A mountainous highway alignment design method and system based on multi-dimensional constraints

Abstract

The application relates to the field of road engineering and relates to a mountainous area highway plane and longitudinal line design method and system based on multidimensional constraints. The method comprises the following steps: acquiring a multidimensional constraint data set, wherein the multidimensional constraint data set comprises design conditions, design control points, terrain data and constraint factors; processing the multidimensional constraint data set by using an improved A-Star algorithm to obtain a design node candidate set, wherein the design node candidate set comprises design control points corresponding to plane intersection points and longitudinal section slope change points; acquiring a preset target function, wherein the target function is used for minimizing total cost; and calculating the design node candidate set according to the preset target function to obtain a feasible highway alignment scheme set. The application breaks the traditional serial design mode of 'plane first and longitudinal section later', avoids repeated iteration of backtracking and adjusting the plane after discovering that the longitudinal slope is out of limit and the vertical curve radius is not in line with the regulation in the longitudinal section design stage, and improves the design efficiency.

Description

Technical Field

[0001] This invention relates to the field of road engineering, and more specifically, to a method and system for designing the horizontal and vertical alignment of mountain highways based on multidimensional constraints. Background Technology

[0002] In mountainous road design, horizontal alignment and vertical profile design need to comprehensively consider multi-dimensional constraints such as topography, geology, hydrology, and ecological red lines. Existing technologies generally adopt a sequential design mode of horizontal alignment first and then vertical profile design. The horizontal alignment stage only determines the route direction based on two-dimensional topography and cannot dynamically predict the feasibility of the vertical profile. After problems are discovered in the vertical profile design stage, the horizontal route needs to be adjusted back, forming an iterative process of "horizontal alignment → vertical profile → horizontal alignment". This leads to repeated design, increased costs, extended design cycle, and reduced design efficiency and scheme quality. Summary of the Invention

[0003] The purpose of this invention is to provide a method and system for designing the horizontal and vertical alignment of mountain highways based on multidimensional constraints, so as to improve the above-mentioned problems.

[0004] To achieve the above objectives, the embodiments of this application provide the following technical solutions:

[0005] On the one hand, embodiments of this application provide a method for designing the horizontal and vertical alignment of mountain highways based on multidimensional constraints, the method comprising:

[0006] Obtain a multidimensional constraint dataset, which includes design conditions, design control points, terrain data, and constraint factors;

[0007] The improved A-Star algorithm is used to process the multidimensional constraint dataset to obtain a design node candidate set, which includes design control points corresponding to plane intersections and longitudinal slope change points.

[0008] Obtain a preset objective function, which is used to minimize the total cost;

[0009] The set of candidate design nodes is calculated based on the preset objective function to obtain a set of feasible highway route selection schemes.

[0010] Secondly, embodiments of this application provide a horizontal and vertical alignment design system for mountainous highways based on multidimensional constraints, the system comprising:

[0011] The first acquisition module is used to acquire a multidimensional constraint dataset, which includes design conditions, design control points, terrain data, and constraint factors.

[0012] The first processing module is used to process the multidimensional constraint dataset using the improved A-Star algorithm to obtain a design node candidate set, which includes design control points corresponding to plane intersections and longitudinal slope change points.

[0013] The second acquisition module is used to acquire a preset objective function, which is used to minimize the total cost.

[0014] The second processing module is used to calculate the candidate set of design nodes according to the preset objective function to obtain a set of feasible highway route selection schemes.

[0015] Thirdly, embodiments of this application provide an apparatus, which includes a memory and a processor. The memory is used to store a computer program; the processor is used to execute the computer program to implement the steps of the above-described method for designing the horizontal and vertical alignment of mountain roads based on multidimensional constraints.

[0016] Fourthly, embodiments of this application provide a readable storage medium storing a computer program, which, when executed by a processor, implements the steps of the above-described method for designing the horizontal and vertical alignment of mountain roads based on multidimensional constraints.

[0017] The beneficial effects of this invention are as follows:

[0018] This invention acquires a multi-dimensional constraint dataset, which, compared to existing technologies that fail to fully integrate constraints such as terrain, geology, and ecological red lines, leading to conflicts between route selection and the environment, can proactively avoid risks in areas with unfavorable geological conditions and ecologically sensitive areas, thus reducing the risk of environmental conflicts. Furthermore, an improved A-Star algorithm is used to simultaneously process horizontal and vertical profile nodes, breaking the traditional sequential design pattern of "horizontal first, then vertical profile." This avoids the iterative backtracking and adjustment of the horizontal plane after discovering excessive longitudinal slope or non-compliant vertical curve radii during the vertical profile design stage, significantly shortening the design cycle and improving design efficiency. By using the minimization of total cost as the objective function to select solutions, it overcomes the shortcomings of existing technologies in evaluating solutions in a single way. Simultaneous optimization of horizontal intersections and vertical slope change points can reduce engineering costs such as cut and fill, and demolition. Moreover, the coordinated horizontal and vertical design makes the alignment combination more harmonious, reducing driving safety hazards such as poor visibility, centrifugal force, and slope superposition, ultimately achieving improved design efficiency and optimized design quality.

