CN115180001B - Train operation control method and system - Google Patents
Train operation control method and system Download PDFInfo
- Publication number
- CN115180001B CN115180001B CN202210892075.XA CN202210892075A CN115180001B CN 115180001 B CN115180001 B CN 115180001B CN 202210892075 A CN202210892075 A CN 202210892075A CN 115180001 B CN115180001 B CN 115180001B
- Authority
- CN
- China
- Prior art keywords
- train
- train operation
- curve
- solution space
- running
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 238000000034 method Methods 0.000 title claims abstract description 99
- 230000008569 process Effects 0.000 claims abstract description 51
- 241000282461 Canis lupus Species 0.000 claims abstract description 45
- 238000005457 optimization Methods 0.000 claims abstract description 16
- 238000004422 calculation algorithm Methods 0.000 claims abstract description 13
- 238000006243 chemical reaction Methods 0.000 claims abstract description 10
- 238000012795 verification Methods 0.000 claims abstract description 10
- 238000004134 energy conservation Methods 0.000 claims abstract description 4
- 241000282421 Canidae Species 0.000 claims description 18
- 238000004904 shortening Methods 0.000 claims description 9
- 238000005265 energy consumption Methods 0.000 claims description 7
- 238000004364 calculation method Methods 0.000 claims description 6
- 238000010276 construction Methods 0.000 claims description 6
- 230000001133 acceleration Effects 0.000 claims description 4
- 230000006870 function Effects 0.000 description 16
- 238000010586 diagram Methods 0.000 description 10
- 238000004590 computer program Methods 0.000 description 9
- 238000003860 storage Methods 0.000 description 7
- 238000012986 modification Methods 0.000 description 5
- 230000004048 modification Effects 0.000 description 5
- 238000012545 processing Methods 0.000 description 5
- 238000011156 evaluation Methods 0.000 description 4
- 230000008901 benefit Effects 0.000 description 3
- 230000006399 behavior Effects 0.000 description 2
- 230000033228 biological regulation Effects 0.000 description 2
- 230000008859 change Effects 0.000 description 2
- 238000012804 iterative process Methods 0.000 description 2
- 239000011159 matrix material Substances 0.000 description 2
- 230000002776 aggregation Effects 0.000 description 1
- 238000004220 aggregation Methods 0.000 description 1
- 230000004075 alteration Effects 0.000 description 1
- 230000003416 augmentation Effects 0.000 description 1
- 239000002131 composite material Substances 0.000 description 1
- 230000007423 decrease Effects 0.000 description 1
- 230000003247 decreasing effect Effects 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 230000009977 dual effect Effects 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 230000006872 improvement Effects 0.000 description 1
- 230000003993 interaction Effects 0.000 description 1
- 238000004519 manufacturing process Methods 0.000 description 1
- 238000013178 mathematical model Methods 0.000 description 1
- 238000010606 normalization Methods 0.000 description 1
- 238000011022 operating instruction Methods 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 230000002035 prolonged effect Effects 0.000 description 1
- 230000009467 reduction Effects 0.000 description 1
- 238000011160 research Methods 0.000 description 1
- 230000004044 response Effects 0.000 description 1
- 238000012360 testing method Methods 0.000 description 1
- 230000007704 transition Effects 0.000 description 1
Images
Classifications
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B61—RAILWAYS
- B61L—GUIDING RAILWAY TRAFFIC; ENSURING THE SAFETY OF RAILWAY TRAFFIC
- B61L27/00—Central railway traffic control systems; Trackside control; Communication systems specially adapted therefor
- B61L27/10—Operations, e.g. scheduling or time tables
Landscapes
- Engineering & Computer Science (AREA)
- Mechanical Engineering (AREA)
- Train Traffic Observation, Control, And Security (AREA)
Abstract
The embodiment of the application provides a train operation control method and system, which can be applied to a dynamic adjustment scene in the train operation process. Firstly, through constructing an objective function and constraint conditions, a train operation process is rapidly modeled based on a conversion process of traction, idle running and braking working conditions. And then accurately solving based on a gray wolf optimization algorithm to obtain a train running curve. And finally, verifying the effectiveness of the train operation curve from a plurality of train operation indexes, and applying the train operation curve passing verification to train operation control. The method provided by the embodiment of the application not only improves the modeling speed and solving precision, but also considers a plurality of train operation indexes in the train operation process to comprehensively verify the effectiveness of the train operation curve so as to achieve the important factors of safety, energy conservation, comfort and the like, and greatly enhances the dynamic adjustment capability of the train.
