Self-adaptive high-speed train tracking operation control method and system considering driving scene

Abstract

The invention provides a self-adaptive high-speed train tracking operation control method and system considering a driving scene, and belongs to the technical field of train operation control. Train operation state data are acquired, and data statistics and processing are carried out; processing and classifying the train driving characteristic data by using a pre-trained driving style recognition model to obtain a driving style recognition result; according to an MIC algorithm, obtaining a driving characteristic parameter having the maximum correlation with the driving style and a corresponding parameter value; according to the driving characteristic parameters and the corresponding parameter values, model prediction control and driving style online identification are combined, the MPC model is used for controlling the trains in different driving modes and driving styles, and self-adaptive group operation of the high-speed rail trains is achieved. According to the invention, the flexibility and adaptability of the high-speed rail train formation group trains are improved, and a self-adaptive control mode oriented to multiple scenes and multiple modes is realized.

Description

Technical Field

[0001] The present invention relates to the technical field of train operation control, and in particular to an adaptive high-speed train tracking operation control method and system taking driving scenarios into consideration. Background Art

[0002] As the brain and central nervous system of high-speed rail systems, the train control system (TCS) plays a vital role in ensuring safe and efficient train operation. Currently, typical train control systems used in high-speed rail include CTCS-3 (China Train Control System), CTCS-2, ETCS (European Train Control System), ATC (Automatic Train Control System), and other fixed-section control modes. To meet growing passenger demand, high-speed rail train density is continuously increasing. As train density continues to increase, existing operating intervals are approaching the system's design limits. Therefore, fixed-section control technology is unable to meet the demand for further improvements in operational efficiency. Currently, high-speed train group operation is an important technical means to further shorten train operating intervals and improve operational efficiency. It is the core and key to railway development both domestically and internationally.

[0003] The theory and practice of virtual formations are already being actively developed and researched. Virtual formation uses train-to-train communication technology to replace existing physical coupler links to achieve train formation and operation. During the train formation and operation process, the virtual formation train fleet can be treated as a single train, and the rear train will always follow the front train while maintaining a certain tracking distance from the front train. Train virtual formation technology can monitor and maintain the tracking distance between trains with less infrastructure intervention. Compared with traditional train control methods, it greatly shortens the tracking distance between trains and improves the operating efficiency of existing lines. By forming and unforming trains while the train is in motion, that is, using virtual formation to form train fleets, line capacity can be further increased.

[0004] Unlike moving blocks, virtual train formation offers a new approach to improving operating efficiency. However, designing a real-time control algorithm to rationally resolve conflicts among multiple objectives remains a challenging issue in the coordinated control of virtual coupled train systems. Over the years, many active efforts have been made to address the coordinated control of high-speed train control systems. Among these efforts, model predictive control (MPC) algorithms are capable of handling conflicts among multiple constraints and offer unique advantages in addressing certain types of multi-objective control problems. Although numerous studies have significantly improved the performance of MPC systems, enhancing control accuracy and stability, most current train control systems utilize only a set of fixed parameters. However, using a fixed set of parameters may not address driving style requirements and mode selection in a wide range of scenarios (e.g., for very conservative or very aggressive driving conditions). Therefore, there is a need to further enhance the flexibility and adaptability of MPC in train formation. Current research and simulations typically assume that the lead train has a single, deterministic behavior pattern, which simplifies the complexity of train behavior and spacing control. While railway operations are highly organized and strictly planned according to train schedules, real-time changes in the behavior of leading trains, including temporary or random adjustments to planned speed profiles, require following trains to adaptively adjust their behavior and spacing. This adaptability highlights the flexibility of model predictive control (MPC) methods in train platooning and demonstrates the system's ability to handle complex transportation environments and dynamic conditions.

