CN113997915A - Big data-based automatic train operation ATO (automatic train operation) accurate parking control method - Google Patents
Big data-based automatic train operation ATO (automatic train operation) accurate parking control method Download PDFInfo
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- CN113997915A CN113997915A CN202111420453.6A CN202111420453A CN113997915A CN 113997915 A CN113997915 A CN 113997915A CN 202111420453 A CN202111420453 A CN 202111420453A CN 113997915 A CN113997915 A CN 113997915A
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60T—VEHICLE BRAKE CONTROL SYSTEMS OR PARTS THEREOF; BRAKE CONTROL SYSTEMS OR PARTS THEREOF, IN GENERAL; ARRANGEMENT OF BRAKING ELEMENTS ON VEHICLES IN GENERAL; PORTABLE DEVICES FOR PREVENTING UNWANTED MOVEMENT OF VEHICLES; VEHICLE MODIFICATIONS TO FACILITATE COOLING OF BRAKES
- B60T8/00—Arrangements for adjusting wheel-braking force to meet varying vehicular or ground-surface conditions, e.g. limiting or varying distribution of braking force
- B60T8/17—Using electrical or electronic regulation means to control braking
- B60T8/1701—Braking or traction control means specially adapted for particular types of vehicles
- B60T8/1705—Braking or traction control means specially adapted for particular types of vehicles for rail vehicles
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60T—VEHICLE BRAKE CONTROL SYSTEMS OR PARTS THEREOF; BRAKE CONTROL SYSTEMS OR PARTS THEREOF, IN GENERAL; ARRANGEMENT OF BRAKING ELEMENTS ON VEHICLES IN GENERAL; PORTABLE DEVICES FOR PREVENTING UNWANTED MOVEMENT OF VEHICLES; VEHICLE MODIFICATIONS TO FACILITATE COOLING OF BRAKES
- B60T8/00—Arrangements for adjusting wheel-braking force to meet varying vehicular or ground-surface conditions, e.g. limiting or varying distribution of braking force
- B60T8/17—Using electrical or electronic regulation means to control braking
- B60T8/172—Determining control parameters used in the regulation, e.g. by calculations involving measured or detected parameters
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Abstract
The invention provides a big data-based automatic train operation ATO (automatic train operation) accurate parking control method, which comprises the following steps: determining a target control model based on the target braking piecewise curve and the target function; determining a target optimal solution corresponding to initial operation data of the target train based on the target control model; performing braking control on the target train based on the target optimal solution; wherein the objective function comprises: a first objective function corresponding to the stop error of the target train; a second target function corresponding to the braking energy consumption of the target train; and the third target function corresponds to the acceleration change rate of the target train. According to the big data-based automatic train operation ATO accurate parking control method, the target control model with good performance is determined, the target optimal solution can be efficiently calculated, the target train is subjected to brake control according to the target optimal solution, and therefore accurate parking can be achieved.
Description
Technical Field
The invention relates to the technical field of rail transit, in particular to a big data-based Automatic Train Operation (ATO) accurate parking control method.
Background
Train arrival and stop are generally controlled by applying a Proportional Integral Derivative (PID) control algorithm to brake the train at present. However, during the actual braking process of the train, the PID control algorithm has obvious defects, such as: the parking distance has errors, the abnormal condition resistance is weak, and the like. Therefore, when the train starts to stop and brake, the distance error measured by the system is large, and the stopping accuracy of the train is seriously influenced.
Disclosure of Invention
The invention provides a big data-based automatic train operation ATO (automatic train operation) accurate parking control method, which is used for solving the technical problem that accurate parking of a train cannot be realized in the prior art.
The invention provides a big data-based automatic train operation ATO (automatic train operation) accurate parking control method, which comprises the following steps:
determining a target control model based on the target braking piecewise curve and the target function;
determining a target optimal solution corresponding to initial operation data of the target train based on the target control model;
performing braking control on the target train based on the target optimal solution;
wherein the objective function comprises:
a first objective function corresponding to the stop error of the target train;
a second target function corresponding to the braking energy consumption of the target train;
and the third target function corresponds to the acceleration change rate of the target train.
In one embodiment, the determining a target optimal solution corresponding to initial operation data of a target train based on the target control model includes:
determining an optimal solution set corresponding to the initial operation data based on the target control model;
determining the target optimal solution in the optimal solution set according to the preset priority of the target function;
wherein the preset priority of the first objective function is highest.
In one embodiment, the determining the target optimal solution in the optimal solution set according to the preset priority of the objective function includes:
determining the solution with the minimum docking error in the optimal solution set as the optimal solution according to the first objective function;
taking the optimal solution as the target optimal solution when the optimal solution is one;
and under the condition that the optimal solution is multiple, determining the target optimal solution in the multiple optimal solutions based on a first preset weight corresponding to the second objective function and a second preset weight corresponding to the third objective function.
