Method, system and device for generating recommended driving curve of virtual marshaling train

Abstract

The present invention discloses a method, system, and device for generating a recommended driving curve for a virtual marshaling train, relating to the field of rail transit signal control technology. The method comprises: constructing a dynamic equation for the virtual marshaling train; based on the dynamic equation, according to different operating stages of the virtual marshaling train and in combination with urban rail operation characteristics, constructing a first optimization function, a second optimization function, a third optimization function, and a fourth optimization function corresponding to the outbound traction stage, the inter-station cruising stage, the interval speed regulation stage, and the inbound parking stage; solving each optimization function to obtain the control input of each train unit in the virtual marshaling train at each operating stage. The present invention can solve the problem of asynchronous operation under the existing virtual marshaling train operation control architecture, improve the situation where emergency braking is easily triggered when entering and leaving the station, improve the efficiency of entering and leaving the station, and reduce the difference in the stopping time of the virtual marshaling train.

Description

Technical Field

[0001] The present invention relates to the technical field of rail transit signal control, and in particular to a method, system and device for generating a recommended driving curve for a virtual marshaled train. Background Art

[0002] As an important means of alleviating traffic pressure and meeting the travel needs of urban residents, urban rail transit (hereinafter referred to as "URT") construction has achieved remarkable results in recent years. Currently, the rapid expansion of the URT network and the increasingly prominent uneven temporal and spatial distribution of passenger flow and its irregular dynamic changes are placing higher demands on the further optimization of vehicle and line resources and the matching of capacity and volume.

[0003] In response to the above needs, virtual coupling (VC) technology is a widely recognized solution. Virtual coupling technology can greatly shorten the running distance of train units that are not physically coupled, allowing them to provide transportation operation services like physically coupled trains. Virtual coupling technology can realize online, dynamic and flexible adjustment of vehicle configuration and marshaling methods, thereby improving the effective utilization rate of vehicle and line resources, which can not only meet the large capacity demand during peak passenger flow, but also reduce the vehicle empty rate during off-peak and off-peak periods. Therefore, by developing virtual marshaling technology, it is possible to safely and efficiently control train units to execute online dynamic marshaling and disassembly as planned, and maintain stable operation and synchronous control at small intervals in a virtual marshaling manner. This can reduce train operation energy consumption and save transportation costs without reducing service quality, which is of great significance to the green and sustainable development of urban rail transit.

[0004] Existing research on virtual marshaling train operation control generally focuses on real-time tracking control between train units, while there is less research on train unit control targets (i.e., driving strategy design). The mainstream urban rail inter-station operation control mode is that the lead train operates independently according to conventional operating indicators, and the follower train does its best to catch up with the preceding train, and reduces the distance between the two cars as much as possible under the premise of safety distance constraints. However, the operating efficiency and actual performance of the virtual marshaling train in this mode, for example: the safety distance constraint increases with speed, forcing the two cars to increase the distance at high speed (currently usually up to tens of meters); the subway platform stipulates that train units must maintain a small distance when stopping (currently usually a few meters). In this case, the lead train still operates independently according to the subway single-car operation strategy without considering the tracking ability of the follower train, and will not be able to achieve the performance of the virtual marshaling train stopping synchronously at the platform. The existing virtual marshaling train operation control scheme has the following problems:

[0005] (1) It is difficult to ensure the synchronization of virtual train operations: Existing research lacks the design of driving strategies for the pilot train in the virtual train. The pilot train still adopts the same driving strategy as the original single train, such as the maximum speed operation and energy-saving operation mode. Therefore, when the pilot train clears the low speed limit section and starts to accelerate, the following train is still in the low speed limit section, resulting in asynchronous operation of the virtual train.

[0006] (2) The process of entering and exiting the station is inefficient and prone to emergency braking: Since the tracking distance between train units is large at high speeds and small at low speeds, adjacent train units need to reduce or increase the distance between each other during the process of entering and exiting the station. During the parking phase, the following train directly enters the station and stops at the target parking position. Since it is affected by the status of the preceding train, it is very easy to cause overspeed emergency braking.

[0007] (3) There is a large difference in stop time between train units: According to the existing virtual marshaling train operation control mode, when switching from high-speed operation to braking and stopping, the two trains maintain a large distance due to the safety distance constraint, but the platform requires small distance stopping. Therefore, at the start of braking, the distance between the leading train and the stopping point is much smaller than the distance between the following train and the stopping point. In addition, the leading train adopts a single-vehicle independent braking and stopping strategy. Due to the safety distance constraint, the following train has to reduce its own speed and travel a longer distance to reach the stopping point. Therefore, it cannot stop synchronously with the preceding train, resulting in a large difference in stop time, which greatly affects the operating efficiency of the subway platform. Summary of the Invention

[0008] Based on this, an embodiment of the present invention provides a method, system and device for generating a recommended driving curve for a virtual marshaling train to solve the problem of operation asynchrony under the existing virtual marshaling train operation control architecture, improve the situation where emergency braking is easily triggered when entering and exiting the station, improve the efficiency of entering and exiting the station, and reduce the stop time difference of the virtual marshaling train.

[0009] To achieve the above objectives, the embodiments of the present invention provide the following solutions:

[0010] A method for generating a recommended driving curve for a virtual train formation, comprising:

[0011] Constructing a dynamic equation for a virtual marshaled train; the virtual marshaled train includes: a plurality of train units; among the plurality of train units, one is a lead train and the rest are follower trains;

[0012] Constructing a first optimization function, a second optimization function, a third optimization function and a fourth optimization function based on the dynamic equation;

[0013] Solving the first optimization function, the second optimization function, the third optimization function, and the fourth optimization function respectively to obtain control inputs for each train unit in the virtual marshaled train at each operating phase; the operating phases include: a departure traction phase, an inter-station cruising phase, a section speed regulation phase, and an entry stop phase;

[0014] Determining a recommended driving curve for the virtual train assembly according to control inputs in all operation phases;

[0015] The first optimization function includes: a first objective function constructed with the goal of ensuring that the lead train reaches the ceiling speed within a set acceleration time and that the following train maintains a tracking distance with the preceding train with a set safety margin during the outbound traction phase;

[0016] The second optimization function includes: a second objective function constructed with the goal of maintaining the cruising speed of both the lead train and the following train during the inter-station cruising phase;

[0017] The third optimization function includes: in the section speed regulation phase, a third objective function constructed with the goal of the lead train regulating its speed at maximum acceleration or maximum deceleration and the following train following the lead train within a safe speed range;

[0018] The fourth optimization function includes: during the station parking phase, a fourth objective function is constructed with the goal of ensuring that all train units in the virtual marshaled train stop at the set parking position on the platform within the set parking time.