[0005] Therefore, for high-speed railway train formation tracking control, how to design the control system to meet different driving modes and driving styles while meeting the requirements of following, comfort and accuracy, so as to provide a better driving experience for different driving modes and styles of high-speed railway train automatic driving systems, this issue needs further research. Summary of the Invention

[0006] The object of the present invention is to provide an adaptive high-speed train tracking operation control method and system taking driving scenarios into consideration, so as to solve at least one technical problem existing in the above-mentioned background technology.

[0007] In order to achieve the above object, the present invention adopts the following technical solutions:

[0008] In a first aspect, the present invention provides an adaptive high-speed train tracking operation control method considering driving scenarios, comprising:

[0009] Obtain train operation status data, and perform data statistics and processing;

[0010] The pre-trained driving style recognition model is used to process and classify the train driving feature data to obtain the driving style recognition results;

[0011] According to the MIC algorithm, the driving characteristic parameters with the greatest correlation with the driving style and the corresponding parameter values ​​are obtained;

[0012] Based on the driving characteristic parameters and their corresponding parameter values, model predictive control and online driving style identification are combined, and the MPC model is used to control trains with different driving modes and styles, realizing adaptive group operation of high-speed trains.

[0013] As a further limitation of the first aspect of the present invention, the training of the driving style recognition model includes: extracting historical trajectory data and driving characteristic data of the tracked train, including the acceleration of the vehicle, the spatial distance between the vehicle and the preceding vehicle, the time distance between the vehicle and the preceding vehicle, the relative speed between the vehicle and the preceding vehicle, and the acceleration characteristic variables of the vehicle, and using a Gaussian mixture model to identify the driver's style.

[0014] As a further limitation of the first aspect of the present invention, the correlation between the characteristic variable and the driving style is calculated using a MIC algorithm; wherein the MIC algorithm measures the linear or nonlinear correlation between two variables by evaluating the mutual information of continuous variables using an optimal data binning technique, and the mutual information of two discrete vectors is defined as:

[0015]

[0016] Among them MI D is the mutual information of two discrete vectors x and y, p(x,y) is the joint probability mass function of x and y, and p(x) and p(y) are the marginal mass functions of x and y.

[0017] As a further limitation of the first aspect of the present invention, in the MPC adaptive control stage, the corresponding optimal driving mode and driving style are selected based on the driving style pattern and time series pattern provided by the data preprocessing layer and the feature extraction layer.

[0018] As a further limitation of the first aspect of the present invention, the control model objective function is:

[0019]

[0020] The constraints are:

[0021] s i (k+1)=s i (k)+v i (k)T s ,

[0022] v i (k+1)=v i (k)+(u i (k)-w i(k)-g i (k))T s ,

[0023] d safe ≥0,

[0024] 0≤v i (k)≤v max ,

[0025] 0≤v i (k)≤v lim (s k ),

[0026] -u br ≤u i (k)≤u dr ,

[0027] -j max ≤j i (k)≤j max ,

[0028]

[0029] where:

[0030] d i (k)=s i-1 (k)-s i (k)-L,

[0031]

[0032] In the objective function formula, the first term is to control the tracking interval d between the rear vehicle and the front vehicle. i (k) size and expected interval d des (k) The second term is to control the speed of the following vehicle v i (k) Approaching the speed of the preceding vehicle, the third term controls the acceleration u of the following vehicle i (k) changes, K D , K v , K u are weight coefficients respectively; expected interval d des (k), maximum train speed v max , maximum train acceleration u dr , maximum train jerk j max Related to the train's control mode, driving style classification, and operating conditions; d safe (k) safety distance, d0 is a constant; v max is the maximum speed of the train, v lim (s k ) is the train at s k Speed ​​limit at the line location; di (k) is the real-time spatial distance between the vehicle and the preceding vehicle.