[0019] Other features and advantages of the invention will be set forth in the following description, and will be apparent in part from the description, or may be learned by practicing embodiments of the invention. The objects and other advantages of the invention may be realized and obtained by means of the structures particularly pointed out in the written description and the accompanying drawings. Attached Figure Description

[0020] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of the present invention and should not be regarded as a limitation on the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0021] Figure 1 This is a schematic diagram of the process for designing the horizontal and vertical alignment of mountain roads based on multidimensional constraints, as described in an embodiment of the present invention.

[0022] Figure 2 This is a schematic diagram of the horizontal and vertical alignment design system for mountain roads based on multidimensional constraints, as described in an embodiment of the present invention.

[0023] Figure 3 This is a schematic diagram of the equipment structure for designing the horizontal and vertical alignment of mountain roads based on multidimensional constraints, as described in an embodiment of the present invention.

[0024] The diagram is labeled as follows: 800, Equipment for designing horizontal and vertical alignment of mountain roads based on multidimensional constraints; 801, Processor; 802, Memory; 803, Multimedia component; 804, I / O interface; 805, Communication component; 901, First acquisition module; 902, First processing module; 903, Second acquisition module; 904, Second processing module. Detailed Implementation

[0025] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. The components of the embodiments of the present invention described and shown in the accompanying drawings can generally be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely to illustrate selected embodiments of the invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without inventive effort are within the scope of protection of the present invention.

[0026] It should be noted that similar reference numerals and letters in the following figures indicate similar items; therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures. Furthermore, in the description of this invention, terms such as "first," "second," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance. Example 1:

[0027] This embodiment provides a method for horizontal and vertical alignment design of mountain highways based on multidimensional constraints. It can be understood that a scenario can be set up in this embodiment, such as a scenario of route design in mountainous and hilly areas.

[0028] See Figure 1 The figure shows that the method includes steps S1, S2, S3 and S4.

[0029] Step S1: Obtain a multidimensional constraint dataset, which includes design conditions, design control points, terrain data, and constraint factors.

[0030] In this step, the design conditions include technical grade, design speed, cross section, red line width, and design flood frequency; the design control points include the starting point, the ending point, basic control points (towns, industrial parks, industrial and mining enterprises, comprehensive transportation hubs, intersecting roads), and control projects (specific extra-large bridges, extra-long tunnels); the topographic data includes contour line data and topographic turning points; the constraints include flood level, basic farmland, demolition restrictions, adverse geological conditions, and protected areas.

[0031] Step S2: Process the multidimensional constraint dataset using the improved A-Star algorithm to obtain a design node candidate set, which includes design control points corresponding to plane intersections and longitudinal slope change points;

[0032] Understandably, the A-Star algorithm, as an effective heuristic algorithm for finding the shortest path in a graph, is very suitable for the core idea of ​​horizontal and vertical coordination in this invention. It is used to construct a joint search algorithm model for plane intersection points and slope change points. This model searches for plane intersection points and slope change points in the longitudinal section simultaneously in three-dimensional space.

[0033] Before step S2, there are steps S21, S22, S23, and S24, which specifically include:

[0034] Step S21: Construct the flood impact constraint boundary based on the flood level data and design flood frequency included in the constraint factors to obtain the first constraint boundary;

[0035] Step S22: Based on the basic farmland distribution map and land use vector data included in the constraint factors, the second constraint boundary is obtained through GIS spatial overlay analysis;

[0036] Step S23: Delineate the unfavorable geological mandatory avoidance area based on the unfavorable geological range line included in the constraint factors to obtain the third constraint boundary;

[0037] Step S24: Spatial association and integration are performed based on the first constraint boundary, the second constraint boundary, and the third constraint boundary, along with the design conditions, design control points, and terrain data, to obtain a standardized multidimensional constraint dataset.

[0038] In this embodiment, targeted constraint boundaries are constructed by classification, and the standardized fusion of multi-source data is achieved by using GIS spatial association integration technology. For the first time, scattered hydrological, land, and geological constraints are transformed into a unified data foundation that can directly support the subsequent improved A-Star algorithm search. This not only avoids the conflicts between route selection and restricted areas caused by missing or unintegrated constraint data in traditional design, but also provides accurate and standardized constraint basis for the collaborative search of horizontal and vertical alignments. From the data source, it ensures the compliance and accuracy of the subsequent design node candidate set generation, and lays a data foundation for breaking the bottleneck of serial design of "horizontal alignment first, then longitudinal alignment".