[0019] The present invention also provides a system for generating a recommended driving curve for a virtual train formation, comprising:

[0020] A dynamic equation construction module is used to construct the dynamic equations of a virtual marshaled train; the virtual marshaled train includes: a plurality of train units; among the plurality of train units, one is a lead train and the rest are follower trains;

[0021] An optimization function construction module, configured to construct a first optimization function, a second optimization function, a third optimization function, and a fourth optimization function based on the dynamic equation;

[0022] a solving module, configured to solve the first optimization function, the second optimization function, the third optimization function, and the fourth optimization function, respectively, to obtain control inputs for each train unit in the virtual marshaled train at each operating phase; the operating phases comprising: an outbound traction phase, an inter-station cruising phase, an interval speed regulation phase, and an inbound parking phase;

[0023] a curve generating module, configured to determine a recommended driving curve for the virtual marshaled train according to control input quantities in all operation phases;

[0024] The first optimization function includes: a first objective function constructed with the goal of ensuring that the lead train reaches the ceiling speed within a set acceleration time and that the following train maintains a tracking distance with the preceding train with a set safety margin during the outbound traction phase;

[0025] The second optimization function includes: a second objective function constructed with the goal of maintaining the cruising speed of both the lead train and the following train during the inter-station cruising phase;

[0026] The third optimization function includes: in the section speed regulation phase, a third objective function constructed with the goal of the lead train regulating its speed at maximum acceleration or maximum deceleration and the following train following the lead train within a safe speed range;

[0027] The fourth optimization function includes: during the station parking phase, a fourth objective function is constructed with the goal of ensuring that all train units in the virtual marshaled train stop at the set parking position on the platform within the set parking time.

[0028] The present invention also provides an electronic device, including a memory and a processor, wherein the memory is used to store a computer program, and the processor runs the computer program to enable the electronic device to execute the above-mentioned method for generating a recommended driving curve for a virtual train formation.

[0029] According to the specific embodiments provided by the present invention, the present invention discloses the following technical effects:

[0030] The embodiment of the present invention is based on the dynamic equation and is based on the different operating stages of virtual marshaling trains, combined with the operating characteristics of urban rail transit. It constructs a first optimization function corresponding to the outbound traction stage, a second optimization function corresponding to the inter-station cruising stage, a third optimization function corresponding to the interval speed regulation stage, and a fourth optimization function corresponding to the station parking stage. By solving each optimization function, the control input of each train unit in the virtual marshaling train at each operating stage is obtained. The present invention can generate recommended driving curves in real time for tracking control of the lead train and the following train, solving the problem of asynchronous operation under the existing virtual marshaling train operation control architecture; the operation process of the virtual marshaling train is finely divided into stages to construct and solve the corresponding optimization functions, improving the situation where emergency braking is easily triggered when entering and exiting the station, and improving the efficiency of entering and exiting the station; the optimization function is separately constructed and solved during the station parking stage, reducing the difference in the stop time of the virtual marshaling train. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in 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.

[0032] Figure 1 A flowchart of a method for generating a recommended driving curve for a virtual train set provided in an embodiment of the present invention;

[0033] Figure 2 A schematic diagram of the speed limit of the inter-station track line provided in an embodiment of the present invention;

[0034] Figure 3 A curve effect diagram generated during the outbound traction phase provided by an embodiment of the present invention;

[0035] Figure 4 A curve rendering generated during the inter-station cruise phase provided by an embodiment of the present invention;

[0036] Figure 5 This is a curve effect diagram generated during the interval speed regulation phase provided by an embodiment of the present invention;

[0037] Figure 6 A curve effect diagram generated during the parking phase provided by an embodiment of the present invention;

[0038] Figure 7 A speed-time diagram of a recommended driving curve provided by an embodiment of the present invention;

[0039] Figure 8 A speed-position diagram of a recommended driving curve provided by an embodiment of the present invention;

[0040] Figure 9 A pitch-time diagram of a recommended driving curve provided by an embodiment of the present invention;

[0041] Figure 10 An acceleration-time diagram of a recommended driving curve provided by an embodiment of the present invention;

[0042] Figure 11 A schematic diagram of the structure of a system for generating a recommended driving curve for a virtual train set provided by an embodiment of the present invention;

[0043] Figure 12 This is a structural diagram of the overall control system composed of a virtual train recommended driving curve generation system provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0044] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0045] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the present invention is further described in detail below with reference to the accompanying drawings and specific embodiments.

[0046] Example 1

[0047] A feasible solution to the problems of the existing technology is to rationally plan the recommended driving curves for each train unit in the virtual train formation, and then have each train unit follow its own recommended driving curve to achieve the overall operation goals of the virtual train formation. This embodiment provides a method for quickly and easily generating recommended driving curves for each train unit.

[0048] The main concepts of the method for generating recommended driving curves for virtual marshaling trains in this embodiment are as follows: establishing a dynamic equation for a virtual marshaling train; designing the objective function and constraints of the optimal control problem based on different inter-station operation phases of the virtual marshaling train and the characteristics of urban rail operations; and executing a solution algorithm to solve the optimal control problem.

[0049] See also Figure 1 The method for generating a recommended driving curve for a virtual train of this embodiment specifically includes:

[0050] Step 101: Constructing a dynamic equation of a virtual train set; the virtual train set includes: a plurality of train units; among the plurality of train units, one is a lead train and the rest are follower trains.

[0051] Step 102: Constructing a first optimization function, a second optimization function, a third optimization function, and a fourth optimization function based on the dynamic equation.

