0 mode, adjust the BCU settings accordingly, and load the proxy model parameters for non-EB0 mode. Among them, if the ratio of the train's maximum available electric braking force to the maximum braking force demand is less than a predetermined threshold (e.g., 120%), non-EB0 mode will be selected; otherwise, EB0 mode will be selected.

[0113] This embodiment includes two modules: a target running curve planning module and a speed following control module. The former generates a target running curve based on train status, track information, etc., while the latter periodically outputs traction and braking levels based on the target running curve to control the train.

[0114] The target running curve planning module uses the train theoretical model and surrogate model as the basis for calculation, and the line speed limit as the constraint, to generate a target running curve that meets the planned running time requirements. This target running curve includes the target speed (i.e., the VS curve) at each location from the starting point to the stopping point. Figure 5 As shown.

[0115] The execution flow of the target curve planning module is as follows: Figure 6 As shown. The steps are explained below:

[0116] (1) Step 1: Obtain train status, line speed limit, gradient information, operation plan, etc.

[0117] Obtain train status, line speed limits, gradient information, and operation plans from TCMS, CCU, local files, etc.

[0118] (2) Step 2: Clarify planning information: planning start point Ps, planning end point Pe, planning initial velocity Vt0;

[0119] The planning start point Ps, planning end point Pe, and planning initial velocity Vt0 are obtained from the input information.

[0120] (3) Step 3: Determine whether the EB0 mode meets the conditions based on braking capacity, train attributes and predicted braking force requirements;

[0121] Based on the current train traction characteristics, train attributes, and the maximum braking force requirement during the train's stopping phase, determine whether the EB0 mode meets the stopping requirements. If it does, it is determined to be the EB0 mode and the BCU is set to the EB0 mode; otherwise, it is determined to be the non-EB0 mode and the BCU is set to the non-EB0 mode.

[0122] (4) Step 4.1: Assign the EB0 mode agent model parameters to the precise parking agent model parameters, [Vd=Ve,Sd=Se,Rd=Re,Td=Te];

[0123] (5) Step 4.2: Assign the non-EB0 mode agent model parameters to the precise parking agent model parameters, [Vd=Va,Sd=Sa,Rd=Ra,Td=Ta];

[0124] (6) Step 5: Plan the target operation curve for the precise parking stage, i.e., the target operation curve for the [Pe-Sd,Pe] stage;

[0125] The target operation curve for the precise parking stage is planned. The starting point of this stage is Pe-Sd, the ending point is the planned ending point Pe, the starting speed of this stage is Vd, the ending speed is 0, the stage time is Td, and Sd is the braking distance of the precise parking stage.

[0126] (7) Step 6: Plan the target operation curve for the precise parking pre-stage, i.e. the target operation curve for the [Pe-St-Sd,Pe-Sd] stage;

[0127] This step is used to plan the target running curve for the precise parking pre-stage. The starting point of this stage is Pe-St-Sd, and the ending point is the planned endpoint Pe-Sd. This stage is a transition stage, and its purpose is to ensure that the train reaches the designated position [Pe-Sd] as close as possible to the initial speed Vd and initial stage Rd during actual operation. Therefore, in this stage, the planned curve is designed according to two requirements: 1) the stage is fixed at Rd; 2) the duration of this stage is Th. The stage Rd and the transition stage duration Th need to be determined through optimization calculations based on the train model and track model; St is the train displacement under the fixed stage Rd and duration Th.

[0128] (8) Step 7: Plan the target running curve for the regular stage, that is, the target running curve in the interval [Ps, Pe-St-Sd];

[0129] This step is used to plan the target operation curve for the regular stage. The train speed, train class, and train position at the end of this stage should be consistent with the train speed, train class, and train position at the beginning of the "precise stopping pre-stage" in step 6.

[0130] (9) Step 8: Output the complete target running curve;

[0131] In this step, the target running curves of the three stages are combined into a complete target running curve and sent to the speed following control module.

