CN114298398B - 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 dynamic tracking operation optimization method of a high-speed train based on an elastic adjustment strategy. Comprising the following steps: according to the actual arrival information of the train, the operation plan of the front train and the initial operation strategy of the interval, an elastic adjustment mechanism is designed, and the static crowd searching algorithm is utilized to perform off-line optimization on tracking trains, wherein the off-line optimization comprises train departure intervals and train operation strategies; and then in the interval, acquiring real-time running state information and surrounding environment information of the front vehicle, and carrying out online optimization on the running adjustment strategy of the tracked train in the remaining interval by combining the transportation requirement through an elastic adjustment strategy and a dynamic crowd searching algorithm to acquire the optimal running interval in the current state, so that the running efficiency of the train is improved until the train reaches a terminal station on the premise of ensuring the safety. 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 running 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, high-speed railways in China rapidly develop, and the largest operation network in the world is formed. By virtue of the advantages of large traffic volume, long traffic distance, high speed, all weather and the like, the high-speed railway gradually becomes the first choice traffic mode for long-distance travel in the public. However, as the density of the road network is larger and larger, the running environment of the train is more and more complex, the demand for passenger transport is increasing, and the running efficiency of the train needs to be improved. The operation saturation is basically achieved along the dense railway line of the population in China by taking the high iron of the jinghu as an example, and the construction demonstration work of the two lines of the jinghu is started; the European Union Shift2Rail project progress report indicates that the European high speed Rail passenger demand is expected to increase by 50% in 2050. Along with the increase of passenger traffic, the railway transportation department becomes one of the units with the largest energy consumption in national economy of China, and meanwhile, higher requirements are put forward on travel experiences such as passenger alignment point rate, comfort level and the like. Therefore, the construction of the high-speed railway is quickened, and meanwhile, the transportation capacity and the operation quality of the existing high-speed railway are further improved, so that the method is one of the main problems facing the current situation.
In the tracking operation process, the train tracking interval is affected by the running speed of the front train and the back train and the control strategy to dynamically change. The train tracking interval is shortened, so that the running capacity of a line is increased on one hand, and the safety tracking running process of the train is challenged on the other hand. Meanwhile, frequent switching of train operation conditions may increase the loss of a train traction braking system, generate more operation energy consumption, and also bring poor riding experience to passengers, so that the train operation stability is poor. Therefore, the high-speed train tracking operation is a multi-objective optimization process which needs to meet the requirements of safety, high efficiency, energy conservation, stability and the like at the same time, and how to balance each optimization objective and improve the train operation efficiency on the basis of safe operation is a core problem of the train tracking operation.
In recent years, elasticity has received increasing attention in the traffic field, often used to describe the ability of a system to recover performance in response to disturbances, and different adjustment methods can be selected as needed during recovery. The elastic adjustment mechanism in the running process of the train can continuously evaluate the tracking state of the train, quantify the deviation degree of the actual interval and the optimal interval of the two trains, and adopt corresponding adjustment strategies according to the change of the operation environment to recover the tracking state to the optimal tracking state.
In the prior art, the high-speed train tracking operation method based on the elastic adjustment strategy is not deeply researched through research at home and abroad.
Disclosure of Invention
The embodiment of the invention provides a dynamic tracking operation optimization method for a high-speed train based on an elastic adjustment strategy, so as to effectively improve the operation efficiency of the high-speed train.
In order to achieve the above purpose, the present 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:
Collecting train tracking static information, wherein the train tracking static information comprises train basic parameters, line parameters, a front train operation plan and an initial interval operation strategy;
Before a tracking train starts, a tracking interval elastic adjustment mechanism is established, and a tracking operation multi-target optimization model is established according to the tracking interval elastic adjustment mechanism and the train tracking static information; acquiring the optimal departure interval of the train by using a static crowd searching algorithm;
Acquiring real-time running state information and temporary speed limiting information of a front train;
After the tracking train starts according to the optimal departure interval, the tracking operation multi-target optimization model is solved by adopting a dynamic crowd searching algorithm based on the real-time operation state information and the temporary speed limit information of the preceding train, and the dynamic optimal tracking operation strategy of the train in the residual interval is obtained through iterative optimization, so that the train is controlled to operate until the train reaches a terminal.