[0033] As a further limitation of the first aspect of the present invention, after performing group control for one control cycle, predictive control for the next cycle is performed, and the predictive control output is cyclically performed. Taking into account the fact that during actual driving, there may be situations in which the train triggers braking or the trajectory characteristics change due to changes in driving status or interruptions in vehicle-to-vehicle and vehicle-to-ground communication, it is necessary to compare the continuously output predictive control results with the newly acquired historical trajectory, and adjust the algorithm online according to the different comparison results.

[0034] In a second aspect, the present invention provides an adaptive high-speed train tracking and operation control system that takes driving scenarios into consideration, comprising:

[0035] Acquisition module, used to obtain train operation status data and perform data statistics and processing;

[0036] The recognition module is used to process and classify the train driving feature data using a pre-trained driving style recognition model to obtain a driving style recognition result;

[0037] A calculation module, configured to obtain the driving characteristic parameter having the greatest correlation with the driving style and the corresponding parameter value according to the MIC algorithm;

[0038] The control module is used to combine model predictive control and online driving style identification based on driving characteristic parameters and corresponding parameter values, and use the MPC model to control trains with different driving modes and styles to achieve adaptive group operation of high-speed trains.

[0039] In a third aspect, the present invention provides a non-transitory computer-readable storage medium, which is used to store computer instructions. When the computer instructions are executed by a processor, the adaptive high-speed train tracking operation control method considering the driving scenario as described in the first aspect is implemented.

[0040] In a fourth aspect, the present invention provides a computer device comprising a memory and a processor, wherein the processor and the memory communicate with each other, the memory stores program instructions that can be executed by the processor, and the processor calls the program instructions to execute the adaptive high-speed train tracking operation control method considering the driving scenario as described in the first aspect.

[0041] In a fifth aspect, the present invention provides an electronic device comprising: a processor, a memory, and a computer program; wherein the processor is connected to the memory, and the computer program is stored in the memory. When the electronic device is running, the processor executes the computer program stored in the memory so that the electronic device executes instructions for implementing the adaptive high-speed train tracking operation control method considering the driving scenario as described in the first aspect.

[0042] The beneficial effects of the present invention are: further improving the flexibility and adaptability of high-speed train formation group trains, proposing a high-speed train control model based on the combination of a new type of MPC and driving style, no longer using fixed and unified parameters and models to design the control system, but instead matching and selecting the corresponding rear vehicle control mode and driving style in real time according to the actual driving status and driving mode of the leading train, realizing an adaptive control method for multiple scenarios and multiple modes, improving the adaptability and effectiveness of the control method, and solving the problem of adaptive and efficient tracking operation control of high-speed train groups in different scenarios.

[0043] Additional advantages of the present invention will be more clearly given in the following description or learned through practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0045] Figure 1 This is a flow chart of an adaptive high-speed train tracking operation control algorithm considering driving scenarios according to an embodiment of the present invention. DETAILED DESCRIPTION

[0046] The embodiments of the present invention are described in detail below. Examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present invention and are not to be construed as limiting the present invention.

[0047] Those skilled in the art will understand that unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by those skilled in the art to which this invention belongs.

[0048] It should also be understood that terms, such as those defined in commonly used dictionaries, should be understood to have a meaning consistent with their meaning in the context of the prior art and will not be interpreted in an idealized or overly formal sense unless as defined herein.

[0049] Those skilled in the art will appreciate that, unless otherwise stated, the singular forms "a," "an," "said," and "the" used herein may also include plural forms. It should be further understood that the term "comprising" used in the specification of the present invention refers to the presence of the stated features, integers, steps, operations, elements, and / or components, but does not preclude the presence or addition of one or more other features, integers, steps, operations, elements, and / or groups thereof.

[0050] In the description of this specification, reference to the terms "one embodiment," "some embodiments," "examples," "specific examples," or "some examples" means that the specific features, structures, materials, or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. Moreover, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples. Those skilled in the art may combine and integrate different embodiments or examples described in this specification, as well as features of different embodiments or examples, unless otherwise contradictory.

