CN112977556A - Train braking curve segmentation method and system and train control method - Google Patents
Train braking curve segmentation method and system and train control method Download PDFInfo
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Abstract
The invention provides a method and a system for segmenting a train braking curve and a train control method, wherein the method for segmenting the train braking curve comprises the following steps: obtaining braking performance parameters of a train braking curve; determining a braking curve segmented optimization model, wherein the braking curve segmented optimization model adopts an optimization model based on a particle swarm algorithm; and determining the sectional parameters of the train braking curve through the braking curve sectional optimization model based on the braking performance parameters. The train braking curve segmentation method provided by the invention provides a quick approximate optimal solution for selecting the segmentation points of the train braking curve, can be used for realizing the optimal segmentation of the train braking curve of any vehicle type, any braking curve and any number of segments, and has good application value.
Description
Technical Field
The invention belongs to the field of rail transit, and particularly relates to a train braking curve segmentation method, a train braking curve segmentation system and a train control method.
Background
A train operation control system (train control system for short) is important driving equipment for ensuring the safe operation of a train and improving the transportation efficiency and comprises vehicle-mounted equipment and ground equipment. An Automatic Train Protection system (ATP) is a vehicle-mounted device of a Train control system, and generates a braking distance curve based on Train braking performance parameters according to information such as line data and temporary speed limit provided by ground devices.
When a train distance curve is calculated by a vehicle-mounted system, in order to simplify calculated amount and improve operation efficiency, a train braking curve with dense train braking performance parameter points is generally subjected to segment approximation processing, namely dense braking performance parameters are segmented, the conventional braking curve segmentation scheme adopts a direct segmentation method, the deceleration is divided into 3-6 segments, and the segmentation mode has no theoretical support and is not optimal segmentation. Because different types of train braking curves have larger difference and a unified train braking curve segmentation method does not exist, how to segment the train braking curve is realized, so that the braking distance curve and the related braking mode curve have good performance and the calculation efficiency is a difficult problem in a vehicle-mounted system.
Disclosure of Invention
Aiming at the problems, the invention provides a method for segmenting a train braking curve, which comprises the following steps:
obtaining braking performance parameters of a train braking curve;
determining a braking curve segmented optimization model, wherein the braking curve segmented optimization model adopts an optimization model based on a particle swarm algorithm;
and determining the sectional parameters of the train braking curve through the braking curve sectional optimization model based on the braking performance parameters.
Further, determining the braking curve segment optimization model comprises:
setting particle swarm parameters including speed segmentation points of a train braking curve;
and setting the objective function as the maximum braking distance minimum corresponding to the particles.
Further, determining the segment parameters of the train braking curve through the braking curve segment optimization model comprises:
initializing the particle swarm based on the braking performance parameters, and setting a plurality of speed segmentation points corresponding to each particle of the particle swarm, wherein each segmentation point corresponds to one speed in the braking performance parameters.
Further, initializing a particle population based on the braking performance parameter includes:
dividing the braking curve into N sections by using N-1 speed sectional points;
setting segmented parameters of the braking curves of the N segments to form an N-1 dimensional space;
setting M parameter particles distributed in a space;
wherein the position x of the m-th particlemAnd velocity vmCan be respectively represented as
Wherein, 0<n<N，0<m≤M，xmnAn nth velocity segment point representing an mth particle; v. ofmnThe moving speed of the nth speed segment point of the mth particle is shown.
Further, determining the segment parameters of the train braking curve through the braking curve segment optimization model comprises:
and reversely calculating the maximum braking distance corresponding to the particles according to the maximum speed and the minimum deceleration of each segment.
Further, reversely calculating the maximum braking distance corresponding to the particle according to the maximum speed and the minimum deceleration of each segment comprises:
and calculating the sectional braking distance according to the deceleration, the highest speed and the lowest speed of each section, and summing the sectional braking distances of the sections to obtain the maximum braking distance based on one particle.
Further, reversely calculating the maximum braking distance corresponding to the particle according to the maximum speed and the minimum deceleration of each segment comprises:
the train braking curve is divided into N sections, and the deceleration and the highest speed of the nth section are respectively anAnd vn，0<n≤N；
The maximum braking distance d of the train can be obtained by reverse calculationmax
Wherein v isnIs the highest speed, v, of the nth sectionn-1Is the highest speed of the n-1 th segment, when n =1, vn-1Is a braking target speed; a isnIs the deceleration of the nth segment.
