CN114298398A - High-speed train dynamic tracking operation optimization method based on elastic adjustment strategy - Google Patents
High-speed train dynamic tracking operation optimization method based on elastic adjustment strategy Download PDFInfo
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Abstract
The invention provides a high-speed train dynamic tracking operation optimization method based on an elastic adjustment strategy. The method comprises the following steps: designing an elastic adjustment mechanism according to actual arrival information of the train, a running plan of a forward train and an initial running strategy of an interval, and performing off-line optimization on the tracked train by using a static crowd search algorithm, wherein the off-line optimization comprises departure intervals and the train running strategy; then, in the interval, the real-time running state information and the surrounding environment information of the front train are collected, the running adjustment strategy of the tracked train in the rest interval is optimized on line through an elastic adjustment strategy and a dynamic crowd search algorithm in combination with the transportation demand, and the optimal running interval in the current state is obtained, so that the running efficiency of the train is improved on the premise of ensuring the safety until the terminal station is reached. The invention can be applied to an automatic driving system or a driving auxiliary system, can provide a safe, efficient, energy-saving and stable driving strategy, and effectively improves the operation efficiency of a high-speed train.
Description
Technical Field
The invention relates to the technical field of high-speed train operation control, in particular to a high-speed train dynamic tracking operation optimization method based on an elastic adjustment strategy.
Background
In recent years, the high-speed railway in China is rapidly developed, and the largest operation network in the world is formed. By virtue of the advantages of large transportation capacity, long distance, high speed, all weather and the like, the high-speed railway gradually becomes a preferred traffic mode for long-distance travel in the public. However, with the increasing road network density, the train operation environment is increasingly complex, the passenger demand is increasing, and the train operation efficiency needs to be improved urgently. Along the dense-population railway line in China, the high-speed rail of Jingu is taken as an example to basically reach the capacity saturation, and the construction demonstration work of the second line of Jingu is started; the european union Shift2Rail project progress reports indicate that european passenger demand for high-speed railways is expected to increase by 50% by 2050. Along with the increase of passenger capacity, the railway transportation department has become one of the units with the largest energy consumption in national economy in China, and meanwhile, higher requirements on trip experiences such as the passenger alignment point rate, the comfort level and the like are provided. Therefore, when the construction of the high-speed railway is accelerated, the further improvement of the transportation capacity and the operation quality of the existing high-speed railway is one of the main problems facing currently.
In the tracking operation process, the train tracking interval is influenced by the operation speed and the control strategy of the front and the rear trains. The train tracking interval is shortened, on one hand, the operation capacity of a line is increased, and on the other hand, the train tracking operation process is challenged. Meanwhile, the loss of a train traction braking system can be increased by frequently switching the train operation conditions, more operation energy consumption is generated, poorer riding experience can be brought to passengers, and the train operation stability is poor. Therefore, the tracking operation of the high-speed train is a multi-objective optimization process which needs to meet the requirements of safety, high efficiency, energy conservation, stability and the like, and the core problem of the tracking operation of the train is how to balance the optimization objectives and improve the operation efficiency of the train on the basis of the safe operation.
In recent years, elasticity has received increasing attention in the traffic field, often describing the ability of the system to recover performance in response to disturbances, and different adjustment methods may be selected as required during recovery. An elastic adjustment mechanism in the running process of the train can continuously evaluate the tracking state of the train, quantize the deviation degree of the actual interval and the optimal interval of the two trains, adopt a corresponding adjustment strategy according to the change of the operation environment and restore the tracking state to the optimal tracking state.
Throughout the research at home and abroad, no deep research is carried out on the high-speed train tracking operation method based on the elastic adjustment strategy in the prior art.
Disclosure of Invention
The embodiment of the invention provides a high-speed train dynamic tracking operation optimization method based on an elastic adjustment strategy, so as to effectively improve the operation efficiency of a high-speed train.