Preferably, before the train is tracked, a tracking interval elastic adjustment mechanism is established, which comprises:
Taking the actual tracking interval of the tracking train as an evaluation object, and establishing a tracking interval elastic adjustment mechanism, 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 ζ is the optimal spacing factor; in order to track the minimum safe tracking interval between the train and the preceding train, the following calculation process is performed:
Wherein, Tracking the train running distance in the response time of a driver; /(I)To track the service braking distance at the current running speed of the train; /(I)Is a safety protection distance; /(I)Is the length of the train; /(I)The emergency braking distance of the train i-1;
the calculation process of the tracking state evaluation model is as follows:
Wherein, The actual tracking interval of the front and rear vehicles is calculated as follows:
Wherein, The actual running position of the train i; /(I)Is the actual operating position of train i-1.
In combination with the operation condition of the preceding train at the next moment, the train elastic adjustment strategy provides a control command for tracking the train at the next moment, and specifically comprises the following steps:
when Q i epsilon (1 + xi), ++ infinity in the time-course of which the first and second contact surfaces, the actual tracking interval of the train is less than the minimum safe tracking interval, i.e Tracking the braking condition of the train at the next moment;
When Q i epsilon (1, 1+ζ), the train tracking state is "less spaced", i.e If the front train is in a braking working condition at the next moment, tracking that the train should also adopt the braking working condition at the next moment, otherwise, adopting an idle working condition;
when Q i epsilon [ 1/(1+epsilon), 1], the train tracking state is "moderate in interval", i.e.) Wherein epsilon is a tracking efficiency factor, if the preceding train is in a braking working condition at the next moment, the tracking train is in an idle working condition at the next moment, otherwise, a cruising working condition is adopted;
When Q i epsilon (- ≡, 1/(1+epsilon)), the train tracking state is "over-spaced", i.e.) And epsilon is a tracking efficiency factor, if the preceding train is in an idle working condition or a braking working condition at the next moment, the tracking train is required to adopt a cruising working condition at the next moment, and otherwise, the tracking train is required to adopt a traction working condition.
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 elasticity adjustment strategy, train operation efficiency, operation energy consumption and working condition switching times are taken as optimization targets, and a train tracking operation multi-target optimization model is established, which comprises the following steps:
min G(ΦC,ΦE,ΦN)
the calculation formula of the performance index comprises the following steps:
Speed constraint: v lim -v is greater than or equal to 0
Tracking interval constraints: l act-Lsafe is greater than or equal to 0
Train operation stability constraints: s-0.2 is less than or equal to 0
Departure interval constraint: h act-Hmin is greater than or equal to 0
Wherein phi C、ΦE、ΦN respectively represents the operation efficiency, the operation energy consumption and the working condition conversion frequency; t act is the actual running time of the train; u is the train operation condition; t u is the running time of the train under the working condition u; f is the train output control force; n change is the switching times of the whole course; h act、Hmin is the actual departure interval and the minimum departure interval of the train respectively; v lim is the current maximum allowable speed of the train; s represents the running stability of the train, and the calculation formula is as follows:
wherein sigma and c are width coefficient and center position respectively; a (t) is the acceleration of the train at the moment t; Δt is the time interval.
Preferably, the acquiring the optimal departure interval of the train by using the static crowd searching algorithm includes:
Initializing basic parameters of train and line information, acquiring an operation plan and an interval operation strategy expected by a front train, taking a departure interval, a tracking efficiency factor and an optimal interval factor as decision variables, and initializing a population, wherein the calculation formula is as follows:
Wherein G is the current evolution algebra; n p is population size; x G is the initial population at the current algebra G; The j-th individual in the G generation population; h j is the departure interval of the jth individual; epsilon j is the tracking efficiency factor for the jth individual; ζ j is the optimal separation factor for the j-th individual, the individual representing a train, and ζ j represents the optimal departure separation for the j-th train.
Preferably, after the tracked train starts according to the optimal departure interval, the tracking operation multi-objective optimization model is solved by adopting a dynamic crowd searching algorithm based on the real-time operation state information and the temporary speed limit information of the preceding train, and a tracking operation strategy of the train in the remaining interval is obtained through iterative optimization, so that the train is controlled to operate until the train reaches a terminal station, and the method comprises the following steps:
After the tracking train starts according to the optimal departure interval, executing dynamic adjustment among stations of the running strategy of the tracking train, setting a timing interval, and solving the tracking running multi-target optimization model by adopting a dynamic crowd searching algorithm based on the real-time running state information and the temporary speed limit information of the preceding train;
The processing procedure of the dynamic crowd searching algorithm comprises the following steps:
The basic parameters of train and line information are initialized, the tracking efficiency factor and the optimal interval factor are taken as decision variables, the train operation efficiency, the operation energy consumption and the working condition switching times are taken as optimization targets, and the initial group calculation formula is as follows:
Wherein G is the current evolution algebra; n p is population size; x G is the initial population at the current algebra G; The j-th individual in the G generation population; epsilon j is the tracking efficiency factor for the jth individual; ζ j is the optimal spacing factor for the j-th individual;
The current running state of the front train and the running strategy in the residual section are obtained, the current running state of the tracking train and the running distance in the residual section are obtained, the dynamic optimal tracking running strategy of the train in the residual section is obtained through cyclic iteration optimizing at a timing interval t c seconds, and the running of the train is controlled until the train reaches a 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 running efficiency of a high-speed train. The train running 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.