Description
Technical Field
The application relates to the field of rail transit, in particular to a train operation control method and system.
Background
In recent years, urban rail transit has rapidly evolved. By the end of 2021, 50 cities in China have provided a total of 283 urban rail transit operation lines, and the total length of the operation lines reaches 9206.8 km. The urban rail transit system has the advantages of small departure interval, short tracking time and strong space-time difference of passenger flow. Meanwhile, certain instability exists, and various sudden scenes such as signal faults, sudden large passenger flows, geological weather disasters and the like can be frequently dealt with. Once train delay occurs, if the processing is not timely, the disturbance of an operation system and the aggregation of passengers can be caused, and a certain influence is generated on a traffic system. In order to reduce the influence of sudden scenes on rail transit operation and improve emergency response capability, related researches on regulation and control integrated technology are carried out in the industry, wherein higher requirements are put forward on the dynamic regulation capability of a train.
Scheduled run-time adjustment is an important way to achieve dynamic adjustment of an inter-zone train. If the front station bursts large passenger flow, the dispatching center can improve the passing capacity of the interval by shortening the planned running time of the train and evacuate the detained passengers as soon as possible. For the scene of train signal fault or operation interruption, the scheduling center can increase the planned operation time division of the subsequent train, and prevent aggravating delay. In the face of the burst scenes, the train responds to the plan change command of the dispatching center in real time, and the time division of the planned operation is prolonged or shortened according to the current train state. The traditional adjustment method can only switch a plurality of fixed operation grades, and the adjustable space is limited and has uncertainty. Meanwhile, the traditional method only considers the safety index requirement, and does not consider the influence of factors such as line conditions, train dynamics characteristics and the like on the train operation process, so that the operation control is not energy-saving and optimal.
Disclosure of Invention
In order to solve one of the technical defects, the embodiment of the application provides a train operation control method and a train operation control system.
According to a first aspect of embodiments of the present application, there is provided a train operation control method, the method including:
constructing an objective function and constraint conditions according to train operation parameters;
constructing a solution space according to the train operation process and the conversion process of the traction working condition, the idle working condition and the braking working condition in the train operation process based on the objective function and the constraint condition, and limiting the solution space range;
solving the solution space to obtain a train operation curve in the limited solution space range;
acquiring a train operation index of the train running in the train operation curve according to the train operation curve, and verifying the effectiveness of the train operation curve according to the acquisition result of the train operation index;
and controlling the train operation according to the train operation curve passing the validity verification.
According to a second aspect of embodiments of the present application, there is provided a train operation control system, the system comprising a processor configured with processor-executable operating instructions to perform the train operation control method according to the first aspect of embodiments of the present application.
The train operation control method provided by the embodiment of the application can be applied to a dynamic adjustment scene in the train operation process. Firstly, through constructing an objective function and constraint conditions, a train operation process is rapidly modeled based on a conversion process of traction, idle running and braking working conditions. And then accurately solving based on a gray wolf optimization algorithm to obtain a train running curve. And finally, verifying the effectiveness of the train operation curve from a plurality of train operation indexes, and applying the train operation curve passing verification to train operation control. The method provided by the embodiment of the application not only improves the modeling speed and solving precision, but also considers a plurality of train operation indexes in the train operation process to comprehensively verify the effectiveness of the train operation curve so as to achieve the important factors of safety, energy conservation, comfort and the like, and greatly enhances the dynamic adjustment capability of the train.
Drawings
The accompanying drawings, which are included to provide a further understanding of the application and are incorporated in and constitute a part of this application, illustrate embodiments of the application and together with the description serve to explain the application and do not constitute an undue limitation to the application. In the drawings:
fig. 1 is a flowchart of a train operation control method according to embodiment 1 of the present application;
fig. 2 is a schematic diagram of a train speed curve of a time-division shortening scenario in a simple route scenario according to embodiment 1 of the present application;
FIG. 3 is a schematic diagram of a train speed curve of a planned run time increase scenario in a simple route scenario as described in example 1 of the present application;
fig. 4 is a schematic diagram of a train speed curve of a time-division shortening scenario in a complex line scenario according to embodiment 1 of the present application;
fig. 5 is a schematic diagram of a train speed curve of a planned operation time division increase scenario in a complex line scenario according to embodiment 1 of the present application.