[0051] To facilitate understanding of the present invention, the present invention is further explained below with reference to specific embodiments in conjunction with the accompanying drawings. However, the specific embodiments do not constitute a limitation on the embodiments of the present invention.

[0052] Those skilled in the art should understand that the drawings are merely schematic diagrams of embodiments, and the components in the drawings are not necessarily necessary for implementing the present invention.

[0053] In this invention, we aim to solve the problems of high-speed railway train trajectory prediction and train group tracking control, further improve the flexibility and adaptability of MPC in formation group trains, and propose a high-speed railway train control model based on the combination of MPC and driving style. We intend to realize adaptive control methods for multiple scenarios and multiple modes, and improve the adaptability and pertinence of the control method.

[0054] First, we analyze the driving characteristics of high-speed trains. The operating state of high-speed trains is influenced by factors such as the operating environment and system performance, resulting in a high degree of uncertainty. Drivers control trains at the speed permitted by the Automatic Train Protection (ATP) system. Due to the dynamic changes in line capacity demand and the complex and ever-changing operating environment, drivers' driving styles are highly variable, and trajectory differences are particularly pronounced. Based on the behaviors and attitudes of high-speed train drivers and their actual driving conditions, driving styles are categorized according to scenarios commonly encountered in actual railway train operation. Then, driving style recognition based on the Gaussian Mixture Model (GMM) is used to identify the current scenario and driving state of the tracked train, clarifying the driver's multiple driving characteristics over a short period of time. This allows for the identification of driving characteristics for high-speed train tracking operations, providing input for short-term train trajectory prediction.

[0055] By combining model predictive control with online driving style identification, and based on the previous accurate driving style classification, driving characteristic parameter results, and trajectory prediction results, a train group operation control method based on model predictive control (MPC) is proposed. This model can design control systems that meet different driving modes and driving styles for different scenarios, and realize the adaptive selection of driving style and control model for tracking trains, while meeting the requirements of followability, comfort, and accuracy. It provides a better driving experience for high-speed railway train automatic driving systems with different driving modes and styles, so as to meet the styles and preferences of different train driving scenarios.

[0056] After one cycle of group control, predictive control is performed for the next cycle, and the predictive control output is cyclically applied. However, in actual driving, temporary conditions such as changes in driving state or interruptions in vehicle-to-vehicle and vehicle-to-ground communication may occur, causing train braking or trajectory characteristics to change. Therefore, it is necessary to compare the continuously output predictive control results with the newly acquired historical trajectory. Based on the different comparison results, the algorithm is adjusted online to achieve online adjustment of high-speed train adaptive group operation control.

[0057] Example 1

[0058] In this embodiment 1, an adaptive high-speed train tracking operation control system that takes driving scenarios into consideration is first provided, including: an acquisition module for acquiring train operation status data and performing data statistics and processing; an identification module for processing and classifying train driving characteristic data using a pre-trained driving style recognition model to obtain a driving style recognition result; a calculation module for obtaining driving characteristic parameters and corresponding parameter values ​​that are most correlated with the driving style based on the MIC algorithm; and a control module for combining model predictive control and online driving style recognition based on the driving characteristic parameters and corresponding parameter values, and using an MPC model to control trains with different driving modes and driving styles, thereby realizing adaptive group operation of high-speed trains.

[0059] In this embodiment, based on the above-mentioned system, an adaptive high-speed train tracking operation control method considering driving scenarios is implemented, including: obtaining train operation status data and performing data statistics and processing; using a pre-trained driving style recognition model to process and classify train driving characteristic data to obtain driving style recognition results; obtaining driving characteristic parameters with the greatest correlation with driving style and corresponding parameter values ​​based on the MIC algorithm; combining model predictive control and online driving style recognition based on the driving characteristic parameters and corresponding parameter values, and using the MPC model to control trains with different driving modes and driving styles to achieve adaptive group operation of high-speed trains.