[0039] Step S2 further includes steps S25, S26, S27, S28, and S29, which specifically include:

[0040] Step S25: Extract DEM data included in the terrain data;

[0041] Step S26: Calculate based on the DEM data to obtain a first calculation result and a second calculation result. The first calculation result includes the average terrain slope in the neighborhood of the current point, and the second calculation result includes the average curvature of the contour lines in the neighborhood of the current point.

[0042] In this step, both the first and second calculation results are based on real-time calculations of DEM data. The specific process for the first calculation result is as follows: a 100-200m square neighborhood is defined with the node as the center, the raster elevation within the neighborhood is extracted, the slope of each raster is calculated using the third-order inverse distance squared weighted difference method, and then the arithmetic mean is taken. The specific process for the second calculation result is as follows: a 100-200m neighborhood is defined with the node as the center, and contour lines or elevations within the neighborhood are extracted from the DEM data; to calculate the average curvature of the contour lines, the curvature of the fitted line segments is calculated and then averaged; to calculate the terrain undulation, the difference between the highest and lowest elevations within the neighborhood is taken.

[0043] Step S27: Adjust the search step size according to the first calculation result to obtain the adjusted search step size;

[0044] In this step, the specific calculation process for the adjusted search step size is as follows:

[0045] ;

[0046] In the above formula, , These represent the maximum and minimum search step sizes set by the user, respectively. This indicates the first calculation result; This means mapping slope values ​​to angles and normalizing them; This represents the sensitivity coefficient, used to adjust the degree of drastic change in step size with slope. It can be set by the user, α∈[0.3, 1.0]. For every 10% increase in slope, α increases by 0.1. For example, when the terrain is mountainous, α=0.8; in hilly areas, α=0.5; and in plains areas, α=0.3, ensuring that the step size adjustment conforms to the actual terrain complexity.

[0047] Step S28: Adjust the search direction according to the second calculation result to obtain the adjusted search range;

[0048] Step S28 further includes steps S281, S282, and S283, which specifically include:

[0049] Step S281: Obtain the basic search half-angle and curvature influence coefficient;

[0050] Step S282: Calculate the search sector half-angle based on the basic search half-angle, the curvature influence coefficient, and the second calculation result;

[0051] In this step, the specific process of searching for the sector half-angle is as follows:

[0052] ;

[0053] In the above formula, Indicates a basic search term using half-width characters; This indicates the second calculation result; This represents the curvature influence coefficient. The more complex the terrain, the more... The larger the value, the wider the search range, in order to find the optimal path to avoid obstacles, such as... hour, =10, hour, =20.

[0054] Step S283: Determine the adjusted search range based on the half-angle of the search sector.

[0055] In this step, the specific calculation process for the adjusted search range is as follows:

[0056] ;

[0057] In the above formula, This represents the basic direction vector pointing from the current point to the next major design control point; This indicates a search for a half-angle sector.

[0058] Step S29: Perform a search based on the adjusted search step size and the adjusted search range to obtain a candidate set of design nodes.

[0059] Step S29 further includes steps S291, S292, S293, and S294, which specifically include:

[0060] Step S291: Calculate the cumulative actual cost from the starting point to the current design control point to obtain the third calculation information;

[0061] In this step, the calculation of the cumulative actual cost from the starting point to the current design control point is a comprehensive cost function, consisting of multiple weighted components, specifically:

[0062] ;

[0063] In the above formula, This indicates third-party computational information; , , , : These are the weighting coefficients for each item, set by the user based on the specific circumstances of the project, and can be assigned values ​​using expert experience. This represents the spatial length of the k-th route segment; The estimated earthwork volume generated by the k-th route segment can be calculated using DEM elevation and standard cross-sectional area. Specifically, the DEM elevation along the route segment is extracted first to determine the ground line, and the cut and fill height is obtained by combining the design elevation. Then, the cut and fill area per meter is calculated according to the standard cross-sectional form, and multiplied by the spatial length of the route segment to obtain the estimated earthwork volume. This represents the penalty cost incurred when the k-th route crosses environmentally sensitive areas such as ecological red lines and water source protection areas; This represents the cost of land acquisition and demolition penalties involved in the k-th segment of the route.