[0052] Among them, the first optimization function includes: in the outbound traction phase, a first objective function is constructed with the goal of ensuring that the lead train reaches the ceiling speed within a set acceleration time and the following train maintains a tracking distance with a set safety margin with the leading train.

[0053] The second optimization function includes: a second objective function constructed with the goal of maintaining the cruising speed of the lead train and the following train during the inter-station cruising phase.

[0054] The third optimization function includes: in the section speed regulation stage, a third objective function is constructed with the goal of adjusting the speed of the lead train at the maximum acceleration or maximum deceleration and the following train following the lead train at a speed change within a safe range.

[0055] The fourth optimization function includes: during the station parking phase, a fourth objective function is constructed with the goal of ensuring that all train units in the virtual marshaled train stop at the set parking position on the platform within the set parking time.

[0056] In addition, the first optimization function, the second optimization function and the third optimization function also include: a first constraint condition; the first constraint condition includes: track line speed limit, train performance limit, comfort limit and minimum tracking distance limit between adjacent train units.

[0057] The fourth optimization function also includes: a first constraint condition and a second constraint condition; the second constraint condition includes: an inter-station running time constraint and a train target parking position constraint.

[0058] Step 103: Solve the first optimization function, the second optimization function, the third optimization function, and the fourth optimization function respectively to obtain the control input of each train unit in the virtual train formation in each operation phase; the operation phase includes: outbound traction phase, inter-station cruising phase, interval speed regulation phase, and in-station parking phase.

[0059] Step 104: Determine a recommended driving curve for the virtual train set according to the control inputs of all operation phases.

[0060] In one example, the specific process of constructing the dynamic equation of the virtual train in step 101 is as follows:

[0061] Establish the dynamic equations x for a single virtual train set i (k+1)=f i (x i (k),u i (k)), where x i (k)=[s i (k),v i (k),a i (k)] T represents the state of the i-th train unit in a virtual train consisting of n train units at time k, s i (k),v i (k),a i (k) represents the position, velocity and acceleration of the train unit at time k, u i (k) represents the control input of the train at time k, f i represents the train dynamics function, which has the following expanded form:

[0062]

[0063] v i (k+1)=v i (k)+τa i (k)

[0064] a i (k+1)=u i(k)+g i (s i (k),v i (k));

[0065] Among them, τ represents the time calculation step, s i (k+1),v i (k+1),a i (k+1) represents the position, velocity and acceleration of the train unit at time k+1, g i It represents the external force acting on the train during operation, which can be expressed as

[0066] g i (s i (k),v i (k))=e i (v i (k))+r i (s i (k))+p i (s i (k));

[0067] Among them, the basic resistance e i (v i (k))=c0+c1v i (k)+c2(v i (k)) 2 , c0, c1, c2 are Davis equation coefficients, and the additional resistance of the curve Cr0, Cr1 are resistance coefficients, R(s i (k)) is the curve radius at the location of the train unit, and the slope resistance p i (s i (k))=P(s i (k))g,P(s i (k)) represents the slope of the train unit, and g represents the acceleration due to gravity.

[0068] In one example, each optimization function and corresponding constraint conditions in step 102 are mainly introduced.

[0069] Consider a complete virtual marshalling station operation process, assuming that the platform starting point is position zero, the departure time is zero time, and the interval operation time is t f The interval operation process consists of four operating phases: outbound traction, inter-station cruising, interval speed regulation, and inbound braking. Different operating objectives are designed for each phase.

[0070] 1) The following design is made for the outbound traction stage:

[0071] In this phase, the virtual train should reach the ceiling speed as quickly as possible, which is related to the track speed limit. Therefore, the lead train should accelerate to reach the ceiling speed as quickly as possible, and the following train should maintain the smallest possible tracking distance with the preceding train, leaving a safety margin. Therefore, the expression of the first objective function in this phase is:

[0072]

[0073] Among them, t a Indicates the maximum train running time allocated to the outbound traction phase during the interval operation. This time can be obtained by Calculated, is the acceleration parameter set according to experience, v max represents the ceiling speed determined by the track speed limit; k represents the time; L represents the length of the train unit, d i (k) represents the minimum tracking distance of the i-th train unit at time k, which is related to the position, speed and acceleration of the front and rear trains and can be written as d i (k) = D(s i-1 (k),v i-1 (k),a i-1 (k).s i (k),v i (k),a i (k)), considering the safety protection distance is h i (k)=H(s i-1 (k),v i-1 (k),a i-1 (k).s i (k),v i (k),a i (k),...), then it is necessary to satisfy d i (k) = h i (k)+Δ(k), where Δ(k) represents the margin reserved in advance for the train operation control error. i represents the control input of the i-th train unit; n represents the total number of train units in the virtual train; Q1 represents the weight coefficient corresponding to the speed of the pilot train accelerating to the ceiling; v1(k) represents the speed of the pilot train at time k; Q i The weight coefficient that indicates the distance between the following train and the train in front of it; s i-1 (k) represents the position of the i-1th train unit at time k; s i (k) represents the position of the i-th train unit at time k.

[0074] At this stage, the virtual train formation needs to meet the constraints of track speed limit, train performance limit, comfort limit, and minimum tracking distance between adjacent train units. That is, the expression of the first constraint is as follows:

[0075] v i (k)≤v EBI (s i (k))

[0076] u min ≤u i (k)≤u max

[0077] j min ≤u i (k+1)-u i (k)≤j max

[0078] d i (k)≤s i-1 (k)-Ls i (k)

[0079] Among them, v EBI (s i (k)) is the speed limit of the track line, u min and u max is the minimum braking acceleration and maximum traction acceleration that the train unit can provide, j min and j max is the maximum impact rate and minimum impact rate of the train unit, u i (k+1) represents the control input of the i-th train unit at time k+1.