[0060] like Figure 1 As shown, the adaptive high-speed train tracking operation control method considering driving scenarios provided by the present invention includes the following process steps:

[0061] The first step is to obtain train operation status data and perform data statistics and processing; the second step is to use a pre-trained driving style recognition model to process and classify the train driving characteristic data to obtain the driving style recognition results; the third step is to obtain the driving characteristic parameters with the greatest correlation with the driving style and the corresponding parameter values ​​based on the MIC algorithm; the fourth step is to combine model predictive control and online driving style recognition, and use the MPC model to control trains with different driving modes and driving styles, realizing adaptive group operation of high-speed trains.

[0062] In this embodiment, the driving style recognition model is trained based on a Gaussian Mixture Model (GMM). The specific training method steps are as follows:

[0063] Step 1: Data preparation stage: historical trajectory collection and processing based on real high-speed train operation data;

[0064] Organize driving characteristic data: characteristic variables such as train speed, relative speed, spatial interval, time interval, acceleration, jerk, etc., and use these variables and their statistics (maximum value, minimum value, average value) for driving style classification in step 2.

[0065] Step 2: Driving style recognition phase: The driving style is identified through the driving style recognition model. Based on the Gaussian mixture model unsupervised clustering algorithm, the optimal cluster label of the preceding vehicle is obtained according to the manually labeled features. The label contains three styles: aggressive, moderate, and conservative.

[0066] The historical trajectory data and driving characteristics of the trains tracked in step 1 are extracted and used for unsupervised clustering to identify driving styles. This approach primarily uses historical data between the train and the preceding vehicle, including characteristic variables such as the train's acceleration, the spatial distance between them, the time headway between them, the relative speed between them, and the train's jerk, to identify the driver's style using a Gaussian mixture model. The GMM can be represented by a weighted sum of sub-Gaussian components, with each cluster modeled using a different Gaussian distribution function.

[0067] For a given observed variable X = {x}, GMM can be described as the following combined distribution function:

[0068]

[0069] Among them, k is the total number of sub-Gaussian models, α k is the weight of each Gaussian mixture model, indicating the proportion of each Gaussian model in the entire model, and its value reflects the influence of the current model on the entire model. k Satisfy the condition α k >0 and is the probability density function of the kth Gaussian distribution, is the general term for Gaussian distribution parameters, μ k , are the mean and variance of the Gaussian distribution, which satisfy the following probability density expression:

[0070] The Gaussian mixture model parameter solution requires the use of the maximum likelihood estimation concept. The GMM model can be trained using the expectation maximization (EM) maximum likelihood estimation algorithm. This algorithm iteratively calculates the maximum cost function and obtains an approximate Gaussian mixture model parameter set θ* that satisfies the following formula:

[0071]

[0072] Step 3: Driving style feature extraction: Based on the MIC method, basic features highly correlated with group labels are evaluated to identify feature variables with a high degree of correlation with driving style, such as expected train interval, expected train speed, relative speed, minimum safe distance, maximum and minimum speeds, and acceleration.

[0073] Feature extraction mainly involves analyzing the correlation between driving style and driving characteristic variables, extracting variables with a high correlation with driving style, and using the main correlated variables in the training of the train adaptive control model. Since the degree of correlation between different characteristic variables and driving style varies among the characteristic variables of the train driver's driving style, in order to reduce the dependence of the driving style recognition layer on the characteristic variables and eliminate non-correlated characteristic variables, the MIC algorithm is used to calculate the correlation between the characteristic variables and driving style. Among them, MIC can evaluate the mutual information of continuous variables by applying the optimal data binning technology and measure the linear or nonlinear correlation between two variables. The mutual information of two discrete vectors is defined as:

[0074]

[0075] Among them MI D is the mutual information of two discrete vectors, p(x,y) is the joint probability mass function of x and y. p(x) and p(y) are the marginal mass functions of x and y.

