CN116756808A - Railway line shape optimization method and device, electronic equipment and storage medium - Google Patents
Railway line shape optimization method and device, electronic equipment and storage medium Download PDFInfo
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Abstract
The application provides a railway line shape optimizing method, a device, electronic equipment and a storage medium, wherein the railway line shape optimizing method comprises the following steps: carrying out sectional processing on the initial line shapes, and determining parameters to be optimized of each section of initial line shapes, decision search spaces of the parameters to be optimized and optimization constraints of the parameters to be optimized; solving an optimal decision string which meets optimization constraint and enables an objective function to be minimum in a decision search space of each initial line shape by adopting an approximate dynamic programming method; the objective function is used for representing the railway linear building cost determined by the decision string; and updating parameters to be optimized by adopting an optimal decision, and updating the initial line shape according to the updated parameters to be optimized to obtain an optimized line shape. And an optimal construction strategy which enables the railway construction cost to be minimum is searched by adopting an approximate dynamic programming method, so that the initial line shape is optimized, the optimal line shape with the minimum construction cost is obtained, and the railway line shape design efficiency is greatly improved.
Description
Technical Field
The application relates to the technical field of railway design, in particular to a railway line shape optimization method, a railway line shape optimization device, electronic equipment and a storage medium.
Background
With the development of urban mass economy, the scale of urban high-speed railways presents a situation of increasing year by year. Because of the shortage of land resources in urban areas, the dense population and the complex existing road network, constructing high-speed railways in urban areas faces many challenges.
At present, when a new railway is introduced into an existing station, the new railway may be built in parallel with the existing railway, and when a new railway line shape is designed, it is required to ensure that the new railway is as close to the existing railway as possible, and does not invade the existing railway land boundary, and at the same time, it is also required to minimize the railway construction cost. At present, the design efficiency of the related art adopting a mode of manually designing a new railway line shape is low.
Disclosure of Invention
The embodiment of the application aims to provide a railway line shape optimizing method, a railway line shape optimizing device, electronic equipment and a storage medium, which are used for improving railway line shape design efficiency.
In a first aspect, an embodiment of the present application provides a railway line optimization method, including: carrying out segmentation processing on the initial line shape, and determining parameters to be optimized of each segment of the initial line shape, a decision search space of the parameters to be optimized and optimization constraints of the parameters to be optimized; solving an optimal decision string which meets the optimization constraint and minimizes an objective function in the decision search space of each initial line shape by adopting an approximate dynamic programming method; the objective function is used for representing the construction cost of the railway line determined by the decision string; and updating the parameters to be optimized by adopting the optimal decision, and updating the initial line shape according to the updated parameters to be optimized to obtain an optimized line shape.
In the implementation process of the scheme, an optimal construction strategy which enables the railway construction cost to be minimum is searched by adopting an approximate dynamic programming method, so that the initial line shape is optimized, the optimal line shape with the minimum construction cost is obtained, and the railway line shape design efficiency is greatly improved.
In an implementation manner of the first aspect, the adopting an approximate dynamic programming method to solve an optimal decision string that meets the optimization constraint and minimizes an objective function in the decision search space of each initial line shape includes: and solving an optimal decision string which meets the optimization constraint and minimizes an objective function in the decision search space of each initial line shape by adopting an approximate dynamic programming method based on approximate strategy iteration.
In the implementation process of the scheme, the optimal solution is solved by adopting an approximate dynamic programming algorithm based on approximate strategy iteration, so that the optimization efficiency of the railway linear optimization method is further improved, and the railway linear design efficiency is further improved.
In one implementation of the first aspect, the objective function includes an instant cost function for calculating an instant cost and a future cost function for calculating a future cost.
In the implementation process of the scheme, future cost is considered by the objective function, so that the influence of decision making on cost can be evaluated more comprehensively by the railway line-shaped optimization method, and the railway line-shaped design effect is effectively improved.
In one implementation of the first aspect, the future cost function is a linear approximation function, an exponential decay approximation function, a polynomial approximation function, or a neural network approximation function.
In the implementation process of the scheme, the future cost function can be represented by a linear approximation function, an exponential decay approximation function, a polynomial approximation function or a neural network approximation function, so that the railway line optimization method can be suitable for more application scenes, and the adaptability of the railway line optimization method is improved.
In one implementation manner of the first aspect, the building cost includes: one or more of earthworks costs, bridge construction costs, tunnel construction costs, railway line construction costs, building demolition costs, and road right costs.
In the implementation process of the scheme, the objective function fully considers all costs of the railway line shape, so that the railway line shape optimized by the railway line shape optimization method can fully consider all costs, and the design effect of the railway line shape is improved; meanwhile, the railway line shape optimizing method can be suitable for more application scenes, and the adaptability of the railway line shape optimizing method is improved.