[0064] Step S292: Calculate the estimated cost from the current design control point to the endpoint to obtain the fourth calculation information;

[0065] In this step, the specific calculation process for the estimated cost is as follows:

[0066] ;

[0067] In the above formula, ; This represents the Euclidean straight-line distance from the current node to the endpoint. This represents an average unit distance cost coefficient, which can be estimated based on the overall topography and geological conditions of the project area. Specifically, it involves: first, classifying the area's topography type (e.g., mountains, hills) and geological grade (e.g., ordinary foundation, adverse geology); then, statistically analyzing historical data on highway construction under similar topography and geology to calculate the average unit distance cost (including earthwork, basic protection, and other foundation costs); and finally, making minor adjustments based on the actual project conditions to determine the final cost. .

[0068] Step S293: Construct a comprehensive cost function based on the third calculation information and the fourth calculation information;

[0069] It is understandable that the comprehensive cost function is specifically as follows:

[0070] ;

[0071] In the above formula, Represents the comprehensive cost function; and These represent the third and fourth calculation information, respectively.

[0072] Step S294: Determine the candidate set of design nodes based on the integrated cost function within the adjusted search step size and the adjusted search range.

[0073] In this embodiment, starting from the starting point, based on the dynamically adjusted search step size and direction, within the range of the search step size and direction, each selection... The node with the smallest value is expanded to generate adjacent state nodes. If the requirements are met, the design node is included in the candidate set; if the cumulative slope length exceeds the maximum slope length limit or the slope exceeds the allowable range before the current plane intersection point, a slope change point is added immediately, and the subsequent plane intersection point positions and elevations are recalculated to ensure that the longitudinal profile design requirements are met.

[0074] Step S3: Obtain a preset objective function, which is used to minimize the total cost;

[0075] In this step, the preset objective function is specifically:

[0076] ;

[0077] In the above formula, This represents ∑(earthwork cost + bridge cost + tunnel cost); It represents ∑(area crossing the ecological red line × penalty coefficient); It represents ∑(occupied basic farmland area × penalty coefficient + demolition area × penalty coefficient); , , These represent the weight coefficients of each sub-item.

[0078] Step S4: Calculate the candidate set of design nodes according to the preset objective function to obtain a set of feasible highway route selection schemes.

[0079] Step S4 further includes steps S41, S42, and S43, which specifically include:

[0080] Step S41: Combine the design control points in the candidate design node set to obtain candidate route selection schemes;

[0081] Step S42: Obtain constraint information, which includes planar design parameters, longitudinal section design parameters, and planar-longitudinal coordination design parameters.

[0082] In this step, the horizontal design parameters include straight section length, circular curve radius, spiral curve length, A-value, horizontal curve length, small-angle horizontal curve length, and special horizontal alignment combinations; the vertical profile design parameters include longitudinal slope, slope length, transition slopes, average slope of continuous uphill and downhill sections, vertical curve radius and length, etc.; the horizontal and vertical coordination design parameters include the correspondence between horizontal curves, vertical curves and circular curves, and the composite slope, etc. In addition, it also includes control point constraints, i.e., the distance between the route and all control points must be less than a preset threshold; and restricted area constraints, i.e., the route must not intersect with any restricted areas.

[0083] Step S43: Based on the preset objective function and the constraint information, the candidate route selection schemes are screened to generate a route selection design scheme.

[0084] In this step, the goal of minimizing total cost is combined with multi-dimensional technical constraints to select multiple feasible solutions from the candidate solutions that combine economic efficiency and technical compliance. This avoids the one-sidedness of traditional solution comparison, which only focuses on technical indicators or single costs, and achieves synergistic optimization of technical compliance and economic efficiency. Example 2:

[0085] This invention not only provides a technical solution for automatic algorithm generation, but also provides a human-computer interaction scenario, forming a closed-loop process of designer trial design - system calculation and verification - two-way optimization and adjustment, as detailed below:

[0086] 1) Designers manually select the intersection points of the planes.

[0087] ① Interactive operation

[0088] Based on the 3D terrain model and the overall route alignment, designers manually select the initial planar intersection point (JD point) location on the interface. The system responds to the operation in real time, automatically generating design lines that meet the requirements of planar and longitudinal profile design specifications, and annotating key parameters.

[0089] ② Dynamic assistance

[0090] Based on the selected JD point, combined with the control point direction and contour line orientation, the system automatically generates a recommended search area (displayed as a semi-transparent fan or polygon), prompting the designer with the preferred direction for subsequent JD points. For example, if the current JD point is near a valley with dense contour lines, the system will highlight the recommended area along the contour line direction.

[0091] 2) The system automatically calculates the horizontal and vertical design parameters.

[0092] ① Calculation of planar indices

[0093] The system automatically calculates the plane curve parameters based on the coordinates of adjacent JD points, including: curve radius (R), transition curve length (Ls); tangent length (T), curve length (L), etc., and compares them with the minimum specifications.

