CN113032876B - Method and system for laying overpasses for automatic relocation of existing roads along railway lines - Google Patents

Abstract

The invention discloses a method, system, terminal and readable storage medium for laying an overpass for automatically relocating an existing road along a railway line. The method includes: step S1: constructing an optimization model of the road and railway overpass; step S2: Divide the study area into cell grids, and decompose the path search into two non-intersecting segment searches and intersecting segment searches; Step S3: perform path search on the two non-intersecting segments and intersecting segments based on the optimization model; Step S4: Utilize the step The N paths, M paths and P partial lines of intersections in S3 are spliced and integrated to form several complete lines, and the optimal line is determined with the optimization function as the objective function, and the plane and vertical section curves are carried out based on the optimal line. Fitting to get the final route scheme. The invention decomposes the path search into the non-intersection section and the intersection section, weakens the coupling interaction between the connection point and the intersection point, and realizes the optimization of the highway and the railway overpass.

Description

Method and system for laying overpasses for automatic relocation of existing roads along railway lines

technical field

The invention belongs to the technical field of railway design, and in particular relates to a method, system, terminal and readable storage medium for laying interchanges for automatically changing existing roads along a railway line.

Background technique

Railway is an important national infrastructure and the main artery of the national economy. It plays an important role in my country's economic and social development. In order to meet the needs of road transportation, the number of roads built and under construction has increased year by year, and the existing road network has become more dense. In the process of railway construction, a large number of existing roads and existing or newly-built railway lines intersect in many cities and regions, and are restricted by the intersection angle, effective clearance and alignment of the intersections. With limited transportation safety and traffic capacity, illegal roads will inevitably be rebuilt. Under the circumstance of limited time and energy, engineers often only rely on their own experience to subjectively compare several feasible solutions that satisfy constraints and control investment to make final decisions, which may lead to more serious problems. Valuable programs are left out. Therefore, it is urgent to propose an intelligent design method for road and railway interchanges that not only considers various constraints such as road construction and road network intersection, but also meets the goal of saving project investment.

Road and railway overpass optimization belongs to a category under the route optimization framework. Compared with the route optimization between two fixed endpoints, it is also necessary to consider the enhanced constraints of road network intersection and connection with existing roads. The research on road and railway interchanges at home and abroad is mainly divided into three stages: in the first stage, for each road intersection, only a set of alternative solutions close to the optimal solution is provided, and the limited solutions are compared and selected. The optimal route scheme that satisfies constraints and investment costs, the method includes the subjectivity and randomness of the designer. The second stage: An accurate solution is proposed, but the changes of terrain and complex constraints are difficult to be expressed by explicit functions, and there are theoretical shortcomings. The third stage: With the continuous innovation of optimization methods and the growth of computer computing power, a heuristic algorithm that does not rely on explicit functions is proposed. The genetic algorithm is widely used in interchanges, but it is still limited to manual setting. An initial route scheme, in the case of determining the start and end points of the route, optimizes the route in the study area and finds a route scheme that meets the constraints, which has certain limitations and is difficult to apply to the actual situation.

Considering the investment cost of the line as the specific distance between the start and end points of the interchange, the design of the interchange can be transformed into a shortest path search problem between the start and the end. The present invention is aimed at the situation that the starting and ending points of the road and the railway overpass are not fixed, and how to realize the route optimization is an urgent need of the present invention.

SUMMARY OF THE INVENTION

The purpose of the present invention is to deal with the intersection of the existing road and the new railway, to deal with the construction of the intersection road and make it meet the road design, road network intersection constraints, maintain the traffic capacity of the existing road, rebuild the local road, and find the optimal reconstruction scheme problem , provides a new technical idea to realize the design of the reconstruction section, and proposes a hierarchical optimization strategy to solve the position of the connection point between the reconstruction section and the existing road and the intersection of the reconstruction section and the railway. By decomposing the core problem into non-intersecting sections The optimization and the two-stage reorganization of the intersection end and the non-intersection section make the changes of the connection point and the intersection independent of each other, weaken the coupling interaction between the two, and realize the optimization of highway and railway overpasses.

On the one hand, the present invention provides a method for laying an overpass for automatically relocating an existing road along a railway line, comprising the following steps:

Step S1: constructing an optimization model of the road and railway overpass line, the optimization module includes: decision variables, constraints and optimization functions in the research area;

Step S2: Divide the study area into cell grids, and then determine the connection start and end cell sets S, E and the cell sets L and L on the left and right sides of the railway in the study area according to the existing roads and railways. R, and then decompose the path search into two non-intersecting segment searches and cross segment searches;

Among them, the path between the cell set S and the cell set L on the same side of the railway and the path between the cell set E and the cell set R on the same side are the two non-intersecting section paths; the cell set L The path with the cell set R is an intersection path;

Step S3: performing path search on two non-intersecting sections and intersecting sections based on the optimization model;

Wherein, performing a line search on the two non-intersecting segments to obtain M paths between the cell set S and the cell set L and N paths between the cell set E and the cell set R; and The path search is performed at the intersection to obtain H local lines of the intersection;

Search with the optimization function in the optimization model as the objective function, and the route obtained by the search satisfies the constraints in the optimization model;

Step S4: Using the N paths, the M paths, and the H cross-section local lines in step S3 to splicing and integrating to form several complete lines, and using the optimization function as the objective function to determine the optimal line , and perform the curve fitting of the plane and the longitudinal section based on the optimal route to obtain the final route scheme.

Optionally, in step S3, the DT algorithm is used in the process of performing path search on two non-intersecting segments based on the optimization model, and the generalized distance value of the cell in the DT algorithm is defined with the optimization function, and the implementation process is as follows:

For the path search process between cell set S and cell set L:

A-1: Take the cell in the cell set L as the fixed endpoint, and select a cell Li in the cell set L[L ₁ ,L ₂ ,...,L _{p ]} as the fixed point of the S→L segment path endpoint, p is the total number of cells in the cell set L;

A-2: Take the cell _Li as the fixed endpoint and use the DT algorithm to search to obtain the DT value of each cell in the cell set S. If the cell DT value is not positive infinity, keep the cell and form a line Partial path; otherwise, discard a partial path corresponding to the cell, wherein several feasible paths are obtained by taking the cell _Li as the fixed endpoint;

A-3: Select the path with the smallest DT value from the several feasible paths as the path from the cell set S with the cell Li as the fixed _endpoint to the cell set L;

A-4: Traverse the cells in the cell set L, and repeat steps A-1-A-3, and finally obtain M paths between the cell set S and the cell set L;

For the path search process between cell set E and cell set R:

B-1: Take a cell in the cell set R as a fixed endpoint, and select a cell R _i in the cell set R[R ₁ ,R ₂ ,...,R _q ] as the fixed point of the E→R segment path endpoint, q is the total number of cells in the cell set R;

B-2: Use the DT algorithm to search with the cell R _i as the fixed endpoint to obtain the DT value of each cell in the cell set E. If the cell DT value is greater than 0, the cell is reserved and a local path is formed ; otherwise, discard a local path corresponding to the cell, wherein, taking the cell R _i as a fixed endpoint to obtain several feasible paths;

B-3: Select the path with the smallest DT value from the several feasible paths as the path from the cell set E to the cell set R with the cell R _i as the fixed endpoint;

B-4: Traverse the cells in the cell set R, and repeat steps B-1-B-3, and finally obtain N paths between the cell set E and the cell set R.