Detailed Description
In order to make the technical solutions and advantages of the embodiments of the present application more apparent, the following detailed description of exemplary embodiments of the present application is given with reference to the accompanying drawings, and it is apparent that the described embodiments are only some of the embodiments of the present application and not exhaustive of all the embodiments. It should be noted that, in the case of no conflict, the embodiments and features in the embodiments may be combined with each other.
Example 1
As shown in fig. 1, the present embodiment proposes a train operation control method, which includes:
s101, constructing an objective function and constraint conditions according to train operation parameters.
Specifically, the method proposed in this embodiment is a process of modeling, solving and verifying based on a mathematical angle. Therefore, if the train operation process is described from a mathematical point of view, it is necessary to discretize the entire train operation process so that the model is more stable. In this embodiment, first, a discrete interval is set, and the whole train running process is discretized according to the discrete interval, so as to form N discrete sub-processes. It should be noted that for a model, the smaller the discrete interval, the higher the accuracy of the model. However, too small a discrete interval causes a problem of an increase in solving time. Therefore, it is necessary to balance the relationship between the two according to the actual scene requirements, and usually a fixed discrete interval of 1m can satisfy most of the scene requirements. Of course, the discrete interval may be appropriately adjusted according to the actual scene, and the present embodiment is not particularly limited.
After discretizing the train operation process, objective functions and constraints are constructed from the train operation parameters. The train operation parameters comprise train speed, stress condition, distance, operation time and the like. Specifically, firstly, considering that the planned operation time and energy conservation are the main optimization indexes of the embodiment, and for the objective function, the fewer the optimization targets are, the higher the model solving precision is, therefore, the dual targets of the optimization indexes and the solving precision are comprehensively considered in the embodiment to define the objective function as follows:
wherein P is a penalty coefficient in operation time, and takes the value of [0,1 ]]The larger the P value is, the higher the requirement is for operation time, the lower the energy consumption requirement is, F i ，ΔS i ，T i The traction force, the running distance and the running time of the train output of each discrete sub-process are respectively represented, and N is the number of the discrete sub-processes.
According to the overall state of the train running process, the present embodiment classifies constraint conditions into speed limiting constraint and dynamic constraint. In order to ensure driving safety, the train is required not to exceed the limit speed in the running process of the train. The limiting speed mainly comprises a train construction speed, a line speed limit, an outbound speed limit and the like. Thus, in this embodiment, the speed limit constraint is as follows:
V i ＜V ebi
wherein V is i ，V ebi The train speed and the limit speed for the i-th discrete sub-process are represented, respectively.
For dynamic constraints, that is, in each discrete sub-process, the state transition of the train is constrained by the maximum traction and braking forces to satisfy newton's second law of motion. Thus, in this embodiment, the kinetic constraints are as follows:
wherein F is i ，B i ，w i ，a i ，V i ，S i ，ΔS i ，T i Respectively representing the traction force, the braking force, the resistance, the acceleration, the speed, the accumulated running distance, the unit running distance and the accumulated running time of the ith discrete sub-process train; f (F) max ，B max Respectively representing the maximum traction force and the maximum braking force which can be output by the train; g is the train weight.
S102, constructing a solution space according to the train operation process and the conversion process of the traction working condition, the idle working condition and the braking working condition in the train operation process based on the objective function and the constraint condition, and limiting the solution space range.
In this embodiment, the scene is shortened by splitting the train operation process into the scheduled operation time division and the scene is increased by the scheduled operation time division. And then shortening the scene according to the planned operation time and increasing the scene according to the planned operation time to determine the value intervals of the traction working condition, the idle working condition and the braking working condition. And finally, constructing a solution space through a value interval and determining the initialization positions of the traction working condition, the idle working condition and the braking working condition in the solution space. By taking the conversion process of traction, idle running and braking working conditions in the train running process as the basis of a construction solution space, the model can be quickly constructed on line, and the whole model can support various dynamic adjustment requirements in the train running process.