[0094] ② Calculation of longitudinal section parameters

[0095] Using the selected JD point as a reference, the system automatically generates a longitudinal profile line along the route and calculates: the slope (i) and cumulative slope length between adjacent JD points; the location of the slope change point, the vertical curve radius (Rv), and the elevation.

[0096] Verify whether the maximum longitudinal slope, minimum slope length, and other specifications are met.

[0097] 3) Detection and optimization prompts for indicators exceeding limits

[0098] ① Real-time verification

[0099] The system monitors and calculates the horizontal and vertical profile indicators in real time. Once it finds that an indicator does not meet the specifications (such as the horizontal curve radius R < the minimum value specified, or the longitudinal slope i > the maximum allowable value), it immediately triggers an early warning mechanism.

[0100] ②Visual prompts

[0101] On the 3D model and design drawings, mark the locations exceeding the limits (such as horizontal curve sections or longitudinal slope sections) with red highlighting, and pop up a prompt box to display the specific exceeding indicators and specification requirements. For example: "The current horizontal curve radius R=200m, which is less than the specification requirement of 300m. Please adjust the position of point JD."

[0102] ③ Optimization suggestions

[0103] Based on the exceedance type, the system provides designers with a variety of optimization strategies to choose from:

[0104] ④ Planar indicators exceed limits

[0105] If the curve radius R is less than the standard value, the new radius after moving the current JD point ΔL (ΔL=5-10m) along the contour line is calculated first; if it is still not satisfied, the scheme of inserting a new JD point is triggered (the insertion point position is determined by the A* algorithm pre-search).

[0106] ⑤ Longitudinal section indicators exceed limits

[0107] If the longitudinal slope i > the maximum allowable value, the optimal location of the slope change point (to minimize the amount of cut and fill) is calculated by horizontal and vertical alignment planning based on the elevation difference between adjacent JD points, and new vertical curve parameters are generated.

[0108] 4) Designer Decisions and Iterative Optimization

[0109] Design Adjustment: Based on system prompts, designers can choose to accept an optimization suggestion and perform an action (such as moving the JD point or adding a slope change point), or adjust the design independently. After each action, the system immediately recalculates the metrics and updates the display.

[0110] Multiple options comparison: For complex road sections, designers can try multiple adjustment options. The system automatically saves the index data and 3D models of different options and supports comparative analysis (such as displaying the earthwork volume and cost estimate of each option) to assist designers in decision-making.

[0111] 5) Final verification of horizontal and vertical synergy

[0112] Overall assessment: After the designer completes the preliminary design of the horizontal and vertical profiles, the system performs the final verification, applying objective functions and constraints to perform joint verification of the horizontal and vertical profiles.

[0113] Output report: If all indicators pass the verification, the system generates a design compliance report; if there are still collaboration issues, the system displays the conflict locations and optimization suggestions in the form of charts, supporting designers to make further adjustments until the requirements are met.

[0114] This human-computer interaction algorithm deeply integrates the designer's professional experience with the system's intelligent computing, which can give full play to the designer's subjective initiative and make efficient use of computers for verification and optimization, so as to achieve the accuracy and efficiency of road alignment design. Example 3:

[0115] like Figure 2 As shown, this embodiment provides a multi-dimensional constraint-based horizontal and vertical alignment design system for mountainous highways. The system includes a first acquisition module 901, a first processing module 902, a second acquisition module 903, and a second processing module 904, specifically including:

[0116] The first acquisition module 901 is used to acquire a multidimensional constraint dataset, which includes design conditions, design control points, terrain data and constraint factors.

[0117] The first processing module 902 is used to process the multidimensional constraint dataset using the improved A-Star algorithm to obtain a design node candidate set, which includes design control points corresponding to plane intersections and longitudinal slope change points.

[0118] The second acquisition module 903 is used to acquire a preset objective function, which is used to minimize the total cost;

[0119] The second processing module 904 is used to calculate the design node candidate set according to the preset objective function to obtain a set of feasible highway route selection schemes.

[0120] In one specific embodiment of this disclosure, the first processing module is preceded by a first processing unit, a second processing unit, a third processing unit, and a fourth processing unit, specifically including:

[0121] The first processing unit is used to construct the flood impact constraint boundary based on the flood level data and design flood frequency included in the constraint factors, and obtain the first constraint boundary.

[0122] The second processing unit is used to obtain the second constraint boundary by using GIS spatial overlay analysis based on the basic farmland distribution map and land use vector data included in the constraint factors.

[0123] The third processing unit is used to delineate the unfavorable geological mandatory avoidance area based on the unfavorable geological range line included in the constraint factors, and obtain the third constraint boundary;

[0124] The fourth processing unit is used to spatially correlate and integrate the first constraint boundary, the second constraint boundary, and the third constraint boundary with the design conditions, design control points, and terrain data to obtain a standardized multidimensional constraint dataset.