[0080] 2) The following design is made for the inter-station cruise phase:

[0081] In this stage, the virtual train should maintain cruising speed. The specific implementation method is that both the lead train and the following train should maintain cruising speed as much as possible. Therefore, the expression of the second objective function in this stage is:

[0082]

[0083] Among them, t a Indicates the running time of the largest train unit allocated to the outbound traction phase during the interval operation; t c Indicates the maximum train running time allocated to the inter-station cruising phase during the interval operation; v cruise Indicates the set cruising speed during the inter-station cruising phase; v i (k) represents the speed of the i-th train unit at time k; u i represents the control input of the i-th train unit; u i (k) represents the control input of the i-th train unit at time k; Q v Indicates the weight coefficient corresponding to the following train and the lead train running at the same speed; Qu Indicates the weight coefficient corresponding to the stable cruise reduction control adjustment of all train units, increasing Q u (u i (k)) 2 The purpose is to avoid frequent acceleration changes; v1(k) represents the speed of the lead train at time k. During the cruise phase, the virtual train must also meet constraints such as track speed limits, train performance limits, comfort limits, and the minimum tracking distance between adjacent train units. This is the first constraint, and the specific expression is not repeated here.

[0084] 3) The following design is made for the interval speed regulation stage:

[0085] When there is a change in the speed limit of the track line in the section, the ceiling speed of the virtual marshaling train will change. At this time, the train unit is required to be able to switch from the current stable cruising speed to another higher or lower cruising speed. The operating goal of the section speed regulation process should be to complete the cruising speed change as quickly as possible. Then the lead train should adjust the speed at the maximum acceleration or deceleration, and the following train should also follow the speed change as much as possible under the premise of considering safety. In addition, when the low speed limit jumps to the high speed limit, the operation integrity of the virtual marshaling train must be considered. The lead train cannot accelerate immediately after clearing the low speed limit section, and wait for the following train to clear the low speed limit section before accelerating together. Therefore, the expression of the third objective function of this stage is:

[0086]

[0087] Among them, t1 represents the initial time of the interval speed regulation phase, t2 represents the end time of the interval speed regulation phase, and t2-t1 represents the maximum train running time allocated to the interval speed regulation phase during the interval operation process. This time can be calculated by Calculated, is the acceleration parameter set according to experience, v goal represents the target ceiling speed at the end of the section speed regulation phase, L represents the length of the train unit, s i-1 (k) represents the position of the i-1th train unit at time k, s i (k) represents the position of the i-th train unit at time k, d i (k) represents the minimum tracking distance between trains, which is related to the position, speed and acceleration of the preceding and following trains and can be written as d i (k) = D(s i-1 (k),v i-1 (k),a i-1 (k).s i (k),v i (k),a i (k)), considering the safety protection distance h i(k)=H(s i-1 (k),v i-1 (k),a i-1 (k).s i (k),v i (k),a i (k),...), needs to satisfy d i (k) = h i (k)+Δ(k), where Δ(k) represents the margin reserved in advance for the control error.

[0088] This stage is similar to the outbound traction stage. The virtual train must also meet constraints such as track speed limit, train performance limit, comfort limit, and minimum tracking distance between adjacent train units, namely the first constraint condition. The specific expression will not be repeated here.

[0089] 4) The following design is made for the parking phase:

[0090] In this stage, all train units in the virtual formation should stop accurately and synchronously at the platform parking position within the specified time. Therefore, the expression of the fourth objective function of this stage is:

[0091]

[0092] Among them, t i represents the time when the i-th train unit stops at the platform; t i-1 represents the time when the i-1th train unit stops at the platform. At this stage, in addition to satisfying the first constraint conditions, such as track speed limit, train performance limit, comfort limit, and minimum tracking distance between adjacent train units, the virtual train must also satisfy the second constraint condition. The specific expression of the first constraint condition is not repeated here. The expression of the inter-station running time constraint in the second constraint condition is: The target train parking position constraint in the second constraint condition is: Among them, t f The inter-station running time set for the dispatch plan, is the platform parking position of train unit i.

[0093] In one example, the solution process of step 103 is as follows:

[0094] (1) Initialize the train state of the virtual marshaling train, use the optimization algorithm to solve the first optimization function, and obtain the control input of each train unit in the virtual marshaling train during the outbound traction phase.

[0095] (2) Based on the train state at the end of the outbound traction phase, the optimization algorithm is used to iterate the second optimization function and the third optimization function multiple times to obtain the control input of each train unit in the virtual marshaling train during the inter-station cruising phase and the control input of each train unit in the virtual marshaling train during the interval speed regulation phase; wherein, one iteration is performed for each speed regulation until the speed remains unchanged. Specifically:

[0096] For the Tth iteration, the control input of the interval speed regulation stage under the T-1th iteration is used as the initial state input of the second optimization function. The optimization algorithm is used to solve the second optimization function to obtain the control input of the inter-station cruising stage under the Tth iteration of each train unit in the virtual marshaling train; among them, in the first iteration, the train state at the end of the out-station traction stage is used as the initial state input of the second optimization function.

[0097] If speed regulation is required in the Tth iteration, the train state at the end of the inter-station cruising phase in the Tth iteration is used as the initial state input of the third optimization function. The third optimization function is solved using the optimization algorithm to obtain the control input of each train unit in the virtual marshaling train in the interval speed regulation phase in the Tth iteration.

[0098] If no speed regulation is required in the Tth iteration, the control input of each train unit in the virtual marshaling train in the inter-station cruising stage at the Tth iteration will be used as the final control input in the inter-station cruising stage, and the control input of each train unit in the virtual marshaling train in the interval speed regulation stage at the T-1th iteration will be used as the final control input in the interval speed regulation stage.

[0099] (3) When the speed remains unchanged, the train state at the end of the inter-station cruising phase is used as the initial state input of the fourth optimization function. The fourth optimization function is solved using the optimization algorithm to obtain the control input of each train unit in the virtual marshaling train during the station stop phase.

[0100] In practical applications, a more specific implementation process of the above steps 103 and 104 is as follows:

[0101] Initialize s i (0),v i (0),a i (0),k=0.

[0102] First, the recommended driving curve optimization problem for the outbound traction phase is solved. Next, the train state at the end of the outbound traction phase is used as the initial state input to solve the recommended driving curve optimization problem for the inter-station cruising phase. If there is a speed limit change in the interval and speed adjustment is required, the state at the end of the cruising phase is input into the interval speed adjustment optimization problem for solution. After the speed adjustment is completed, the end state is used as the input for the new cruising phase. Finally, the train state at the end of the inter-station cruising phase is used as the initial state input to solve the recommended driving curve optimization problem for the inbound parking phase. In the process of solving the problems in each of the above stages, algorithms such as sequential quadratic programming and active set method can be used to solve the optimization problem model.