Specifically, for a line with a short distance between stations, the operation conditions of the train mainly comprise three conditions of traction, idle running and braking. Based on the 'three-stage' optimal control theory, for a section with simpler speed limit and line characteristics, the optimal curve can be realized through 'one-time traction-idle running working condition conversion'. For curves with complex speed limit or obvious line characteristics, the optimal curve is realized through multiple traction-idle working condition conversion. For more complex speed limits and line characteristics, the optimal curve is also considered for braking conditions. Based on the above, the present embodiment constructs a solution space based on the objective function and constraint conditions according to the train operation process and the conversion process of the traction working condition, the idle working condition and the braking working condition in the train operation process, wherein the dimension of the solution space is related to the line speed limit and the line characteristics. In this embodiment, the construction process of the solution space is described by using two scenes, i.e., a simple line scene and a complex line scene, and other scenes can be extended on the basis of the description.
In a simple line scenario, the present embodiment subdivides it into two sub-scenarios: the scheduled run time shortening scenario and the scheduled run time increasing scenario are shown in fig. 2 and 3, respectively.
Corresponding to FIG. 2, the train is at S 1 The position receives the adjustment command, and the train needs higher running speed to meet the adjustment requirement due to the shortening of the planned running time, so S 1 The working condition point of the position can be switched to traction working condition, and the value interval of idle working condition is in [ S ] 1 ，S 2 ]In between, if the optimal solution of the idle running condition is S 1 Then means S 1 Position of the positionThe device does not need to be switched to traction working conditions. The setting principle of the solution space can be led out, namely, all working condition combination modes are provided as far as possible, and a solving algorithm determines whether certain working conditions are needed according to the searching process. The solution space initialization of the time-division shortened scene in the planning operation under the simple line scene is shown in the following table, and the dimension is 1 dimension, namely the optimization problem of the idle running switching position is solved.
| Sequence number | Working conditions of | Marking | Initializing a position |
| 1 | Traction | ○ | S 1 |
| 2 | Coasting | △ | [S 1 ，S 2 ] |
Corresponding to fig. 3, the train needs to be run at a reduced speed to meet the adjustment requirement due to the scheduled run time increase. Thus S is 1 The working condition can be switched into braking, and the value interval of idle working condition is in S 1 ，S 2 ]The solution space initialization settings are shown in the following table.
| Sequence number | Working conditions of | Marking | Initializing a position |
| 1 | Braking system | ○ | S 1 |
| 2 | Coasting | △ | [S 1 ，S 2 ] |
In a complex line scene, according to the same idea, the embodiment divides the complex line scene into two sub-scenes: the scheduled run time shortening scenario and the scheduled run time increasing scenario are shown in fig. 4 and 5, respectively.
Corresponding to fig. 4, since the line characteristics are complex, the speed-limiting descending area exists in front of the line, and the whole train running process can not be completed through one-time traction-idle running, so that traction and idle running working conditions need to be added behind the speed-limiting descending area. The corresponding solution space initialization position is shown in the following table, and the dimension of the solution space in the scene is 3-dimensional, and the problem of optimization of the switching positions of three working conditions of idle running, traction and idle running is solved.
| Sequence number | Working conditions of | Marking | Heuristic setup |
| 1 | Traction | ○ | S 1 |
| 2 | Coasting | △ | [S 1 ，S 2 ] |
| 3 | Traction | ○ | [S 2 ，S k ] |
| 4 | Coasting | △ | [S k ，S 3 ] |
For FIG. 5, for the scenario of scheduled runtime augmentation, give S 1 The position is braking, and the traction and idle running conditions are increased in the subsequent working conditions. The initial position for the solution space is shown in the following table.
| Sequence number | Working conditions of | Marking | Heuristic setup |
| 1 | Braking system | ○ | S 1 |
| 2 | Coasting | △ | [S 1 ，S 2 ] |
| 3 | Traction | □ | [S 2 ，S k ] |
| 4 | Coasting | △ | [S k ，S 3 ] |
In order to prevent an invalid solution which does not meet constraint requirements from being searched in a subsequent solving process, the searching efficiency of an algorithm is further improved, and the range of a solution space is limited in the embodiment. Specifically, the fastest speed curve and the slowest speed curve of the train from the current position to the end point are calculated respectively. The fastest speed curve is then taken as the upper boundary constraint of the solution space, and the slowest speed curve is taken as the lower boundary constraint of the solution space. All solutions within the feasible solution space thus constructed can meet the speed limit, terminal and start constraints.
And S103, solving the solution space to obtain a train operation curve in the limited solution space range.