Optionally, in step A-2, taking cell _Li as the fixed endpoint and adopting the DT algorithm to search to obtain the DT value of each cell in the cell set S as follows:

A-2-1: Preprocess the search area, that is, the search area is determined according to the research area on the side where the cell set S is located, _wherein the distance from any cell in the search area to the fixed endpoint Li is used Under the condition of the maximum limited slope i _max and the allowable spread coefficient γ, the following formula is satisfied:

Among them, R(R _x , R _y , R _z ) represents the xyz coordinates of any cell in the search area, LR(LR _x , LR _y , LR _z ) represents the xyz coordinates of the fixed endpoint Li, and _{i max} represents the maximum road The limit slope, γ represents the spread coefficient;

If it is not satisfied, mark the cell in the search area as a forbidden cell, and do not participate in the two-way scanning process;

A-2-2: Initialize the generalized distance value of all non-prohibited cells in the search area;

A-2-3: Scan the search area in both directions, and update the generalized distance value of each cell to form a generalized distance map, where the generalized distance value of each cell in the generalized distance map is the DT value of the cell.

Optionally, the process of constructing the generalized distance map in step A-2-3 is as follows:

A-2-3-1: construct a neighborhood template, when scanning each cell, place the center position of the neighborhood target on the cell;

Among them, when any cell P is scanned, P is taken as the starting point of the local line, and any cell G in the neighborhood template is taken as the end point of the local line;

A-2-3-2: When cell G does not belong to S[S ₁ ,S ₂ ,...,S _m ] in the search area, use a straight line to connect cell G and P; or when cell G When it belongs to S[S ₁ ,S ₂ ,...,S _m ], use curve smooth connection, where a is the forward path direction connected to the target cell, b is the existing road direction of cell G, and m is the cell the number of cells in the set S;

A-2-3-3: Connect cells P and G to obtain a local circuit and form a local circuit scheme with structures, and judge whether the local path scheme satisfies the constraints, if not, discard the local circuit, and transfer to Go to step A-2-3-1, reselect the cells in the field template; if satisfied, go to step A-2-3-4;

Step A-2-3-4: Calculate the generalized distance value between cell P and cell G, and update the generalized distance value of cell P;

Step A-2-3-5: Iteratively execute the scanning process described in A _2-3-1 , A _2-3-2 , A _2-3-3 , and A _2-3-4 until the generalization of each cell is reached The distance value no longer changes to get a generalized distance map.

Optionally, in step S3, the process of performing a path search on the intersection to obtain H local lines of the intersection is as follows: iteratively searches for the local lines between the cell set L and the cell set R, and retains the local lines that meet the constraints. line, as follows:

Taking any cell Li in the cell set L as the center, draw a vertical line to the railway and take the vertical line as the angle bisector, and rotate the vertical lines to the left and right by 90°-A _min to obtain the straight line a and Straight line b, Amin is the minimum intersection angle that meets the requirements of the specification;

In the cell set R, the Q cells contained in the fan-shaped area formed by the straight line a and the straight line b are the fan-shaped feasible solution cell set;

Connect the cell Li and the Q cells in the fan-shaped feasible solution cell set by straight lines respectively to form Q local lines;

Detecting whether the Q local lines meet the constraint conditions, and if not, eliminating them; if so, generating the plane, longitudinal section, bridge and tunnel line schemes of the local lines;

Iteratively searches all cells in the cell set L according to the above process, and generates a local route scheme of P intersections.

Optionally, in step S4, the curve fitting of the plane and the vertical section is performed based on the optimal route scheme to obtain the final route scheme: according to the optimal route scheme, the feature points in the path are selected by using the chord tangent distance method as Plane intersection, and match the minimum radius of circular curve and length of transition curve according to the specification to generate the initial plane line; on the basis of the plane initial line plan, initialize the longitudinal section plan according to the minimum amount of earthwork; the optimal, the overall optimization of the line is obtained The final plan and longitudinal section optimization route scheme, such as the use of nonlinear mesh adaptive direct search algorithm (Nonlinear optimization with the mesh adaptive direct search, NOMAD) for the overall optimization of the route. Since this process is an implementation process in the prior art, it will not be described in detail.

Optionally, the decision variables in the research area in step S1 are expressed as:

Plane intersection HIP's(X _H ,Y _H ,R _H ,L _H0 ,L _H1 );

Vertical section slope point VIP's (K _V ,H _V ,R _V );

Reconstruction of bridges and tunnels on the line MBU's (KB _S ,KB _E ,KU _S ,KU _E );

The starting and ending points and intersection points of the reconstructed line VCC's (I, S, E);

X _H and Y _H are the abscissa and ordinate of the plane intersection, R _H is the intersection radius corresponding to the plane intersection, L _H0 and L _H1 are the lengths of the front and rear spirals corresponding to the plane intersection; K _V and H _V are The mileage and elevation of the changing slope point, R _V is the radius of the vertical curve corresponding to the changing slope point; KB _S and KB _E are the starting and ending mileage corresponding to the bridge, KU _S and KU _E are the corresponding starting mileage and Termination mileage; I represents the intersection of the optimized route scheme and the new railway, S represents the initial connection point of the optimized route scheme on the existing road, and E represents the termination connection point of the optimized route scheme on the existing road;

In step S1, the optimization function is the minimum engineering cost, which includes: earthwork engineering cost, road surface cost, right-of-way cost, bridge engineering cost and tunnel engineering cost, which are expressed as follows:

F(HPI's,VPI's,MBU's,VCC's)=min(C _E +C _P +C _R +C _B +C _U )

In the formula, F(HPI's, VPI's, MBU's, VCC's) is the engineering cost based on the decision variables, C _E , C _P , C _R , C _B , C _U are the earthwork cost, pavement cost, road right fee, bridge Engineering fee, tunnel engineering fee;

The constraints in the optimization model include: geometric line constraints, intersection constraints, connection constraints and existing structural constraints.

Optionally, in step S2, in the process of dividing the research area into a unit grid, the calculation formula of the width width of the unit grid is as follows:

width=2T _min +J _min

J _min represents the minimum clip straight line length, and T _min represents the minimum tangent length at the maximum turning angle, which is calculated by the following formula:

In the formula, α _max is the maximum allowable turning angle of the line design, and R _min is the minimum curve radius of the road.