[0103] Building on the aforementioned method for generating recommended driving curves for virtual trains, the present invention has designed an automatic generation system for recommended driving curves for virtual trains. This system can automatically generate recommended driving curves within a limited timeframe, combining any form of safety protection distance calculation model and inter-station operation index requirements. The system employs an algorithm that accelerates the solution of recommended driving curves for virtual train intervals to meet the real-time requirements of curve generation. This algorithm analyzes the mathematical characteristics of the inter-station operation optimization model for virtual trains and, based on the target operational performance for each stage, provides approximate optimal solutions for each stage that align with typical subway driving behavior. This algorithm can then rapidly calculate the recommended driving curves for virtual trains.

[0104] The specific curve solving algorithm design within the system is as follows:

[0105] 1) For the outbound traction phase, the operating goal of the virtual marshaling train is to reach the ceiling speed as quickly as possible under the constraints. Therefore, an approximate optimal solution to this optimization problem is that the leading train runs at the fastest speed, while the trailing train maintains the minimum tracking distance with the leading train as much as possible. Based on the above strategy, an approximate optimal solution can be obtained as the leading and trailing train units adopt the maximum tractive acceleration u that can be achieved under their respective constraints. i (k)=max(u i (k)), satisfy

[0106]

[0107] 2) For the inter-station cruising stage, the operation goal of the virtual marshaling train in this stage is to maintain a stable speed as much as possible. According to the analysis of the force characteristics of the train operation, it can be concluded that in order to keep the train speed unchanged, the control acceleration of the train unit must be equal to the resistance it receives. Therefore, the optimal solution for the control acceleration of the lead train in this stage is u1(k)=g1(s1(k),v1(k)). If the following train does not reach the ceiling speed at this time, it continues to run with the minimum tracking distance from the lead train to meet v i (k)≤vEBI (s i (k)); If the following train has also reached the ceiling speed, it will also overcome the resistance and output the control acceleration u i (k) = g i (s i (k),v i (k)). Controlling acceleration also needs to satisfy the vehicle traction and braking characteristic constraints and impact rate constraints.

[0108] 3) For the section speed regulation stage, the operating goal of the virtual marshaling train in this stage is to change from one cruising speed to another as quickly as possible. Therefore, the approximate optimal solution for this stage is that the lead train outputs the maximum acceleration or deceleration while satisfying the impact rate constraint and traction braking characteristic constraint, so that the train unit can quickly adjust the speed. Then the control acceleration of the lead train is u1(k)=max(u1(k)) or u1(k)=min(u1(k)), and the following train maintains the minimum tracking distance with the lead train. The control acceleration also needs to satisfy the vehicle traction braking characteristic constraint and the impact rate constraint.

[0109] 4) During the stop phase, the virtual train's operational goal is to ensure that both trains stop synchronously as much as possible while ensuring the required running time and stopping accuracy. Based on the operational mechanism of minimizing the time difference between virtual train stops, the lead train and the following train must meet the following operational strategies during the stop phase:

[0110] ① Stopping stage 1: The lead train uses heavy braking to increase the speed difference with the following train, so that the following train has the speed conditions to quickly approach the lead train; the following train approaches the lead train as close as possible without exceeding the safety distance.

[0111] ② Stopping stage 2: Based on the speed difference between the two trains, the lead train decelerates at a small braking rate to maintain the speed difference as much as possible, so that the following train can quickly narrow the gap with the lead train during this stage.

[0112] ③ Stop phase 3: The train units are close to their respective parking points. The lead train and the following train stop quickly at the same time using a single braking (constant braking rate) to complete the virtual train platform parking.

[0113] Based on the above parking phase operation strategy, combined with a large number of recommended driving curve solution data for the optimization problem of the station parking phase, with the help of data fitting and empirical analysis, the approximate optimal solution adopted by the pilot train during the station parking phase is: in is the initial speed when entering the braking stop section, m1 and m2 are negative constant values, so that the acceleration of the pilot train gradually decreases as its own speed decreases. When v1(k)=v1 0When m1 + m2 is equal to the acceleration at the end of the inter-station cruise segment, the following train always maintains a minimum tracking distance from the lead train. Acceleration control throughout the entire phase must meet vehicle traction and braking characteristic constraints and impact rate constraints.

[0114] At this point, approximate optimal solutions for all stages of inter-station operation of virtual marshaling trains have been obtained. Combined with the safety protection distance and inter-station operation plan, the recommended driving curve for the virtual marshaling train can be calculated in a relatively short time (on the order of seconds, meeting the requirement of generating the next inter-station recommended driving curve during the train stop).

[0115] The method of the above embodiment is further described in detail below in conjunction with a specific inter-station operation scenario of a virtual marshaled train.

[0116] Taking a virtual train consisting of two train units as an example, other scenarios with multiple train units can be derived by analogy. The specific inter-station operation scenario uses the test line from platform 1 to platform 2 of Hebei CRRC Railway Vehicle Equipment Co., Ltd. The specific data is shown in Table 1. The speed limit of the inter-station track line is as follows: Figure 2 shown.

[0117] Table 1 Inter-station operation scenario data

[0118] name Numerical Distance between stations 1063.85m Pilot train departure location 257.30m Follow train departure location 156.74m Pilot train parking location 1321.15m Follow the train's parking position 1220.59m Train unit length 94.64m Starting distance / parking distance 5.92m Parking accuracy error requirements <30cm Runtime requirements 100±5s Maximum allowable departure time difference 2s Upper limit of parking time difference 4s

[0119] The safety protection calculation method for virtual marshaling trains can adopt the full-time and space safety protection method in existing literature. The present invention is applicable to various safety protection calculation methods. Only one protection method is selected here as an example.