In one embodiment, the performing braking control on the target train based on the target optimal solution includes:
determining a target braking parameter of the target train based on the target optimal solution;
under the condition that the current operation data of the target train is determined to be not matched with the target braking parameters, braking control is carried out on the target train;
wherein the target braking parameters include at least one of: target braking time of each segment, target speed of each segment point, target acceleration of each segment, and target braking distance of each segment.
In one embodiment, the performing braking control on the target train in the case that it is determined that the current operation data of the target train does not match the target braking parameter includes:
determining whether the target train reaches a target stopping point under the condition that the current operation data of the target train is determined not to be matched with the target braking parameters;
determining whether the target train reaches a next target section point in the case that the target train is determined not to reach the target stop point;
under the condition that the target train reaches the next target segmentation point, switching the current acceleration of the target train to the target acceleration corresponding to the next target segmentation point;
and keeping the current acceleration unchanged under the condition that the target train does not reach the next target segmentation point.
In one embodiment, the determining whether the target train reaches the target stopping point in the case that the current operation data of the target train is determined not to match the target braking parameter includes:
under the condition that the current operation data of the target train is determined to be not matched with the target braking parameters, determining the current speed of the target train and the current braking distance of the target train through laser signals;
determining whether the target train reaches the target stopping point based on the current speed and the current braking distance.
The invention also provides a big data-based automatic train operation ATO precise parking control device, which comprises:
the first determining module is used for determining a target control model based on a target braking piecewise curve and a target function;
the second determining module is used for determining a target optimal solution corresponding to the initial operation data of the target train based on the target control model;
the control module is used for carrying out braking control on the target train based on the target optimal solution;
wherein the objective function comprises:
a first objective function corresponding to the stop error of the target train;
a second target function corresponding to the braking energy consumption of the target train;
and the third target function corresponds to the acceleration change rate of the target train.
The invention also provides electronic equipment which comprises a memory, a processor and a computer program which is stored on the memory and can run on the processor, wherein the processor executes the program to realize the steps of any one of the big data-based automatic train operation ATO precise parking control methods.
The present invention also provides a non-transitory computer readable storage medium having stored thereon a computer program which, when executed by a processor, implements the steps of the big data based automatic train operation ATO precision parking control method as any one of the above.
The invention also provides a computer program product comprising a computer program which, when executed by a processor, implements the steps of any of the big data based automatic train operation ATO precise parking control methods described above.
According to the big data-based automatic train operation ATO accurate parking control method, the target control model with good performance can be determined through the target braking sectional curve and the target function, so that the target optimal solution can be efficiently calculated, the target train is subjected to braking control according to the target optimal solution, and accurate parking can be realized.
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In order to more clearly illustrate the technical solutions of the present invention or the prior art, the drawings needed for the description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and those skilled in the art can also obtain other drawings according to the drawings without creative efforts.
FIG. 1 is a schematic flow chart of a big data-based automatic train operation ATO precise parking control method provided by the invention;
FIG. 2 is a schematic diagram comparing an ideal braking curve and an actual braking curve of the big data-based automatic train operation ATO precise parking control method provided by the invention;
FIG. 3 is a schematic diagram comparing an ideal braking curve and a sectional braking curve of the big data-based automatic train operation ATO precise parking control method provided by the invention;
FIG. 4 is a schematic flow chart of a big data-based automatic train operation ATO precise parking control method provided by the invention;
FIG. 5 is a schematic diagram comparing an ideal braking curve and a big data optimal braking sectional curve of the big data-based automatic train operation ATO precise stopping control method provided by the invention;
FIG. 6 is a schematic structural diagram of a big data-based automatic train operation ATO precise parking control device provided by the present invention;
fig. 7 is a schematic structural diagram of an electronic device provided by the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention clearer, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings, and it is obvious that the described embodiments are some, but not all embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
In the related art, the process of performing brake control on a train is specifically as follows:
at present, Automatic Train Operation (ATO) systems adjust Train positioning through positioning equipment which is installed in a station area and at a preset position, and accurate parking is realized. The positioning apparatus includes, for example: train-ground Communication (TWC) loops, transponders, and the like.
Under the Protection of the recommended speed of a Train Protection system (ATP), the command speed of the ATO is used as target input, the speed and the distance of a speed and distance measuring module are used as feedback, and the PID control algorithm is used for output control of the Train operation level, so that stable operation of the trains between stations and stations is realized; and finally, the accurate parking function is realized according to the position correction of the platform position correction intersection.
The core idea of the PID control algorithm is to judge whether the last execution is accurate or not and whether the next execution needs to be corrected or not in a feedback mode. When the feedback time is neglected, the PID controlled train braking curve can theoretically approach the optimal braking curve infinitely.
However, during the actual braking process of the train, the PID regulation has obvious defects, and the specific conditions are as follows:
(1) there is an error in the parking distance. The system uses the data of the last cycle in the calculation, the data need to be transmitted to ATP and then ATO for safety reasons, and the transmission time needs about 200ms, so that the parking distance has an error of 50 cm.