In an implementation manner of the first aspect, the segmenting the initial line shape includes: carrying out sectional treatment on the initial line shape of the horizontal plane of the railway design base line; the parameters to be optimized comprise: two sections of adjacent tangential intersection point coordinates of the initial line shape of the horizontal plane, the radius of the circular curve of the initial line shape of the horizontal plane and the length of the transition curve; the updating of the initial line shape according to the updated parameter to be optimized to obtain an optimized line shape comprises the following steps: and updating the initial line shape of the horizontal plane according to the updated parameters to be optimized to obtain the optimized line shape of the horizontal plane.
In the implementation process of the scheme, the initial alignment comprises a horizontal initial alignment, namely the railway alignment optimization method can optimize the horizontal initial alignment so as to obtain a horizontal optimization alignment, so that the railway alignment optimization method can be applied to a horizontal alignment optimization scene, the use scenes of the railway alignment optimization method are enriched, and the adaptability of the railway alignment optimization method is improved.
In an implementation manner of the first aspect, the segmenting the initial line shape includes: carrying out sectional treatment on the initial line shape of the vertical section of the railway design base line; the parameters to be optimized comprise: two sections of adjacent tangential intersection coordinates of initial linearities of the vertical sections and the radius of a circular curve of the initial linearities of the vertical sections; the updating of the initial line shape according to the updated parameter to be optimized to obtain an optimized line shape comprises the following steps: and updating the initial profile of the vertical section according to the updated parameters to be optimized to obtain the optimized profile of the vertical section.
In the implementation process of the scheme, the initial line shape comprises a vertical section initial line shape, namely the railway line shape optimizing method can optimize the vertical section initial line shape so as to obtain a vertical section optimizing line shape, so that the railway line shape optimizing method can be applied to a vertical section line shape optimizing scene, the use scene of the railway line shape optimizing method is enriched, and the adaptability of the railway line shape optimizing method is improved.
In a second aspect, an embodiment of the present application provides a railroad line optimization apparatus, including: the system comprises a parameter determining module, an optimal decision acquiring module and an optimal linear acquiring module, wherein,
the parameter determining module is used for carrying out sectional processing on the initial line shape and determining parameters to be optimized of each section of the initial line shape, decision search spaces of the parameters to be optimized and optimization constraints of the parameters to be optimized;
the optimal decision acquisition module is used for solving an optimal decision string which meets the optimization constraint and enables an objective function to be minimum in the decision search space of each initial line shape by adopting an approximate dynamic programming method; the objective function is used for representing the construction cost of the railway line determined by the decision string;
the optimized line shape acquisition module is used for updating the parameters to be optimized by adopting the optimal decision, and updating the initial line shape according to the updated parameters to be optimized to obtain an optimized line shape.
In a third aspect, an embodiment of the present application provides an electronic device, including: the device comprises a processor, a memory and a communication bus, wherein the processor and the memory complete communication with each other through the communication bus; the memory has stored therein computer program instructions executable by the processor which, when read and executed by the processor, perform the method of the first aspect or any one of the possible implementations of the first aspect.
In a fourth aspect, embodiments of the present application provide a computer readable storage medium having stored thereon computer program instructions which, when read and executed by a processor, perform the method provided by the first aspect or any one of the possible implementations of the first aspect.
Additional features and advantages of the application will be set forth in the description which follows, and in part will be apparent from the description, or may be learned by practice of the embodiments of the application. The objectives and other advantages of the application will be realized and attained by the structure particularly pointed out in the written description and claims thereof as well as the appended drawings.
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In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings that are needed in the embodiments of the present application will be briefly described below, it should be understood that the following drawings only illustrate some embodiments of the present application and should not be considered as limiting the scope, and other related drawings can be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a schematic view of a land boundary between an existing railway and a newly designed railway in the related art according to an embodiment of the present application;
FIG. 2 is a schematic flow chart of a railway line optimization method according to an embodiment of the present application;
FIG. 3 is a schematic view of a railway line distribution provided by an embodiment of the present application;
fig. 4 is a schematic diagram of a horizontal plane initial linear HA in a certain scenario provided by an embodiment of the present application;
FIG. 5 is a schematic diagram showing a comparison between a horizontal plane optimization line shape and an artificial design line shape obtained by the railway line shape optimization method according to the embodiment of the present application;
FIG. 6 is a schematic diagram showing a comparison between a vertical section optimized line shape and an artificial design line shape obtained by the railway line shape optimizing method according to the embodiment of the present application;
FIG. 7 is a schematic structural view of a railway line optimization device according to an embodiment of the present application;
fig. 8 is a schematic structural diagram of an electronic device according to an embodiment of the present application.
Detailed Description
The technical solutions in the embodiments of the present application will be described below with reference to the accompanying drawings in the embodiments of the present application. The following examples are only for more clearly illustrating the technical aspects of the present application, and thus are merely examples, and are not intended to limit the scope of the present application.
Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs; the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the application; the terms "comprising" and "having" and any variations thereof in the description of the application and the claims and the description of the drawings above are intended to cover a non-exclusive inclusion.
In the description of embodiments of the present application, the technical terms "first," "second," and the like are used merely to distinguish between different objects and are not to be construed as indicating or implying a relative importance or implicitly indicating the number of technical features indicated, a particular order or a primary or secondary relationship. In the description of the embodiments of the present application, the meaning of "plurality" is two or more unless explicitly defined otherwise.
Reference herein to "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment may be included in at least one embodiment of the application. The appearances of such phrases in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments. Those of skill in the art will explicitly and implicitly appreciate that the embodiments described herein may be combined with other embodiments.
In the description of the embodiments of the present application, the term "and/or" is merely an association relationship describing an association object, and indicates that three relationships may exist, for example, a and/or B may indicate: a exists alone, A and B exist together, and B exists alone. In addition, the character "/" herein generally indicates that the front and rear associated objects are an "or" relationship.
At present, when a new railway is introduced into an existing station, the new railway is inevitably built in parallel with the existing railway. Since existing railways already occupy a certain land area, existing railways have a fixed land use boundary, and new railways will also have their land boundaries. In designing a new railway, in order to save the railway floor space and maintain the safe operation of the existing railway, the new railway needs to be as close as possible to the existing railway without encroaching on the land use boundary of the existing railway.
As shown in fig. 1, fig. 1 shows the land use boundaries of the existing railway and the land use boundaries of the newly designed railway. The area between the land boundary line of the existing railway and the land use boundary of the newly designed railway is called a sandwich area. In general, the area of the interlayer is relatively long and narrow, and commercial exploitation is difficult, so that the land in the interlayer area needs to be shared when a new railway is constructed. Therefore, when a new railway is designed manually, the construction cost of the railway needs to be considered as a point. The design efficiency of the railway construction with lower cost is lower by manually searching the optimal strategy, and the optimal construction strategy may be missed, so that the design effect is poor.
Based on the above, the embodiment of the application provides a railway line shape optimizing method, which adopts an approximate dynamic programming method to search an optimal construction strategy for minimizing the railway construction cost, so as to optimize the initial line shape, obtain the optimal line shape with the minimum construction cost, and greatly improve the railway line shape design efficiency.
The railway line shape optimizing method can be applied to a new railway design scene of introducing a new railway into an existing station, and can be directly applied to the new railway design scene of the new station.
Referring to fig. 2, an embodiment of the present application provides a railway line optimization method, which includes:
step S110: carrying out sectional processing on the initial line shapes, and determining parameters to be optimized of each section of initial line shapes, decision search spaces of the parameters to be optimized and optimization constraints of the parameters to be optimized;
step S120: solving an optimal decision string which meets optimization constraint and enables an objective function to be minimum in a decision search space of each initial line shape by adopting an approximate dynamic programming method; wherein the objective function is used to characterize the construction cost of the railway line determined by the decision string;
step S130: and updating parameters to be optimized by adopting an optimal decision, and updating the initial line shape according to the updated parameters to be optimized to obtain an optimized line shape.
The following describes each of the above steps in detail:
first, step S110 will be described in detail:
the initial line shape in the step S110 may be obtained by manual design, or may be a randomly generated initial line shape. It can be understood that when the initial line shape is an initial line shape designed manually, the railway optimization method directly optimizes the initial line shape designed manually, so that the calculated amount of the railway line shape optimization method is greatly reduced, and the design efficiency of the railway line shape is improved; when the initial line is a randomly generated initial line, the railway optimization method is high in automation degree, and the design efficiency of the railway line can be improved.
The manner of segmenting the initial line shape in step S110 may be: the initial line shape is segmented at the inflection point of the initial line shape. For example, if a certain initial line shape has 6 inflection points except the start point and the end point, the initial line shape is subjected to segmentation processing at the 6 inflection points, and 7 segments are obtained in total.
It is understood that step S110 further includes, before segmenting the initial line shape: and carrying out parameterization treatment on the initial line shape. For details of the parameterization process, please refer to the prior art.
It will be appreciated that referring to fig. 3, the railway line is a three-dimensional curve, and the projection of the three-dimensional curve on the horizontal plane may be determined as a horizontal plane line HA, and the projection of the three-dimensional curve on the vertical plane perpendicular to the horizontal plane may be determined as a vertical plane line VA. The three-dimensional curve for characterizing the railway line shape can be obtained by a horizontal line shape HA and a vertical line shape VA.
As an alternative embodiment of the above-mentioned railway line shape optimizing method, step S110 performs a segmentation process on the initial line shape, including: and carrying out sectional treatment on the initial line shape of the horizontal plane of the railway design base line. The parameters to be optimized include: the intersection point coordinates of tangent lines of the initial line shapes of two adjacent horizontal planes, the radius of a circular curve of the initial line shapes of the horizontal planes and the length of a transition curve. Step S130, updating the initial line shape according to the updated parameters to be optimized to obtain an optimized line shape, which comprises the following steps: and updating the initial line shape of the horizontal plane according to the updated parameters to be optimized to obtain the optimized line shape of the horizontal plane.