[0120] By using line parameters, safety protection distances, and operational index requirements as inputs to the virtual train recommended driving curve automatic generation system, the system can automatically generate the curve in real time according to the virtual train section recommended driving curve generation method proposed in this invention. The system implementation process for a specific scenario is as follows:

[0121] (1) During the outbound traction phase, the pilot train starts at the maximum traction acceleration of 0.8 m / s that the vehicle can output under overload conditions. 2 Quickly start acceleration, and the following train always maintains a 3km / h margin considering the control error of the train controller and the emergency brake intervention curve, and tries to catch up with the leading train. This phase ends when the speed of the leading train reaches the ceiling speed, and the end status of the virtual marshaling train is input into the inter-station cruise phase optimization module. The whole process meets the impact rate of 0.3m / s 3 The curve effect diagram generated at this stage is as follows Figure 3 shown.

[0122] (2) For the inter-station cruising phase, the lead train maintains a constant speed, calculates the running resistance according to the line slope, curve radius and its own speed, and outputs a control acceleration equal to the resistance to ensure uniform speed operation. The following train has not reached the ceiling speed at this time, so it continues to maintain a safe tracking distance with the lead train on the basis of considering the control error. Since the speed limit in the interval has a jump from low speed limit to high speed limit, the rear of the virtual marshaling train is considered to be maintained. When the following train clears the low speed limit section, the cruising phase ends, and the end state is used as the initial input for the interval speed regulation phase. The whole process meets the impact rate of 0.3m / s 3 The curve effect diagram generated at this stage is as follows Figure 4 shown.

[0123] (3) During the interval speed regulation phase, the pilot train shall provide a maximum traction acceleration of 0.8 m / s according to the overload load. 3 The following vehicle accelerates and maintains a safe tracking distance from the lead train while taking into account the control error. When the new ceiling speed is reached, the optimization process of the inter-station cruise phase is resumed. The recommended driving curve for transitioning from high speed limit to low speed limit can be obtained by analogy. The whole process meets the impact rate of 0.3m / s. 3 The curve effect diagram generated at this stage is as follows Figure 5 shown.

[0124] (4) For the parking phase at the station, according to the analysis of reducing the parking time difference in the invention content and previous experience, the acceleration change law of the pilot train adopts The following train maintains a safe tracking distance with the preceding train while taking into account the control error. When the two train units approach the stopping point, they apply the brakes once for precise stopping. The whole process meets the impact rate of 0.3m / s 3 The curve effect diagram generated at this stage is as follows Figure 6 shown.

[0125] At this point, the generation of the recommended driving curve between virtual train stations is completed. The complete curve effect is as follows: Figure 7 、 Figure 8 、 Figure 9 and Figure 10 The curve index parameters are shown in Table 2. The curve is automatically output by the system to the automatic driving system of the corresponding train unit for actual vehicle tracking, thereby achieving the goal of synchronous operation, synchronous departure, and synchronous entry and stop between stations of virtual marshaling trains.

[0126] Table 2 Recommended driving curve performance parameters

[0127] Performance indicators Numerical Departure time difference 0s Parking time difference 1.2s Runtime 97.8s Maximum spacing between train units 64.94m Pilot train stopping error 0cm Following train stop error 0cm Recommended driving curve generation time 3.12s

[0128] In order to solve the problems of asynchronous operation between virtual marshaling trains, time difference in stopovers, and easy triggering of emergency braking when entering and leaving the station, the present invention designs a method for generating recommended driving curves between stations for virtual marshaling trains, so that the train units can achieve the requirements of indicators such as synchronous operation between stations by tracking the recommended driving curves. The innovation of this method lies in that it proposes to generate the recommended driving curves for virtual marshaling trains through collaborative planning, taking meeting the operation requirements of urban rail trains as the goal, and collaboratively planning the driving strategies of all train units (including pilot trains and following trains) to obtain the recommended driving curves for virtual marshaling trains. The on-board automatic driving system achieves the goal of collaborative operation between stations by tracking the recommended driving curves. The advantages of this method are that it can design the driving strategies of virtual marshaling trains in combination with any form of safety protection distance; it can optimize the driving strategies of virtual marshaling trains in combination with different operation plans and requirements, and improve the synchronization of interval operation and entry and exit requirements of virtual marshaling trains; it can cope with emergencies of temporary speed limit adjustments, and the method meets real-time requirements.

[0129] Example 2

[0130] In order to execute the method corresponding to the above-mentioned embodiment 1 and achieve the corresponding functions and technical effects, a system for generating a recommended driving curve for a virtual train is provided below.

[0131] See also Figure 11 , the system comprising:

[0132] The dynamic equation construction module 201 is used to construct the dynamic equation of a virtual marshaled train; the virtual marshaled train includes: a plurality of train units; among the plurality of train units, one is a lead train and the rest are follower trains.

[0133] The optimization function construction module 202 is used to construct a first optimization function, a second optimization function, a third optimization function and a fourth optimization function based on the dynamic equation.

[0134] The solution module 203 is used to solve the first optimization function, the second optimization function, the third optimization function and the fourth optimization function respectively to obtain the control input of each train unit in the virtual train formation in each operation stage; the operation stage includes: the outbound traction stage, the inter-station cruising stage, the interval speed regulation stage and the station stop stage.

[0135] The curve generating module 204 is configured to determine a recommended driving curve for the virtual train set according to control inputs in all operation phases.

[0136] Among them, the first optimization function includes: in the outbound traction phase, a first objective function is constructed with the goal of ensuring that the lead train reaches the ceiling speed within a set acceleration time and the following train maintains a tracking distance with a set safety margin with the leading train.

[0137] The second optimization function includes: a second objective function constructed with the goal of maintaining the cruising speed of the lead train and the following train during the inter-station cruising phase.

[0138] The third optimization function includes: in the section speed regulation stage, a third objective function is constructed with the goal of adjusting the speed of the lead train at the maximum acceleration or maximum deceleration and the following train following the lead train at a speed change within a safe range.

[0139] The fourth optimization function includes: during the station parking phase, a fourth objective function is constructed with the goal of ensuring that all train units in the virtual marshaled train stop at the set parking position on the platform within the set parking time.