In order to achieve the purpose, the invention adopts the following technical scheme.
A high-speed train dynamic tracking operation optimization method based on an elastic adjustment strategy comprises the following steps:
acquiring train tracking static information, wherein the train tracking static information comprises train basic parameters, line parameters, a preceding train operation plan and an initial interval operation strategy;
before a tracked train starts, establishing a tracking interval elastic adjustment mechanism, and establishing a tracking operation multi-target optimization model according to the tracking interval elastic adjustment mechanism and the train tracking static information; obtaining the optimal departure interval of the train by using a static crowd search algorithm;
collecting real-time running state information and temporary speed limit information of a preceding train;
and after the tracked train starts according to the optimal departure interval, solving the tracked operation multi-target optimization model by adopting a dynamic crowd search algorithm based on the real-time operation state information and the temporary speed limit information of the front train, obtaining a dynamically optimal tracked operation strategy of the train in the remaining interval through iterative optimization, and controlling the train to operate until the train reaches the terminal station.
Preferably, before the tracked train starts, the establishing of the tracking interval flexible adjustment mechanism includes:
establishing a tracking interval elastic adjustment mechanism by taking the actual tracking interval of the tracking train as an evaluation object, wherein the tracking interval elastic adjustment mechanism comprises an optimal tracking interval model, a tracking state evaluation model and a train elastic adjustment strategy;
optimal tracking interval between tracking train and preceding train
Is calculated as follows:
wherein xi is the optimal spacing factor;
to track the minimum safe tracking interval between the train and the preceding train, the calculation process is as follows:
wherein the content of the first and second substances,
tracking the train running distance within the reaction time of a driver;
tracking the service braking distance of the train at the current running speed;
the distance is a safety protection distance;
is the train length;
the emergency braking distance of the train i-1;
the calculation process of the tracking state estimation model is as follows:
wherein the content of the first and second substances,
the actual tracking interval of the front vehicle and the rear vehicle is calculated as follows:
wherein the content of the first and second substances,
the actual running position of the train i;
is the actual operating position of train i-1.
Combining the running condition of the preceding train at the next moment, the train elasticity adjustment strategy provides the control command at the next moment for the tracking train, and the method specifically comprises the following steps:
when Q isiWhen e (1+ xi, infinity), the train actual tracking interval is less than the minimum safe tracking interval, i.e.
Tracking the braking condition of the train at the next moment;
when Q isiWhen the element belongs to (1,1+ xi), the train tracking state is 'interval is small', namely
If the preceding train is in the braking working condition at the next moment, the tracking train also adopts the braking working condition at the next moment, otherwise, the coasting working condition is adopted;
when Q isi∈[1/(1+ε),1]The train tracking state is 'interval moderate', that is
Wherein epsilon is a tracking efficiency factor, if the previous train is in a braking working condition at the next moment, the tracking train adopts an idling working condition at the next moment, otherwise, the tracking train adopts a cruising working condition;
when Q isiWhen the value is within the range of E (- ∞, 1/(1+ ε)), the train tracking state is "excessive interval", that is, the train tracking state is
And epsilon is a tracking efficiency factor, if the previous train is in an idle working condition or a braking working condition at the next moment, the tracking train adopts a cruising working condition at the next moment, and otherwise, a traction working condition is adopted.
Preferably, the establishing a tracking operation multi-objective optimization model according to the tracking interval elastic adjustment mechanism and the train tracking static information includes:
according to the tracking state evaluation model and the elastic adjustment strategy, a train tracking operation multi-target optimization model is established by taking train operation efficiency, operation energy consumption and working condition switching times as optimization targets, and the method specifically comprises the following steps:
min G(ΦC,ΦE,ΦN)
performance index calculation formula:
speed constraint: v. oflim-v≥0
Tracking interval constraint: l isact-Lsafe≥0
And (3) restraining the train running stability: s-0.2 is less than or equal to 0
Departure interval constraint: hact-Hmin≥0
Wherein phiC、ΦE、ΦNRespectively representing the operation efficiency, the operation energy consumption and the working condition conversion frequency; t isactThe actual running time of the train is obtained; u is the train operation condition; t is tuThe running time of the train under the working condition u; f is train output control force; n is a radical ofchangeThe switching times in the whole process; hact、HminRespectively an actual departure interval and a minimum departure interval of the train; v. oflimThe current maximum allowable speed of the train; s represents the train running stability, and the calculation formula is as follows:
wherein, σ and c are respectively a width coefficient and a central position; a (t) is the acceleration of the train at the time t; Δ t is the time interval.