Specifically, in the prior art, there are many solution algorithms that can implement solution to the solution space. In order to improve the solving speed and accuracy, the embodiment adopts a gray wolf optimization algorithm to solve the solution space. The gray wolf optimization algorithm has the advantages of high solving precision, high convergence speed and the like, the solving time is less than 0.1s, and the requirement of online calculation can be met. The natural process of the group hunting behavior simulated by the gray wolf optimization algorithm mainly comprises three stages: tracking and chasing approaching hunting; pursuing and surrounding harassment hunting objects until the harassment hunting objects stop moving; attack prey. The corresponding mathematical model is as follows:
(1) Location update
After the target prey is determined by the lead of the wolf, the wolf members are dispersed around the prey, and the search position is updated in each iteration. The definition of the location update is as follows:
in the formula (1-1), t represents the number of iterations,
position vector representing prey in the t-th iteration,/->
A position vector representing a gray wolf; the formula (1-2) shows the position update of the wolves, and the wolves canRefreshing to a location near the prey,
is a coefficient vector, and is calculated as follows:
wherein,,
is a convergence factor, decreasing linearly from 2 to 0,/as the iterative process proceeds>
Is a random number, and has a value range of 0,1]Between them.
(2) Search and capture direction
In order to simulate hunting behavior of the wolves, it is assumed that the alpha wolves, the beta wolves and the delta wolves can search the position of the hunting object, the three are also excellent individuals in the wolf group which can find the hunting object, the positions of the hunting object are judged by utilizing the positions of the three, and other wolves continuously move according to the positions of the alpha wolves, the beta wolves and the delta wolves.
Wherein, the formula (1-5), the formula (1-6) shows the positions of the wolf group individuals, which need to be updated, by taking alpha wolf, beta wolf and delta wolf as tracking targets respectively, the formula (1-7) shows the commands of integrating the alpha wolf, the beta wolf and the delta wolf, and the average of the three is taken as the final searching direction.
Convergence factor of gray wolf optimization algorithm
Can be used to force the algorithm to perform a global search or converge to an optimal solution, as the iterative process proceeds,/->
The value of (2) decreases linearly from 2 to 1, corresponding to +.>
The value of (a) is equal to [ -a, a]Internal changes, when
The wolf group is forced to explore outwards when +.>
The wolf group is forced to converge towards the prey. />
Is the value range [0,2 ]]Is a random number representing the effect of the position of the wolf on the prey, ++>
And when the influence weight is large, the influence is small. At the same time due to->
Unlike +.>
Is linearly reduced, ++>
Is set to avoidThe search for a trapping localized optimum plays an important role.
Based on the principle of the gray wolf optimization algorithm, the embodiment initializes the number X and the positions of the wolves in the gray wolf population in the solution space, and sets the maximum iteration times; calculating the adaptability of each wolf in the wolf population, and storing the first Y wolves with the best adaptability; updating the position of (X-Y) only wolves in the wolf population according to the adaptability of each wolf; calculating the fitness of each wolf in the wolf population, updating the fitness and the position of the previous Y wolves, adding one to the iteration number until the iteration number reaches the maximum iteration number, and outputting the optimal position of each wolf in the wolf population; and obtaining a train running curve according to the optimal position and the limited solution space range of each wolf of the wolf population.
S104, acquiring a train operation index of the train running in the train operation curve according to the train operation curve, and verifying the validity of the train operation curve according to the acquisition result of the train operation index;
s105, controlling the train operation according to the train operation curve passing the validity verification.