In a second aspect, the present invention provides a system of a method for laying interchanges based on automatically changing existing roads along a railway line, comprising:

The optimization model building module is used to construct the optimization model of the road and railway overpass, the optimization module includes: decision variables, constraints and optimization functions in the research area;

The meshing module is used to mesh the study area with cells;

The search section division module is used to determine the starting and ending cell sets S, E and the cell sets L and R on the left and right sides of the railway in the study area for the connection between the local reconstruction road and the existing road in the study area according to the existing roads and railways, and then search the path. Decomposed into two non-intersecting segment searches and cross segment searches;

Among them, the path between the cell set S and the cell set L on the same side of the railway and the path between the cell set E and the cell set R on the same side are the two non-intersecting section paths; the cell set L The path with the cell set R is an intersection path;

a non-intersecting segment path search module, configured to perform a path search on two non-intersecting segments based on the optimization model;

Cross segment path search module, which is used to perform path search on the cross segment;

Wherein, performing a line search on the two non-intersecting segments to obtain M paths between the cell set S and the cell set L and N paths between the cell set E and the cell set R; and The path search is performed at the intersection to obtain P partial lines of the intersection;

Search with the optimization function in the optimization model as the objective function, and the route obtained by the search satisfies the constraints in the optimization model;

The optimal line acquisition module uses the N paths, the M paths and the P cross-section local lines in step S3 for splicing and integration to form several complete lines, and the optimization function is used as the objective function to determine optimal route;

The final route plan obtaining module is used for obtaining the final route plan by performing curve fitting on the plane and vertical section based on the optimal route plan.

In a third aspect, the present invention also provides a terminal, comprising a processor and a memory, the memory stores a computer program, and the processor invokes the computer program to execute: an interchange for automatically changing an existing road along a railway line The steps of the channel routing method.

In a fourth aspect, the present invention also provides a readable storage medium storing a computer program, the computer program being invoked by the processor to execute: the steps of a method for laying interchanges for automatically relocating existing roads along a railway line.

beneficial effect

In the design process of the road and railway overpass, the starting and ending points of the reconstructed roads and the intersections with the newly built railways are not fixed, the starting and ending points of the lines are subject to connection constraints, the intersections are subject to cross constraints, and the lines are subject to a large number of geometric and linear constraints and both In the case of structural constraints, the layout method provided by the present invention proposes an optimization model of road and railway overpass lines, and the optimization model includes information of geographic information, optimization functions, constraints and decision variables. Based on the optimization model, a hierarchical optimization strategy is introduced, which divides the generation of the reconstruction segment into two stages: the generation of the intersection point and the generation of the start and end points of the reconstruction road. The two-stage reorganization with the non-intersection section makes the generation of connection points and intersections independent of each other, weakens the coupling interaction between the two, simplifies the optimization problem, and realizes the intelligent optimization of road and railway interchanges.

The scheme generated by the layout method of the present invention is further compared with the experienced manual design scheme, and the results show that the method can effectively improve the cost of local reconstruction road projects under the condition of satisfying all the constraints, and has a high degree of automation, High efficiency and strong practicability.

Description of drawings

FIG. 1 is a schematic diagram of a research area provided by an embodiment of the present invention.

FIG. 2 is an optimization model of a road and railway overpass route scheme provided by an embodiment of the present invention.

FIG. 3 is a geometric schematic diagram of a cell width provided by an embodiment of the present invention.

FIG. 4 is a schematic diagram of a set of S, L, R, and E cells provided by an embodiment of the present invention.

FIG. 5 is a schematic diagram of a neighborhood template and a bidirectional scanning direction in a DT algorithm provided by an embodiment of the present invention.

6 is a schematic diagram of the connection of local lines in the reconstructed roads S→L and E→R provided by an embodiment of the present invention (1: P does not belong to S; 2: P belongs to S).

FIG. 7 is an optimized path tree generated from S→L segments provided by an embodiment of the present invention.

FIG. 8 is a schematic diagram of a local line connection in the L→R section of a reconstructed road provided by an embodiment of the present invention.

FIG. 9 is a schematic diagram of a chord cut support distance method provided by an embodiment of the present invention.

FIG. 10 is a schematic flowchart of the layout method provided in Embodiment 1 of the present invention.

Detailed ways

The present invention provides a method, a system, a terminal and a readable storage medium for laying an overpass for automatically relocating an existing road along a railway line. Based on the communication ability, the design problem of local road reconstruction is realized, and the optimal reconstruction scheme is found. The present invention will be further described below with reference to the embodiments.

Example 1:

As shown in FIG. 10 , the present invention provides a method for laying an overpass for automatically relocating an existing road along a railway line, comprising the following steps:

S ₁ : Establish an optimization model of road and railway overpass lines.

Among them, a comprehensive geographic information model of the study area is established, and the plane intersection information of the road (including the intersection coordinates, radius, and length of the front and rear transition curves), the position of the vertical section slope change point (including mileage, elevation and vertical curve radius), bridges and tunnels are introduced. The location (including mileage and elevation) and the intersection of the rebuilt road and the new railway, and the two connection points with the existing road are used as decision variables. Taking finding the lowest comprehensive cost as the optimization function (objective function), and considering various constraints (including linear geometric constraints, intersection constraints, connection constraints and existing structural constraints), an optimization model of road and railway overpass routes is established.

For the research area, in the present embodiment 1, at a distance of X meters from the intersection position I of the existing road and the new railway, (this distance is generally determined by the designer based on experience, since the distance between the road and the railway overpass is less than 1km, this paper Take this distance as 500m.) Identify points A and B along the existing road to construct an elliptical area. Take two points A and B as the ellipse focus, and the expansion line coefficient γ=2i _max /i _D is the eccentricity, where i _D is the average slope of the ground elevation, and i _max is the maximum restricted slope of the road. Take the study area as the circumscribed quadrilateral of the ellipse, subdivide the study area into cell squares with a width of w, and obtain information including topography (elevation), geology (geotechnical characteristics), unit cost (unit price information of the project), existing buildings ( Comprehensive geographic information model (CGIM) of geographic information such as factories, roads, railways, residential areas).

It should be noted that the average gradient i _D of the above ground elevation and the maximum restricted gradient i _max of the road are parameters for the entire existing road in the study area. In other feasible embodiments, the scope of the study area may be determined by selecting other rules according to the location of the existing road and the newly built railway, which is not specifically limited in the present invention.

By determining the decision variable, the mathematical expression of the objective function is established, wherein, the decision variable selected in this embodiment 1 is expressed as:

Plane intersection HIP's(X _H ,Y _H ,R _H ,L _H0 ,L _H1 );

Vertical section slope point VIP's (K _V ,H _V ,R _V );

Bridges and tunnels MBU's (KB _S ,KB _E ,KU _S ,KU _E ) in the reconstruction section;

The start and end points of the reconstruction segment and the intersection point VCC's(I,S,E).

In HIP: X _H and Y _H are the abscissa and ordinate of the plane intersection, R _H is the intersection radius corresponding to the plane intersection, and L _H0 and L _H1 are the lengths of the front and rear transition curves corresponding to the plane intersection.

In VIP: K _V and H _V are the mileage and elevation of the variable slope point, and R _V is the radius of the vertical curve corresponding to the variable slope point.

In MBU: KB _S and KB _E are the starting mileage and ending mileage corresponding to the bridge, and KU _S and KU _E are the starting mileage and ending mileage corresponding to the tunnel.

In VCC: I represents the intersection of the optimized route scheme and the new railway, S represents the initial connection point of the optimized route scheme on the existing road, and E represents the termination connection point of the optimized route scheme on the existing road.