(2) Resistance to abnormal conditions is weak. Because the system is adjusted in real time, when the system delay is large or the data error is large, the system can be adjusted by mistake, the reverse standard exceeding is basically generated after the adjustment, and the system can be recovered to be normal only after being adjusted back and forth for multiple times. However, if a problem occurs during the stop period, the train cannot be stopped.
In addition to the above reasons, too many transponders cannot be laid on the lines due to the cost problem, so that when the train starts to stop and brake, the error of the system measurement distance is large, and the braking performance of the train is affected. The actual braking curve and the ideal braking curve of the train are shown in figure 2. Referring to fig. 2, it can be seen that in the actual braking process, the train speed frequently generates stage jump, which seriously affects the train stopping accuracy and the passenger riding comfort.
The method for controlling automatic train operation ATO precise parking based on big data provided by the invention is described in detail by specific embodiments and application scenarios thereof in combination with the accompanying drawings.
Fig. 1 is a schematic flow chart of a big data-based automatic train operation ATO precise stop control method provided by the invention. Referring to fig. 1, the method for controlling automatic train operation ATO precise stop based on big data provided by the invention comprises the following steps: step 110, step 120 and step 130.
Step 110, determining a target control model based on a target braking piecewise curve and a target function;
wherein the objective function comprises:
a first objective function corresponding to the stop error of the target train;
a second target function corresponding to the braking energy consumption of the target train;
and the third target function corresponds to the acceleration change rate of the target train.
The execution main body of the big data-based automatic train operation ATO precise parking control method can be electronic equipment, a component in the electronic equipment, an integrated circuit or a chip. The electronic device may be a mobile electronic device or a non-mobile electronic device. By way of example, the mobile electronic device may be a mobile phone, a tablet computer, a notebook computer, a palm top computer, a vehicle-mounted electronic device, a wearable device, an ultra-mobile personal computer (UMPC), a netbook or a Personal Digital Assistant (PDA), and the like, and the non-mobile electronic device may be a server, a Network Attached Storage (NAS), a Personal Computer (PC), and the like, and the present invention is not limited in particular.
The following describes the technical solution of the present invention in detail by taking an example in which a computer executes the method for controlling the automatic train operation ATO precise stop based on big data provided by the present invention.
Optionally, in step 110, in order to simplify the calculation amount and improve the calculation efficiency, the present invention adopts a segmented method to process the target train braking curve. The target train refers to a rail transit train including, for example, a subway, a high-speed rail, or a motor car, and the present invention is not particularly limited.
As shown in FIG. 3, assuming the target train braking segment curve is an ideal braking curve, the ideal braking curve can be divided into N segments, each segment of the braking curve is driven at a constant acceleration deceleration, and each segment is marked as Ni(i ═ 1,2, 3.., n), and the segment start point position is denoted as si-1The segment start velocity is denoted vi-1The segment start time is denoted as ti-1The position of the segment end point is marked as siThe segment ending velocity is denoted viAnd the segment end time is denoted as ti。
Wherein s is0The corresponding sectional point is the point where the target train passes through the initial transponder and starts braking, the position of the sectional point is always kept unchanged, and the initial braking speed of the sectional point is recorded as v0。snThe corresponding point is the point where the target train stops at the platform and the speed is zero.
According to the kinematic equation, it can be known that:
the segment acceleration is:
segmented road length of
Braking distance of target train
Optionally, the location s of the initial transponder is known0Is a fixed value from the target parking point and is recorded as SstSince different subway lines require different parking errors, e is uniformly used in the inventionsIndicating a docking error. In addition, when the subway stops accurately, the subway needs to follow the stop time in the train schedule, and the speed and the acceleration of the target train are both zero at the time of reaching the target stop point. At the same time, to ensure the safe transportation of the subwayThe running speed between stations should not exceed the limited speed value vlimitThe braking acceleration of the train should not exceed the maximum deceleration alimitAnd because the acceleration of the target train changes too fast, great impact can be generated, the riding comfort of passengers is influenced, and therefore certain constraint is also provided for the acceleration.
Optionally, if the improvement of the target train stopping accuracy is taken as an optimization target, the absolute value of the target train stopping error is taken as a control index, and the smaller the required error is, the better the required error is. The first objective function is noted as: j. the design is a square1＝S-Sst。
Alternatively, if the optimization is aimed at improving the riding comfort of passengers, the acceleration change rate of the target train during traveling is as small as possible. Changing the acceleration rate u of the target trainiAs a control variable, the second objective function is noted as
Optionally, in addition to the stopping accuracy and the riding comfort, the braking energy consumption of the target train is also a key point of great attention of the current operators, and the braking energy consumption of the target train is also used as one of the control indexes, and the smaller the braking energy consumption is, the better the braking energy consumption is. The third objective function is:
wherein the content of the first and second substances,
Qifor the target train in NiBraking energy consumption of the segments, MiIs the target train quality for the braking segment.