Preferably, the obtaining of the optimal departure interval of the train by using the static crowd search algorithm includes:
initializing basic parameters of train and line information, acquiring a predicted operation plan and an interval operation strategy of a preceding train, and initiating a population by taking a departure interval, a tracking efficiency factor and an optimal interval factor as decision variables, wherein the calculation formula is as follows:
wherein G is the current evolution algebra; n is a radical ofpThe population scale is adopted; xGIs an initial population under the current evolution algebra G;
is the jth individual in the G generation population; hjThe departure interval for the jth individual; epsilonjA tracking efficiency factor for the jth individual; xijOptimal separation factor for jth individual, individual representing train, xijRepresenting the optimal departure interval for the jth train.
Preferably, after the tracked train starts according to the optimal departure interval, the tracking operation multi-target optimization model is solved by using a dynamic crowd search algorithm based on the real-time operation state information and the temporary speed limit information of the preceding train, a dynamically optimal tracking operation strategy of the train in the remaining interval is obtained through iterative optimization, and the train operation is controlled until the train reaches the terminal station, including:
executing dynamic adjustment between tracking train operation strategy stations after a tracking train starts according to the optimal departure interval, setting a timing interval, and solving the tracking operation multi-target optimization model by adopting a dynamic crowd search algorithm based on the real-time operation state information and the temporary speed limit information of the advancing train;
the processing procedure of the dynamic crowd search algorithm comprises the following steps:
initializing basic parameters of train and line information, taking a tracking efficiency factor and an optimal interval factor as decision variables, taking train operation efficiency, operation energy consumption and working condition switching times as optimization targets, and adopting a starting population calculation formula as follows:
wherein G is the current evolution algebra; n is a radical ofpThe population scale is adopted; xGIs an initial population under the current evolution algebra G;
is the jth individual in the G generation population; epsilonjA tracking efficiency factor for the jth individual; xijAn optimal interval factor for the jth individual;
acquiring the current running state of a preceding train and the running strategy in the rest interval, acquiring the current running state of a tracked train and the running distance in the rest interval, and timing at intervals of tcAnd second, circularly and iteratively optimizing to obtain a dynamic optimal tracking operation strategy of the train in the rest interval, and controlling the train to operate until the train reaches the terminal station.
According to the technical scheme provided by the embodiment of the invention, the method provided by the embodiment of the invention can be applied to an automatic driving system or a driving auxiliary system, can provide a safe, efficient, energy-saving and stable driving strategy, and effectively improves the operation efficiency of a high-speed train. The train operation efficiency can be improved on the premise of ensuring safety until the terminal station is reached.
Additional aspects and advantages of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings needed to be used in the description of the embodiments are briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.
Fig. 1 is a schematic flow chart of a high-speed train dynamic tracking operation optimization method based on an elastic adjustment strategy according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of a mechanism for adjusting elasticity according to an embodiment of the present invention;
FIG. 3 is a flowchart of a static crowd search algorithm provided by an embodiment of the present invention;
FIG. 4 is a flowchart of a static crowd search algorithm provided by an embodiment of the present invention;
fig. 5(a) is a graph of a change relationship between the operating speed and the distance between the tracked train and the preceding train in the non-interference scene according to the embodiment of the present invention;
fig. 5(b) is a graph showing a change relationship between the actual separation between the tracking train and the preceding train and the minimum safe separation difference value in the non-interference scene according to the embodiment of the present invention;
fig. 6(a) is a graph of a change relationship between the operating speed and the distance between a tracked train and a leading train in a temporary speed-limiting interference scene according to an embodiment of the present invention;
fig. 6(b) is a graph showing a change relationship between an actual interval between a tracked train and a preceding train and a minimum safe interval difference value in a temporary speed-limiting interference scene according to an embodiment of the present invention.