It will be appreciated that, as shown in FIG. 3, a horizontal planeThe initial linear HA is formed by combining a straight line segment, a circular curve and a transition curve. Therefore, when the initial horizontal line shape is optimized, the parameters to be optimized comprise the coordinates of the intersection point of the tangent lines of two adjacent initial horizontal line shapes, namely the coordinates of the HPI point and the radius R of the circular curve of the initial horizontal line shape H Length of transition curve L S 。
The mathematical expression for the initial linear shape of the horizontal plane with N segments is:
wherein, (x) 0 ,y 0 ) And (x) N+1 ,y N+1 ) Coordinates of two endpoints of the initial line shape of the horizontal plane are respectively; (x) i ,y i ) Representing coordinates of an ith HPI point; r is R Hi Representing the radius of the circular curve in the ith HA segment; ls (Ls) i Representing the transition curve length in the i-th segment.
The optimization constraints of the initial linear shape of the horizontal plane include:
R Hi -R HCmin ≥0
Lsi-Ls Cmin ≥0
R Hi α i -L HCmin ≥0
L Tmin -L Ti ≤0
wherein R is HCmin Representing the minimum allowable radius of the circular curve in HA; ls (Ls) Cmin Representing the minimum length of the transition curve in HA; l (L) HCmin Representing the minimum length of a circular curve in HA; l (L) T Representing the tangential length between two adjacent segments in the HA; l (L) Tmin Representing L in HA T Is a minimum length of (c).
As an alternative embodiment of the above-mentioned railway line shape optimizing method, step S110 performs a segmentation process on the initial line shape, including: and carrying out sectional treatment on the initial profile of the longitudinal section of the railway design base line. The parameters to be optimized include: and the intersection point coordinates of the tangent lines of the initial lineages of the two sections of adjacent vertical sections and the radius of the circular curve of the initial lineages of the vertical sections. Step S130, updating the initial line shape according to the updated parameters to be optimized to obtain an optimized line shape, which comprises the following steps: and updating the initial profile of the vertical section according to the updated parameters to be optimized to obtain the optimized profile of the vertical section.
It will be appreciated that as shown in fig. 3, a section of initial vertical section line VA is formed by combining straight line sections and circular curves, and the position of the tangential intersection VPI of each two adjacent sections of initial vertical section line on VA is represented by mileage K and design elevation E.
The mathematical expression for the initial profile shape with M segments is:
wherein, (K) 0 ，E 0 ) Sum (K) M+1 ，E M+1 ) Respectively representing coordinates of two endpoints of the initial line shape of the horizontal plane; (K) j ，E j ) Representing the position coordinates of the j-th VPI point; r is R Vj Representing the radius of the circular curve in the jth VA segment.
Optimization constraints for initial profile shape of the profile include:
G i ≤G max
R Vi -R VCmin ≥0
wherein G is max Representing the maximum allowable gradient in VA; r is R VCmin Representing the minimum allowable radius of the VA allowable middle circle curve; l (L) Smin Representing the minimum allowable length of the slope in VA.
Step S120 is described in detail below:
first, the approximate dynamic programming method in step S120 is described:
as an alternative embodiment of the above-described railway line optimization method, the objective function in step S120 includes an instant cost function for calculating an instant cost and a future cost function for calculating a future cost.
The approximate dynamic programming method (Approximate Dynamic Programming, ADP) is an algorithm for solving the optimal decision problem by making explicit decisions on the near term and using approximationsThe reflected future behavior implicitly assumes the best decision after time t+t to estimate the cost of the current state for each time step T, namely:
wherein s is t A state of time step t; u is decision; u is a decision search space; e (·) is the solution expectation; alpha i For future discount coefficients; t is the time required for future cost function calculation; l (·) is the instant cost u for calculating the instant cost;a future cost function for calculating a future cost;
thus, an optimal decision stringThe method comprises the following steps:
as an alternative embodiment of the above-mentioned railway line optimization method, the future cost function may be selected from a linear approximation function, an exponential decay approximation function, a polynomial approximation function, or a neural network approximation function, wherein:
linear approximation functions, assuming that the relation of future cost and current state can be described by a straight line, i.e. the future cost function is a linear function of the current state;
an exponential decay approximation function assuming that future cost decays exponentially over time, i.e. the future cost function is the current state multiplied by an exponential function;
the polynomial approximation function assumes that the relation of future cost to current state can be described by a polynomial function, i.e. the future cost function is some polynomial function of the current state;
the neural network approximates a function assuming that the relationship of future cost to current state can be described by a neural network, i.e., the future cost is equal to the output of the neural network to the current state.