Further, the minimum deceleration a of each segmentnAs a function of the speed of the segment:
v is the speed of the nth segment.
Further, determining the segment parameters of the train braking curve through the braking curve segment optimization model comprises:
the optimal position of the self experience can be judged according to experience in the particle moving process, and the optimal position of the population is obtained through population communication; each particle continuously learns the optimal position experienced by the particle and the optimal position of the group, and the position of the particle is updated so that the position of the particle tends to the optimal solution; position x of m-th particlemAnd velocity vmThe update process is as follows:
vmn(t) and xmn(t) is the current velocity and position of the nth dimension variable of the mth particle, vmn(t +1) and xmn(t +1) is the updated velocity and position of the nth dimension variable of the mth particle, ω is the inertial weight of the particle, φ1And phi2As learning factors, r1(t) and r2(t) are [0,1 ]]With a random factor of pbest, evenly distributed in the current iterationmnThe nth dimension variable, gbest, for the optimal position currently experienced by the mth particle itselfnAnd the nth dimension variable is the current optimal position of the particle swarm.
The invention also provides a train braking control method, which comprises the following steps:
calculating speed subsection parameters in the braking process by adopting the train braking curve subsection method;
and calculating a braking distance curve according to the segmentation parameters for ATP vehicle control.
The invention also provides a train braking curve segmentation system, comprising:
the train braking system comprises an acquisition unit, a control unit and a control unit, wherein the acquisition unit is used for acquiring braking performance parameters of a train braking curve;
the model determining unit is used for determining a braking curve segmented optimization model, and the braking curve segmented optimization model adopts an optimization model based on a particle swarm algorithm;
and the segmented execution unit is used for determining segmented parameters of the train braking curve through the braking curve segmented optimization model based on the braking performance parameters.
The invention also provides a train brake curve segmentation system, which comprises at least one processor and at least one memory;
the memory stores a computer program that executes a train brake curve segmentation method, and the processor invokes the computer program in the memory to execute the train brake curve segmentation method described above.
The train braking curve segmentation method, the train braking curve segmentation system and the train control method provided by the invention provide a quick approximate optimal solution for selecting the segmentation points of the train braking curve, can be used for realizing the optimal segmentation of the train braking curve of any vehicle type, any braking curve and any number of segments, and have good application value.
Additional features and advantages of the invention will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. The objectives and other advantages of the invention will be realized and attained by the structure particularly pointed out in the written description and claims hereof as well as the appended drawings.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in 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 illustrates a flow chart of a method for segmenting a train braking curve according to an embodiment of the present invention;
FIG. 2 shows a brake curve of a Standard China Multi-Motor train set with 16 Long passengers in a consist according to an embodiment of the invention;
FIG. 3 shows a braking distance curve for a Standard Motor train Unit in China for 16 long passenger consists according to an embodiment of the present invention;
FIG. 4 illustrates a comparison of approximate pre-and post-approximation train brake curves for particle swarm optimization 6 segment braking in accordance with an embodiment of the present invention;
FIG. 5 illustrates a comparison graph of pre-and post-approximation braking distance curves for a 6-segment brake for particle swarm optimization of the 6-segment brake, according to an embodiment of the present invention;
FIG. 6 illustrates a schematic diagram of a train brake curve segmentation system in accordance with an embodiment of the present invention;
FIG. 7 illustrates another train brake curve segmentation system configuration in accordance with an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, 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.
The embodiment of the invention provides a train brake curve segmentation method based on Particle Swarm optimization (PSO for short), which obtains optimal segmentation parameters by constructing a brake distance target function and verifies the performance of the segmentation method through simulation.
As shown in fig. 1, the method for segmenting the train braking curve according to the embodiment of the present invention includes the following steps:
step 1: obtaining braking performance parameters of a train braking curve;
step 2: determining a braking curve segmented optimization model, wherein the braking curve segmented optimization model adopts an optimization model based on a particle swarm algorithm;
and step 3: and determining the sectional parameters of the train braking curve through a braking curve sectional optimization model based on the braking performance parameters.