[0132] In the above steps, the planning sequence is from the endpoint Pe to the starting point, and then backwards. The sequence is: precise parking stage, precise parking pre-stage, and regular operation stage. The starting point for the precise parking stage is [Pe-Sd, Pe], the starting point for the precise parking pre-stage is [Pe-Sd-St, Pe-Sd], and the starting point for the regular operation stage is [Ps, Pe-Sd-St]. A schematic diagram of the process is shown in Figure 10.

[0133] Where Vd, Sd, Rd, and Td are surrogate model parameters, Ps and Pe are analytical input data for the planning algorithm, Th is a conventional value, and St is the back-calculated value from the theoretical model. Sd represents the braking distance during the precise parking phase.

[0134] Sd is the braking distance Sd in the precise parking stage surrogate model [Vd,Sd,Rd,Td}.

[0135] The value St is calculated by the software based on the theoretical model. In this stage, the train's state at the end of the stage is known (train speed is Vd, train class is Rd, and train position is Sd). The train travels at a fixed class Rd for a time Th during this stage. Therefore, the time Th can be calculated iteratively from the state {Vd, Rd, Sd} using the theoretical model. The distance the train travels within the time Th is St.

[0136] Understandably, the execution flow of this target running curve planning module details how to achieve precise stopping of urban rail transit through a systematic process, from acquiring train status information to outputting the final target running curve. First, the system collects information about the train's current status, line speed limits, gradient information, and operating plans from various sources (such as TCMS, CCU, and local files). Next, based on this information, the system determines the train's start and end points, as well as its initial speed, laying the foundation for subsequent running curve planning. The system also evaluates the train's braking capacity and attributes to determine if the conditions for the EB0 mode are met, selecting the appropriate mode and assigning corresponding surrogate model parameters to the selected mode. Then, target running curves are planned separately for the precise stopping stage and the pre-precise stopping stage. These curves reflect the train's speed changes and positional relationships as it approaches the stopping point, ensuring the train stops accurately according to predetermined parameters. In the normal stage, the train runs at the set speed and level until it enters the more precise stopping control stage. Finally, these phased target running curves are integrated into a complete running curve for use by the speed following control module to achieve precise control and stopping throughout the entire journey. This process not only optimizes the efficiency of train operation, but also greatly improves the accuracy and safety of stopping.

[0137] The speed following control module adjusts the train's traction and braking forces based on the generated target running curve. This module uses a linear quadratic regulator (LQR) as its control module and the target running curve as the following target. It periodically outputs control levels to achieve train control, ensuring that the deviation between the train speed and the speed on the target running curve at the same position is less than 1 km / h during operation, and that the changes in train dynamics during operation are as follows: Figure 8 As shown, the execution flow of the speed following control module is as follows: Figure 7 As shown, the steps are explained below.

[0138] (1) Step 1: Select the proxy model based on EB0 mode / non-EB0 mode.

[0139] Select the agent model based on the current train's EB0 mode / non-EB0 mode. If the current mode is EB0, assign the EB0 mode agent model parameters to the precise parking agent model parameters, [Vd=Ve,Sd=Se,Rd=Re,Td=Te]. Otherwise, assign the non-EB0 mode agent model parameters to the precise parking agent model parameters, [Vd=Va,Sd=Sa,Rd=Ra,Td=Ta].

[0140] (2) Step 2: Obtain the target operating curve for the current cycle from the target operating curve planning module, and obtain train status information and line information from TCMS, CCU, etc.

[0141] (3) Step 3: Determine whether to stop.

[0142] (4) Step 4: Determine whether the precise parking stage has been entered.

[0143] Determine whether to enter the precise parking stage based on the distance from the current location to the parking spot. If the precise parking stage is entered, proceed to step 5; otherwise, proceed to steps 6-8.

[0144] (5) Step 5: Output fixed level bit Rd.

[0145] (6) Step 6: Extract the target running curve segment from the current location to the stop station.

[0146] (7) Step 7: Obtain the current cycle train operating conditions, traction braking force, and train speed.