Detailed Description
Reference will now be made in detail to embodiments of the present invention, examples of which are illustrated in the accompanying drawings, wherein like reference numerals refer to the same or similar elements or elements having the same or similar function throughout. The embodiments described below with reference to the accompanying drawings are illustrative only for the purpose of explaining the present invention, and are not to be construed as limiting the present invention.
As used herein, the singular forms "a", "an", "the" and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms "comprises" and/or "comprising," when used in this specification, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof. It will be understood that when an element is referred to as being "connected" or "coupled" to another element, it can be directly connected or coupled to the other element or intervening elements may also be present. Further, "connected" or "coupled" as used herein may include wirelessly connected or coupled. As used herein, the term "and/or" includes any and all combinations of one or more of the associated listed items.
It will be understood by those skilled in the art that, unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. It will be further understood that terms, such as those defined in commonly used dictionaries, should be interpreted as having a meaning that is consistent with their meaning in the context of the prior art and will not be interpreted in an idealized or overly formal sense unless expressly so defined herein.
For the convenience of understanding the embodiments of the present invention, the following description will be further explained by taking several specific embodiments as examples in conjunction with the drawings, and the embodiments are not to be construed as limiting the embodiments of the present invention.
The processing flow of the high-speed train dynamic tracking operation optimization method based on the elastic adjustment strategy is shown in fig. 1, and specifically comprises the following processing steps:
step S1: before tracking the departure of a train, acquiring basic parameters and line parameters of the train, and receiving a running plan and an initial interval running strategy of the train ahead;
step S2: and executing static planning in the tracking train operation strategy station.
Step S2.1: the tracking interval elastic adjustment mechanism shown in fig. 2 is established by taking the actual tracking interval of the tracked train as an evaluation object, and comprises an optimal tracking interval model, a tracking state evaluation model and a train elastic adjustment strategy.
Optimal tracking interval between tracking train and preceding train
Is calculated as follows:
wherein xi is the optimal spacing factor;
to track the minimum safe tracking interval between the train and the preceding train, the calculation process is as follows:
wherein the content of the first and second substances,
tracking the train running distance within the reaction time of a driver;
tracking the service braking distance of the train at the current running speed;
the distance is a safety protection distance;
is the train length;
is the emergency braking distance of train i-1.
The calculation process of the tracking state estimation model is as follows:
wherein the content of the first and second substances,
the actual tracking interval of the front vehicle and the rear vehicle is calculated as follows:
wherein the content of the first and second substances,
the actual running position of the train i;
is the actual operating position of train i-1.
Combining the running condition of the preceding train at the next moment, the train elasticity adjustment strategy provides the control command at the next moment for the tracking train, and the method specifically comprises the following steps:
when Q isiWhen e (1+ xi, infinity), the train actual tracking interval is less than the minimum safe tracking interval, i.e.
Tracking the braking condition of the train at the next moment;
when Q isiWhen the element belongs to (1,1+ xi), the train tracking state is 'interval is small', namely
If the preceding train is in the braking working condition at the next moment, the tracking train is in the braking working condition at the next moment, otherwise, the coasting working condition is adopted.
When Q isi∈[1/(1+ε),1]The train tracking state is 'interval moderate', that is
Where epsilon is the tracking efficiency factor. If the preceding train is in the braking working condition at the next moment, the following train is in the coasting working condition at the next moment, otherwise, the cruising working condition is adopted.