The objective function selected in Embodiment 1 is the lowest engineering cost, which includes earthwork engineering cost, road surface cost, right-of-way cost, bridge engineering cost, and tunnel engineering cost. That is, the objective function with the lowest comprehensive cost is constructed by the engineering cost, and the specific expression is as follows:

F(HPI's,VPI's,MBU's,VCC's)=min(C _E +C _P +C _R +C _B +C _U )

Among them, the earthwork cost C _E is calculated by formula (1):

为挖方的单位成本，

为填方的单位成本，

为将土方运至填埋场的单位成本，

为从借土坑运土方的单位成本，

为挖方量，

为填方量，当计算将土方运至填埋场的全部费用时，

为运至填埋场的土石方总体积，当计算从借土坑运土方的全部费用时，

为从借土坑运借土的土石方总体积。In the formula,

is the unit cost of excavation,

is the unit cost of filling,

For the unit cost of moving earth to the landfill,

is the unit cost of moving earth from the borrow pit,

is the excavation amount,

is the fill volume, when calculating the full cost of transporting the earth to the landfill,

For the total volume of earth and stone transported to the landfill, when calculating the full cost of transporting earth from the borrow pit,

It is the total volume of earth and stone for borrowing soil from the borrowing pit.

Pavement cost C _P , calculated by formula (2):

C _{P =} up WL (2)

In the formula, up is the unit cost of the _pavement , W is the width of the pavement, and L is the length of the pavement.

The right-of-way toll C _R is calculated by formula (3):

C _R =n×w ² ×u _R (3)

In the formula, n is the total number of cells occupied by the optimized route (reconstruction section) in the geographic information model, w ² is the area of the cell, and u _R is the unit cost of the right of way.

The bridge engineering cost C _B is calculated by formula (4):

where u _Bi is the unit length cost of the ith bridge, L _Bi is the bridge length of the ith bridge, Ni is the total number of piers of the _ith bridge, C _Ai is the unit cost of the piers, and z is the number of bridges .

The tunnel engineering cost C _U is calculated by formula (5):

In the formula, u _Ui is the unit length cost of the ith tunnel, L _Ui is the length of the ith tunnel, and h is the number of tunnels.

It should be noted that, in this embodiment 1, considering the cost issue, the objective function is to take the lowest comprehensive cost of reconstruction as the objective function. In other feasible embodiments, the objective function may also be set from other cost perspectives or consider more factors to form the comprehensive cost. , which can be adaptively adjusted according to actual needs, which is not specifically limited in the present invention.

Constraints: By consulting the "Code for Road Alignment Design" (JTGD20-2017), determine the geometric alignment constraints, intersection constraints, connection constraints and existing structural constraints.

(1): geometric line constraints

具体约束表示如下：1) Note that the search path has n1 intersection points in the plane (in this embodiment, the search path obtained by using the DT algorithm is a polyline, and the turning point of the polyline is regarded as the intersection), and the plane curve radius, the length of the clip line and the steering angle corresponding to the intersection point are respectively is R _i , R _i ,

The specific constraints are expressed as follows:

分别为线路平面允许的最大曲线半径、线路平面允许的最小夹直线长、线路平面允许的最小转向角。R _min , L _min ,

They are the maximum curve radius allowed on the line plane, the minimum clip line length allowed on the line plane, and the minimum steering angle allowed on the line plane.

具体约束表示如下：2) Note that the search path has n2 slope-changing points in the longitudinal section, and the vertical curve radius, slope and slope length corresponding to the slope-changing points are:

The specific constraints are expressed as follows:

分别为线路纵断面允许的最小竖曲线半径、线路纵断面允许的最大坡度、线路纵断面所允许的最小坡长、线路纵断面所允许的最大坡长。

They are the minimum vertical curve radius allowed for the line profile, the maximum slope allowed for the line profile, the minimum slope length allowed for the line profile, and the maximum slope length allowed for the line profile.

(2): Cross constraint

1) When the road intersects with the newly built railway, the formed intersection angle A _C should not exceed the specified minimum intersection angle A _min , and the road and the railway should be orthogonal as far as possible:

A _min ≤A _C ≤90° (8)

2) The clearance of the intersection should ensure that there is enough distance between the road and the railway to ensure the safety of vehicles:

Vh≤|H _road -H _rail | (9)

3) When the road is at the intersection, the road must be kept straight to ensure good visibility when driving.

(3): Connection constraints:

When a partially reconstructed road is connected to an existing road, in order to ensure the smooth connection of the reconstructed road, it is necessary to ensure that the starting and ending points of the reconstructed road are located on the existing road, and the azimuth angle of the connecting point between the reconstructed road and the existing road should be consistent:

α _exist = α _cross (10)

α _exist and α _cross are the line azimuth of the existing road at the intersection of the existing road and the reconstructed road, and the line azimuth of the reconstructed road at the same position (ie, the starting point of the reconstructed road).

(4): Existing Structural Constraints Existing structural constraints can be divided into two types: the restricted area of existing buildings determined in the comprehensive geographic information model and the structural requirements of new buildings.

1) Forbidden areas: The search path should avoid interfering with existing structures in the Comprehensive Geographic Model (CGIM). Take the set UF as all the cells representing the forbidden area in the _CGIM ; set _UC as the cells occupied by the search path. To ensure that the search path avoids the forbidden area, the intersection of _UF and _UC must be empty:

2) Structural requirements: The height and length of the bridge and the aperture of the tunnel should meet the requirements in the "Code for Road Alignment Design" (JTGD20-2017).

S ₂ : Divide the study area into cell grids, and then determine the connection start and end cell sets S and E of the connection between the partially reconstructed roads and the existing roads in the study area and the cell sets L and L on the left and right sides of the railway according to the existing roads and railways. R, and then decompose the path search into two non-intersecting segment searches and intersecting segment searches. _In this embodiment, the specific execution process of step S2 is as follows:

S _2-1 : Obtain a rectangular study area.

S _2-2 : Divide the rectangular research area into cells with a width of width to ensure that the minimum clip line length and tangent length between the two cells are met. The formula for calculating width is as follows:

width=2T _min +J _min (12)

J _min represents the minimum clip straight line length, and T _min represents the minimum tangent length at the maximum turning angle, which is calculated by the following formula:

In the formula, α _max is the maximum allowable turning angle of the line design, generally taking 70° to 90°, and R _min is the minimum curve radius of the road.

S _2-3 : Define the start and end cell sets S and E of the connection between the partially reconstructed road and the existing road, and the cell sets L and R on the left and right sides of the railway. Among them, taking the newly built railway as the baseline, in order to increase the diversity of line schemes, the cells in the first column and the second column on the left side of the forward direction of the newly built railway are taken as the set of cells on the left near the railway L[L ₁ ,L ₂ ,. ..,L _p ], in the same way, take the cells in the first column and the second column on the right side of the new railway's forward direction, which is the set of cells on the right near the railway R[R ₁ ,R ₂ ,...,R _q ] . Taking the new railway line as the boundary, the set of cells that cross the existing road on the same side as L[L ₁ ,L ₂ ,...,L _p ] is taken as the connection starting point set S of the local reconstruction road and the existing road [S ₁ ,S ₂ ,...,S _m ], the set of cells that cross the existing road on the same side as R[R ₁ ,R ₂ ,...,R _q ] is taken as the local reconstruction road and the The set E[E ₁ ,E ₂ ,...,E _n ] of connecting end points of existing roads. In other feasible embodiments, the range of the set of cells can be adaptively adjusted based on the location of the existing road and the newly built railway, which is not specifically limited in the present invention, such as expanding the range of cells along the existing road or Expand the range of cells along the newly built railway.