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In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for the description of the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a schematic flow chart of a dynamic tracking operation optimization method of a high-speed train based on an elastic adjustment strategy according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of an elastic adjustment mechanism 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 invention;
FIG. 4 is a flowchart of a static crowd search algorithm provided by an embodiment of the invention;
FIG. 5 (a) is a graph showing the change of the speed-distance between the tracking train and the preceding train in the non-interference scenario according to the embodiment of the present invention;
Fig. 5 (b) is a diagram showing a change relation between actual intervals between a tracking train and a front train and a minimum safety interval difference in a non-interference scenario provided by an embodiment of the present invention;
FIG. 6 (a) is a graph showing a change relation between the running speed and the distance between the tracked train and the preceding train in a temporary speed-limiting interference scenario according to an embodiment of the present invention;
fig. 6 (b) is a diagram illustrating a change relation between actual intervals between a tracking train and a preceding train and a minimum safety interval difference in a temporary speed-limiting interference scenario according to an embodiment of the present invention.
Detailed Description
Embodiments of the present invention are described in detail below, examples of which are illustrated in the accompanying drawings, wherein the same or similar reference numerals refer to the same or similar elements or elements having the same or similar functions throughout. The embodiments described below by referring to the drawings are exemplary only for 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 expressly stated otherwise, as understood by those skilled in the art. 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. The term "and/or" as used herein 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 purpose of facilitating an understanding of the embodiments of the invention, reference will now be made to the drawings of several specific embodiments illustrated in the drawings and in no way should be taken to limit the embodiments of the invention.
The process flow of the high-speed train dynamic tracking operation optimization method based on the elastic adjustment strategy is shown in figure 1, and specifically comprises the following processing steps:
Step S1: before tracking train dispatching, acquiring basic parameters and line parameters of a train, and receiving a dispatching train operation plan and an initial interval operation strategy;
Step S2: and executing static programming in the tracking train operation strategy station.
Step S2.1: the actual tracking interval of the tracking train is taken as an evaluation object, and a tracking interval elastic adjustment mechanism shown in fig. 2 is established, which 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 trainIs calculated as follows:
wherein ζ is the optimal spacing factor; in order to track the minimum safe tracking interval between the train and the preceding train, the following calculation process is performed:
Wherein, Tracking the train running distance in the response time of a driver; /(I)To track the service braking distance at the current running speed of the train; /(I)Is a safety protection distance; /(I)Is the length of the train; /(I)Is the emergency braking distance of the train i-1.
The calculation process of the tracking state evaluation model is as follows:
Wherein, The actual tracking interval of the front and rear vehicles is calculated as follows:
Wherein, The actual running position of the train i; /(I)Is the actual operating position of train i-1.
In combination with the operation condition of the preceding train at the next moment, the train elastic adjustment strategy provides a control command for tracking the train at the next moment, and specifically comprises the following steps:
when Q i epsilon (1 + xi), ++ infinity in the time-course of which the first and second contact surfaces, the actual tracking interval of the train is less than the minimum safe tracking interval, i.e Tracking the braking condition of the train at the next moment;
When Q i epsilon (1, 1+ζ), the train tracking state is "less spaced", i.e If the preceding train is in a braking working condition at the next moment, the tracking train also adopts the braking working condition at the next moment, otherwise, adopts an idle working condition.
When Q i epsilon [ 1/(1+epsilon), 1], the train tracking state is "moderate in interval", i.e.)Where ε is the tracking efficiency factor. If the preceding train is in a braking working condition at the next moment, the following train is tracked to adopt an idle working condition at the next moment, otherwise, the preceding train adopts a cruising working condition.