The train braking curve in the step 1 is to obtain a train braking curve to be segmented and approximated, namely a train braking deceleration curve (such as EBD or SBD), the segmentation method of the embodiment of the invention is used for solving segmentation parameters of the train control curve, the train braking curve is approximately segmented according to the finally obtained segmentation parameters, so that an approximate broken line of the train braking curve is obtained and is used for calculating a braking distance curve, namely a speed-distance curve, and ATP controls the train based on the speed-distance curve. Further, other related control curves may also be calculated based on the braking distance curve.
Obtaining the braking performance parameters of the train braking curve refers to obtaining multiple sets of speed and deceleration parameters determined according to the train braking curve, i.e., a series of one-to-one corresponding speed and deceleration values. The train braking curve can be viewed as a curve made up of a series of points, each corresponding to a value of speed and deceleration. The brake performance parameters (i.e. the brake deceleration of the train at different speeds) of the train brake curve directly obtained from the vehicle are usually dense (for example, 1 km/h-5 km/h is a speed segment interval), and a large amount of time is required in the actual calculation. For the train with the maximum speed of 350km/h, the number of parameters is about 350 when 1km/h is graded, and even if the grading speed is 5km/h, the number of parameters is more than 70. Therefore, the parameters of dozens of segments or hundreds of segments are reduced to 6 segments or less by the segmentation method of the embodiment of the invention.
Without loss of generality, the embodiment of the invention describes the train braking curve segmentation process by taking a braking scene with a target braking speed of 0 as an example, but the target speed can be other specified speeds other than 0.
In the step 2, a braking curve segmented optimization model is determined, and the braking curve segmented optimization model of the embodiment of the invention adopts an optimization model based on a particle swarm algorithm. An optimization model based on particle swarm optimization requires determining particle parameter settings and objective functions. In the embodiment of the invention, the particle swarm parameters are set as parameters including speed segmentation points of a train braking curve, and the objective function is set as the minimum maximum braking distance corresponding to the particles. Each particle parameter is a solution of the objective function, and the particle parameter which enables the maximum braking distance to be optimal is selected as the optimal solution of the objective function through calculation, namely the finally determined section parameter. Thus, the optimal braking distance MIN (d) is established by taking the train braking curve segment points as particle swarm parametersmax) And optimizing the model for the brake curve segment of the objective function.
In step 3, determining the segment parameters of the train braking curve through the braking curve segment optimization model comprises: segmenting a train braking curve, initializing a particle swarm based on the braking performance parameters, setting a plurality of speed segmentation points corresponding to each particle of the particle swarm, and setting one speed in the braking performance parameters corresponding to each segmentation point. The braking curve is divided into a plurality of segments (at least two segments) through the speed segmentation point, so that the highest speed and the minimum deceleration of each segment curve can be determined, and the maximum braking distance corresponding to the particles is reversely calculated according to the highest speed and the minimum deceleration of each segment. And moving and calculating the particles by adopting a particle swarm algorithm, and solving the particles with the optimal braking distance as an optimal solution.
The following is an exemplary description of the process of brake curve segmentation using particle swarm optimization.
The particle swarm algorithm is a random search algorithm based on swarm cooperation and developed by simulating foraging behavior of bird swarms. It is assumed that the parameters to be optimized may constitute a D-dimensional space, in which several particles are distributed. Each particle corresponds to a solution of the objective function, and the optimal solution of the objective function is searched in the D-dimensional space at a certain vector velocity.
In the embodiment of the invention, the objective function of the braking curve segmented optimization model, namely the braking distance curve segmented particle swarm optimization model, is that the maximum braking distance corresponding to the particles meets the minimum. Braking distance is a key objective in describing the segment optimality of train braking curves. In the braking calculation, the braking distance is generally simplified to be the sum of the braking lost motion distance and the effective braking distance, and two factors determining the lost motion distance are the initial braking speed and the lost motion time. Since the idle running time is not influenced by the braking sections, the effective braking distance under the condition of the same braking initial speed is an index for evaluating the section optimality of the train braking curve.
The train braking curve can be divided into N sections, and the braking deceleration and the highest speed of each section are respectively anAnd vn，0<N is less than or equal to N, and the maximum braking distance d of the train can be obtained through reverse calculationmax。
Specifically, the segment braking distance is calculated by the deceleration, the highest speed and the lowest speed of each segment (i.e., the highest speed of the next deceleration segment), and the segment braking distances of the respective segments are summed to obtain the maximum braking distance based on one particle. In the process of calculating the segmentation parameters, one particle is a particle position, and the particles at different positions formed by the movement of the particle can be regarded as different particles, namely different segmentation parameter solution spaces.