S ₃ : Use the DT algorithm to perform line search on two non-intersecting segments. In this embodiment, the DT algorithm is used to search the path between the cell set S and the cell set L and the path between the cell set E and the cell set R on the same side. In other feasible embodiments, On the basis of the objective function and satisfying the constraints, the method of obtaining the path of two non-intersecting segments can also be applied to the present invention.

For the path search process between cell set S and cell set L:

A-1: Take the cell in the cell set L as the fixed endpoint, and select a cell Li in the cell set L[L ₁ ,L ₂ ,...,L _{p ]} as the fixed point of the S→L segment path endpoint, p is the total number of cells in the cell set L;

A-2: Take the cell _Li as the fixed endpoint and use the DT algorithm to search to obtain the DT value of each cell in the cell set S. If the cell DT value is not positive infinity, keep the cell and form a line Partial path; otherwise, discard a partial path corresponding to the cell, wherein several feasible paths are obtained by taking the cell _Li as the fixed endpoint;

A-3: Select the path with the smallest DT value from the several feasible paths as the path from the cell set S with the cell Li as the fixed _endpoint to the cell set L;

A-4: Traverse the cells in the cell set L, and repeat steps A-1-A-3, and finally obtain M paths between the cell set S and the cell set L, M≤p.

Similarly, for the path search between the cell set E and the cell set R, N paths between the cell set E and the cell set R are obtained, N≤q, q is the total number of cells in the cell set L number.

Among them, the basic principles of the DT algorithm mentioned above are as follows:

Distance transformation is a common transformation in imagery, which converts an image into a grayscale image (distance map), in which the grayscale value of each cell is the distance from the cell to its Minimum distance to the closest background point (target point). The neighborhood template is composed of n × n cells, which can be represented by a matrix M _d , where the value of each element d (r→c) represents the center of the template and the pixels in its neighborhood (the rth row, the th the local distance between cells in column c). In the route design, the engineering cost is extended into a generalized distance. Use the neighborhood template to scan the study area in both forward and reverse directions.

Based on the principle of the above-mentioned DT algorithm, the process that the present invention utilizes the DT algorithm to search is as follows:

S _3-2-1 : Preprocess the study area on the left side of the newly built railway line.

The cells in the study area should be highly accessible, and a cell L _i in the cell set L[L ₁ ,L ₂ ,...,L _p ] is used as a fixed endpoint to perform a path search on the cell set S The process of , as an example, that is, the distance from any cell in the study area to the fixed endpoint Li must ensure that the height difference between the two cells can be overcome under the condition of using the maximum limit slope i _max and the allowable spread coefficient γ, that is, it satisfies:

where R(R _x , R _y , R _z ) represents the coordinates of any cell in the study area on the left, and LR(LR _x , LR _y , LR _z ) represents the selected fixed endpoint Li in the unit L _near the railway Coordinates, i _max represents the maximum restricted slope of the road, and γ represents the extension coefficient. If there is a cell that fails the high accessibility check, i.e. it conflicts with Formula 14, the cell is marked as a forbidden cell, and the scanning of this forbidden area will be ignored during the bidirectional scanning process described in S _3-3-2 .

S _3-2-2 : Initialize the generalized distance value V _DT of all cells in the study area on the left, as shown in formula 15, that is, the fixed endpoint is 0, and other pixels are positive infinity. Generalized distance means extending the concept of distance in distance change to comprehensive cost, that is, the comprehensive cost determined by (C _E +C _P +C _R +C _B +C _U ), the number of which represents the generalized distance value, D represents the free unit, Its generalized distance value is positive infinity, _LR represents a fixed endpoint, and its generalized distance value is zero, as shown in the following formula (15):

S _3-2-3 : Starting from the upper left corner of the image, scan the non-prohibited cells in the order of forward (from top to bottom, left to right), and then reverse (from bottom to top, right to left). For all pixels, update the generalized distance value of the pixel point to form a generalized distance map.

Among them, the construction process of the generalized distance map includes the following steps:

S _3-2-3-1 : Build a 5×5 neighborhood template. When scanning each cell, place the center of the neighborhood template on the cell. When any cell P is scanned, take P as the starting point of the local line, and make the center of the neighborhood template coincide with P. Take any cell G in the neighborhood template as the end point of the local line.

S _3-2-3-2 : In order to ensure that the local line PG satisfies the connection constraint regulations described in step S _1-4 , when the local line is connected, the connection type between P and G is based on whether G belongs to S[S ₁ , S ₂ , ...,S _m ] are divided into two cases to be dealt with respectively, see Figure 6 for details:

(1): When cell G does not belong to S[S ₁ ,S ₂ ,...,S _m ], use a straight line to connect cell G and P.

(2): When cell G belongs to S[S ₁ ,S ₂ ,...,S _m ], use curve smooth connection, where a is the forward path direction connected to the target cell, and b is the existing path of cell G road direction. At the same time, it is necessary to satisfy the geometrical line constraint regulations described in step _S1-4 .

S _3-2-3-3 : After connecting the cells P and G, a plane longitudinal plane will be formed, that is, the bridge tunnel and roadbed will be set according to the local path, and it will be judged whether the local line meets the corner constraint and self-intersection required by the plane line. Constraints, avoidance constraints and must pass constraints; limited slope constraints, elevation accessibility constraints, elevation trend constraints and spanning constraints required by longitudinal section lines; bridge height constraints, bridge length constraints and clearance constraints; tunnel excavation according to the line Whether the tunnel generated by the automatic layout of the deep demarcation standard meets the constraints of tunnel length and geological conditions; whether the subgrade section meets the requirements of slope aspect and geological conditions. If it is not satisfied, the local route scheme is eliminated, and the process goes to step _S3-2-4-1 , and the cells in the neighborhood template are reselected.

S _3-2-3-4 : satisfy the various constraints described in step S _3-2-3-3 , calculate the generalized distance value between P and G, if it satisfies:

V _DT (G)+d(P→G)＜V _DT (P) (16)

Then update the generalized distance value V _DT (P) of the current cell P, so that V _DT (P)=V _DT (G)+d(P→G), and record the row and column difference between P and G (ΔR,ΔC ) to propagate the distance value from cell P to cell G. Otherwise, do not update and go to step _S3-2-3-1 , and reselect the cells in the neighborhood template.

_S3-2-3-5 : Iteratively execute the scanning process described in _S3-2-3-1 , _S3-2-3-2 , _S3-2-3-3 , _S3-2-3-4 , until the generalized distance value of each cell no longer changes, forming the final distance map.

S _3-2-4 : Through the generalized distance map formed in step S _3-2-3 , find the DT values of all cells in the S set, and take the cell (Si) with the smallest DT value as the target point of Li At the end point of the shortest path, the shortest path scheme from the cell Si to the target point is recursively derived through ΔR and ΔC. The recursion process is the prior art, and the center position of each neighborhood template obtained by recursion is within the neighborhood template. local optimal route.