Alternatively, based on the target braking piecewise curve and the target function, an optimal control problem mathematical model, i.e., a target control model, may be established. Wherein the target control model may be represented by:
optionally, in step 120, according to the target control model in step 110, f (X) is an objective function of the model, and X is a solution of the model. Wherein X may be represented by the formula:
because the situations that the parking precision of the target train reaches the highest, the braking energy consumption is the smallest and the riding comfort of passengers also reaches the highest cannot occur simultaneously, under the condition that the target function F (X) is the smallest, a plurality of braking curve dividing methods exist. At this time, the objective function can obtain an initial velocity v0And determining the unique optimal target solution in different optimal solutions according to the requirements corresponding to different optimal solutions.
It should be noted that the target train braking curve is a theoretical ideal braking sectional curve, and after a target control model is established according to the ideal braking sectional curve and the target function, a target optimal solution is determined. And then according to the initial operation data of the current train, determining a sectional braking curve corresponding to the target optimal solution. For example: according to the ideal braking curve in fig. 3, a segmented braking curve corresponding to the target optimal solution can be obtained.
Optionally, in step 130, a target braking parameter may be determined based on the target optimal solution. And judging whether the target train operates according to the target braking parameters, and then realizing the braking control of the target train.
According to the big data-based automatic train operation ATO accurate parking control method, the target control model with good performance can be determined through the target braking sectional curve and the target function, so that the target optimal solution can be efficiently calculated, the target train is subjected to braking control according to the target optimal solution, and accurate parking can be realized.
In one embodiment, the determining a target optimal solution corresponding to initial operation data of a target train based on the target control model includes:
determining an optimal solution set corresponding to the initial operation data based on the target control model;
determining the target optimal solution in the optimal solution set according to the preset priority of the target function;
wherein the preset priority of the first objective function is highest.
Optionally, the target control model may be converted into a Multi-target optimal solution problem, the model may be solved by using a Multi-target Particle Swarm Optimization (MOPSO), and after updating and iteration are performed based on the principle of the MOPSO optimal solution, the initial speed v of the corresponding target train is obtained0Is called Pareto optimal solution set and is marked as Xk( k 1, 2.. times.j, j is the number of optimal solutions).
Optionally, multiple groups of optimal solutions exist in the optimal solution set, so that a unique target optimal solution needs to be determined according to the preset priority of the target function. The target control model comprises a first target function corresponding to a parking error, a second target function corresponding to braking energy consumption and a third target function corresponding to an acceleration change rate, and the target control model respectively corresponds to three indexes of parking accuracy, braking energy consumption and passenger riding comfort. In the embodiment of the invention, the braking energy consumption and the passenger riding comfort of the target train are considered under the condition of meeting the highest parking accuracy, so that the first target function is provided with the highest preset priority, and the preset priorities of the second target function and the third target function are not specifically limited.
It should be noted that the preset priority of the objective function may be adjusted according to the needs of the operator, and the present invention is not limited specifically.
According to the big data-based automatic train operation ATO accurate parking control method, the control over three indexes of parking accuracy, braking energy consumption and riding comfort of a target train can be realized by setting the preset priority of the target function, and the flexibility of braking control is improved; and based on the first target function with the highest preset priority, the optimal solution of the target can be determined, so that the target train is always in a safe and reliable state when being braked, the error of the accurate parking distance can be reduced, and the accurate parking is realized.
In one embodiment, the determining the target optimal solution in the optimal solution set according to the preset priority of the objective function includes:
determining the solution with the minimum docking error in the optimal solution set as the optimal solution according to the first objective function;
taking the optimal solution as the target optimal solution when the optimal solution is one;
and under the condition that the optimal solution is multiple, determining the target optimal solution in the multiple optimal solutions based on a first preset weight corresponding to the second objective function and a second preset weight corresponding to the third objective function.
Optionally, for the optimal solution set XkFirst, a set of solutions satisfying the highest parking accuracy is selected, that is, an optimal set of solutions having the smallest parking error is selected. If the optimal solution has uniqueness, directly marking as a Pareto target optimal solution X'j(j ∈ k). If the optimal solution does not have uniqueness, the train braking energy consumption at the current moment is taken as a first preset weight w1And a second preset weight w occupied by the riding comfort of passengers2Determining unique target optimal solution X'j。
Optionally, the first preset weight corresponding to the second objective function and the second preset weight corresponding to the third objective function may be preset or adjusted according to the requirement of the operator and the actual operation condition of the target train. For example: the target train braking control performance measurement indexes are a stopping error, braking energy consumption and passenger riding comfort, and then the weight corresponding to the stopping error can be set to be 0.5, the weight corresponding to the braking energy consumption can be set to be 0.3, and the weight corresponding to the passenger riding comfort can be set to be 0.2. Assuming that the target train is the starting train and the target train is in the idle state, it is not necessary to consider w2。
The big data-based automatic train driving ATO accurate parking control method can quickly determine the only target optimal solution according to different target functions, and improves the calculation efficiency of the target control model; through presetting the weight to the objective function setting, can make train energy conservation nature, parking precision, passenger take the travelling comfort and reach a balance.