Specifically, in this embodiment, although the wolf optimization algorithm can perform accurate solution, the solution result still has a certain degree of uncertainty due to the fact that the method belongs to a heuristic search method. Therefore, the embodiment also needs to perform validity verification on the solved train operation curve so as to improve the accuracy of the train operation curve. The embodiment performs validity verification on the train operation curve mainly through comprehensive evaluation of related train operation indexes in the train operation process. The train operation index may include at least one of a safety index, a time-of-operation index, an operation energy consumption index, or a comfort index. The calculation mode of each train operation index is as follows:
index 1: safety index
Assume that the train speed at each discrete location is V i The corresponding protection speed is V ebi The safety evaluation index of each discrete point is defined as follows:
the safety evaluation indexes of the whole operation process are as follows:
index 2: run time index
Assume that the planned operation time of train operation is T plan Error |T between actual operation time T and planned operation time of train error When the I is less than 5s, the standard point is reached, and the generalized bell-shaped membership function is adopted to construct the time division index of operation as follows:
index 3: operation energy consumption index
The running energy consumption E of the train is at the maximum value E max And minimum value E min The structural evaluation index is as follows:
index 4: comfort index
The comfort level is directly characterized by the rate of change of acceleration, i.e. the impact rate:
where a is the acceleration during the train operation and t is the operation time. The smaller the J value, the higher the comfort. The comfort index of the whole train operation process is as follows:
after determining the calculation mode of each train operation index, the importance degree of the four train operation indexes can be defined according to the actual operation condition of the train, and the embodiment provides a definition result as shown in the following table, wherein the larger the number is, the higher the importance degree is indicated:
and constructing a pairwise comparison matrix according to the importance degree of each train operation index. Through calculation, the matrix has complete consistency, and through consistency test, the normalized feature vector can be used as a weight vector, and the corresponding weight of each train operation index is finally obtained:
z i ＝[0.3889，0.2778，0.1667，0.1667]
in the process of running various train indexes w i After normalization, a composite score Q is calculated:
only if the comprehensive score Q meets a certain requirement, the effectiveness of the train running curve can be verified, and the train running curve which finally passes the verification is used as the on-line optimized speed of the train to control the train running. Therefore, the method and the device can fully consider the index requirements of safety, time-division operation, operation energy consumption, comfort and the like on the basis of optimizing the train operation curve, and can bring about reduction of operation cost and improvement of passenger traveling experience.
Example 2
Corresponding to embodiment 1, the present embodiment proposes a train operation control system, the system comprising a processor configured with operation instructions executable by the processor to perform the steps of:
constructing an objective function and constraint conditions according to train operation parameters;
constructing a solution space according to the train operation process and the conversion process of the traction working condition, the idle working condition and the braking working condition in the train operation process based on the objective function and the constraint condition, and limiting the solution space range;
solving the solution space to obtain a train operation curve in the limited solution space range;
acquiring a train operation index of the train running in the train operation curve according to the train operation curve, and verifying the effectiveness of the train operation curve according to the acquisition result of the train operation index;
and controlling the train operation according to the train operation curve passing the validity verification.
Since the embodiments of the train operation control system and the embodiments of the train operation control method section correspond to each other, the embodiments of the train operation control system will refer to the description of the embodiments of the train operation control method section, and will not be repeated here.
Example 3
The embodiment of the invention also provides a computer readable storage medium, wherein a computer program is stored on the computer readable storage medium, and when the computer program is executed by a processor, the steps of the train operation control method are realized.
Since the embodiments of the computer readable storage medium portion and the embodiments of the train operation control method portion correspond to each other, the embodiments of the storage medium portion are referred to the description of the embodiments of the train operation control method portion, and are not repeated here.
It will be appreciated by those skilled in the art that embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein. The solutions in the embodiments of the present application may be implemented in various computer languages, for example, object-oriented programming language Java, and an transliterated scripting language JavaScript, etc.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
In this application, unless specifically stated and limited otherwise, the terms "mounted," "connected," "secured," and the like are to be construed broadly, and may be, for example, fixedly connected, detachably connected, or integrally formed; may be mechanically connected, may be electrically connected or may communicate with each other; can be directly connected or indirectly connected through an intermediate medium, and can be communicated with the inside of two elements or the interaction relationship of the two elements. The specific meaning of the terms in this application will be understood by those of ordinary skill in the art as the case may be.
While preferred embodiments of the present application have been described, additional variations and modifications in those embodiments may occur to those skilled in the art once they learn of the basic inventive concepts. It is therefore intended that the following claims be interpreted as including the preferred embodiments and all such alterations and modifications as fall within the scope of the application.
It will be apparent to those skilled in the art that various modifications and variations can be made in the present application without departing from the spirit or scope of the application. Thus, if such modifications and variations of the present application fall within the scope of the claims and the equivalents thereof, the present application is intended to cover such modifications and variations.