When Q i epsilon (- ≡, 1/(1+epsilon)), the train tracking state is "over-spaced", i.e.)Where ε 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 track train is in the cruising working condition at the next moment, otherwise, the traction working condition is adopted.
Step S2.2: according to the tracking state evaluation model and the elasticity adjustment strategy, train operation efficiency, operation energy consumption and working condition switching times are taken as optimization targets, and a train tracking operation multi-target optimization model is established, which comprises the following steps:
min G(ΦC,ΦE,ΦN)
the calculation formula of the performance index comprises the following steps:
Speed constraint: v lim -v is greater than or equal to 0
Tracking interval constraints: l act-Lsafe is greater than or equal to 0
Train operation stability constraints: s-0.2 is less than or equal to 0
Departure interval constraint: h act-Hmin is greater than or equal to 0
Wherein phi C、ΦE、ΦN respectively represents the operation efficiency, the operation energy consumption and the working condition conversion frequency; t act is the actual running time of the train; u is the train operation condition; t u is the running time of the train under the working condition u; f is the train output control force; n change is the switching times of the whole course; h act、Hmin is the actual departure interval and the minimum departure interval of the train respectively; v lim is the current maximum allowable speed of the train; s represents the running stability of the train, and the calculation formula is as follows:
wherein sigma and c are width coefficient and center position respectively; a (t) is the acceleration of the train at the moment t; Δt is the time interval.
Step S2.3: further, the optimal train departure interval and the running strategy of the static optimal train are obtained by using a static crowd searching algorithm, and a flow chart of the static crowd searching algorithm is shown in fig. 3. The method specifically comprises the following steps:
Basic parameters such as trains, line information and the like are initialized, an operation plan and an interval operation strategy which are expected by a front train are obtained, a train departure interval, a tracking efficiency factor and an optimal interval factor are taken as decision variables, and an initial population calculation formula is as follows:
Wherein G is the current evolution algebra; n p is population size; x G is the initial population at the current algebra G; The j-th individual in the G generation population; h j is the departure interval of the jth individual; epsilon j is the tracking efficiency factor for the jth individual; ζ j is the optimal spacing factor for the j-th individual. The above individuals represent trains, and ζ j represents the optimal departure interval of the j-th train.
Step S3: the high-speed train vehicle-mounted equipment is responsible for collecting real-time running state information (position, speed, acceleration and running working condition) and temporary speed limiting information from the wireless block center, and receiving the real-time running state information of the front train through the train communication system.
Step S4: after the tracked train starts according to the optimal departure interval, executing the dynamic adjustment among stations of the tracked train operation strategy, and specifically comprising the following steps:
step S4.1: further, a tracking interval elastic adjustment mechanism is established.
Step S4.1: further, a tracking operation multi-objective optimization model is established.
Step S4.2: further, with t c =120 seconds as a timing interval, a dynamic crowd searching algorithm is adopted, and the dynamic optimal tracking operation strategy of the train in the remaining interval is obtained through iterative optimization until the train reaches a terminal, wherein a flow chart of the dynamic crowd searching algorithm is shown in fig. 4. The method specifically comprises the following steps:
Basic parameters such as trains, line information and the like are initialized, the current running state of the front train and the running strategy in the remaining interval are obtained, the current running state of the tracked trains and the running distance in the remaining interval are obtained, the tracking efficiency factor and the optimal interval factor are taken as decision variables, and the initial population calculation formula is as follows:
Wherein G is the current evolution algebra; n p is population size; x G is the initial population at the current algebra G; The j-th individual in the G generation population; epsilon j is the tracking efficiency factor for the jth individual; ζ j is the optimal spacing factor for the j-th individual.
The above algorithms and processes may be implemented in some common computer languages, such as c#, c++, matlab, and the like.
In the embodiment, the train model is assumed to be CRH380AL, the maximum allowable speed is 350km/h, and the minimum departure interval is 120s.
From the above data and by the method of the invention, the following experimental results can be obtained:
The train is controlled by using an elastic adjustment strategy, and fig. 5 is a graph of an operation result of the tracked train under the condition of no external interference, wherein the graph comprises a change relation of an operation speed-distance curve between the tracked train and a front train section and a difference value between an actual interval and a minimum safe tracking interval, and the elastic adjustment strategy can ensure that the train tracking interval keeps an optimal operation state. Fig. 6 is a diagram of the running result of a tracking train under the condition that the preceding train is suddenly decelerated due to temporary speed limit, including the running speed-distance curve between the tracking train and the preceding train and the change relation between the actual interval and the minimum safety tracking interval difference, the elastic adjustment strategy can ensure that the train tracking state is recovered to the optimal state in time when the preceding train is suddenly decelerated due to temporary speed limit, and the tracking interval is always larger than the minimum safety tracking interval.