As an alternative embodiment of the above-described railway line optimization method, the construction cost includes: one or more of earthworks costs, bridge construction costs, tunnel construction costs, railway line construction costs, building demolition costs, and road right costs, wherein:
cost of earthwork C E The calculation method of (1) is as follows:
wherein u is c And u f The unit of excavation and the unit of filling are respectively the unit of the square per m 3 ；A ci And A fi The i-th cross section is the square area and the filling area respectively; n is n 1 The number of the cross sections is; l (L) Ei Is the length of the ith cross section; omega 0 When=1, the cross section is a full-cut section; omega 1 When=1, the cross section is a full filled section; omega 2 When=1, the cross section is a half-filled half-cut section;
bridge construction cost C B The calculation method of (1) is as follows:
wherein n is 2 The number of bridges in the whole railway line; u (u) Bi The unit construction cost of the ith bridge is the unit of the/m; l (L) Bi The length of the ith bridge; c (C) Ai The bridge abutment construction cost for the ith bridge is shown in the unit of the bridge abutment construction cost;
tunnel construction cost C T The calculation method of (1) is as follows:
wherein n is 3 The number of tunnels in the whole railway line; u (u) Ti The unit construction cost of the ith tunnel is the unit of the ith tunnel per meter; l (L) Ti The length of the ith tunnel; c (C) Pi The building cost for the ith tunnel portal is shown in the unit of the building cost;
railway line construction cost C L The calculation method of (1) is as follows:
C L ＝u L ×L L
wherein u is L The unit construction cost of the railway line is expressed as the unit per m; l (L) L Is the length of the railway line shape;
building demolition cost C BD The calculation method of (1) is as follows:
wherein n is 4 For the number of buildings to be dismantled; u (u) H Unit demolition cost for the ith building to be demolished in units of @/m 2 ；A i The building area of the ith building to be dismantled;
road right cost C R The calculation method of (1) is as follows:
wherein n is 5 Is influenced by railway line shapeIs the land mass number; u (u) R The unit cost of the kth land block affected by the railway line is expressed as the unit of the (m/m) 2 ；A k Is the area of the kth land block affected by the railway line shape.
For ease of understanding, the decision search space in step S120 is explained in a horizontal plane initial line shape:
referring to FIG. 4, FIG. 4 shows a horizontal plane initial linear HA in a scene, where HPI 0 And HPI 7 Respectively the start and end of HA, [ HPI ] 0 ，HPI 1 ]、[HPI 1 ，HPI 2 ]、[HPI 2 ，HPI 3 ]、[HPI 3 ，HPI 4 ]、[HPI 4 ，HPI 5 ]、[HPI 5 ，HPI 6 ]And [ HPI ] 6 ，HPI 7 ]Representing the first to seventh segments respectively, decomposing the initial linear optimization problem into the seven-stage decision problem by adopting an approximate dynamic programming method, wherein the state space S of the kth stage k Is thatThe decision search space for the kth stage is therefore:
x k ∈[x lower ，x upper ]
y k ∈[y lower ，y upper ]
R Hk ∈[R Hlower ，R Hupper ]
L Sk ∈[L Slower ，L Supper ]
wherein x is lower And x upper Respectively represent x k Lower and upper limits of (2); y is lower And y upper Respectively represent y k Lower and upper limits of (2); r is R Hlower And R is Hupper Respectively represent R Hk Lower and upper limits of (2); l (L) Slower And L Supper Respectively represent L Sk Lower and upper limits of (2);
similarly, the state space S of the kth stage in the initial profile of the longitudinal section k Is thatThe decision search space of the kth stage is:
K k ∈[K lower ，K upper ]
E k ∈[E lower ，E upper ]
R Vk ∈[R Vlower ，R Vupper ]
wherein K is lower And K upper Respectively K k Lower and upper limits of (2); e (E) lower And E is upper Respectively E k Lower and upper limits of (2); r is R Vlower And R is Vupper R is respectively Vk Lower and upper limits of (2).
As an optional implementation manner of the above-mentioned railway line optimization method, step S120 adopts an approximate dynamic programming method to solve an optimal decision string that satisfies an optimization constraint and minimizes an objective function in a decision search space of each initial line shape, and includes: and solving an optimal decision string which meets the optimization constraint and minimizes the objective function in the decision search space of each initial line shape by adopting an approximate dynamic programming method based on approximate strategy iteration.