In one embodiment, the performing braking control on the target train based on the target optimal solution includes:
determining a target braking parameter of the target train based on the target optimal solution;
under the condition that the current operation data of the target train is determined to be not matched with the target braking parameters, braking control is carried out on the target train;
wherein the target braking parameters include at least one of: target braking time of each segment, target speed of each segment point, target acceleration of each segment, and target braking distance of each segment.
Optionally, determining a target optimal solution X'jThen, according to X'jNamely, the initial braking speed of the train is determined to be v0And the target braking parameter of the target train when the mass is M. The target braking parameters include, for example: target brake curve segment point and target speed v of each segment pointiTarget brake time T of each segmenti(Ti＝ti-ti-1) Further, the target acceleration a of each segment is obtainediAnd each sectional target braking distance SiTherefore, an optimal train braking sectional curve can be obtained.
Optionally, the current operation state of the target train is analyzed, and current operation data of the target train is obtained. And judging whether the target train operates according to the target braking parameters, namely under the condition that the current operation data of the target train is not matched with the target braking parameters, performing braking control on the target train according to the optimal train braking sectional curve corresponding to the target braking parameters.
According to the big data-based automatic train operation ATO accurate parking control method, whether the target train operates according to the target optimal solution can be determined in real time by matching the target brake parameters with the current operation data of the target train, and the target train is subjected to brake control according to the matching result, so that the timeliness of brake control is improved, and meanwhile, the efficiency of brake control is also improved.
In one embodiment, the performing braking control on the target train in the case that it is determined that the current operation data of the target train does not match the target braking parameter includes:
determining whether the target train reaches a target stopping point under the condition that the current operation data of the target train is determined not to be matched with the target braking parameters;
determining whether the target train reaches a next target section point in the case that the target train is determined not to reach the target stop point;
under the condition that the target train reaches the next target segmentation point, switching the current acceleration of the target train to the target acceleration corresponding to the next target segmentation point;
and keeping the current acceleration unchanged under the condition that the target train does not reach the next target segmentation point.
Optionally, it is first determined whether the train reaches the target stopping point and the target train is in a stable state. If the conditions are met, the target train has already realized accurate parking. If the target train does not reach the target stopping point, the current running state of the target train is judged firstly, namely whether the target train runs to the next braking target section point is judged, if the target train does not move to the next braking target section point, no treatment is carried out, and the current braking acceleration of the target train is kept unchanged. And when the target train runs to the next braking target subsection point, switching the original acceleration into the next braking subsection target acceleration corresponding to the target optimal solution, and continuing the judgment process until the target train is accurately and stably stopped.
Optionally, when it is determined that the current operation data of the target train does not match the target braking parameter, performing braking control on the target train, further includes: the target speed of each segmentation point can be directly determined according to the target optimal solution. And under the condition that the actual running speed of the segmentation point of the target train is not matched with the target speed of the segmentation point, the target train is subjected to braking control according to the target speed of the segmentation point corresponding to the optimal target solution, so that the target train is controlled in real time.
The big data-based train automatic driving ATO accurate parking control method provided by the invention can reasonably adopt a sectional type uniform deceleration braking mode to enable a target train to run at a constant acceleration in a braking sectional curve, so that delay caused by real-time changing acceleration is reduced, and the anti-interference performance of a braking control process is improved.
In one embodiment, the determining whether the target train reaches the target stopping point in the case that the current operation data of the target train is determined not to match the target braking parameter includes:
under the condition that the current operation data of the target train is determined to be not matched with the target braking parameters, determining the current speed of the target train and the current braking distance of the target train through laser signals;
determining whether the target train reaches the target stopping point based on the current speed and the current braking distance.
Optionally, a laser receiver is installed at a target stopping point of the target train, and is used for receiving laser emitted by a laser emitter installed at a Vehicle-mounted position, determining a current speed of the target train and a current braking distance of the target train according to a time interval between laser emission and laser reception, converting the current speed and the current braking distance into electric signals and transmitting the electric signals to the interlocking device, and feeding the electric signals back to a Vehicle-mounted Controller (VOBC), so that the target train is reasonably controlled to uniformly reduce the speed according to target braking parameters, and accurate arrival stopping is realized.
Optionally, the laser speed and distance measuring device is generally high in precision and low in cost compared with a transponder which is expensive in manufacturing cost, and the hardware cost can be reduced while the measurement precision is ensured by determining the current speed of the target train and the current braking distance of the target train by using the laser signal.
According to the big data-based precise parking control method for the automatic train operation ATO, provided by the invention, by adopting laser ranging, not only can the system error be reduced, the parking precision of a target train be improved, but also the line development cost can be saved.
In order to further explain the scheme, the invention also provides a specific embodiment of the automatic train operation ATO precise parking control method based on the big data. A specific application embodiment of the present invention utilizes a big data platform to implement the present solution.