The method is suitable for train tracking operation optimization in a high-speed railway system of the unidirectional double trains, and is particularly suitable for train dynamic tracking operation optimization in a complex interference environment. The method is also applicable to quasi-point transportation requirements, and can be realized by modifying a multi-objective optimization model.
In summary, the method for optimizing the dynamic tracking operation of the high-speed train based on the elastic adjustment strategy according to the embodiment of the invention can be used for optimizing the tracking operation process of the high-speed train, and has the following beneficial effects:
(1) By adopting the 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 searching 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 system can be used for a train operation control system to guide safe, efficient, energy-saving and stable tracking operation of a high-speed train.
Those of ordinary skill in the art will appreciate that: the drawing is a schematic diagram of one embodiment and the modules or flows in the drawing are not necessarily required to practice the invention.
From the above description of embodiments, it will be apparent to those skilled in the art that the present invention may be implemented in software plus a necessary general hardware platform. Based on such understanding, the technical solution of the present invention may be embodied essentially or in a part contributing to the prior art in the form of a software product, which may be stored in a storage medium, such as a ROM/RAM, a magnetic disk, an optical disk, etc., including several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute the method described in the embodiments or some parts of the embodiments of the present invention.
In this specification, each embodiment is described in a progressive manner, and identical and similar parts of each embodiment are all referred to each other, and each embodiment mainly describes differences from other embodiments. In particular, for apparatus or system embodiments, since they are substantially similar to method embodiments, the description is relatively simple, with reference to the description of method embodiments in part. The apparatus and system embodiments described above are merely illustrative, wherein the elements illustrated as separate elements may or may not be physically separate, and the elements shown as elements may or may not be physical elements, may be located in one place, or may be distributed over a plurality of network elements. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment. Those of ordinary skill in the art will understand and implement the present invention without undue burden.
The present invention is not limited to the above-mentioned embodiments, and any changes or substitutions that can be easily understood by those skilled in the art within the technical scope of the present invention are intended to be included in the scope of the present invention. Therefore, the protection scope of the present invention should be subject to the protection scope of the claims.
Claims (2)
1. The high-speed train dynamic tracking operation optimization method based on the elastic adjustment strategy is characterized by comprising the following steps of:
Collecting train tracking static information, wherein the train tracking static information comprises train basic parameters, line parameters, a front train operation plan and an initial interval operation strategy;
Before a tracking train starts, a tracking interval elastic adjustment mechanism is established, and a tracking operation multi-target optimization model is established according to the tracking interval elastic adjustment mechanism and the train tracking static information; acquiring the optimal departure interval of the train by using a static crowd searching algorithm;
Acquiring real-time running state information and temporary speed limiting information of a front train;
After the tracked train starts according to the optimal departure interval, solving the tracked operation multi-target optimization model by adopting a dynamic crowd searching algorithm based on the real-time operation state information and the temporary speed limit information of the preceding train, and obtaining a dynamic optimal tracked operation strategy of the train in the remaining interval through iterative optimization, and controlling the train to operate until the train reaches a terminal station;
before the train is tracked, an elastic adjustment mechanism of a tracking interval is established, and the method comprises the following steps:
Taking the actual tracking interval of the tracking train as an evaluation object, and establishing a tracking interval elastic adjustment mechanism, 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 ζ is the optimal spacing factor; in order to track the minimum safe tracking interval between the train and the preceding train, the following calculation process is performed:
Wherein, Tracking the train running distance in the response time of a driver; /(I)To track the service braking distance at the current running speed of the train; /(I)Is a safety protection distance; /(I)Is the length of the train; /(I)The emergency braking distance of the train i-1;
the calculation process of the tracking state evaluation model is as follows:
Wherein, For the actual tracking interval of the front and rear vehicles, the calculation process is as follows:
Wherein, The actual running position of the train i; /(I)The actual running position of the train i-1;
in combination with the operation condition of the preceding train at the next moment, the train elastic adjustment strategy provides a control command for tracking the train at the next moment, and specifically comprises the following steps:
when Q i epsilon (1 + xi), ++ infinity in the time-course of which the first and second contact surfaces, the actual tracking interval of the train is less than the minimum safe tracking interval, i.e Tracking the braking condition of the train at the next moment;