[0076] Step 4: In the MPC adaptive control stage, based on the driving style pattern and time series pattern given by the data preprocessing layer and feature extraction layer, the corresponding optimal driving mode and driving style are selected. The MPC control parameters are calculated according to step 3.

[0077] The objective function of the control model is:

[0078]

[0079] The constraints are:

[0080] s i (k+1)=s i (k)+v i (k)T s ,

[0081] v i (k+1)=v i (k)+(u i (k)-w i (k)-g i (k))T s ,

[0082] d safe ≥0,

[0083] 0≤vi (k)≤v max ,

[0084] 0≤u i (k)≤v lim (s k ),

[0085] -u br ≤u i (k)≤u dr ,

[0086] -j max ≤j i (k)≤j max ,

[0087]

[0088] where:

[0089] d i (k)=s i-1 (k)-s i (k)-L,

[0090]

[0091] In the objective function formula, the first term is to control the tracking interval d between the rear vehicle and the front vehicle. i (k) size and expected interval d des (k) The second term is to control the speed of the following vehicle v i (k) Approaching the speed of the vehicle in front. The third term is to control the acceleration u of the vehicle behind. i (k) changes, run as smoothly as possible. K D , K v , K u are the weight coefficients of the three target items. p Indicates the model prediction time, u i (k), w i (k), g i (k) represents the train traction acceleration, unit basic resistance and unit additional resistance respectively; a br Indicates the maximum unit braking force; T s represents the sampling interval; j max Indicates the maximum acceleration of the train; L indicates the length of the train;

[0092] Expected interval d des (k), maximum train speed v max , maximum train acceleration u dr , maximum train jerk j maxIt is related to the train's control mode, driving style classification and operating conditions, and needs to be discussed on a case-by-case basis.

[0093] d safe (k) Safety distance is a safety constraint that ensures that the two trains do not collide after emergency braking at any time t = k. d0 is a constant. Considering the error in the actual position of the trains, a safety margin can be set, such as d0 = 10m.

[0094] v max is the maximum speed of the train, v lim (s k ) is the train at s k The speed limit at the route location.

[0095] d i (k) is the real-time spatial distance between the vehicle and the preceding vehicle, which must satisfy the constraint that the distance is greater than or equal to the minimum safety distance.

[0096] The MPC adaptive control mode includes two major categories: V2V communication mode and moving block control mode. The V2V communication mode includes three subcategories: radical, moderate and conservative; the moving block control mode is divided into two subcategories: relative braking mode and moving braking mode.

[0097] The control parameters such as the expected train interval, expected train speed, relative speed, safety distance, maximum and minimum speeds and acceleration are determined according to steps 2 and 3.

[0098] Step 5: Train control mode online adjustment stage

[0099] After one control cycle of group control, the next cycle of predictive control begins, with the predictive control output cyclically repeated. However, in real-world driving, temporary conditions such as changes in driving state or interruptions in vehicle-to-vehicle or vehicle-to-ground communication can trigger braking or alter trajectory characteristics. Therefore, the continuously output predictive control results must be compared with newly acquired historical trajectories. Based on the comparison results, the algorithm is adjusted online, repeating steps 2 and 3.

[0100] Example 2

[0101] This embodiment 2 provides a non-transitory computer-readable storage medium for storing computer instructions. When the computer instructions are executed by a processor, the above-mentioned adaptive high-speed train tracking operation control method considering driving scenarios is implemented. The method includes:

[0102] Obtain train operation status data, and perform data statistics and processing;

[0103] The pre-trained driving style recognition model is used to process and classify the train driving feature data to obtain the driving style recognition results;

[0104] According to the MIC algorithm, the driving characteristic parameters with the greatest correlation with the driving style and the corresponding parameter values ​​are obtained;

[0105] Based on the driving characteristic parameters and their corresponding parameter values, model predictive control and online driving style identification are combined, and the MPC model is used to control trains with different driving modes and styles, realizing adaptive group operation of high-speed trains.