[0147] (8) Step 8: Calculate the current cycle traction braking level according to the LQR algorithm and output it to the DCU / BCU.

[0148] Based on the current cycle train operating conditions, traction braking force, and train speed obtained above, with the impact rate as a constraint and the target running curve as the tracking target, the traction braking level is calculated and sent to the DCU / BCU.

[0149] (9) Step 9: Wait for the calculation cycle to end.

[0150] According to the set execution cycle, subtract the execution time of steps 2-8, and call the Sleep function to wait.

[0151] Understandably, the speed following control module uses a linear quadratic regulator (LQR) as its control algorithm. It dynamically adjusts traction and braking levels using data received from the target operating curve planning module, as well as real-time train status and track information obtained from the Train Control Management System (TCMS) and the Onboard Computing Unit (CCU). First, based on whether the train currently meets the conditions for EB0 or ​​non-EB0 mode, an appropriate surrogate model is selected and relevant parameters are loaded. Then, the module detects whether the train has reached the predetermined stopping stage and executes corresponding control strategies, such as fixing the braking level or continuing to adjust the traction and braking force. Throughout the process, the LQR algorithm periodically calculates and outputs the optimal control level based on the actual train operation and the requirements of the target operating curve, ensuring that the deviation between the train speed and the target operating curve remains less than 1 km / h, thereby achieving high-precision and high-safety stopping control. This process, through continuous monitoring and adjustment, ensures that the train can accurately correspond to its ideal position and speed on the operating curve at any given time, ultimately achieving the goal of precise stopping.

[0152] The execution flow of the functional module is as follows Figure 9 The specific process is as follows:

[0153] This functional module is a key component of the urban rail transit precision parking system, its core function being to ensure that the train can perform precise stops based on real-time conditions and predetermined stopping requirements. The process begins with collecting crucial train operation data, including the train's traction and braking characteristics, various physical attributes, and the predicted maximum braking force requirement during the stopping phase. Based on this data, the system assesses whether the current operating conditions are suitable for the electric braking to zero speed (EB0) mode. If conditions permit, the target operating curve planning module generates an operating curve suitable for the EB0 mode; if not, it generates an operating curve suitable for non-EB0 modes. The generated target operating curve is then passed to the speed following control module, which uses a linear quadratic regulator (LQR) control algorithm to finely control the train, ensuring that the train's actual trajectory precisely matches the target operating curve, thereby achieving high-precision stopping under various operating conditions.

[0154] In summary, after applying this invention, urban rail trains can achieve a parking accuracy of 15cm in EB0 mode and 20cm in non-EB0 mode.

[0155] Optionally, specific examples in this embodiment can refer to the examples described in the above embodiments, and will not be repeated here.

[0156] In specific implementations of this invention, the above embodiments can be consulted, and the corresponding technical effects can be achieved.

[0157] The present invention also provides an apparatus for implementing a precise parking method, comprising the following modules:

[0158] Information acquisition module: used to acquire current train status information and track information;

[0159] Mode selection module: used to select the parking mode based on the current status information of the train and the line information, wherein the parking mode includes EB0 mode or non-EB0 mode;

[0160] Model building module: used to build a proxy model for the corresponding parking mode. The method for building the proxy model includes: selecting an initial level and initial speed; conducting a single test and obtaining the braking time and braking distance of the single test, which is based on a predetermined EB0 mode or non-EB0 mode; and building a proxy model in the EB0 mode or non-EB0 mode based on the results of the single test. The proxy model includes four parameters: initial level, initial speed, braking time, and braking distance.

[0161] The target operation curve planning module is used to plan the target operation curve from the current location to the predetermined parking point based on the selected parking mode, the constructed agent model, and the theoretical model. The target operation curve includes three stages: the normal operation stage, the precise parking preparatory stage, and the precise parking stage.