Wherein v isnIs the highest speed, v, of the nth sectionn-1Is the highest speed of the n-1 th segment, when n =1, vn-1Is a braking target speed; a isnIs the deceleration of the nth segment. Without loss of generality, v0=0,vnFor braking the initial speed, the maximum value is vmax,vmaxThe maximum speed at which the train is allowed to operate.
Based on the train braking curve, the train braking deceleration a is a function of the speed v, anFor the deceleration of the nth segment, in order to make the maximum braking distance calculated for each segment more accurate, the minimum deceleration of each segment is obtained as the segmented deceleration a based on the relationship between the deceleration and the speednMinimum deceleration a of nth segmentnComprises the following steps:
v is the speed of the nth segment. Specifically, anAnd inquiring the acquired brake performance parameters. For example, after dividing the dense braking performance parameters (the parameter values corresponding to the deceleration-speed correspondence curve points) given by the vehicle into 6 speed segments, there will be a minimum deceleration in each segment.
In the embodiment of the invention, the speed change is described by adopting deceleration, the speed change can also be described by adopting acceleration, and the value of the acceleration is negative in the braking process. Deceleration in this application is to be understood in a broad sense, i.e. the rate of change of speed during braking.
When the brake curve segmentation parameters are calculated, the train brake curve is segmented firstly, random segmentation can be adopted for initialization segmentation, and the number N of the segments can be set according to actual needs, such as 2 segments, 3 segments, 6 segments or more. Initializing a particle swarm based on the segmented train brake curve.
Setting N sections of braking curve segment parameters to form N-1 dimensional space with M parameter particles distributed and the position x of the M-th particlemAnd velocity vmCan be respectively represented as
Wherein, 0<n<N，0<m≤M，xmnAn nth velocity segment point, i.e., an nth dimension variable, representing the mth particle; v. ofmnThe moving speed of the nth speed segment point of the mth particle is shown. The highest and lowest speeds of the segment can be determined by the speed segmentation point.
Each particle has a memory function, and the optimal position pbest experienced by the particle can be judged according to experience in the moving process of the particlemAnd obtaining the optimal position gbest of the population through population exchange. Each particle passing through successive pbestmAnd gbest learning, updating the self position to make the self position tend to the optimal solution. Position x of m-th particlemAnd velocity vmThe update process is as follows:
wherein v ismn(t) and xmn(t) is the current velocity and position of the nth dimension variable of the mth particle, vmn(t +1) and xmn (t +1) are the updated velocity and position of the nth dimension variable of the mth particle, ω is the inertial weight of the particle, φ 1 and φ 2 are learning factors, r1(t) and r2(t) are [0,1]With a random factor of pbest, evenly distributed in the current iterationmnIs the m-th particleThe nth dimension variable of the optimal position, gbest, of previous self-experiencenThe nth dimension variable of the current optimal position of the population of the particle swarm is changed in each iteration r1(t) and r2(t), so that the diversity of individuals in the particle swarm can be guaranteed.
Obtaining a new moved particle position through iteration, calculating the maximum braking distance corresponding to the particle based on the parameters corresponding to the new particle position, namely, the speed parameters obtained according to the segmentation points and the like, evaluating and comparing the particle, updating the optimal position experienced by the particle and the optimal position of the group, considering the optimal position of the group as the optimal solution obtained through calculation when the optimal position of the group is not changed or iterated for a specified number of times, and calculating a braking distance curve according to the particle position. The braking distance curve includes an Emergency Brake Deceleration curve (EBD) and a Service Brake Deceleration curve (SBD), which are used as basic elements of a train speed monitoring curve. Further, other related braking mode curves may be generated based on the SBD and the EBD, including a Service Brake Intervention curve (SBI), an Emergency Brake Intervention curve (EBI), a Warning curve (Warning, W), and an allowed Speed (P). Specifically, the EBI can be calculated by EBD; the SBI, W or P curves can be calculated by EBD and SBD.