[0140] In practical applications, see Figure 12 The virtual train recommended driving curve generation system of this embodiment is connected to the automatic train supervision system, the trackside resource management system, and the virtual train real-time control system. The automatic train supervision system provides the virtual train recommended driving curve generation system with operation plans and performance requirements. The trackside resource management system provides the virtual train recommended driving curve generation system with electronic maps and trackside resource occupancy information. The virtual train recommended driving curve generation system provides the virtual train real-time control system with recommended driving curve tracking control capabilities. Furthermore, the hardware foundation of the virtual train recommended driving curve generation system is a personal computer, server, industrial computer, or cloud device.

[0141] As for the system disclosed in the embodiment, since it corresponds to the method disclosed in the first embodiment, the description is relatively simple, and the relevant parts can be referred to the description of the method part.

[0142] This embodiment is based on the method of the first embodiment to design a virtual train station recommended driving curve automatic generation system. Without changing the interfaces of other modules, it can automatically generate the required virtual train station recommended driving curves in real time according to the line data and station operation index requirements for tracking and control of the lead train and the following train, thus meeting the actual application needs of the project.

[0143] Example 3

[0144] This embodiment provides an electronic device, including a memory and a processor. The memory is used to store a computer program, and the processor runs the computer program to enable the electronic device to execute the method for generating a recommended driving curve for a virtual train formation according to the first embodiment.

[0145] In addition, an embodiment of the present invention further provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the method for generating a recommended driving curve for a virtual train set according to the first embodiment.

[0146] All the above embodiments have the following advantages:

[0147] (1) The problem of asynchronous operation under the existing virtual train operation control architecture is solved.

[0148] It is proposed to generate the recommended driving curve of virtual train formation through collaborative planning, and the automatic driving system of each train unit can achieve the goal of synchronous operation by tracking the curve.

[0149] (2) The situation where emergency braking is easily triggered when entering and leaving the station has been improved, thereby improving the efficiency of entering and leaving the station. The inter-station operation of the virtual marshaling train is constructed into a phased optimization problem. By carefully considering the operation of all train units in the exit traction phase and the braking and parking phase, the safe and synchronous operation of the virtual marshaling train is guaranteed.

[0150] (3) The stopping time difference of virtual trains is reduced. By analyzing the mechanism of reducing the stopping time difference, the operation strategies of the lead train and the following train during the braking stop phase are designed. On this basis, the optimization problem of this phase is constructed and solved, and the recommended driving curve for the optimization of the synchronous stopping of virtual trains is obtained.

[0151] The various embodiments in this specification are described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the various embodiments can be referenced to each other.

[0152] This document uses specific examples to illustrate the principles and implementation methods of the present invention. The above examples are only intended to help understand the method and core concept of the present invention. At the same time, those skilled in the art will find that the specific implementation methods and application scopes may vary based on the concept of the present invention. In summary, the contents of this specification should not be construed as limiting the present invention.

Claims

1. A method for generating a recommended driving curve for a virtual train, characterized in that: include: Construct the dynamic equations of the virtual train formation; The virtual marshaled train comprises: a plurality of train units; among the plurality of train units, one is a lead train and the rest are follower trains; Constructing a first optimization function, a second optimization function, a third optimization function and a fourth optimization function based on the dynamic equation; Solving the first optimization function, the second optimization function, the third optimization function, and the fourth optimization function respectively to obtain control inputs for each train unit in the virtual marshaled train at each operating phase; the operating phases include: a departure traction phase, an inter-station cruising phase, a section speed regulation phase, and an entry stop phase; Determining a recommended driving curve for the virtual train assembly according to control inputs in all operation phases; The first optimization function includes: a first objective function constructed with the goal of ensuring that the lead train reaches the ceiling speed within a set acceleration time and that the following train maintains a tracking distance with the preceding train with a set safety margin during the outbound traction phase; The second optimization function includes: a second objective function constructed with the goal of maintaining the cruising speed of both the lead train and the following train during the inter-station cruising phase; The third optimization function includes: in the section speed regulation phase, a third objective function constructed with the goal of the lead train regulating its speed at maximum acceleration or maximum deceleration and the following train following the lead train within a safe speed range; The fourth optimization function includes: during the station parking phase, a fourth objective function is constructed with the goal of ensuring that all train units in the virtual marshaled train stop at a set parking position on the platform within a set parking time; Solving the first optimization function, the second optimization function, the third optimization function, and the fourth optimization function respectively to obtain the control input of each train unit in the virtual marshaling train at each operation stage specifically includes: Initializing the train state of the virtual marshaling train, solving the first optimization function using an optimization algorithm, and obtaining the control input of each train unit in the virtual marshaling train during the outbound traction phase; Based on the train state at the end of the outbound traction phase, the optimization algorithm is used to iteratively solve the second and third optimization functions multiple times to obtain the control input of each train unit in the virtual marshaling train during the inter-station cruising phase and the control input of each train unit in the virtual marshaling train during the interval speed regulation phase. The iteration is performed once for each speed regulation until the speed remains constant. When the speed remains unchanged, the train state at the end of the inter-station cruising phase is used as the initial state input of the fourth optimization function. The fourth optimization function is solved using the optimization algorithm to obtain the control input of each train unit in the virtual marshaling train during the station stop phase.

2. The method for generating a recommended driving curve for a virtual train according to claim 1, wherein: Based on the train status at the end of the outbound traction phase, the optimization algorithm is used to iteratively solve the second optimization function and the third optimization function multiple times to obtain the control input of each train unit in the virtual marshaling train during the inter-station cruising phase and the control input of each train unit in the virtual marshaling train during the section speed regulation phase. Specifically, the following are obtained: For the Tth iteration, the control input of the interval speed regulation phase at the T-1th iteration is used as the initial state input of the second optimization function. The second optimization function is solved using the optimization algorithm to obtain the control input of the inter-station cruising phase at the Tth iteration for each train unit in the virtual marshaling train. Among them, in the first iteration, the train state at the end of the outbound traction phase is used as the initial state input of the second optimization function. If speed regulation is required at the Tth iteration, the train state at the end of the inter-station cruise phase at the Tth iteration is used as the initial state input of the third optimization function. The third optimization function is solved using the optimization algorithm to obtain the control input of each train unit in the virtual marshaling train at the interval speed regulation phase at the Tth iteration. If no speed regulation is required in the Tth iteration, the control input of each train unit in the virtual marshaling train in the inter-station cruising stage at the Tth iteration will be used as the final control input in the inter-station cruising stage, and the control input of each train unit in the virtual marshaling train in the interval speed regulation stage at the T-1th iteration will be used as the final control input in the interval speed regulation stage.