When Q i epsilon (1, 1+ζ), the train tracking state is "less spaced", i.e If the front train is in a braking working condition at the next moment, tracking that the train should also adopt the braking working condition at the next moment, otherwise, adopting an idle working condition;
when Q i epsilon [ 1/(1+epsilon), 1], the train tracking state is "moderate in interval", i.e.) Wherein epsilon is a tracking efficiency factor, if the preceding train is in a braking working condition at the next moment, the tracking train is in an idle working condition at the next moment, otherwise, a cruising working condition is adopted;
When Q i epsilon (- ≡, 1/(1+epsilon)), the train tracking state is 'oversized interval', wherein epsilon is a tracking efficiency factor, if the preceding train is in an idle working condition or a braking working condition at the next moment, the tracking train should take a cruising working condition at the next moment, otherwise, a traction working condition is taken;
the method for establishing a tracking operation multi-target optimization model according to the tracking interval elastic adjustment mechanism and the train tracking static information comprises the following steps:
according to the tracking state evaluation model and the elasticity adjustment strategy, taking train operation efficiency, operation energy consumption and working condition switching times as optimization targets, and establishing a train tracking operation multi-target optimization model, wherein the method specifically comprises the following steps:
min G(ΦC,ΦE,ΦN)
the calculation formula of the performance index comprises the following steps:
Speed constraint: v lim -v is greater than or equal to 0
Tracking interval constraints: l act-Lsafe is greater than or equal to 0
Train operation stability constraints: s-0.2 is less than or equal to 0
Departure interval constraint: h act-Hmin is greater than or equal to 0
Wherein phi C、ΦE、ΦN respectively represents the operation efficiency, the operation energy consumption and the working condition conversion frequency; t act is the actual running time of the train; u is the train operation condition; t u is the running time of the train under the working condition u; f is the train output control force; n change is the switching times of the whole course; h act、Hmin is the actual departure interval and the minimum departure interval of the train respectively; v lim is the current maximum allowable speed of the train; s represents the running stability of the train, and the calculation formula is as follows:
Wherein sigma and c are width coefficient and center position respectively; a (t) is the acceleration of the train at the moment t; Δt is the time interval;
after the tracked train starts according to the optimal departure interval, the tracking operation multi-target optimization model is solved by adopting a dynamic crowd searching algorithm based on the real-time operation state information and the temporary speed limit information of the preceding train, and a tracking operation strategy of the train, which is dynamically optimal in the remaining interval, is obtained through iterative optimization, and the train operation is controlled until the train reaches a terminal station, and the method comprises the following steps:
After the tracking train starts according to the optimal departure interval, executing dynamic adjustment among stations of the running strategy of the tracking train, setting a timing interval, and solving the tracking running multi-target optimization model by adopting a dynamic crowd searching algorithm based on the real-time running state information and the temporary speed limit information of the preceding train;
The processing procedure of the dynamic crowd searching algorithm comprises the following steps:
The basic parameters of train and line information are initialized, the tracking efficiency factor and the optimal interval factor are taken as decision variables, the train operation efficiency, the operation energy consumption and the working condition switching times are taken as optimization targets, and the initial group calculation formula is as follows:
Wherein G is the current evolution algebra; n p is population size; x G is the initial population at the current algebra G; The j-th individual in the G generation population; epsilon j is the tracking efficiency factor for the jth individual; ζ j is the optimal spacing factor for the j-th individual;
The current running state of the front train and the running strategy in the residual section are obtained, the current running state of the tracking train and the running distance in the residual section are obtained, the dynamic optimal tracking running strategy of the train in the residual section is obtained through cyclic iteration optimizing at a timing interval t c seconds, and the running of the train is controlled until the train reaches a terminal station.
2. The method of claim 1, wherein said obtaining the optimal departure interval of the train using a static crowd search algorithm comprises:
Initializing basic parameters of train and line information, acquiring an operation plan and an interval operation strategy expected by a front train, taking a departure interval, a tracking efficiency factor and an optimal interval factor as decision variables, and initializing a population, wherein the calculation formula is as follows:
Wherein G is the current evolution algebra; n p is population size; x G is the initial population at the current algebra G; The j-th individual in the G generation population; h j is the departure interval of the jth individual; epsilon j is the tracking efficiency factor for the jth individual; ζ j is the optimal separation factor for the j-th individual, the individual representing a train, and ζ j represents the optimal departure separation for the j-th train.
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