[0125] In one specific embodiment of this disclosure, the first processing module includes a fifth processing unit, a sixth processing unit, a seventh processing unit, an eighth processing unit, and a ninth processing unit, specifically including:

[0126] The fifth processing unit is used to extract DEM data included in the terrain data;

[0127] The sixth processing unit is used to perform calculations based on the DEM data to obtain a first calculation result and a second calculation result. The first calculation result includes the average terrain slope in the neighborhood of the current point, and the second calculation result includes the average curvature of the contour lines in the neighborhood of the current point.

[0128] The seventh processing unit is used to adjust the search step size according to the first calculation result to obtain the adjusted search step size;

[0129] The eighth processing unit is used to adjust the search direction based on the second calculation result to obtain the adjusted search range;

[0130] The ninth processing unit is used to perform a search based on the adjusted search step size and the adjusted search range to obtain a candidate set of design nodes.

[0131] In one specific embodiment of this disclosure, the eighth processing unit includes a first acquisition unit, a tenth processing unit, and an eleventh processing unit, specifically comprising:

[0132] The first acquisition unit is used to acquire the basic search half-angle and curvature influence coefficient;

[0133] The tenth processing unit is used to calculate the search sector half-angle based on the basic search half-angle, the curvature influence coefficient, and the second calculation result.

[0134] The eleventh processing unit is used to determine the adjusted search range based on the half-angle of the search sector.

[0135] In one specific embodiment of this disclosure, the ninth processing unit includes a twelfth processing unit, a thirteenth processing unit, a fourteenth processing unit, and a fifteenth processing unit, specifically comprising:

[0136] The twelfth processing unit is used to calculate the cumulative actual cost from the starting point to the current design control point and obtain the third calculation information;

[0137] The thirteenth processing unit is used to calculate the estimated cost from the current design control point to the endpoint, and obtain the fourth calculation information;

[0138] The fourteenth processing unit is used to construct a comprehensive cost function based on the third calculation information and the fourth calculation information;

[0139] The fifteenth processing unit is used to determine the design node candidate set based on the integrated cost function within the adjusted search step size and the adjusted search range.

[0140] In one specific embodiment of this disclosure, the second processing module includes a sixteenth processing unit, a second acquisition unit, and a seventeenth processing unit, specifically comprising:

[0141] The sixteenth processing unit is used to combine the design control points in the candidate design node set to obtain candidate route selection schemes;

[0142] The second acquisition unit is used to acquire constraint information, which includes planar design indicators, longitudinal section design indicators, and planar-longitudinal coordination design indicators.

[0143] The seventeenth processing unit is used to filter candidate route selection schemes based on the preset objective function and the constraint information, and generate a route selection design scheme.

[0144] It should be noted that the specific methods by which each module performs operations in the system described in the above embodiments have been described in detail in the embodiments related to the method, and will not be elaborated here. Example 4:

[0145] Corresponding to the above method embodiments, this embodiment also provides a multi-dimensional constraint-based mountain road horizontal and vertical alignment design device. The multi-dimensional constraint-based mountain road horizontal and vertical alignment design device described below and the multi-dimensional constraint-based mountain road horizontal and vertical alignment design method described above can be referred to each other.

[0146] Figure 3 This is a block diagram illustrating a multi-dimensional constraint-based horizontal and vertical alignment design device 800 for mountainous roads, according to an exemplary embodiment. Figure 3 As shown, the multi-dimensional constraint-based mountain road horizontal and vertical alignment design device 800 may include: a processor 801 and a memory 802. The multi-dimensional constraint-based mountain road horizontal and vertical alignment design device 800 may also include one or more of a multimedia component 803, an I / O interface 804, and a communication component 805.

[0147] The processor 801 controls the overall operation of the multi-dimensional constraint-based mountain road alignment design device 800 to complete all or part of the steps in the multi-dimensional constraint-based mountain road alignment design method. The memory 802 stores various types of data to support the operation of the multi-dimensional constraint-based mountain road alignment design device 800. This data may include, for example, instructions for any application or method operating on the multi-dimensional constraint-based mountain road alignment design device 800, as well as application-related data such as contact data, sent and received messages, images, audio, video, etc. The memory 802 can be implemented using any type of volatile or non-volatile storage device or a combination thereof, such as Static Random Access Memory (SRAM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Erasable Programmable Read-Only Memory (EPROM), Programmable Read-Only Memory (PROM), Read-Only Memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk. The multimedia component 803 may include a screen and an audio component. The screen may be, for example, a touchscreen, and the audio component is used to output and / or input audio signals. For example, the audio component may include a microphone for receiving external audio signals. The received audio signals may be further stored in the memory 802 or transmitted via the communication component 805. The audio component also includes at least one speaker for outputting audio signals. I / O interface 804 provides an interface between processor 801 and other interface modules, such as keyboards, mice, and buttons. These buttons can be virtual or physical. Communication component 805 is used for wired or wireless communication between the multi-dimensional constraint-based mountain road alignment design device 800 and other devices. Wireless communication includes Wi-Fi, Bluetooth, Near Field Communication (NFC), 2G, 3G, or 4G, or a combination thereof. Therefore, the corresponding communication component 805 may include a Wi-Fi module, a Bluetooth module, and an NFC module.