[0162] Speed ​​following control module: Used to periodically receive the train's current status information and the complete target running curve, and perform the following operations:

[0163] Obtain the target running curve at the current moment, and obtain the current train status information and track information;

[0164] Determine if the precise parking phase has been entered:

[0165] If it enters, a fixed-level position will be output to stop the vehicle;

[0166] If the target running curve from the current position to the stop is not entered, the train operating condition, traction braking force, and train speed at the current moment are obtained. Based on the obtained train operating condition, traction braking force, and train speed at the current moment, with the impact rate as a constraint, and with the target running curve from the current position to the stop as the tracking target, the traction braking level is calculated and sent to the DCU / BCU.

[0167] Repeat the above steps periodically until parking is complete.

[0168] It is understood that the embodiments described herein can be implemented in hardware, software, firmware, middleware, microcode, or a combination thereof. For hardware implementation, the processing unit can be implemented in one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field-programmable gate arrays (FPGAs), general-purpose processors, controllers, microcontrollers, microprocessors, other electronic units for performing the functions described herein, or combinations thereof.

[0169] For software implementation, the techniques described herein can be implemented by units that perform the functions described herein. The software code can be stored in memory and executed by a processor. For example... Figure 11 As shown, the electronic device includes a memory 10 and a processor 20; wherein the memory 10 is used to store a readable program, which is loaded and executed by the processor 20 to implement the precise parking method as described above.

[0170] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0171] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working process of the above-described apparatus and unit can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0172] In the embodiments provided in this application, it should be understood that the disclosed apparatus and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative. For instance, the division of modules is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple modules or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be an indirect coupling or communication connection through some interfaces, devices, or units, and may be electrical, mechanical, or other forms.

[0173] In addition, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0174] If a function is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solutions of the embodiments of this application, or the parts that contribute to the prior art, or parts of the technical solutions, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, ROM, RAM, magnetic disks, or optical disks. It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0175] One or more embodiments in this application are intended to cover all such substitutions, modifications, and variations that fall within the broad scope of this application. Therefore, any omissions, modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of one or more embodiments in this application should be included within the protection scope of this application.

Claims

1. A precise parking method, characterized in that, Includes the following steps: Obtain current train status information and route information; The stopping mode is selected based on the current train status information and line information. The stopping mode includes EB0 mode or non-EB0 mode. Constructing a proxy model for the corresponding parking mode, wherein the method for constructing the proxy model for the parking mode includes: Select the initial stage and initial velocity; A single test is conducted and the braking time and braking distance of the single test are obtained, the single test being conducted based on a predetermined EB0 mode or a non-EB0 mode; Based on the results of a single test, a proxy model is constructed in either EB0 mode or non-EB0 mode. This proxy model includes four parameters: initial level, initial velocity, braking time, and braking distance. Based on the selected parking mode, the constructed agent model, and the theoretical model, a target operation curve from the current location to the predetermined parking point is planned. This target operation curve includes three stages: the normal operation stage, the precise parking preparatory stage, and the precise parking stage. During the precise parking phase, a proxy model is used to plan the first target running curve; In the preliminary stage of precise parking, a theoretical model is used to plan the second target operation curve; During the normal operation phase, the operating curve of the third objective is planned using theoretical models; The third target operation curve, the second target operation curve, and the first target operation curve of the three stages are integrated to form a complete target operation curve. The complete target operation curve is sent to the speed following control module to control the train to stop. In the precise stopping pre-stage, the train is made to run at a fixed level.

2. The precise parking method as described in claim 1, characterized in that, The method for selecting EB0 mode or non-EB0 mode based on the current status information and track information of the train includes: obtaining the current status information and track information of the train from the train control and management system, the train on-board unit and local files; analyzing the train traction characteristics, train attributes and predicted maximum braking force demand; and calculating the ratio of the train's maximum available electric braking force to the maximum braking force demand. If the ratio is less than a predetermined threshold value, the non-EB0 mode is selected; otherwise, the EB0 mode is selected.

3. The precise parking method as described in claim 1, characterized in that, In the construction steps of the proxy model, the selected initial stage is smaller than the stage corresponding to the train's common braking deceleration, and the initial speed is greater than the electric braking force exit threshold speed.