S ₄ : Iteratively searches to obtain the local line of the intersection. In this embodiment 1, the non-intersecting segment search is performed first, and then the intersecting segment search is performed. In other feasible embodiments, the intersecting segment search is performed first, and then the non-intersecting segment search is performed, or both are performed concurrently and synchronously.

_The step S4 specifically includes the following steps:

_S4-1 : As shown in FIG. 8, take the i-th (i=1, 2, 3..., M) cell Li in L, draw a vertical line to the new railway from the center of the cell, and use the The vertical line is the angle bisector, and the vertical lines are rotated to the left and right by 90°-A _min respectively, which are called straight line a and straight line b. cross angle). The sector area formed by the straight lines a and b contains Q cells in R[R ₁ , R ₂ ,...,R _q ], and the Q cells are called a sector-shaped feasible solution cell set.

S _4-2 : Connect Li and any cell Rj in the fan-shaped feasible solution cell set with a straight line to form a local circuit. Li and the Q cells are respectively connected to form Q local lines whose intersections must satisfy the intersection angle constraint.

_S4-3 : Detect the vertical clearance constraint at the intersection point I of the local line of the intersection, see the cross constraint regulation in the constraint conditions for details, if the detection fails, the local scheme of the intersection is rejected, and go to _S4-6 . Step _S4-4 is performed in sequence if the detection is passed.

S _4-4 : Use the same steps as S _3-2-3-3 to generate detailed plan, longitudinal section, bridge and tunnel route scheme between _{Li and R j .}

S _4-5 : Repeat steps S _4-2 , S _4-3 , S _4-4 , and S _4-5 , iterate the connection between Li and the cells in the fan-shaped feasible solution cell set, and finally generate no more than Q pieces Cross section local route scheme.

S _4-6 : iteratively search all cells in L, repeat steps S _4-1 , S _4-2 , S _4-3 , S _4-4 , S _4-5 to generate no more than M*Q intersections Section local line scheme, that is, H cross section local lines.

S5: Integrate the generated two non _- intersecting sections and intersecting sections to form several complete lines, and use the optimization function as an objective function to determine an optimal line, and perform plane and vertical section curves based on the optimal line Fitting to get the final route scheme. Specifically include the following steps:

_S5-1 : Combine the S→L segment path corresponding to the cell Li and the E→R segment path corresponding to the cell Rj in the local scheme of each intersection to form a complete line, and refresh the line S→L→R→E The generalized distance value of , recorded as D _{i,j ,} is added to the distance set U _Di,j .

S _5-2 : Repeat step S _5-1 until the M*Q cross-section local line schemes obtain the corresponding generalized distance value of the complete local line.

S _5-3 : Sort the generalized distance value set U _Di,j according to the generalized distance value from small to large, and the minimum generalized distance scheme min(D _i,j ) is the optimal scheme with the lowest comprehensive cost, indicating the ith in L The cell is connected to the jth cell in R, and the S→L and E→R segment paths are combined to form the optimal route.

S _5-4 : According to the optimal route obtained in step S _5-3 , the feature points in the path are selected as plane intersection points by using the chord tangent distance method, and the minimum radius of the circular curve and the length of the transition curve are matched according to the specification to generate an initial flat line.

S _5-5 : On the basis of the initial route plan on the plane, initialize the longitudinal section plan according to the minimum amount of earthwork.

S _5-6 : On the basis of the road and railway overpass route scheme optimization model, the nonlinear mesh adaptive direct search algorithm (Nonlinear optimization with the mesh adaptive direct search, NOMAD) is used to carry out the overall optimization of the route, and the final result is obtained. The plan and profile optimization route scheme.

As shown in FIG. 9 , the final circuit scheme is obtained. Since the implementation process of the above steps is in the prior art, it will not be described in detail.

To sum up, the layout method provided in Embodiment 1 of the present invention divides the path search problem into path searches of intersecting segments and non-intersecting segments, so that the generation of connection points and intersecting points are independent of each other, weaken the coupling interaction between the two, and simplify the Optimization problem, realize intelligent optimization of interchange between roads and railways.

Example 2:

On the basis of Embodiment 1, Embodiment 2 of the present invention provides a system of a method for laying interchanges based on automatic relocation of existing roads along a railway line, including:

The optimization model building module is used to construct the optimization model of the road and railway overpass, the optimization module includes: decision variables, constraints and optimization functions in the research area;

The meshing module is used to mesh the study area with cells;

The search section division module is used to determine the starting and ending cell sets S, E and the cell sets L and R on the left and right sides of the railway in the study area for the connection between the local reconstruction road and the existing road in the study area according to the existing roads and railways, and then search the path. Decomposed into two non-intersecting segment searches and cross segment searches;

Among them, the path between the cell set S and the cell set L on the same side of the railway and the path between the cell set E and the cell set R on the same side are the two non-intersecting section paths; the cell set L The path with the cell set R is an intersection path;

a non-intersecting segment path search module, configured to perform a path search on two non-intersecting segments based on the optimization model;

Cross segment path search module, which is used to perform path search on the cross segment;

Wherein, performing a line search on the two non-intersecting segments to obtain M paths between the cell set S and the cell set L and N paths between the cell set E and the cell set R; and The path search is performed at the intersection to obtain H local lines of the intersection;

Search with the optimization function in the optimization model as the objective function, and the route obtained by the search satisfies the constraints in the optimization model;

The optimal line acquisition module uses the N paths, the M paths and the H cross-section local lines in step S3 for splicing and integration to form several complete lines, and the optimization function is used as the objective function to determine optimal route;

The final route plan obtaining module is used for obtaining the final route plan by performing curve fitting on the plane and vertical section based on the optimal route plan.

Wherein, the implementation process of each module is described with reference to the foregoing method, and details are not repeated here. It should be understood that the specific implementation process of the above-mentioned unit modules refers to the content of the method, and the present invention will not go into details here, and the division of the above-mentioned functional module units is only a division of logical functions, and there may be other division methods in actual implementation. For example, multiple units or components may be combined or may be integrated into another system, or some features may be omitted, or not implemented. Meanwhile, the above-mentioned integrated units may be implemented in the form of hardware, and may also be implemented in the form of software functional units.

Example 3:

On the basis of Embodiment 1, an embodiment of the present invention provides a terminal, which includes a processor and a memory, the memory stores a computer program, and the processor invokes the computer program to execute:

S ₁ : Establish an optimization model of road and railway overpass lines. S ₂ : Divide the study area into cell grids, and then determine the connection start and end cell sets S and E of the connection between the partially reconstructed roads and the existing roads in the study area and the cell sets L and L on the left and right sides of the railway according to the existing roads and railways. R, and then decompose the path search into two non-intersecting segment searches and intersecting segment searches. S ₃ : Use the DT algorithm to perform line search on two non-intersecting segments. S ₄ : Iteratively searches to obtain the local line of the intersection. S5: Integrate the generated two non _- intersecting sections and intersecting sections to form several complete lines, and use the optimization function as an objective function to determine an optimal line, and perform plane and vertical section curves based on the optimal line Fitting to get the final route scheme.