3. The method for generating a recommended driving curve for a virtual train according to claim 1, wherein: The first optimization function, the second optimization function, and the third optimization function all further include: a first constraint condition; the first constraint condition includes: track line speed limit, train performance limit, comfort limit, and minimum tracking distance limit between adjacent train units; The fourth optimization function also includes: a first constraint condition and a second constraint condition; the second constraint condition includes: an inter-station running time constraint and a train target parking position constraint.

4. The method for generating a recommended driving curve for a virtual train according to claim 1, wherein: The expression of the first objective function is: Among them, t a represents the running time of the largest train unit allocated to the outbound traction phase during the interval operation; k represents the time; v max represents the ceiling speed determined by the track speed limit; L represents the length of the train unit, d i (k) represents the minimum tracking distance of the i-th train unit at time k; u i represents the control input of the i-th train unit; n represents the total number of train units in the virtual train; Q1 represents the weight coefficient corresponding to the speed of the pilot train accelerating to the ceiling; v1(k) represents the speed of the pilot train at time k; Q i The weight coefficient that indicates the distance between the following train and the train in front of it; s i-1 (k) represents the position of the i-1th train unit at time k; s i (k) represents the position of the i-th train unit at time k.

5. The method for generating a recommended driving curve for a virtual train according to claim 1, wherein: The expression of the second objective function is: Among them, t a represents the running time of the largest train unit allocated to the outbound traction phase during the interval operation; k represents the time; t c represents the maximum train running time allocated to the inter-station cruising phase during the interval operation; n represents the total number of train units in the virtual marshaling train; v cruise Indicates the set cruising speed during the inter-station cruising phase; v i (k) represents the speed of the i-th train unit at time k; u i represents the control input of the i-th train unit; u i (k) represents the control input of the i-th train unit at time k; Q v It represents the weight coefficient corresponding to the following train and the pilot train running at the same speed, Q u represents the weight coefficient corresponding to the stable cruise reduction control adjustment of all train units; v1(k) represents the speed of the pilot train at time k.

6. The method for generating a recommended driving curve for a virtual train according to claim 1, wherein: The expression of the third objective function is: Among them, t1 represents the initial time of the interval speed regulation phase, t2 represents the end time of the interval speed regulation phase, t2-t1 represents the maximum train running time allocated to the interval speed regulation phase during the interval operation; k represents the time; L represents the length of the train unit, d i (k) represents the minimum tracking distance of the i-th train unit at time k; u i represents the control input of the i-th train unit; n represents the total number of train units in the virtual train; v goal represents the target ceiling speed at the end of the section speed regulation phase; Q1 represents the weight coefficient corresponding to the pilot train accelerating to the ceiling speed; v1(k) represents the speed of the pilot train at time k; Q i The weight coefficient that indicates the distance between the following train and the train in front of it; s i-1 (k) represents the position of the i-1th train unit at time k; s i (k) represents the position of the i-th train unit at time k.

7. The method for generating a recommended driving curve for a virtual train according to claim 1, wherein: The expression of the fourth objective function is: Among them, u i represents the control input of the i-th train unit; n represents the total number of train units in the virtual train; t i represents the time when the i-th train unit stops at the platform; t i-1 Indicates the time when the i-1th train unit stops at the platform.

8. A virtual train recommended driving curve generation system, characterized in that: include: Dynamic equation building module, used to build the dynamic equations of virtual train formation; The virtual marshaled train comprises: a plurality of train units; among the plurality of train units, one is a lead train and the rest are follower trains; An optimization function construction module, configured to construct a first optimization function, a second optimization function, a third optimization function, and a fourth optimization function based on the dynamic equation; A solution module is used to solve the first optimization function, the second optimization function, the third optimization function and the fourth optimization function respectively to obtain the control input of each train unit in the virtual marshalling train at each operation stage; the operation stage includes: outbound traction stage, inter-station cruising stage, interval speed regulation stage and station parking stage; the first optimization function, the second optimization function, the third optimization function and the fourth optimization function are solved respectively to obtain the control input of each train unit in the virtual marshalling train at each operation stage, specifically including: initializing the train state of the virtual marshalling train, using the optimization algorithm to solve the first optimization function, and obtaining the virtual marshalling train. The control input of each train unit in the virtual marshaling train during the outbound traction phase is obtained; based on the train state at the end of the outbound traction phase, the optimization algorithm is used to iteratively solve the second optimization function and the third optimization function multiple times to obtain the control input of each train unit in the virtual marshaling train during the inter-station cruising phase and the control input of each train unit in the virtual marshaling train during the interval speed regulation phase; wherein, one iteration is performed each time the speed is regulated until the speed remains unchanged; when the speed remains unchanged, the train state at the end of the inter-station cruising phase is used as the initial state input of the fourth optimization function, and the optimization algorithm is used to solve the fourth optimization function to obtain the control input of each train unit in the virtual marshaling train during the station parking phase; a curve generating module, configured to determine a recommended driving curve for the virtual marshaled train according to control input quantities in all operation phases; The first optimization function includes: a first objective function constructed with the goal of ensuring that the lead train reaches the ceiling speed within a set acceleration time and that the following train maintains a tracking distance with the preceding train with a set safety margin during the outbound traction phase; The second optimization function includes: a second objective function constructed with the goal of maintaining the cruising speed of both the lead train and the following train during the inter-station cruising phase; The third optimization function includes: in the section speed regulation phase, a third objective function constructed with the goal of the lead train regulating its speed at maximum acceleration or maximum deceleration and the following train following the lead train within a safe speed range; The fourth optimization function includes: during the station parking phase, a fourth objective function is constructed with the goal of ensuring that all train units in the virtual marshaled train stop at the set parking position on the platform within the set parking time.

9. An electronic device, characterized in that: The electronic device comprises a memory and a processor, wherein the memory is used to store a computer program, and the processor runs the computer program to enable the electronic device to execute the method for generating a recommended driving curve for a virtual train set according to any one of claims 1 to 7.

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