It can be understood that the strategy iteration is a method for solving the optimal solution by using an approximate dynamic programming algorithm, and the mathematical expression of the strategy iteration method adopted by the embodiment of the application is as follows:
wherein J is * (s t ) Representing an optimal cost function value; t represents the time required for future cost function calculation; f(s) t ，u t ) The representation is from s t To s t+1 State transition functions of (2); l(s) t * ，u t * ) Representing the optimal value of each step;
the strategy iteration comprises two steps of strategy evaluation PEV, which is a solution for solving the self-consistent condition, i.e. a cost function J (s t ) By feedingDetermining a policy pi k (·)，J(s t ) The solution can be found by:
wherein H represents Hamiltonian, but here the Hamiltonian's optimum value J * (s t ) Is replaced by an estimated value
Policy improvement PIM finds a better policy pi by weakening the HJB equation (Hamilton-Jacobi-Bellman equation) k+1 (s t ) Weakening refers to the optimal J in the real Hamiltonian at each approximation dynamic programming iteration * (s t ) Estimated isSubstituted.
In the embodiment of the application, in order to quickly obtain the approximate optimal solution, the cost function and the strategy both adopt the approximate function to set parameters, specifically:
wherein the cost function (i.e., criticizer Critic) is a parameterized function with parameter ω; the strategy (i.e. the Actor) is a parameterized function with a parameter θ;
the cost function and strategy are updated by designing a loss function using a gradient descent method, wherein the update rule of the parameterized cost function is:
minimizing the following average squared error based on self-consistent conditions at the iterative PEV of step k:
the update rule of the parameterized policy is:
minimally weaken the HJB equation:
loop iteration of PEV and PIM solves the parameterized cost function J (s t The method comprises the steps of carrying out a first treatment on the surface of the ) And parameterized policiesThe iteration of PEV and PIM will gradually switch to the HJB solution.
Finally, the step S130 is described in detail:
step S130 may update the value of the parameter to be optimized by using the optimal decision, and update the initial line shape according to the updated value of the parameter to be optimized, so as to finally obtain the optimized line shape.
It will be appreciated that, after step S130, further includes: outputting the optimized line shape when the optimized line shape meets the optimization constraint.
In order to verify the effectiveness of the railway line optimization method provided by the embodiment of the application, the embodiment of the application performs verification under a certain real scene, and specifically comprises the following steps:
a road segment of a high-speed project with a length of 21 km and a design speed of 250 km/h is selected for verification. The high-speed railway project is located in urban areas and is parallel to the existing common-speed railway. In order to meet the operation safety of the existing railway, the manual linear distance of the primary design stage is kept at a certain safety distance from the common speed railway. Because the linear standard of the common speed railway is not consistent with that of the high speed railway, the long and narrow land used between the designed line and the existing line is difficult to be minimized. The parameter settings for the above-described railway line optimization method are shown in table 1.
TABLE 1 parameter set-up conditions
| Project | Numerical value |
| Search Width/m | 200 |
| Map accuracy | 1∶2000 |
| Safe distance/m | 25 |
| Intersection search Range/m | 50 |
| Intersection point search accuracy | 50 |
| Radius value search range/m | 1000 |
| Moderation curve length value search range/m | 50 |
The comparison of the horizontal plane optimized line shape and the manual design line shape obtained by the railway line shape optimizing method is shown in fig. 5, the comparison of the vertical plane optimized line shape and the manual design line shape obtained by the railway line shape optimizing method is shown in fig. 6, and the front-back comparison of building projects and building cost is shown in table 2.
Table 2 building program and construction cost comparison
| Project | Manually designed line shape | Optimized line shape |
| Full length/km of line | 21.235 | 21.024 |
| Filling (m) 3 ) | 5648 | 5624 |
| Digger (m) 3 ) | 124560 | 124875 |
| Bridge (km) | 17.685 | 17.584 |
| Area of detachment (m) 2 ) | 14253 | 13597 |
| Saving the removal area/% | — | 4.61 |
| The space between two wires (m) 2 ) | 164825 | 159238 |
| Saving the occupied area between two wires | — | 3.39 |
| Total cost/ten thousand yuan | 343585 | 334136 |
| /％ | — | 2.75 |
As can be seen from table 2, the three-dimensional railway line shape optimized by the above-described railway line shape optimizing method achieves a saving of 3.03% in terms of total cost. Thereby verifying the effectiveness of the railway line optimization method.
Referring to fig. 7, based on the same inventive concept, an embodiment of the present application further provides a railway line-shaped optimizing apparatus 200, which includes: a parameter determination module 210, an optimal decision acquisition module 220, and an optimal alignment acquisition module 230, wherein,
the parameter determining module 210 is configured to perform a segmentation process on an initial line shape, and determine a parameter to be optimized of each segment of the initial line shape, a decision search space of the parameter to be optimized, and an optimization constraint of the parameter to be optimized;
the optimal decision obtaining module 220 is configured to solve, in the decision search space of each initial line shape, an optimal decision string that satisfies the optimization constraint and minimizes an objective function by using an approximate dynamic programming method; the objective function is used for representing the construction cost of the railway line determined by the decision string;
the optimized line shape obtaining module 230 is configured to update the parameter to be optimized by using the optimal decision, and update the initial line shape according to the updated parameter to be optimized to obtain an optimized line shape.