4. The precise parking method as described in claim 1, characterized in that, In the construction of the proxy model, a single test is repeated multiple times, with each single test set according to the same initial conditions and parking mode; the average braking time and average braking distance of multiple single tests are calculated.

5. The precise parking method as described in claim 1, characterized in that, The steps for planning the target travel curve from the current location to the designated parking point include: Determine the planned starting point Ps and planned ending point Pe of the train, and set the planned initial speed Vt0; During the precise stopping phase, based on the selected agent model and the actual train operation status, a first target running curve from Pe-Sd to Pe is planned, where the starting speed is set to Vd and the ending speed is 0, so that the precise stopping phase is completed within the time Td. In the precise parking pre-stage, a theoretical model is used to plan the second target running curve from Pe-St-Sd to Pe-Sd, so that the train runs at a fixed level Rd and meets the duration Th of the precise parking pre-stage. During the regular operation phase, a third target operation curve is planned from Ps to Pe-St-Sd to ensure that the train speed and class during the regular operation phase are consistent with the starting point of the precise stopping phase. By using the target operating curve planned above, the train can be controlled to stop precisely from its current position to the predetermined stopping point. Where Sd is the braking distance during the precise stopping phase, and St is the train displacement under a fixed level Rd and a duration Th.

6. The precise parking method as described in claim 1, characterized in that, The speed following control module periodically receives the train's current status information and the target running curve.

7. The precise parking method as described in claim 6, characterized in that, The steps for sending the complete target running curve to the speed following control module to control the train to stop include: Obtain the target running curve at the current moment, and obtain the current train status information and track information; Determine if the precise parking phase has been entered: If it enters, a fixed-level position will be output to stop the vehicle; If not entered, extract the target running curve from the current position to the stop station; obtain the train operating conditions, traction braking force, and train speed at the current moment; based on the obtained train operating conditions, traction braking force, and train speed at the current moment, with the impact rate as a constraint, and with the target running curve from the current position to the stop station as the tracking target, calculate the traction braking level and send it to the DCU / BCU. Repeat the above steps periodically until parking is complete.

8. An apparatus for achieving a precise parking method, characterized in that, Includes the following modules: Information acquisition module: used to acquire current train status information and track information; Mode selection module: used to select the parking mode based on the current status information of the train and the line information, wherein the parking mode includes EB0 mode or non-EB0 mode; Model building module: used to build a proxy model for the corresponding parking mode. The method for building the proxy model includes: selecting an initial level and initial speed; conducting a single test and obtaining the braking time and braking distance of the single test, which is based on a predetermined EB0 mode or non-EB0 mode; and building a proxy model in the EB0 mode or non-EB0 mode based on the results of the single test. The proxy model includes four parameters: initial level, initial speed, braking time, and braking distance. The target operation curve planning module is used to plan the target operation curve from the current location to the predetermined parking point based on the selected parking mode, the constructed agent model, and the theoretical model. The target operation curve includes three stages: the normal operation stage, the precise parking preparatory stage, and the precise parking stage. Speed ​​following control module: Used to periodically receive the train's current status information and the complete target running curve, and perform the following operations: Obtain the target running curve at the current moment, and obtain the current train status information and track information; Determine if the precise parking phase has been entered: If it enters, a fixed-level position will be output to stop the vehicle; If the target running curve from the current position to the stop is not entered, the train operating condition, traction braking force, and train speed at the current moment are obtained. Based on the obtained train operating condition, traction braking force, and train speed at the current moment, with the impact rate as a constraint, and with the target running curve from the current position to the stop as the tracking target, the traction braking level is calculated and sent to the DCU / BCU. Repeat the above steps periodically until parking is complete.

9. An electronic device, characterized in that, include: At least one memory and at least one processor; The at least one memory is used to store a readable program; The at least one processor is configured to invoke the readable program to execute the precise parking method according to any one of claims 1 to 7.

10. A computer-readable medium, characterized in that, The computer-readable medium stores computer instructions that, when executed by a processor, cause the processor to perform the precise parking method according to any one of claims 1 to 7.

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