The method provided by the embodiment of the invention can be used for generating a scientific optimal segmentation scheme for realizing the train braking curves of any vehicle type, any train braking curve and any number of segments, and can be used for automatically segmenting by adopting software. Taking 16 long-passenger marshalling CR400BF type motor train units (CR 400BF for short) as an example, the performance of the brake curve segmentation method based on particle swarm optimization is analyzed. As shown in FIG. 2, the brake curve of a Chinese standard motor train unit with 16 long passengers in a marshalling has the abscissa of speed (km/h) and the ordinate of deceleration (m/s)2) The emergency braking deceleration of the wet rail with wind resistance of the train CR400BF is a non-monotonic curve, and the braking deceleration shows the changing characteristics of increasing and decreasing with the change of the vehicle speed, and the changing rate also changes, fig. 3 is a graphThe brake distance curve of the Chinese standard motor train unit consists of 16 long passengers.
Substituting the CR400BF fine braking deceleration and speed data into equation (1) can obtain the train braking distance at different speeds as shown in fig. 3, with the abscissa as braking distance (km) and the ordinate as speed (km/h). Wherein the maximum braking distance of the train, namely the braking distance of the train when the speed of the train is reduced from 350km/h to 0km/h is 9096.5 m.
According to the analysis, although the shortest braking distance can be obtained by the refined braking model, the operation efficiency is low, and for this reason, the effectiveness of the braking curve segment optimization model is verified by taking 6 segments of braking deceleration segments as an example. With 6 segments, 5 speed segmentation points are required.
The optimization target of the model is the braking distance d described by formula (1)maxAnd (4) minimizing. The particle group has 200 particles, the parameters are the sectional point velocity, the inertia weight omega is 0.72894, and the learning factor phi1And phi2Is 1.49618. Through 200 iterations of the particle swarm optimization algorithm, approximately optimal 6-section braking deceleration can be obtained as shown in fig. 4, wherein the curve is a train braking curve before the approximation, the broken line is the train braking curve after the approximation, the abscissa is the speed (km/m), and the ordinate is the braking deceleration (m/s)2). A braking distance curve is calculated from the train braking curves before and after the approximation, and the relationship between the obtained deceleration and the speed is shown in fig. 5, wherein the inner curve is the braking distance curve calculated from the train braking curve after the approximation, and the outer curve is the braking distance curve calculated from the train braking curve before the approximation. The maximum braking distance of the train, namely the braking distance of the train from 350km/h to 0km/h is 9454.5m, the approximate error with the refined segment is 358m, the relative error is 3.9 percent, the error range can be received, and meanwhile, the efficiency of calculating the braking distance curve is greatly improved.
Based on the same inventive concept, an embodiment of the present invention further provides a train braking curve segmenting system, as shown in fig. 6, the system includes:
the train braking system comprises an acquisition unit, a control unit and a control unit, wherein the acquisition unit is used for acquiring braking performance parameters of a train braking curve;
the model determining unit is used for determining a braking curve segmented optimization model, and the braking curve segmented optimization model adopts an optimization model based on a particle swarm algorithm;
and the segmented execution unit is used for determining segmented parameters of the train braking curve through the braking curve segmented optimization model determined by the model determination unit based on the braking performance parameters obtained by the obtaining unit.
The specific implementation mode of each unit of the train braking curve segmentation system can be obtained according to the method of any embodiment of the invention, and is not described again.
The method of the invention can be realized by a computer or an embedded program controlled system. Accordingly, in accordance with an embodiment of the present invention, there is also provided a train braking curve segmentation system, as shown in fig. 7, the system includes at least one processor and at least one memory; the memory stores a computer program for performing any of the above methods of embodiments of the invention, and the processor calls the computer program in the memory to perform any of the methods of embodiments of the invention.
Further, the memory may be communicatively coupled to the one or more processors and have stored therein instructions executable by the one or more processors to cause the one or more processors to perform the method of the present invention.
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 (12)
1. A method of segmenting a train braking curve, comprising:
obtaining braking performance parameters of a train braking curve;
determining a braking curve segmented optimization model, wherein the braking curve segmented optimization model adopts an optimization model based on a particle swarm algorithm;
and determining the sectional parameters of the train braking curve through the braking curve sectional optimization model based on the braking performance parameters.