For the implementation process of each step, please refer to the specific implementation process of the foregoing method, which will not be repeated here.

Example 4:

On the basis of Embodiment 1, the embodiment of the present invention provides a readable storage medium, which stores a computer program, and the computer program is invoked by a processor to execute: S ₁ : Establish an optimization model of road and railway interchange. S ₂ : Divide the study area into cell grids, and then determine the connection start and end cell sets S and E of the connection between the partially reconstructed roads and the existing roads in the study area and the cell sets L and L on the left and right sides of the railway according to the existing roads and railways. R, and then decompose the path search into two non-intersecting segment searches and intersecting segment searches. S ₃ : Use the DT algorithm to perform line search on two non-intersecting segments. S ₄ : Iteratively searches to obtain the local line of the intersection. S5: Integrate the generated two non _- intersecting sections and intersecting sections to form several complete lines, and use the optimization function as an objective function to determine an optimal line, and perform plane and vertical section curves based on the optimal line Fitting to get the final route scheme.

For the implementation process of each step, please refer to the specific implementation process of the foregoing method, which will not be repeated here.

It should be understood that, in this embodiment of the present invention, the processor may be a central processing unit (Central Processing Unit, CPU), and the processor may also be other general-purpose processors, digital signal processors (Digital Signal Processor, DSP), dedicated integrated Circuit (Application Specific Integrated Circuit, ASIC), off-the-shelf Programmable Gate Array (Field-Programmable Gate Array, FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, and the like. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like. The memory, which may include read-only memory and random access memory, provides instructions and data to the processor. A portion of the memory may also include non-volatile random access memory. For example, the memory may also store device type information.

The readable storage medium is a computer-readable storage medium, which may be an internal storage unit of the controller described in any of the foregoing embodiments, such as a hard disk or a memory of the controller. The readable storage medium may also be an external storage device of the controller, such as a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) equipped on the controller card, flash card (Flash Card) and so on. Further, the readable storage medium may also include both an internal storage unit of the controller and an external storage device. The readable storage medium is used to store the computer program and other programs and data required by the controller. The readable storage medium can also be used to temporarily store data that has been output or is to be output.

It should be emphasized that the examples described in the present invention are illustrative rather than restrictive, so the present invention is not limited to the examples described in the specific implementation manner, and all the examples obtained by those skilled in the art according to the technical solutions of the present invention Other embodiments that do not depart from the spirit and scope of the present invention, whether modified or replaced, also belong to the protection scope of the present invention.

Claims (8)

1. an overpass laying method for automatically changing existing roads along a railway line, is characterized in that: comprise the steps: Step S1: constructing an optimization model of the road and railway overpass line, the optimization model includes: decision variables, constraints and optimization functions in the research area; Step S2: Divide the study area into cell grids, and then determine the connection start and end cell sets S, E and the cell sets L and L on the left and right sides of the railway in the study area according to the existing roads and railways. R, and then decompose the path search into two non-intersecting segment searches and cross segment searches; Among them, the path between cell set S and cell set L on the same side of the railway and the path between cell set E and cell set R on the same side are two non-intersecting section paths; cell set L and cell set The path between lattice sets R is a cross-segment path; Step S3: performing path search on two non-intersecting sections and intersecting sections based on the optimization model; Wherein, performing a line search on the two non-intersecting segments to obtain M paths between the cell set S and the cell set L and N paths between the cell set E and the cell set R; and The path search is performed at the intersection to obtain H local lines of the intersection; Search with the optimization function in the optimization model as the objective function, and the route obtained by the search satisfies the constraints in the optimization model; Step S4: Using the N paths, the M paths, and the H cross-section local lines in step S3 to splicing and integrating to form several complete lines, and using the optimization function as the objective function to determine the optimal line , and perform the curve fitting of the plane and the longitudinal section based on the optimal route to obtain the final route scheme; In step S2, the research area is divided into unit grids, and the calculation formula of the width width of the unit grid is as follows: width=2T _min +J _min J _min represents the minimum clip straight line length, and T _min represents the minimum tangent length at the maximum turning angle, which is calculated by the following formula:

In the formula, α _max is the maximum allowable turning angle of the line design, and R _min is the minimum curve radius of the road; In step S3, the process of performing a path search on the intersection section to obtain H local lines of the intersection section is: iteratively search for the local lines between the cell set L and the cell set R, and retain the local lines that meet the constraints, as follows. : Taking any cell Li in the cell set L as the center, draw a vertical line to the railway and take the vertical line as the angle bisector, and rotate the vertical lines to the left and right by 90°-A _min to obtain the straight line a and Straight line b, Amin is the minimum intersection angle that meets the requirements of the specification; In the cell set R, the Q cells contained in the fan-shaped area formed by the straight line a and the straight line b are the fan-shaped feasible solution cell set; Connect the cell Li and the Q cells in the fan-shaped feasible solution cell set by straight lines respectively to form Q local lines; Detecting whether the Q local lines meet the constraint conditions, if not, eliminating them; if so, generating the plane, longitudinal section, bridge and tunnel line schemes of the local lines; Iteratively searches all cells in the cell set L according to the above process, and generates a local route scheme of H intersections. 2. method according to claim 1, is characterized in that: in step S3, the process of carrying out path search to two non-intersecting segments based on described optimization model adopts DT algorithm, and defines cell in DT algorithm with described optimization function The generalized distance value of , the realization process is as follows: For the path search process between cell set S and cell set L: A-1: Take the cell in the cell set L as the fixed endpoint, and select a cell Li in the cell set L[L ₁ ,L ₂ ,...,L _{p ]} as the fixed point of the S→L segment path endpoint, p is the total number of cells in the cell set L; A-2: Take the cell _Li as the fixed endpoint and use the DT algorithm to search to obtain the DT value of each cell in the cell set S. If the cell DT value is not positive infinity, keep the cell and form a line Partial path; otherwise, discard a partial path corresponding to the cell, wherein several feasible paths are obtained by taking the cell _Li as the fixed endpoint; A-3: Select the path with the smallest DT value from the several feasible paths as the path from the cell set S with the cell Li as the fixed _endpoint to the cell set L; A-4: Traverse the cells in the cell set L, and repeat steps A-1-A-3, and finally obtain M paths between the cell set S and the cell set L; For the path search process between cell set E and cell set R: B-1: Take a cell in the cell set R as a fixed endpoint, and select a cell R _i in the cell set R[R ₁ ,R ₂ ,...,R _q ] as the fixed point of the E→R segment path endpoint, q is the total number of cells in the cell set R; B-2: Use the DT algorithm to search with the cell R _i as the fixed endpoint to obtain the DT value of each cell in the cell set E. If the cell DT value is not positive infinity, keep the cell and form a line Partial path; otherwise, discard a partial path corresponding to the cell, wherein several feasible paths are obtained by taking the cell R _i as the fixed endpoint; B-3: Select the path with the smallest DT value from the several feasible paths as the path from the cell set E to the cell set R with the cell R _i as the fixed endpoint; B-4: Traverse the cells in the cell set R, and repeat steps B-1-B-3, and finally obtain N paths between the cell set E and the cell set R. 3. method according to claim 2 is characterized in that: in step A-2, take cell L _i as fixed endpoint and adopt DT algorithm to search and obtain the process of the DT value of each cell in cell set S is: A-2-1: Preprocess the search area, where the distance from any cell in the search area to the fixed endpoint Li satisfies the following formula under the condition of using the maximum restricted slope _{i max} and the allowable spread coefficient γ:

Among them, R(R _x , R _y , R _z ) represents the xyz coordinates of any cell in the search area, LR(LR _x , LR _y , LR _z ) represents the xyz coordinates of the fixed endpoint Li, and _{i max} represents the maximum road The limit slope, γ represents the spread coefficient; If it is not satisfied, mark the cell in the search area as a forbidden cell, and do not participate in the two-way scanning process; A-2-2: Initialize the generalized distance value of all non-prohibited cells in the search area; A-2-3: Scan the search area in both directions, and update the generalized distance value of each cell to form a generalized distance map, where the generalized distance value of each cell in the generalized distance map is the DT value of the cell. 4. method according to claim 3 is characterized in that: the process of building described generalized distance map in step A-2-3 is as follows: A-2-3-1: Build a neighborhood template, when scanning each cell, place the center position of the neighborhood target on the cell; Among them, when any cell P is scanned, P is taken as the starting point of the local line, and any cell G in the neighborhood template is taken as the end point of the local line; A-2-3-2: When cell G does not belong to S[S ₁ ,S ₂ ,...,S _m ] in the search area, use a straight line to connect cell G and P; or when cell G When it belongs to S[S ₁ ,S ₂ ,...,S _m ], use curve smooth connection, where a is the forward path direction connected to the target cell, b is the existing road direction of cell G, and m is the cell the number of cells in the set S; A-2-3-3: Connect cells P and G to obtain a local circuit and form a local circuit scheme with structures, and judge whether the local path scheme satisfies the constraints, if not, discard the local circuit, and transfer to Go to step A-2-3-1, reselect the cells in the field template; if satisfied, go to step A-2-3-4; Step A-2-3-4: Calculate the generalized distance value between cell P and cell G, and update the generalized distance value of cell P; Step A-2-3-5: Iteratively execute the scanning process described in A _2-3-1 , A _2-3-2 , A _2-3-3 , and A _2-3-4 until the generalization of each cell is reached The distance value no longer changes to get a generalized distance map. 5. The method according to claim 1, wherein: the decision variable in the research area in step S1 is expressed as: Plane intersection HIP's(X _H ,Y _H ,R _H ,L _H0 ,L _H1 ); Vertical section slope point VIP's (K _V ,H _V ,R _V ); Reconstruction of bridges and tunnels on the line MBU's (KB _S ,KB _E ,KU _S ,KU _E ); The starting and ending points and intersection points of the reconstructed line VCC's (I, S, E); X _H and Y _H are the abscissa and ordinate of the plane intersection, R _H is the intersection radius corresponding to the plane intersection, L _H0 and L _H1 are the lengths of the front and rear spirals corresponding to the plane intersection; K _V and H _V are The mileage and elevation of the slope-changing point, R _V is the radius of the vertical curve corresponding to the slope-changing point; KB _S and KB _E are the starting and ending mileage corresponding to the bridge, and KU _S and KU _E are the corresponding starting mileage and Termination mileage; I represents the intersection of the optimized route scheme and the new railway, S represents the initial connection point of the optimized route scheme on the existing road, and E represents the termination connection point of the optimized route scheme on the existing road; In step S1, the optimization function is the minimum engineering cost, which includes: earthwork engineering cost, road surface cost, right-of-way cost, bridge engineering cost and tunnel engineering cost, which are expressed as follows: F(HPI's,VPI's,MBU's,VCC's)=min(C _E +C _P +C _R +C _B +C _U ) In the formula, F(HPI's, VPI's, MBU's, VCC's) is the engineering cost based on the decision variables, C _E , C _P , C _R , C _B , C _U are the earthwork cost, pavement cost, road right fee, bridge Engineering fee, tunnel engineering fee; The constraints in the optimization model include: geometric line constraints, intersection constraints, connection constraints and existing structural constraints. 6. A system based on the method of any one of claims 1-5, characterized in that: comprising: The optimization model building module is used to construct the optimization model of the road and railway overpass line, and the optimization model includes: decision variables, constraints and optimization functions in the research area; The meshing module is used to mesh the study area with cells; The search section division module is used to determine the starting and ending cell sets S, E and the cell sets L and R on the left and right sides of the railway in the study area for the connection between the local reconstruction road and the existing road in the study area according to the existing roads and railways, and then search the path. Decomposed into two non-intersecting segment searches and cross segment searches; Among them, the path between the cell set S and the cell set L on the same side of the railway and the path between the cell set E and the cell set R on the same side are the two non-intersecting section paths; the cell set L The path with the cell set R is an intersection path; a non-intersecting segment path search module, configured to perform a path search on two non-intersecting segments based on the optimization model; The cross section path search module is used to perform path search on the cross section; Wherein, performing a line search on the two non-intersecting segments to obtain M paths between the cell set S and the cell set L and N paths between the cell set E and the cell set R; and Perform path search at the intersection to obtain H local lines of the intersection; Search with the optimization function in the optimization model as the objective function, and the route obtained by the search satisfies the constraints in the optimization model; The optimal line acquisition module uses the N paths, the M paths and the H cross-section local lines in step S3 for splicing and integration to form several complete lines, and the optimization function is used as the objective function to determine optimal route; a final route plan acquisition module, used for obtaining the final route plan by performing curve fitting on the plane and longitudinal section based on the optimal route plan; In the grid division module, the calculation formula of the width width of the unit grid is as follows: width=2T _min +J _min J _min represents the minimum clip line length, and T _min represents the minimum tangent length at the maximum turning angle, which is calculated by the following formula:

In the formula, α _max is the maximum allowable turning angle of the line design, and R _min is the minimum curve radius of the road; Wherein, in the intersection path search module, the path search is performed on the intersection section to obtain H local lines of the intersection section: iteratively searches for the local lines between the cell set L and the cell set R, and retains the local lines that meet the constraints. line, as follows: Taking any cell Li in the cell set L as the center, draw a vertical line to the railway and take the vertical line as the angle bisector, and rotate the vertical lines to the left and right by 90°-A _min to obtain the straight line a and Straight line b, Amin is the minimum intersection angle that meets the requirements of the specification; In the cell set R, the Q cells contained in the fan-shaped area formed by the straight line a and the straight line b are the fan-shaped feasible solution cell set; Connect the cell Li and the Q cells in the fan-shaped feasible solution cell set by straight lines to form Q local lines; Detecting whether the Q local lines meet the constraint conditions, if not, eliminating them; if so, generating the plane, longitudinal section, bridge and tunnel line schemes of the local lines; Iteratively searches all cells in the cell set L according to the above process, and generates a local route scheme of H intersections. 7. A terminal, characterized by comprising a processor and a memory, wherein the memory stores a computer program, and the processor invokes the computer program to execute: the steps of the method according to any one of claims 1-5. 8. A readable storage medium, characterized in that: a computer program is stored, and the computer program is invoked by a processor to execute: the steps of the method according to any one of claims 1-5.

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