When Q isiWhen the value is within the range of E (- ∞, 1/(1+ ε)), the train tracking state is "excessive interval", that is, the train tracking state is
Where epsilon is the tracking efficiency factor. If the preceding train is in the idle working condition or the braking working condition at the next moment, the tracking train adopts the cruising working condition at the next moment, otherwise, the tracking train adopts the traction working condition.
Step S2.2: according to the tracking state evaluation model and the elastic adjustment strategy, a train tracking operation multi-target optimization model is established by taking train operation efficiency, operation energy consumption and working condition switching times as optimization targets, and the method specifically comprises the following steps:
min G(ΦC,ΦE,ΦN)
performance index calculation formula:
speed constraint: v. oflim-v≥0
Tracking interval constraint: l isact-Lsafe≥0
And (3) restraining the train running stability: s-0.2 is less than or equal to 0
Departure interval constraint: hact-Hmin≥0
Wherein phiC、ΦE、ΦNRespectively representing the operation efficiency, the operation energy consumption and the working condition conversion frequency; t isactThe actual running time of the train is obtained; u is the train operation condition; t is tuThe running time of the train under the working condition u; f is train output control force; n is a radical ofchangeIs a whole course cutChanging times; hact、HminRespectively an actual departure interval and a minimum departure interval of the train; v. oflimThe current maximum allowable speed of the train; s represents the train running stability, and the calculation formula is as follows:
wherein, σ and c are respectively a width coefficient and a central position; a (t) is the acceleration of the train at the time t; Δ t is the time interval.
Step S2.3: further, a static crowd search algorithm is used for obtaining the optimal departure interval and the static optimal operation strategy of the train, and a flow chart of the static crowd search algorithm is shown in fig. 3. The method specifically comprises the following steps:
initializing basic parameters such as train and line information, obtaining a predicted operation plan and an interval operation strategy of a preceding train, and taking a departure interval, a tracking efficiency factor and an optimal interval factor as decision variables, wherein an initial population calculation formula is as follows:
wherein G is the current evolution algebra; n is a radical ofpThe population scale is adopted; xGIs an initial population under the current evolution algebra G;
is the jth individual in the G generation population; hjThe departure interval for the jth individual; epsilonjA tracking efficiency factor for the jth individual; xijThe optimal interval factor for the jth individual. The above-mentioned individual representing a train, xijRepresenting the optimal departure interval for the jth train.
Step S3: the high-speed train vehicle-mounted equipment is responsible for acquiring real-time running state information (position, speed, acceleration and running conditions) and temporary speed limit information from a wireless block center, and receiving the real-time running state information of a preceding train through a train-vehicle communication system.
Step S4: after the tracking train starts according to the optimal departure interval, executing dynamic adjustment between tracking train operation strategy stations, and specifically comprising the following steps:
step S4.1: further, a tracking interval flexible adjustment mechanism is established.
Step S4.1: further, a tracking operation multi-objective optimization model is established.
Step S4.2: further, with tcAnd (3) adopting a dynamic crowd search algorithm at a timing interval of 120 seconds, obtaining a dynamic optimal tracking operation strategy of the train in the remaining interval through iterative optimization until the train reaches a terminal station, wherein a flow chart of the dynamic crowd search algorithm is shown in fig. 4. The method specifically comprises the following steps:
initializing basic parameters such as train and line information, acquiring the current running state of a preceding train and a running strategy in a remaining interval, acquiring the current running state of a tracking train and a running distance in the remaining interval, and taking a tracking efficiency factor and an optimal interval factor as decision variables, wherein an initial population calculation formula is as follows:
wherein G is the current evolution algebra; n is a radical ofpThe population scale is adopted; xGIs an initial population under the current evolution algebra G;
is the jth individual in the G generation population; epsilonjA tracking efficiency factor for the jth individual; xijThe optimal interval factor for the jth individual.