[0106] Example 3

[0107] This embodiment 3 provides a computer device, including a memory and a processor, wherein the processor and the memory communicate with each other, the memory stores program instructions executable by the processor, and the processor calls the program instructions to execute the above-mentioned adaptive high-speed train tracking operation control method considering driving scenarios, the method comprising:

[0108] Obtain train operation status data, and perform data statistics and processing;

[0109] The pre-trained driving style recognition model is used to process and classify the train driving feature data to obtain the driving style recognition results;

[0110] According to the MIC algorithm, the driving characteristic parameters with the greatest correlation with the driving style and the corresponding parameter values ​​are obtained;

[0111] Based on the driving characteristic parameters and their corresponding parameter values, model predictive control and online driving style identification are combined, and the MPC model is used to control trains with different driving modes and styles, realizing adaptive group operation of high-speed trains.

[0112] Example 4

[0113] This embodiment 4 provides an electronic device, including: a processor, a memory, and a computer program; wherein the processor is connected to the memory, and the computer program is stored in the memory. When the electronic device is running, the processor executes the computer program stored in the memory to cause the electronic device to execute instructions for implementing the above-mentioned adaptive high-speed train tracking operation control method considering driving scenarios. The method includes:

[0114] Obtain train operation status data, and perform data statistics and processing;

[0115] The pre-trained driving style recognition model is used to process and classify the train driving feature data to obtain the driving style recognition results;

[0116] According to the MIC algorithm, the driving characteristic parameters with the greatest correlation with the driving style and the corresponding parameter values ​​are obtained;

[0117] Based on the driving characteristic parameters and their corresponding parameter values, model predictive control and online driving style identification are combined, and the MPC model is used to control trains with different driving modes and styles, realizing adaptive group operation of high-speed trains.

[0118] It will be understood by those skilled in the art that embodiments of the present invention may be provided as methods, systems, or computer program products. Thus, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0119] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowcharts and / or block diagrams, as well as combinations of processes and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowcharts and / or block diagrams. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0120] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0121] These computer program instructions can also be loaded onto a computer or other programmable data processing device, and a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, so that the instructions executed on the computer or other programmable device provide the functions for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0122] Although the above describes the specific embodiments of the present invention in conjunction with the accompanying drawings, it is not intended to limit the scope of protection of the present invention. Those skilled in the art should understand that various modifications or variations that can be made by those skilled in the art on the basis of the technical solutions disclosed in the present invention without the need for creative work should be included in the scope of protection of the present invention.

Claims

1. An adaptive high-speed train tracking operation control method considering driving scenarios, characterized in that: include: Obtain train operation status data, and perform data statistics and processing; The pre-trained driving style recognition model is used to process and classify the train driving feature data to obtain the driving style recognition results; According to the MIC algorithm, the driving characteristic parameters with the greatest correlation with the driving style and the corresponding parameter values ​​are obtained; Based on the driving characteristic parameters and their corresponding parameter values, model predictive control and online driving style identification are combined, and the MPC model is used to control trains with different driving modes and styles, realizing adaptive group operation of high-speed trains.

2. The adaptive high-speed train tracking operation control method considering driving scenarios according to claim 1 is characterized in that: The training of the driving style recognition model includes: extracting the historical trajectory data and driving characteristic data of the tracked train, including the acceleration of the vehicle, the spatial distance between the vehicle and the preceding vehicle, the time distance between the vehicle and the preceding vehicle, the relative speed between the vehicle and the preceding vehicle, and the acceleration characteristic variables of the vehicle, and using a Gaussian mixture model to identify the driver's style.

