Road route planning system based on multi-search and dynamic cost function optimization

Abstract

The invention relates to the technical field of road route planning, in particular to a road route planning system based on multi-search and dynamic cost function optimization. The system comprises a topographic feature partition processing module, a multi-dimensional cost function construction module, a multi-path parallel generation module, a dynamic path fusion processing module, a post-processing optimization module and a project amount and cost calculation module. The method comprises the following steps: establishing a climbing / downhill differentiated dynamic cost model; a four-path parallel search and partition fusion mechanism is provided; integrating real-time cross detection and a gradient preserving algorithm; and developing a full life cycle cost optimization model. The method has the technical effects that the gradient control precision is + / -1%, path crossing is eliminated, the earth volume is reduced by 25-30%, the downhill accident risk is reduced by 35%, and the design efficiency is improved by 40%. The method is especially suitable for mountainous roads, mine roads and other complex terrain scenes.

Description

Technical Field

[0001] This invention relates to the field of road route planning technology, and more specifically to a road route planning system based on multiple search and dynamic cost function optimization. Background Technology

[0002] With the acceleration of urbanization and the booming development of intelligent transportation systems, efficient and intelligent road route planning has become a key technology for improving efficiency, alleviating congestion, and reducing energy consumption. Users' requirements for intelligent road route planning systems have evolved from simply finding the "shortest path" or "fastest path" to a comprehensive need considering energy consumption, safety, and dynamic trade-offs among multiple objectives. Therefore, researching more intelligent, adaptive, and adaptable intelligent road route planning methods for complex road conditions has significant theoretical value and importance.

[0003] Currently, road engineering design still relies heavily on engineers' experience for manual route selection. This approach suffers from several drawbacks, including insufficient slope control precision, path self-intersection, imbalance between cut and fill volumes, lack of coordination between energy consumption and safety, and poor algorithm adaptability, especially in complex terrain. Specifically, insufficient slope control precision manifests in the difficulty of existing automated design systems to accurately control the entire slope in undulating terrain, particularly when using the same design standards for uphill and downhill sections, leading to unreasonable local slopes. Path self-intersection is evident in the frequent intersections of automatically generated paths, failing to meet road design specifications. Imbalance between cut and fill volumes is reflected in inaccurate earthwork calculations, which can increase project costs by 10-25%. Lack of coordination between energy consumption and safety is manifested in the failure to consider vehicle energy consumption during uphill driving and braking losses during downhill driving, resulting in a lack of lifecycle cost optimization. Poor algorithm adaptability is reflected in the tendency of single-path search algorithms to get trapped in local optima in complex terrain. Summary of the Invention

[0004] To address the aforementioned technical problems, the present invention aims to provide a road route planning system based on multiple search and dynamic cost function optimization. The specific technical solution adopted is as follows: In a first aspect, embodiments of the present invention provide a road route planning system based on multiple search and dynamic cost function optimization, the system comprising: The terrain feature zoning module is used to identify climbing areas, downhill areas, and gentle slope areas based on the digital terrain model; The multi-dimensional cost function construction module is used to build a dynamically weighted multi-dimensional cost function model that includes elevation cost, earthwork cost, curve cost, and energy consumption cost. The multi-path parallel generation module is used to simultaneously execute slope optimization strategy, non-crossing safety strategy, climbing-specific strategy and downhill safety strategy; The dynamic path fusion processing module is used to generate the final path by applying different fusion ratios to different partitions. The post-processing optimization module is used to perform slope adjustment, intersection elimination, and local smoothing. The quantity and cost calculation module is used to calculate earthwork volume and total lifecycle cost.

[0005] Furthermore, the terrain feature partitioning processing module includes: The DEM data import unit is used to input digital terrain model (DTM) data and establish an elevation database. The terrain feature analysis unit is used to obtain the slope of each terrain unit based on the elevation database. The slope zoning coding unit is used to divide different terrain units into climbing areas, downhill areas, and gentle areas according to the slope of different terrain units; the output terrain zoning coding matrix is ​​stored as a terrain feature database.