The above algorithms and processes can be implemented by using some common computer languages, such as C #, C + +, and Matlab languages.
In this embodiment, it is assumed that the train model is CRH380AL, the maximum allowable speed is 350km/h, and the minimum inter-train distance is 120 s.
The following experimental results can be obtained by the method of the present invention based on the above data:
the train is controlled by using an elastic adjustment strategy, fig. 5 is a running result diagram of the tracked train under the condition of no external interference, and the running result diagram comprises a running speed-distance curve between the tracked train and a preceding train and a change relation between an actual interval and a minimum safe tracking interval difference value, and the elastic adjustment strategy can ensure that the train tracking interval keeps an optimal running state. Fig. 6 is a running result diagram of the tracked train when the preceding train suddenly decelerates due to the temporary speed limit, and includes a running speed-distance curve between the tracked train and the preceding train and a change relation between a difference value between an actual interval and the minimum safe tracking interval.
The method is suitable for train tracking operation optimization in a high-speed railway system of one-way double trains, and is particularly suitable for train dynamic tracking operation optimization in a complex interference environment. The method is also suitable for quasi-point transportation requirements and can be realized by modifying the multi-objective optimization model.
In summary, the method for optimizing the high-speed train dynamic tracking operation based on the elastic adjustment strategy according to the embodiment of the invention can be used for optimizing the high-speed train tracking operation process, and has the following beneficial effects:
(1) by adopting a tracking interval elastic adjustment mechanism, the train interval can be shortened on the basis of ensuring the running safety of the train, and the running efficiency of the train is improved;
(2) by adopting a dynamic crowd search algorithm, a real-time optimal tracking interval can be designed according to the change of an operation environment, and the tracking state is restored to the optimal tracking state under the current operation environment by combining an elastic adjustment strategy, so that the dynamic adjustment of train tracking is realized;
(3) the device can be used for a train operation control system to guide the safe, efficient, energy-saving and stable tracking operation of a high-speed train.
Those of ordinary skill in the art will understand that: the figures are merely schematic representations of one embodiment, and the blocks or flow diagrams in the figures are not necessarily required to practice the present invention.
From the above description of the embodiments, it is clear to those skilled in the art that the present invention can be implemented by software plus necessary general hardware platform. Based on such understanding, the technical solutions of the present invention may be embodied in the form of a software product, which may be stored in a storage medium, such as ROM/RAM, magnetic disk, optical disk, etc., and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute the method according to the embodiments or some parts of the embodiments.
The embodiments in the present specification are described in a progressive manner, and the same and similar parts among the embodiments are referred to each other, and each embodiment focuses on the differences from the other embodiments. In particular, for apparatus or system embodiments, since they are substantially similar to method embodiments, they are described in relative terms, as long as they are described in partial descriptions of method embodiments. The above-described embodiments of the apparatus and system are merely illustrative, and the units described as separate parts may or may not be physically separate, and the parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of the present embodiment. One of ordinary skill in the art can understand and implement it without inventive effort.
The above description is only for the preferred embodiment of the present invention, but the scope of the present invention is not limited thereto, and any changes or substitutions that can be easily conceived by those skilled in the art within the technical scope of the present invention are included in the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.
Claims (5)
1. A high-speed train dynamic tracking operation optimization method based on an elastic adjustment strategy is characterized by comprising the following steps:
acquiring train tracking static information, wherein the train tracking static information comprises train basic parameters, line parameters, a preceding train operation plan and an initial interval operation strategy;
before a tracked train starts, establishing a tracking interval elastic adjustment mechanism, and establishing a tracking operation multi-target optimization model according to the tracking interval elastic adjustment mechanism and the train tracking static information; obtaining the optimal departure interval of the train by using a static crowd search algorithm;
collecting real-time running state information and temporary speed limit information of a preceding train;
and after the tracked train starts according to the optimal departure interval, solving the tracked operation multi-target optimization model by adopting a dynamic crowd search algorithm based on the real-time operation state information and the temporary speed limit information of the front train, obtaining a dynamically optimal tracked operation strategy of the train in the remaining interval through iterative optimization, and controlling the train to operate until the train reaches the terminal station.