[0148] In an exemplary embodiment, the mountain road alignment design device 800 based on multidimensional constraints can be implemented by one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors, or other electronic components to execute the above-described mountain road alignment design method based on multidimensional constraints.

[0149] In another exemplary embodiment, a computer-readable storage medium including program instructions is also provided. When executed by a processor, these program instructions implement the steps of the above-described method for designing the horizontal and vertical alignment of mountain roads based on multidimensional constraints. For example, the computer-readable storage medium may be the memory 802 including the program instructions, which may be executed by the processor 801 of the device 800 for designing the horizontal and vertical alignment of mountain roads based on multidimensional constraints to complete the above-described method for designing the horizontal and vertical alignment of mountain roads based on multidimensional constraints. Example 5:

[0150] Corresponding to the above method embodiments, this embodiment also provides a readable storage medium. The readable storage medium described below can be referred to in conjunction with the above-described method for designing the horizontal and vertical alignment of mountain roads based on multidimensional constraints.

[0151] A readable storage medium storing a computer program, which, when executed by a processor, implements the steps of the multi-dimensional constraint-based horizontal and vertical alignment design method for mountain roads described in the above-described method embodiments.

[0152] The readable storage medium can specifically be a USB flash drive, external hard drive, read-only memory (ROM), random access memory (RAM), magnetic disk, or optical disk, or any other readable storage medium capable of storing program code.

[0153] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

[0154] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A method for designing the horizontal and vertical alignment of mountain highways based on multidimensional constraints, characterized in that, include: Obtain a multidimensional constraint dataset, which includes design conditions, design control points, terrain data, and constraint factors; The improved A-Star algorithm is used to process the multidimensional constraint dataset to obtain a design node candidate set, which includes design control points corresponding to plane intersections and longitudinal slope change points. Obtain a preset objective function, which is used to minimize the total cost; The set of candidate design nodes is calculated based on the preset objective function to obtain a set of feasible highway route selection schemes; Specifically, the improved A-Star algorithm is used to process the multidimensional constraint dataset to obtain a candidate set of design nodes, including: Extract DEM data included in the terrain data; The calculation is performed based on the DEM data to obtain a first calculation result and a second calculation result. The first calculation result includes the average terrain slope in the neighborhood of the current point, and the second calculation result includes the average curvature of the contour lines in the neighborhood of the current point. The search step size is adjusted based on the first calculation result to obtain the adjusted search step size; The search direction is adjusted based on the second calculation result to obtain the adjusted search range; A search is performed based on the adjusted search step size and the adjusted search range to obtain a candidate set of design nodes; The specific calculation process for the adjusted search step size is as follows: ； In the above formula, These represent the maximum and minimum search step sizes set by the user, respectively. This indicates the first calculation result; This means mapping slope values ​​to angles and normalizing them; The sensitivity coefficient is used to adjust the degree of change in step size with slope. It can be set by the user, α∈[0.3, 1.0]. For every 10% increase in slope, α increases by 0.

1. When the terrain is mountainous, α=0.8, when the terrain is hilly, α=0.5, and when the terrain is plain, α=0.3, to ensure that the step size adjustment conforms to the actual terrain complexity.

2. The method for designing the horizontal and vertical alignment of mountain highways based on multidimensional constraints according to claim 1, characterized in that, Before processing the multidimensional constrained dataset using the improved A-Star algorithm, the following steps are included: The first constraint boundary is obtained by constructing the flood impact constraint boundary based on the flood level data and design flood frequency included in the constraint factors; The second constraint boundary is obtained by using GIS spatial overlay analysis based on the basic farmland distribution map and land use vector data included in the constraint factors; The third constraint boundary is obtained by delineating the unfavorable geological range line included in the constraint factors to determine the unfavorable geological mandatory avoidance area; Based on the first constraint boundary, the second constraint boundary, and the third constraint boundary, spatial correlation and integration are performed with design conditions, design control points, and terrain data to obtain a standardized multidimensional constraint dataset.