[0006] Furthermore, the terrain feature zoning processing module also includes: using terrain units with a slope of ≥5% as climbing areas; using terrain units with a slope of ≤-4% as descending areas; and using terrain units with a slope between -4% and 5% as flat areas.

[0007] Furthermore, the multi-dimensional cost function construction module includes: The cost components in the multi-dimensional cost function construction module include: elevation cost, earthwork cost, curve cost, and energy consumption cost. The elevation cost component adopts a differentiated model for climbing or descending slopes. When the climbing slope is >7%, the weighting coefficient is 1.5, and when the descending slope is <-5%, the weighting coefficient is 1.8. The earthwork cost component adopts a quadratic model; The cost component of the curve adopts the inverse radius model; The energy cost component adopts a directional correlation model, with the cost for climbing sections being 2.5 times the product of slope and length, and the cost for descending sections being 0.8 times the product of the absolute value of slope and length.

[0008] Furthermore, the multi-path parallel generation module includes: A slope-priority unit is used to execute the slope optimization strategy, including: a circular local search algorithm; A non-intersecting path unit, used to execute the non-intersecting safety policy, including: a vertical direction search algorithm; The hill-climbing optimization unit is used to execute the hill-climbing specific strategy, including: using a sector search area constraint; A downhill safety unit is used to execute the downhill safety strategy, including: employing a curve radius maximization strategy.

[0009] Furthermore, the dynamic path fusion processing module includes: Climbing areas: Climbing-specific routes account for 60-80%, and non-intersecting safe routes account for 20-40%; Downhill areas: 20-40% of the downhill safe routes, and 60-80% of the safe routes without intersections; In gentle slope areas: slope-optimized paths and non-intersecting safe paths each account for 50%; The fusion weight function is: w = 0.5 + 0.5 × cos(2π × p), where p is the relative position of the path.

[0010] Furthermore, the post-processing optimization module includes: The slope adjustment unit is used to compensate for elevation deviations proportionally to the segment length. ΔZ_adjust,k = (S_target×L_k - ΔZ_actual,k) / n; k=1,2,...,n; The path verification unit is used to detect intersections in real time based on the vector cross product method and automatically replace them with safe path points. A smoothing unit is used to apply a three-point moving average filter.

[0011] Furthermore, the quantity and cost calculation module includes: Earthwork volume calculation unit, used to obtain earthwork volume based on the volume surface method; The cost analysis unit is used to obtain the full life cycle cost by combining construction costs and operating costs for a predetermined number of years.

[0012] Furthermore, the system also includes: a CAD integration interface; Supports Autodesk Civil 3D format output; Automatically generate route plan, longitudinal profile, and cross profile; The output BIM model includes road information at the LOD300 level.

[0013] Secondly, a road route planning method based on multiple search and dynamic cost function optimization is provided, the method including the following steps: Identify climbing, descending, and gentle slope areas based on digital terrain models; Establish a dynamic weighted multi-dimensional cost function model that includes elevation cost, earthwork cost, curve cost, and energy consumption cost; Simultaneously execute the slope optimization strategy, the non-crossing safety strategy, the climbing-specific strategy, and the downhill safety strategy; The final path is generated by applying differentiated fusion ratios to different partitions. Implement slope adjustment, intersection elimination, and local smoothing treatment; Calculate the earthwork volume and total lifecycle cost.

[0014] The embodiments of the present invention have at least the following beneficial effects: This invention includes a terrain feature partitioning module, a multi-dimensional cost function construction module, a multi-path parallel generation module, a dynamic path fusion processing module, a post-processing optimization module, and an engineering quantity and cost calculation module. This invention establishes a dynamic cost model differentiated for uphill / downhill sections; proposes a four-path parallel search and partitioning fusion mechanism; integrates real-time cross-detection and slope-preserving algorithms; and develops a full lifecycle cost optimization model. Technical effects: Slope control accuracy ±1%, elimination of path intersections, reduction of earthwork volume by 25-30%, reduction of downhill accident risk by 35%, and improvement of design efficiency by 40%. It is particularly suitable for complex terrain scenarios such as mountain roads and mining roads.