In one embodiment, the big data platform is largely divided into the following modules: the device comprises a data acquisition unit, a data storage and classification unit, a data analysis unit and a data decision unit. The functions of the modules are as follows:
a data acquisition unit: the method comprises the steps of collecting original data from an LKJ (Train operation monitoring and recording device), a transponder, a Train Control and Management System (TCMS) when a Train operates, wherein the data types comprise the position of the Train, the speed of the Train, the time when the Train operates at the current speed, the time when the Train operates to a target position, the total weight of the Train, line data and the like. Wherein the raw data includes historical operating data and current operating data.
A data storage classification unit: establishing a big database according to the data, wherein the stored content comprises the acquired original data and the initial braking speed v of the target train0When the mass is M, obtaining a target optimal solution based on a Pareto optimal solution principle and obtaining the braking time T of each segmented train according to the target optimal solutioniAcceleration of each segment aiBraking distance S of each segmenti. The subsection point division of the optimal braking subsection curve is subjected to the initial braking speed v of the train0Mass M, so first at a speed v0The mass M is a first dividing limit; before the uniqueness of the Pareto optimal solution is not determined, the optimal solution meeting the minimum parking precision may be more than one, and at the moment, the weight w occupied by train braking energy consumption needs to be considered1And weight w occupied by passenger riding comfort2Therefore, w is selected1And w2As a second partition limit. And determining the unique target optimal solution through the first partition boundary and the second partition boundary.
A data analysis unit: analyzing the collected current operation data, and judging the current operation state of the train and the target optimal segmented solution X 'corresponding to the current operation state according to the data storage classification unit'jAnd whether the target train is according to Pareto optimal solution X'jAnd (5) segmented braking. It can be understood that the current operation data can be analyzed by storing the historical operation data and the corresponding historical optimal target solution, and the matched data can be found in the historical operation data based on the current operation data, so that the optimal target solution can be quickly determined, and the calculation process is simplified.
A data decision unit: decision data are formed according to the analysis result of the data analysis unit, and the train is led to be in accordance with Pareto target optimal solution X 'matched with the corresponding running state (corresponding speed, corresponding weight and corresponding weight) in the data storage and classification unit'jTo control train braking.
In one embodiment, fig. 4 is a schematic flow chart of a big data-based automatic train operation ATO precise parking control method provided by the present invention. A laser receiver is arranged at a target stop point, and when a target train enters a platform stop area after a large data platform is built on a vehicle-mounted VOBC, the processing can be carried out according to the steps shown in FIG. 4:
(1) the target train receives data of the initial brake transponder, the big data platform analyzes the data, and the target optimal solution X 'is searched from the data storage classification unit according to the current running state of the target train'jAccording to target optimal solution X'jThe initial braking deceleration is a1Then the target train is decelerated at the initial braking deceleration a1A uniform deceleration is started. Meanwhile, the VOBC turns on a laser transmitter to transmit a laser signal;
(2) the laser receiver receives the transmitted laser signal and calculates the current running speed of the target train and the current braking distance of the target train according to the time interval between the laser transmission and the laser reception. Converting the current running speed and the current braking distance into electric signals and uploading the electric signals to interlocking equipment near a station;
(3) the interlocking device firstly judges whether the train reaches a target stop point snIf the train stops stably, the interlocking device uploads a command of stopping the stable train of the VOBC, and the VOBC feeds back a command of opening a platform door of the interlocking device and prepares to open the train door; and if the train does not reach the target stop point, feeding back the data received and processed by the laser receiver to the VOBC through the interlocking equipment, and judging the current running state of the target train by the VOBC big data platform according to the data analysis unit. Judging whether the train moves to the next braking section point or not, if the train does not move to the next braking section point, the braking system does not carry out any treatment, and the braking acceleration is unchanged; when the train moves to a section point in front of the next braking section, the braking system switches the original acceleration into a Pareto target optimal solution X'jThe next braking segment acceleration is provided, the process is until the train is accurately stopped. And after the train is determined to be stable, the vehicle-mounted VOBC turns off the power supply of the laser transmitter.
Alternatively, the big data optimal braking curve obtained by the embodiment of the invention is shown in fig. 5. The method is based on the combination of an MOPSO algorithm and a big data technology, and reasonably adopts a sectional type train uniform deceleration braking mode, so that the train runs at a constant acceleration in an optimal braking sectional curve, the delay caused by the real-time change of the acceleration of a control system is reduced, and the anti-interference performance of the system is improved.
The big data-based automatic train operation ATO accurate parking control method can quickly realize accurate train positioning and reduce parking errors. Meanwhile, the purposes of less braking energy consumption and improvement of riding comfort can be met, and the requirements of operators and passengers are met to a great extent. Meanwhile, the system uses laser speed and distance measurement with high precision to replace a transponder with high manufacturing cost, thereby saving the line development cost and improving the control precision of the braking system.