As an alternative embodiment of the above-mentioned railway line optimization device, the optimal decision obtaining module 220 is specifically configured to: and solving an optimal decision string which meets the optimization constraint and minimizes an objective function in the decision search space of each initial line shape by adopting an approximate dynamic programming method based on approximate strategy iteration.
As an alternative embodiment of the above-described railway line optimization apparatus, the objective functions employed by the optimal decision-obtaining module 220 include an instantaneous cost function for calculating an instantaneous cost and a future cost function for calculating a future cost.
As an alternative embodiment of the above-described railway line optimization apparatus, the future cost function of the objective function employed by the optimal decision-obtaining module 220 is a linear approximation function, an exponential decay approximation function, a polynomial approximation function, or a neural network approximation function.
As an alternative embodiment of the above-described railway line optimizing apparatus, the construction cost includes: one or more of earthworks costs, bridge construction costs, tunnel construction costs, railway line construction costs, building demolition costs, and road right costs.
As an alternative embodiment of the above-mentioned railway line optimization apparatus, the parameter determination module 210 performs a segmentation process on the initial line shape, including: carrying out sectional treatment on the initial line shape of the horizontal plane of the railway design base line; the parameters to be optimized acquired by the parameter determining module 210 include: two sections of adjacent tangential intersection point coordinates of the initial line shape of the horizontal plane, the radius of the circular curve of the initial line shape of the horizontal plane and the length of the transition curve; the optimizing line shape obtaining module 230 updates the initial line shape according to the updated parameter to be optimized, to obtain an optimizing line shape, including: and updating the initial line shape of the horizontal plane according to the updated parameters to be optimized to obtain the optimized line shape of the horizontal plane.
As an alternative embodiment of the above-mentioned railway line optimization apparatus, the parameter determination module 210 performs a segmentation process on the initial line shape, including: carrying out sectional treatment on the initial line shape of the vertical section of the railway design base line; the parameters to be optimized acquired by the parameter determining module 210 include: two sections of adjacent tangential intersection coordinates of initial linearities of the vertical sections and the radius of a circular curve of the initial linearities of the vertical sections; the optimizing line shape obtaining module 230 updates the initial line shape according to the updated parameter to be optimized, to obtain an optimizing line shape, including: and updating the initial profile of the vertical section according to the updated parameters to be optimized to obtain the optimized profile of the vertical section.
Fig. 8 is a schematic diagram of an electronic device according to an embodiment of the present application. Referring to fig. 8, the electronic device 300 includes: processor 310, memory 320, and communication interface 330, which are interconnected and communicate with each other by a communication bus 340 and/or other forms of connection mechanisms (not shown).
The Memory 320 includes one or more (Only one is shown in the figure), which may be, but is not limited to, a random access Memory (Random Access Memory, RAM), a Read Only Memory (ROM), a programmable Read Only Memory (Programmable Read-Only Memory, PROM), an erasable Read Only Memory (Erasable Programmable Read-Only Memory, EPROM), an electrically erasable Read Only Memory (Electric Erasable Programmable Read-Only Memory, EEPROM), and the like. The processor 310, as well as other possible components, may access, read, and/or write data from, the memory 320.
The processor 310 includes one or more (only one shown) which may be an integrated circuit chip having signal processing capabilities. The processor 310 may be a general-purpose processor, including a central processing unit (Central Processing Unit, CPU), a micro control unit (Micro Controller Unit, MCU), a network processor (Network Processor, NP), or other conventional processor; but may also be a special purpose processor including a digital signal processor (Digital Signal Processor, DSP for short), an application specific integrated circuit (Application Specific Integrated Circuits, ASIC for short), a field programmable gate array (Field Programmable Gate Array, FPGA for short), or other programmable logic device, discrete gate or transistor logic device, discrete hardware components.
The communication interface 330 includes one or more (only one shown) that may be used to communicate directly or indirectly with other devices for data interaction. For example, the communication interface 330 may be an ethernet interface; may be a mobile communications network interface, such as an interface of a 3G, 4G, 5G network; or may be other types of interfaces with data transceiving functionality.
One or more computer program instructions may be stored in the memory 320 and may be read and executed by the processor 310 to implement the railroad linearity optimization method and other desired functions provided by embodiments of the present application.
It is to be understood that the configuration shown in fig. 8 is merely illustrative, and that electronic device 300 may also include more or fewer components than those shown in fig. 8, or have a different configuration than that shown in fig. 8. The components shown in fig. 8 may be implemented in hardware, software, or a combination thereof. For example, the electronic device 300 may be a single server (or other device having computing processing capabilities), a combination of multiple servers, a cluster of a large number of servers, or the like, and may be either a physical device or a virtual device.
The embodiment of the application also provides a computer readable storage medium, and the computer readable storage medium stores computer program instructions which execute the railway line optimization method provided by the embodiment of the application when being read and run by a processor of a computer. For example, the computer-readable storage medium may be implemented as memory 320 in electronic device 300 in FIG. 8.