Claims (7)
1. A train operation control method, the method comprising:
constructing an objective function and constraint conditions according to train operation parameters;
constructing a solution space according to the train operation process and the conversion process of the traction working condition, the idle working condition and the braking working condition in the train operation process based on the objective function and the constraint condition, and limiting the solution space range;
solving the solution space to obtain a train operation curve in the limited solution space range;
acquiring a train operation index of the train running in the train operation curve according to the train operation curve, and verifying the effectiveness of the train operation curve according to the acquisition result of the train operation index;
controlling the train operation according to the train operation curve passing the validity verification;
the construction process of the objective function comprises the following steps: taking the planned running time and energy conservation in the train running parameters as optimization indexes, and constructing the objective function according to the optimization indexes; the objective function is as follows:
wherein P is a penalty coefficient in operation time, and takes the value of [0,1 ]]The larger the P value is, the higher the requirement is for operation time, the lower the energy consumption requirement is, F i ，ΔS i ，T i The traction force, the running distance and the running time of the train output by each discrete sub-process are respectively represented, and N is the number of the discrete sub-processes;
the construction process of the constraint condition comprises the following steps: constructing speed limiting constraints according to the train speed and the limiting speed in each discrete sub-process; constructing dynamic constraints according to the stress state of the train in each discrete sub-process;
the speed limit constraints are as follows:
V i ＜V ebi
wherein V is i ，V ebi Respectively representing the train speed and the limit speed of the ith discrete sub-process;
the kinetic constraints are as follows:
wherein F is i ，B i ，w i ，a i ，V i ，S i ，ΔS i ，T i Respectively representing the traction force, the braking force, the resistance, the acceleration, the speed, the accumulated running distance, the unit running distance and the accumulated running time of the ith discrete sub-process train; f (F) max ，B max Respectively representing the maximum traction force and the maximum braking force which can be output by the train; g is the weight of the train;
the construction process of the solution space comprises the following steps:
splitting the train operation process into a scheduled operation time division shortening scene and a scheduled operation time division increasing scene;
shortening a scene according to the planned operation time division and increasing the scene according to the planned operation time division to determine the value intervals of a traction working condition, an idle working condition and a braking working condition;
and constructing a solution space through the value interval and determining the initialization positions of the traction working condition, the idle working condition and the braking working condition in the solution space.
2. The method of claim 1, wherein prior to said constructing an objective function and constraints based on train operation metrics, the method further comprises:
and setting a discrete interval, and discretizing the train running process according to the discrete interval to obtain a plurality of discrete sub-processes.
3. The method of claim 1, wherein the defining the range of the solution space comprises:
calculating a fastest speed curve and a slowest speed curve of the train from the current position to the end position;
and taking the fastest speed curve as an upper boundary constraint of the solution space and the slowest speed curve as a lower boundary constraint of the solution space.
4. The method of claim 1, wherein the solving the solution space to obtain a train operating curve within the defined solution space is implemented based on a gray wolf optimization algorithm, comprising:
initializing the number X and the positions of the wolves in the wolf population in the solution space, and setting the maximum iteration times;
calculating the adaptability of each wolf in the wolf population, and storing the first Y wolves with the best adaptability;
updating the position of (X-Y) only wolves in the wolf population according to the adaptability of each wolf;
calculating the fitness of each wolf in the wolf population, updating the fitness and the position of the first Y wolves, adding one to the iteration number until the iteration number reaches the maximum iteration number, and outputting the optimal position of each wolf in the wolf population;
and obtaining a train running curve according to the optimal position of each wolf of the wolf population and the limited solution space range.
5. The method of claim 1, wherein the verification process of the validity of the train operation curve comprises:
constructing a train operation index calculation model, and setting the weight of the train operation index;
calculating train operation indexes of the train running in the train operation curve according to the train operation index calculation model;
calculating a train index comprehensive score according to the train running index and the set weight of the train running index when the train runs in the train running curve;
and verifying the effectiveness of the train operation curve according to the train index comprehensive score.
6. The method of claim 5, wherein the train operation indicator comprises at least one of a safety indicator, a time-of-operation indicator, an energy consumption indicator, or a comfort indicator.