2. The train brake curve segmentation method of claim 1, wherein determining a brake curve segmentation optimization model comprises:
setting particle swarm parameters including speed segmentation points of a train braking curve;
and setting the objective function as the maximum braking distance minimum corresponding to the particles.
3. The train brake curve segmentation method of claim 2, wherein determining the segmentation parameters of the train brake curve via the brake curve segmentation optimization model comprises:
initializing the particle swarm based on the braking performance parameters, and setting a plurality of speed segmentation points corresponding to each particle of the particle swarm, wherein each segmentation point corresponds to one speed in the braking performance parameters.
4. The train brake curve segmentation method of claim 3, wherein initializing a population of particles based on the brake performance parameters comprises:
dividing the braking curve into N sections by using N-1 speed sectional points;
setting segmented parameters of the braking curves of the N segments to form an N-1 dimensional space;
setting M parameter particles distributed in a space;
wherein the position x of the m-th particlemAnd velocity vmCan be respectively represented as
Wherein, 0<n<N，0<m≤M，xmnAn nth velocity segment point representing an mth particle; v. ofmnThe moving speed of the nth speed segment point of the mth particle is shown.
5. The train brake curve segmentation method of claim 3, wherein determining the segmentation parameters of the train brake curve via the brake curve segmentation optimization model comprises:
and reversely calculating the maximum braking distance corresponding to the particles according to the maximum speed and the minimum deceleration of each segment.
6. The train braking curve segmentation method of claim 5, wherein reversely calculating the maximum braking distance corresponding to the particles according to the maximum speed and the minimum deceleration of each segment comprises:
and calculating the sectional braking distance according to the deceleration, the highest speed and the lowest speed of each section, and summing the sectional braking distances of the sections to obtain the maximum braking distance based on one particle.
7. The train braking curve segmentation method of claim 6, wherein reversely calculating the maximum braking distance corresponding to the particles according to the maximum speed and the minimum deceleration of each segment comprises:
the train braking curve is divided into N sections, and the deceleration and the highest speed of the nth section are respectively anAnd vn，0<n≤N；
The maximum braking distance d of the train can be obtained by reverse calculationmax
Wherein v isnIs the highest speed, v, of the nth sectionn-1Is the highest speed of the n-1 th segment, when n =1, vn-1Is a braking target speed; a isnIs a subtraction of the nth segmentSpeed.
8. The method of claim 7 wherein the minimum deceleration a for each segment isnAs a function of the speed of the segment:
v is the speed of the nth segment.
9. The train braking curve segmentation method according to any one of claims 3-7, wherein determining the segmentation parameters of the train braking curve through the braking curve segmentation optimization model includes:
the optimal position of the self experience can be judged according to experience in the particle moving process, and the optimal position of the population is obtained through population communication; each particle continuously learns the optimal position experienced by the particle and the optimal position of the group, and the position of the particle is updated so that the position of the particle tends to the optimal solution; position x of m-th particlemAnd velocity vmThe update process is as follows:
vmn(t) and xmn(t) is the current velocity and position of the nth dimension variable of the mth particle, vmn(t +1) and xmn(t +1) is the updated velocity and position of the nth dimension variable of the mth particle, ω is the inertial weight of the particle, φ1And phi2As learning factors, r1(t) and r2(t) are [0,1 ]]With a random factor of pbest, evenly distributed in the current iterationmnThe nth dimension variable, gbest, for the optimal position currently experienced by the mth particle itselfnAnd the nth dimension variable is the current optimal position of the particle swarm.
10. A train brake control method, comprising:
calculating a speed segment parameter during braking using a method according to any one of claims 1-9;
and calculating a braking distance curve according to the segmentation parameters for ATP vehicle control.
11. A train brake curve segmentation system, comprising:
the train braking system comprises an acquisition unit, a control unit and a control unit, wherein the acquisition unit is used for acquiring braking performance parameters of a train braking curve;
the model determining unit is used for determining a braking curve segmented optimization model, and the braking curve segmented optimization model adopts an optimization model based on a particle swarm algorithm;
and the segmented execution unit is used for determining segmented parameters of the train braking curve through the braking curve segmented optimization model based on the braking performance parameters.
12. A train brake curve segmentation system, characterized in that the system comprises at least one processor and at least one memory;
the memory stores a computer program for performing the method of any of claims 1-9, and the processor calls the computer program in the memory to perform the method of any of claims 1-9.
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