In the embodiments provided in the present application, it should be understood that the disclosed apparatus and method may be implemented in other manners. The above-described apparatus embodiments are merely illustrative, for example, the division of the units is merely a logical function division, and there may be other manners of division in actual implementation, and for example, multiple units or components may be combined or integrated into another system, or some features may be omitted, or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be through some communication interface, device or unit indirect coupling or communication connection, which may be in electrical, mechanical or other form.
Further, the units described as separate units may or may not be physically separate, and units displayed as units may or may not be physical units, may be located in one place, or may be distributed over a plurality of network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
Furthermore, functional modules in various embodiments of the present application may be integrated together to form a single portion, or each module may exist alone, or two or more modules may be integrated to form a single portion.
The above description is only an example of the present application and is not intended to limit the scope of the present application, and various modifications and variations will be apparent to those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the protection scope of the present application.
Claims (10)
1. A method of optimizing a railway line, the method comprising:
carrying out segmentation processing on the initial line shape, and determining parameters to be optimized of each segment of the initial line shape, a decision search space of the parameters to be optimized and optimization constraints of the parameters to be optimized;
solving an optimal decision string which meets the optimization constraint and minimizes an objective function in the decision search space of each initial line shape by adopting an approximate dynamic programming method; the objective function is used for representing the construction cost of the railway line determined by the decision string;
and updating the parameters to be optimized by adopting the optimal decision, and updating the initial line shape according to the updated parameters to be optimized to obtain an optimized line shape.
2. The railway line optimization method according to claim 1, wherein said solving an optimal decision string satisfying the optimization constraint and minimizing an objective function in the decision search space of each of the initial lines using an approximate dynamic programming method, comprises:
and solving an optimal decision string which meets the optimization constraint and minimizes an objective function in the decision search space of each initial line shape by adopting an approximate dynamic programming method based on approximate strategy iteration.
3. The railroad line optimization method of claim 1, wherein the objective function includes an instant cost function for calculating an instant cost and a future cost function for calculating a future cost.
4. A method of railway line optimization as claimed in claim 3, wherein the future cost function is a linear approximation function, an exponential decay approximation function, a polynomial approximation function or a neural network approximation function.
5. The railway line optimization method according to any one of claims 1 to 4, characterized in that the construction cost comprises:
one or more of earthworks costs, bridge construction costs, tunnel construction costs, railway line construction costs, building demolition costs, and road right costs.
6. The optimization method of railway line shape according to any one of claims 1 to 4, wherein the segmentation processing of the initial line shape comprises:
carrying out sectional treatment on the initial line shape of the horizontal plane of the railway design base line;
the parameters to be optimized comprise:
two sections of adjacent tangential intersection point coordinates of the initial line shape of the horizontal plane, the radius of the circular curve of the initial line shape of the horizontal plane and the length of the transition curve;
the updating of the initial line shape according to the updated parameter to be optimized to obtain an optimized line shape comprises the following steps:
and updating the initial line shape of the horizontal plane according to the updated parameters to be optimized to obtain the optimized line shape of the horizontal plane.
7. The optimization method of railway line shape according to any one of claims 1 to 4, wherein the segmentation processing of the initial line shape comprises:
carrying out sectional treatment on the initial line shape of the vertical section of the railway design base line;
the parameters to be optimized comprise:
two sections of adjacent tangential intersection coordinates of initial linearities of the vertical sections and the radius of a circular curve of the initial linearities of the vertical sections;
the updating of the initial line shape according to the updated parameter to be optimized to obtain an optimized line shape comprises the following steps:
and updating the initial profile of the vertical section according to the updated parameters to be optimized to obtain the optimized profile of the vertical section.
8. A railroad line optimization apparatus, comprising: the system comprises a parameter determining module, an optimal decision acquiring module and an optimal linear acquiring module, wherein,
the parameter determining module is used for carrying out sectional processing on the initial line shape and determining parameters to be optimized of each section of the initial line shape, decision search spaces of the parameters to be optimized and optimization constraints of the parameters to be optimized;
the optimal decision acquisition module is used for solving an optimal decision string which meets the optimization constraint and enables an objective function to be minimum in the decision search space of each initial line shape by adopting an approximate dynamic programming method; the objective function is used for representing the construction cost of the railway line determined by the decision string;
the optimized line shape acquisition module is used for updating the parameters to be optimized by adopting the optimal decision, and updating the initial line shape according to the updated parameters to be optimized to obtain an optimized line shape.
9. An electronic device, comprising: a processor, a memory, and a bus, wherein,
the processor and the memory complete communication with each other through the bus;
the memory stores program instructions executable by the processor, the processor invoking the program instructions to perform the method of any of claims 1-7.
10. A non-transitory computer readable storage medium storing computer instructions which, when executed by a computer, cause the computer to perform the method of any of claims 1 to 7.
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