2. The method of claim 1, wherein establishing a tracking interval flexible adjustment mechanism prior to the departure of the tracked train comprises:
establishing a tracking interval elastic adjustment mechanism by taking the actual tracking interval of the tracking train as an evaluation object, wherein the tracking interval elastic adjustment mechanism comprises an optimal tracking interval model, a tracking state evaluation model and a train elastic adjustment strategy;
optimal tracking interval between tracking train and preceding train
Is calculated as follows:
wherein xi is the optimal spacing factor;
to track the minimum safe tracking interval between the train and the preceding train, the calculation process is as follows:
wherein the content of the first and second substances,
tracking the train running distance within the reaction time of a driver;
tracking the service braking distance of the train at the current running speed;
the distance is a safety protection distance;
is the train length;
the emergency braking distance of the train i-1;
the calculation process of the tracking state estimation model is as follows:
wherein the content of the first and second substances,
the actual tracking interval of the front vehicle and the rear vehicle is calculated as follows:
wherein the content of the first and second substances,
the actual running position of the train i;
is the actual operating position of train i-1.
Combining the running condition of the preceding train at the next moment, the train elasticity adjustment strategy provides the control command at the next moment for the tracking train, and the method specifically comprises the following steps:
when Q isiWhen e (1+ xi, infinity), the train actual tracking interval is less than the minimum safe tracking interval, i.e.
Tracking the braking condition of the train at the next moment;
when Q isiWhen the element belongs to (1,1+ xi), the train tracking state is 'interval is small', namely
If the preceding train is in the braking working condition at the next moment, the tracking train also adopts the braking working condition at the next moment, otherwise, the coasting working condition is adopted;
when Q isi∈[1/(1+ε),1]The train tracking state is 'interval moderate', that is
Wherein epsilon is a tracking efficiency factor, if the previous train is in a braking working condition at the next moment, the tracking train adopts an idling working condition at the next moment, otherwise, the tracking train adopts a cruising working condition;
when Q isiWhen the value is within the range of E (- ∞, 1/(1+ ε)), the train tracking state is "excessive interval", that is, the train tracking state is
Wherein epsilon is a tracking efficiency factor, if the train moves aheadAnd if the next moment is an idle running working condition or a braking working condition, tracking the train to adopt a cruising working condition at the next moment, otherwise, adopting a traction working condition.
3. The method according to claim 2, wherein the establishing a tracking operation multi-objective optimization model according to the tracking interval elastic adjustment mechanism and the train tracking static information comprises:
according to the tracking state evaluation model and the elastic adjustment strategy, a train tracking operation multi-target optimization model is established by taking train operation efficiency, operation energy consumption and working condition switching times as optimization targets, and the method specifically comprises the following steps:
min G(ΦC,ΦE,ΦN)
performance index calculation formula:
speed constraint: v. oflim-v≥0
Tracking interval constraint: l isact-Lsafe≥0
And (3) restraining the train running stability: s-0.2 is less than or equal to 0
Departure interval constraint: hact-Hmin≥0
Wherein phiC、ΦE、ΦNRespectively representing the operation efficiency, the operation energy consumption and the working condition conversion frequency; t isactThe actual running time of the train is obtained; u is the train operation condition; t is tuThe running time of the train under the working condition u; f is train output control force; n is a radical ofchangeThe switching times in the whole process; hact、HminRespectively an actual departure interval and a minimum departure interval of the train; v. oflimThe current maximum allowable speed of the train; s represents the train running stability, and the calculation formula is as follows:
wherein, σ and c are respectively a width coefficient and a central position; a (t) is the acceleration of the train at the time t; Δ t is the time interval.