The following describes the big data based ATO precise stop control device for train automatic driving according to the present invention, and the big data based ATO precise stop control device for train automatic driving described below and the big data based ATO precise stop control method for train automatic driving described above may be referred to each other.
Fig. 6 is a schematic structural diagram of the automatic train operation ATO precise stop control device based on big data provided by the invention. Referring to fig. 6, the big data based automatic train operation ATO precise stop control device provided by the invention comprises: a first determination module 610, a second determination module 620, and a control module 630.
A first determining module 610 for determining a target control model based on a target braking piecewise curve and a target function;
a second determining module 620, configured to determine, based on the target control model, a target optimal solution corresponding to initial operation data of the target train;
a control module 630, configured to perform braking control on the target train based on the target optimal solution;
wherein the objective function comprises:
a first objective function corresponding to the stop error of the target train;
a second target function corresponding to the braking energy consumption of the target train;
and the third target function corresponds to the acceleration change rate of the target train.
According to the automatic train operation ATO accurate parking control device based on the big data, the target control model with good performance can be determined through the target brake piecewise curve and the target function, so that the target optimal solution can be efficiently calculated, the target train is subjected to brake control according to the target optimal solution, and accurate parking can be achieved.
In one embodiment, the second determining module 620 is specifically configured to:
determining an optimal solution set corresponding to the initial operation data based on the target control model;
determining the target optimal solution in the optimal solution set according to the preset priority of the target function;
wherein the preset priority of the first objective function is highest.
In an embodiment, the second determining module 620 is further specifically configured to:
determining the solution with the minimum docking error in the optimal solution set as the optimal solution according to the first objective function;
taking the optimal solution as the target optimal solution when the optimal solution is one;
and under the condition that the optimal solution is multiple, determining the target optimal solution in the multiple optimal solutions based on a first preset weight corresponding to the second objective function and a second preset weight corresponding to the third objective function.
In an embodiment, the control module 630 is specifically configured to:
determining a target braking parameter of the target train based on the target optimal solution;
under the condition that the current operation data of the target train is determined to be not matched with the target braking parameters, braking control is carried out on the target train;
wherein the target braking parameters include at least one of: target braking time of each segment, target speed of each segment point, target acceleration of each segment, and target braking distance of each segment.
In an embodiment, the control module 630 is further specifically configured to:
determining whether the target train reaches a target stopping point under the condition that the current operation data of the target train is determined not to be matched with the target braking parameters;
determining whether the target train reaches a next target section point in the case that the target train is determined not to reach the target stop point;
under the condition that the target train reaches the next target segmentation point, switching the current acceleration of the target train to the target acceleration corresponding to the next target segmentation point;
and keeping the current acceleration unchanged under the condition that the target train does not reach the next target segmentation point.
In an embodiment, the control module 630 is further specifically configured to:
under the condition that the current operation data of the target train is determined to be not matched with the target braking parameters, determining the current speed of the target train and the current braking distance of the target train through laser signals;
determining whether the target train reaches the target stopping point based on the current speed and the current braking distance.
Fig. 7 illustrates a physical structure diagram of an electronic device, and as shown in fig. 7, the electronic device may include: a processor (processor)710, a communication Interface (Communications Interface)720, a memory (memory)730, and a communication bus 740, wherein the processor 710, the communication Interface 720, and the memory 730 communicate with each other via the communication bus 740. Processor 710 may invoke logic instructions in memory 730 to perform a big data based train autonomous ATO precision stop control method comprising:
determining a target control model based on the target braking piecewise curve and the target function;
determining a target optimal solution corresponding to initial operation data of the target train based on the target control model;
performing braking control on the target train based on the target optimal solution;
wherein the objective function comprises:
a first objective function corresponding to the stop error of the target train;
a second target function corresponding to the braking energy consumption of the target train;
and the third target function corresponds to the acceleration change rate of the target train.
In addition, the logic instructions in the memory 730 can be implemented in the form of software functional units and stored in a computer readable storage medium when the software functional units are sold or used as independent products. Based on such understanding, the technical solution of the present invention may be embodied in the form of a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.
In another aspect, the present invention also provides a computer program product, the computer program product comprising a computer program, the computer program being stored on a non-transitory computer readable storage medium, wherein when the computer program is executed by a processor, the computer is capable of executing the big data based ATO precision parking control method for train automatic driving, which is provided by the above methods, the method comprising:
determining a target control model based on the target braking piecewise curve and the target function;
determining a target optimal solution corresponding to initial operation data of the target train based on the target control model;
performing braking control on the target train based on the target optimal solution;
wherein the objective function comprises:
a first objective function corresponding to the stop error of the target train;
a second target function corresponding to the braking energy consumption of the target train;
and the third target function corresponds to the acceleration change rate of the target train.