[0015] This invention achieves the following improvements: Improved slope control accuracy: overall slope deviation ≤1%, average slope reduction of 12% on climbing sections, and 100% slope compliance rate; Improved path quality: elimination of 100% path self-intersection, reduction of 40% in the number of sharp downhill curves, and 100% compliance rate for minimum curve radius; Enhanced economic efficiency: earthwork volume reduced by 25-30%, construction costs reduced by 15%, and 20-year operating costs reduced by 18%; Improved safety performance: accident risk on downhill sections reduced by 35%, longitudinal slope continuity increased by 40%, and emergency escape zones automatically generated; Improved design efficiency: design cycle shortened by 40%, and efficiency increased by 3 times. Attached Figure Description

[0016] To more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0017] Figure 1 This is a system block diagram of a road route planning system based on multiple search and dynamic cost function optimization, provided as an embodiment of the present invention. Figure 2 This is a flowchart illustrating a road route planning method based on multiple search and dynamic cost function optimization, provided in one embodiment of the present invention. Figure 3 This is another system block diagram of a road route planning system based on multiple search and dynamic cost function optimization provided in one embodiment of the present invention; Figure 4 This is a flowchart illustrating a terrain feature zoning processing module provided in one embodiment of the present invention. Figure 5This is a schematic diagram illustrating the process of configuring weight parameters for different partitions based on a multi-dimensional cost function, according to an embodiment of the present invention. Figure 6 This is a flowchart illustrating a multi-path parallel generation module provided in one embodiment of the present invention. Figure 7 This is a flowchart illustrating a method for merging partitioned paths for different terrain types according to an embodiment of the present invention. Figure 8 This is a flowchart illustrating a method for generating partitioned paths for different terrain types according to an embodiment of the present invention. Figure 9 This is a schematic diagram of a slope adjustment process provided in one embodiment of the present invention; Figure 10 This is a schematic diagram of a process for eliminating intersections according to an embodiment of the present invention; Figure 11 This is a schematic diagram of a smoothing process provided in one embodiment of the present invention; Figure 12 This is a schematic diagram illustrating the earthwork volume calculation process provided in one embodiment of the present invention. Detailed Implementation

[0018] To further illustrate the technical means and effects adopted by the present invention to achieve its intended purpose, the following, in conjunction with the accompanying drawings and preferred embodiments, details the specific implementation, structure, features, and effects of a road route planning system based on multiple search and dynamic cost function optimization proposed according to the present invention. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. Furthermore, specific features, structures, or characteristics in one or more embodiments can be combined in any suitable form.

[0019] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0020] This invention provides a specific implementation method for a road route planning system based on multiple search and dynamic cost function optimization. The system is applicable to scenarios such as mountain highway design, railway line planning and optimization, mine heavy-haul transportation road design, hydropower station access road optimization, oil pipeline accompanying road planning, mountain scenic area sightseeing road design, and forest fire prevention channel planning.

[0021] The following description, in conjunction with the accompanying drawings, details a specific scheme for a road route planning system based on multiple search and dynamic cost function optimization provided by the present invention.

[0022] Please see Figure 1 The diagram illustrates a system block diagram of a road route planning system based on multiple search and dynamic cost function optimization according to an embodiment of the present invention. The system includes the following modules: The terrain feature zoning processing module 10 is used to identify climbing areas, downhill areas, and gentle areas based on the digital terrain model.

[0023] The terrain feature zoning processing module includes: a DEM data import unit, a terrain feature analysis unit, and a slope zoning coding unit.

[0024] The DEM data import unit is used to input digital terrain model (DTM) data and establish an elevation database. In this embodiment of the invention, a 1:2000 digital terrain model (DTM) with an accuracy of ±0.5m is used; the starting elevation is 1056m, the ending elevation is 1280m, and the target slope is 5.5%; the vehicle has a load capacity of 30t and a power-to-weight ratio of 8kW / t.