4. The method according to claim 3, wherein the obtaining of the optimal departure interval of the train by using the static crowd search algorithm comprises:
initializing basic parameters of train and line information, acquiring a predicted operation plan and an interval operation strategy of a preceding train, and initiating a population by taking a departure interval, a tracking efficiency factor and an optimal interval factor as decision variables, wherein the calculation formula is as follows:
wherein G is the current evolution algebra; n is a radical ofpThe population scale is adopted; xGIs an initial population under the current evolution algebra G;
is the jth individual in the G generation population; hjThe departure interval for the jth individual; epsilonjA tracking efficiency factor for the jth individual; xijOptimal separation factor for jth individual, individual representing train, xijRepresenting the optimal departure interval for the jth train.
5. The method according to claim 4, wherein after the tracked train departs according to the optimal departure interval, the tracked operation multi-target optimization model is solved by adopting a dynamic crowd search algorithm based on the real-time operation state information and the temporary speed limit information of the preceding train, a dynamically optimal tracked operation strategy of the train in the remaining interval is obtained through iterative optimization, and the train operation is controlled until the train reaches the terminal station, and the method comprises the following steps:
executing dynamic adjustment between tracking train operation strategy stations after a tracking train starts according to the optimal departure interval, setting a timing interval, and solving the tracking operation multi-target optimization model by adopting a dynamic crowd search algorithm based on the real-time operation state information and the temporary speed limit information of the advancing train;
the processing procedure of the dynamic crowd search algorithm comprises the following steps:
initializing basic parameters of train and line information, taking a tracking efficiency factor and an optimal interval factor as decision variables, taking train operation efficiency, operation energy consumption and working condition switching times as optimization targets, and adopting a starting population calculation formula as follows:
wherein G is the current evolution algebra; n is a radical ofpThe population scale is adopted; xGIs an initial population under the current evolution algebra G;
is the jth individual in the G generation population; epsilonjA tracking efficiency factor for the jth individual; xijAn optimal interval factor for the jth individual;
acquiring the current running state of a preceding train and the running strategy in the rest interval, acquiring the current running state of a tracked train and the running distance in the rest interval, and timing at intervals of tcAnd second, circularly and iteratively optimizing to obtain a dynamic optimal tracking operation strategy of the train in the rest interval, and controlling the train to operate until the train reaches the terminal station.
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| CN115180002A (en) * | 2022-09-13 | 2022-10-14 | 中国铁道科学研究院集团有限公司通信信号研究所 | Multi-train operation situation deduction method and device |
| CN115195821A (en) * | 2022-06-14 | 2022-10-18 | 同济大学 | Method and device for controlling following behavior of rear vehicle and storage medium |
| CN115230777A (en) * | 2022-06-21 | 2022-10-25 | 中国科学院自动化研究所 | Scheduling policy adjustment method and device, electronic equipment and storage medium |
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| CN115195821A (en) * | 2022-06-14 | 2022-10-18 | 同济大学 | Method and device for controlling following behavior of rear vehicle and storage medium |
| CN115195821B (en) * | 2022-06-14 | 2023-09-26 | 同济大学 | Method and device for controlling following behavior of rear vehicle and storage medium |
| CN115230777A (en) * | 2022-06-21 | 2022-10-25 | 中国科学院自动化研究所 | Scheduling policy adjustment method and device, electronic equipment and storage medium |
| CN115230777B (en) * | 2022-06-21 | 2024-01-16 | 中国科学院自动化研究所 | Scheduling policy adjustment method and device, electronic equipment and storage medium |
| CN115180002A (en) * | 2022-09-13 | 2022-10-14 | 中国铁道科学研究院集团有限公司通信信号研究所 | Multi-train operation situation deduction method and device |
| CN115180002B (en) * | 2022-09-13 | 2023-01-31 | 中国铁道科学研究院集团有限公司通信信号研究所 | Multi-train operation situation deduction method and device |
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