3. The method for designing the horizontal and vertical alignment of mountain highways based on multidimensional constraints according to claim 1, characterized in that, The search direction is adjusted based on the second calculation result to obtain the adjusted search range, including: Obtain the basic search half-angle and curvature influence coefficients; The search sector half-angle is calculated based on the basic search half-angle, the curvature influence coefficient, and the second calculation result. The adjusted search range is determined based on the half-angle of the search sector.

4. The method for designing the horizontal and vertical alignment of mountain highways based on multidimensional constraints according to claim 1, characterized in that, The search is performed based on the adjusted search step size and the adjusted search range, including: Calculate the cumulative actual cost from the starting point to the current design control point to obtain the third calculation information; Calculate the estimated cost from the current design control point to the endpoint to obtain the fourth calculation information; Construct a comprehensive cost function based on the third and fourth calculation information; Within the adjusted search step size and the adjusted search range, a candidate set of design nodes is determined based on the comprehensive cost function.

5. A horizontal and vertical alignment design system for mountain highways based on multi-dimensional constraints, characterized in that, include: The first acquisition module is used to acquire a multidimensional constraint dataset, which includes design conditions, design control points, terrain data, and constraint factors. The first processing module is used to process the multidimensional constraint dataset using the improved A-Star algorithm to obtain a design node candidate set, which includes design control points corresponding to plane intersections and longitudinal slope change points. The second acquisition module is used to acquire a preset objective function, which is used to minimize the total cost. The second processing module is used to calculate the candidate set of design nodes according to the preset objective function to obtain a set of feasible highway route selection schemes; The first processing module includes: The fifth processing unit is used to extract DEM data included in the terrain data; The sixth processing unit is used to perform calculations based on the DEM data to obtain a first calculation result and a second calculation result. The first calculation result includes the average terrain slope in the neighborhood of the current point, and the second calculation result includes the average curvature of the contour lines in the neighborhood of the current point. The seventh processing unit is used to adjust the search step size based on the first calculation result to obtain the adjusted search step size, specifically including: ； In the above formula, These represent the maximum and minimum search step sizes set by the user, respectively. This indicates the first calculation result; This means mapping slope values ​​to angles and normalizing them; The sensitivity coefficient is used to adjust the degree of change in step size with slope. It can be set by the user, α∈[0.3, 1.0]. For every 10% increase in slope, α increases by 0.

1. When the terrain is mountainous, α=0.8, when the terrain is hilly, α=0.5, and when the terrain is plain, α=0.3, to ensure that the step size adjustment conforms to the actual terrain complexity. The eighth processing unit is used to adjust the search direction based on the second calculation result to obtain the adjusted search range; The ninth processing unit is used to perform a search based on the adjusted search step size and the adjusted search range to obtain a candidate set of design nodes.

6. The mountain highway horizontal and vertical alignment design system based on multi-dimensional constraints according to claim 5, characterized in that, Prior to the first processing module, the following are included: The first processing unit is used to construct the flood impact constraint boundary based on the flood level data and design flood frequency included in the constraint factors, and obtain the first constraint boundary. The second processing unit is used to obtain the second constraint boundary by using GIS spatial overlay analysis based on the basic farmland distribution map and land use vector data included in the constraint factors. The third processing unit is used to delineate the unfavorable geological mandatory avoidance area based on the unfavorable geological range line included in the constraint factors, and obtain the third constraint boundary; The fourth processing unit is used to spatially correlate and integrate the first constraint boundary, the second constraint boundary, and the third constraint boundary with the design conditions, design control points, and terrain data to obtain a standardized multidimensional constraint dataset.

7. The mountain highway horizontal and vertical alignment design system based on multi-dimensional constraints according to claim 5, characterized in that, The eighth processing unit includes: The first acquisition unit is used to acquire the basic search half-angle and curvature influence coefficient; The tenth processing unit is used to calculate the search sector half-angle based on the basic search half-angle, the curvature influence coefficient, and the second calculation result. The eleventh processing unit is used to determine the adjusted search range based on the half-angle of the search sector.

8. The mountain highway horizontal and vertical alignment design system based on multi-dimensional constraints according to claim 5, characterized in that, The ninth processing unit includes: The twelfth processing unit is used to calculate the cumulative actual cost from the starting point to the current design control point and obtain the third calculation information; The thirteenth processing unit is used to calculate the estimated cost from the current design control point to the endpoint, and obtain the fourth calculation information; The fourteenth processing unit is used to construct a comprehensive cost function based on the third calculation information and the fourth calculation information; The fifteenth processing unit is used to determine the design node candidate set based on the integrated cost function within the adjusted search step size and the adjusted search range.

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