In yet another aspect, the present invention also provides a non-transitory computer-readable storage medium having stored thereon a computer program, which when executed by a processor, implements a big data based automatic train operation ATO precise parking control method provided by the above methods, the method comprising:
determining a target control model based on the target braking piecewise curve and the target function;
determining a target optimal solution corresponding to initial operation data of the target train based on the target control model;
performing braking control on the target train based on the target optimal solution;
wherein the objective function comprises:
a first objective function corresponding to the stop error of the target train;
a second target function corresponding to the braking energy consumption of the target train;
and the third target function corresponds to the acceleration change rate of the target train.
The above-described embodiments of the apparatus are merely illustrative, and the units described as separate parts may or may not be physically separate, and 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.
Through the above description of the embodiments, those skilled in the art will clearly understand that each embodiment can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware. With this understanding in mind, the above-described technical solutions may be embodied in the form of a software product, which can be stored in a computer-readable 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 methods described in the embodiments or some parts of the embodiments.
Finally, it should be noted that: the above examples are only intended to illustrate the technical solution of the present invention, but not to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.
Claims (10)
1. A big data-based Automatic Train Operation (ATO) precise parking control method is characterized by comprising the following steps:
determining a target control model based on the target braking piecewise curve and the target function;
determining a target optimal solution corresponding to initial operation data of the target train based on the target control model;
performing braking control on the target train based on the target optimal solution;
wherein the objective function comprises:
a first objective function corresponding to the stop error of the target train;
a second target function corresponding to the braking energy consumption of the target train;
and the third target function corresponds to the acceleration change rate of the target train.
2. The big-data-based Automatic Train Operation (ATO) precise parking control method according to claim 1, wherein said determining a target optimal solution corresponding to initial operation data of a target train based on said target control model comprises:
determining an optimal solution set corresponding to the initial operation data based on the target control model;
determining the target optimal solution in the optimal solution set according to the preset priority of the target function;
wherein the preset priority of the first objective function is highest.
3. The big-data-based automatic train operation ATO precise stop control method according to claim 2, wherein the determining the target optimal solution in the optimal solution set according to the preset priority of the objective function comprises:
determining the solution with the minimum docking error in the optimal solution set as the optimal solution according to the first objective function;
taking the optimal solution as the target optimal solution when the optimal solution is one;
and under the condition that the optimal solution is multiple, determining the target optimal solution in the multiple optimal solutions based on a first preset weight corresponding to the second objective function and a second preset weight corresponding to the third objective function.
4. The big data based train automatic driving (ATO) precise parking control method according to claim 1, wherein said brake control of said target train based on said target optimal solution comprises:
determining a target braking parameter of the target train based on the target optimal solution;
under the condition that the current operation data of the target train is determined to be not matched with the target braking parameters, braking control is carried out on the target train;
wherein the target braking parameters include at least one of: target braking time of each segment, target speed of each segment point, target acceleration of each segment, and target braking distance of each segment.
5. The big data based train Automatic Train Operation (ATO) precision parking control method according to claim 4, wherein said brake control of said target train in case of determining that said current operation data of said target train does not match said target brake parameter, comprises:
determining whether the target train reaches a target stopping point under the condition that the current operation data of the target train is determined not to be matched with the target braking parameters;
determining whether the target train reaches a next target section point in the case that the target train is determined not to reach the target stop point;
under the condition that the target train reaches the next target segmentation point, switching the current acceleration of the target train to the target acceleration corresponding to the next target segmentation point;
and keeping the current acceleration unchanged under the condition that the target train does not reach the next target segmentation point.
6. The big-data-based train automatic train driving ATO precise parking control method according to claim 5, wherein the determining whether the target train reaches a target parking point in case that it is determined that the current operation data of the target train does not match the target braking parameter comprises:
under the condition that the current operation data of the target train is determined to be not matched with the target braking parameters, determining the current speed of the target train and the current braking distance of the target train through laser signals;
determining whether the target train reaches the target stopping point based on the current speed and the current braking distance.
7. A big data-based Automatic Train Operation (ATO) precise parking control device is characterized by comprising:
the first determining module is used for determining a target control model based on a target braking piecewise curve and a target function;
the second determining module is used for determining a target optimal solution corresponding to the initial operation data of the target train based on the target control model;
the control module is used for carrying out braking control on the target train based on the target optimal solution;
wherein the objective function comprises:
a first objective function corresponding to the stop error of the target train;
a second target function corresponding to the braking energy consumption of the target train;
and the third target function corresponds to the acceleration change rate of the target train.
8. An electronic device comprising a memory, a processor and a computer program stored on the memory and operable on the processor, wherein the processor when executing the program implements the steps of the big data based train automatic train operation ATO precision parking control method according to any of claims 1 to 6.
9. A non-transitory computer readable storage medium having a computer program stored thereon, wherein the computer program when executed by a processor implements the steps of the big data based train automatic driving ATO precision parking control method according to any one of claims 1 to 6.
10. A computer program product comprising a computer program, wherein the computer program when executed by a processor implements the steps of the big data based train automatic driving ATO precision parking control method according to any of claims 1 to 6.
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