3. The adaptive high-speed train tracking operation control method considering driving scenarios according to claim 1 is characterized in that: The MIC algorithm is used to calculate the correlation between characteristic variables and driving style. MIC evaluates the mutual information of continuous variables by applying the optimal data binning technique to measure the linear or nonlinear correlation between two variables. The mutual information of two discrete vectors is defined as: Among them MI D is the mutual information of two discrete vectors x and y, p(x,y) is the joint probability mass function of x and y, and p(x) and p(y) are the marginal mass functions of x and y.

4. The adaptive high-speed train tracking operation control method considering driving scenarios according to claim 1 is characterized in that: In the MPC adaptive control stage, the corresponding optimal driving mode and driving style are selected based on the driving style pattern and time series pattern given by the data preprocessing layer and feature extraction layer.

5. The adaptive high-speed train tracking operation control method considering driving scenarios according to claim 4 is characterized in that: The objective function of the control model is: The constraints are: s i (k+1)=s i (k)+v i (k)T s , v i (k+1)=v i (k)+(u i (k)-w i (k)-g i (k))T s , d safe ≥0, 0≤v i (k)≤v max , 0≤v i (k)≤v lim (s k ), -in br in i (k)≤u dr , -j max ≤j i (k)≤j max , where: d i (k)=s i-1 (k)-s i (k)-L, In the objective function formula, the first term is to control the tracking interval d between the rear vehicle and the front vehicle. i (k) size and expected interval d des (k) The second term is to control the speed of the following vehicle v i (k) Approaching the speed of the preceding vehicle, the third term controls the acceleration u of the following vehicle i (k) changes, K D , K v , K u are weight coefficients respectively; expected interval d des (k), maximum train speed v max , maximum train acceleration u dr , maximum train jerk j max Related to the train's control mode, driving style classification, and operating conditions; d safe (k) safety distance, d0 is a constant; v max is the maximum speed of the train, v lim (s k ) is the train at s k Speed ​​limit at the line location; d i (k) is the real-time spatial distance between the vehicle and the preceding vehicle.

6. The adaptive high-speed train tracking operation control method considering driving scenarios according to claim 1 is characterized in that: After performing group control for one control cycle, predictive control is performed for the next cycle, and the predictive control output is cyclically performed. Considering that in actual driving, there may be situations in which the train triggers braking or trajectory characteristics changes due to changes in driving status or interruptions in communication between vehicles or between vehicles and the ground, it is necessary to compare the continuously output predictive control results with the newly acquired historical trajectory, and adjust the algorithm online according to the different comparison results.

7. An adaptive high-speed train tracking and operation control system considering driving scenarios, characterized in that: include: Acquisition module, used to obtain train operation status data and perform data statistics and processing; The recognition module is used to process and classify the train driving feature data using a pre-trained driving style recognition model to obtain a driving style recognition result; A calculation module, configured to obtain the driving characteristic parameter having the greatest correlation with the driving style and the corresponding parameter value according to the MIC algorithm; The control module is used to combine model predictive control and online driving style identification based on driving characteristic parameters and corresponding parameter values, and use the MPC model to control trains with different driving modes and styles to achieve adaptive group operation of high-speed trains.

8. A non-transitory computer-readable storage medium, characterized in that The non-transitory computer-readable storage medium is used to store computer instructions. When the computer instructions are executed by the processor, the adaptive high-speed train tracking operation control method considering the driving scenario as described in any one of claims 1-6 is implemented.

9. A computer device, characterized in that: It includes a memory and a processor, the processor and the memory communicate with each other, the memory stores program instructions that can be executed by the processor, and the processor calls the program instructions to execute the adaptive high-speed train tracking operation control method considering the driving scenario as described in any one of claims 1 to 6.

10. An electronic device, characterized in that: include: A processor, a memory, and a computer program; wherein the processor is connected to the memory, the computer program is stored in the memory, and when the electronic device is running, the processor executes the computer program stored in the memory to enable the electronic device to execute instructions for implementing the adaptive high-speed train tracking operation control method considering driving scenarios as described in any one of claims 1-6.

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