7. A train operation control system, characterized in that the system comprises a processor configured with operation instructions executable by the processor to perform the train operation control method according to any one of claims 1 to 6.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210892075.XA CN115180001B (en) | 2022-07-27 | 2022-07-27 | Train operation control method and system |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210892075.XA CN115180001B (en) | 2022-07-27 | 2022-07-27 | Train operation control method and system |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN115180001A CN115180001A (en) | 2022-10-14 |
| CN115180001B true CN115180001B (en) | 2023-07-14 |
Family
ID=83520850
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202210892075.XA Active CN115180001B (en) | 2022-07-27 | 2022-07-27 | Train operation control method and system |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN115180001B (en) |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106143540A (en) * | 2015-03-27 | 2016-11-23 | 上海申通地铁集团有限公司 | The self-adaptive computing method of rail transit train closely connected operation controlling curve at full speed |
| CN107336725A (en) * | 2017-05-24 | 2017-11-10 | 北京交通大学 | A kind of online automatic train regulation control method |
| CN111591324A (en) * | 2020-05-29 | 2020-08-28 | 中车青岛四方车辆研究所有限公司 | Heavy-load train energy consumption optimization method based on gray wolf optimization algorithm |
| CN112507464A (en) * | 2020-12-21 | 2021-03-16 | 湘潭大学 | Freight train operation curve optimization method based on improved multi-target wolf algorithm |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US8660723B2 (en) * | 2012-06-29 | 2014-02-25 | Mitsubishi Electric Research Laboratories, Inc. | Method for determining run-curves for vehicles in real-time subject to dynamic travel time and speed limit constraint |
-
2022
- 2022-07-27 CN CN202210892075.XA patent/CN115180001B/en active Active
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106143540A (en) * | 2015-03-27 | 2016-11-23 | 上海申通地铁集团有限公司 | The self-adaptive computing method of rail transit train closely connected operation controlling curve at full speed |
| CN107336725A (en) * | 2017-05-24 | 2017-11-10 | 北京交通大学 | A kind of online automatic train regulation control method |
| CN111591324A (en) * | 2020-05-29 | 2020-08-28 | 中车青岛四方车辆研究所有限公司 | Heavy-load train energy consumption optimization method based on gray wolf optimization algorithm |
| CN112507464A (en) * | 2020-12-21 | 2021-03-16 | 湘潭大学 | Freight train operation curve optimization method based on improved multi-target wolf algorithm |
Also Published As
| Publication number | Publication date |
|---|---|
| CN115180001A (en) | 2022-10-14 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| US11708098B2 (en) | Method and device for optimizing target operation speed curve in ATO of train | |
| CN107577234B (en) | Automobile fuel economy control method for driver in-loop | |
| CN104881527A (en) | ATO (Automatic Train Operation) speed command optimization method of urban rail transit train | |
| Zhou et al. | Smart train operation algorithms based on expert knowledge and reinforcement learning | |
| CN103879414B (en) | A kind of railway locomotive optimized handling method based on self adaptation A-Star algorithm | |
| CN109895794B (en) | Accurate parking method and device of train automatic driving system based on machine learning | |
| CN109978350A (en) | A kind of subway train energy conservation optimizing method based on regime decomposition dynamic programming algorithm | |
| CN106529674A (en) | Multiple-unmanned-aerial-vehicle cooperated multi-target distribution method | |
| CN103847749A (en) | Method for generating double-deck multi-objective locomotive optimized manipulating sequence | |
| CN102275601B (en) | Method and device for intelligently controlling train | |
| CN104808672A (en) | Method and device for optimizing the landing of an aircraft on a runway | |
| CN107677473A (en) | A kind of GRNN rotating machinery fault Forecasting Methodologies based on FOA optimizations | |
| CN112733448B (en) | Parameter self-learning double Q table combined agent establishing method for automatic train driving system | |
| CN107368920A (en) | A kind of off-peak period multi-train movement energy conservation optimizing method | |
| CN108932356A (en) | A kind of ATO speed command energy conservation optimizing method considering Train delay factor | |
| CN106777717A (en) | A kind of ATO speed command energy conservation optimizing methods for considering handling capacity of passengers change | |
| CN114818135A (en) | Train operation curve off-line optimization method, equipment and storage medium | |
| CN103955135A (en) | Method for calculating locomotive optimized operation sequence based on double-layer mode curve | |
| CN111591324B (en) | Heavy-load train energy consumption optimization method based on gray wolf optimization algorithm | |
| CN115180001B (en) | Train operation control method and system | |
| CN112744270B (en) | Rapid and accurate train stopping method based on state identification | |
| CN117034459B (en) | Magnetic suspension train operation optimization method and system based on improved dung beetle optimization algorithm | |
| CN111598311B (en) | Novel intelligent optimization method for train running speed curve | |
| CN114670856B (en) | Parameter self-tuning longitudinal control method and system based on BP neural network | |
| CN114919632A (en) | Traction calculation simulation method and device |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| REG | Reference to a national code |
Ref country code: HK Ref legal event code: DE Ref document number: 40079441 Country of ref document: HK |
|
| GR01 | Patent grant | ||
| GR01 | Patent grant |