[0025] The terrain feature analysis unit is used to obtain the slope of each terrain unit based on the elevation database. The slope zoning coding unit is used to divide different terrain units into climbing areas, downhill areas, and gentle areas according to the slope of different terrain units; the output terrain zoning coding matrix is ​​stored as a terrain feature database.

[0026] Among them, terrain units with a slope of ≥5% are designated as climbing areas; Topographic units with a slope of ≤-4% are designated as downslope areas; Terrain units with a slope between -4% and 5% are designated as flat zones.

[0027] As in one embodiment of the present invention, after executing the terrain feature zoning processing module, the execution result is as follows: the climbing area accounts for 42%, the downhill area accounts for 28%, and the flat area accounts for 30%.

[0028] Please see Figure 4 , Figure 4 A flowchart illustrating the terrain feature zoning processing module.

[0029] The multi-dimensional cost function construction module 20 is used to establish a dynamically weighted multi-dimensional cost function model that includes elevation cost, earthwork cost, curve cost and energy consumption cost.

[0030] The cost components in the multi-dimensional cost function model include: elevation cost, earthwork cost, curve cost, and energy cost. The elevation cost component adopts a differentiated model for climbing or descending slopes. When the climbing slope is >7%, the weighting coefficient is 1.5, and when the descending slope is <-5%, the weighting coefficient is 1.8, thereby strengthening the slope constraint in the climbing area and strengthening the safety constraint in the descending area. The earthwork cost component adopts a quadratic model as follows: If dig; If the square is filled in; Where ΔElev represents the change in elevation of the cut and fill sections.

[0031] The cost component of the curve is modeled using the inverse radius: Downhill and R < 100m; It is uphill and R < 60m; , otherwise.

[0032] The energy cost component adopts a directional correlation model, with the cost for climbing sections being 2.5 times the product of slope and length, and the cost for descending sections being 0.8 times the product of the absolute value of slope and length.

[0033] The dynamic weights of each component are adjusted in real time based on terrain features: w1 = 0.4 + 0.2 × tanh(5 × |slope|) w² = 0.3 - 0.1 × tanh(3 × |slope|) w3 = 0.2 + 0.3 × exp(-0.5 × R) w4=0.1+0.4 / (1+exp(-5×(slope-0.08))) Where slope is the gradient; w1 is the weight value of elevation cost; w2 is the weight value of earthwork cost; w3 is the weight value of curve cost; w4 is the weight value of energy consumption standby; exp is the exponential function with the natural constant e as the base; and tanh is the hyperbolic tangent function.

[0034] Please see Figure 5 , Figure 5 A flowchart illustrating the process of configuring weight parameters for different partitions based on a multi-dimensional cost function.

[0035] The weight of the climbing area is as follows: Elevation cost has the highest weight, specifically 0.55: Strengthen slope control; The second highest energy cost is 0.20: Optimizing climbing energy consumption; The cost of earthwork is relatively low, specifically 0.15: the constraints on the amount of work should be appropriately relaxed; The weight of the downhill area is: The curve cost has the highest weight, specifically 0.45: prioritizing downhill safety. The elevation cost is moderate, specifically 0.30: controlling the downhill slope. The energy cost is the lowest, specifically 0.05: the energy consumption impact is smaller when going downhill; The weight of the smooth area is: Earthwork cost has the highest weighting, specifically 0.65: with economic efficiency as the primary objective; Other cost weights are relatively low: terrain constraints are relatively lenient.

[0036] The multi-path parallel generation module 30 is used to simultaneously execute the slope optimization strategy, the non-crossing safety strategy, the climbing-specific strategy, and the downhill safety strategy.

[0037] Four independent search strategies are used to generate feature paths simultaneously: The slope priority unit is used to execute the slope optimization strategy, including: based on a circular local search algorithm, the search radius is configurable, and in this embodiment of the invention, the search radius can be set to r=5-20m, with the goal of minimizing elevation cost; A non-intersecting path unit is used to execute the non-intersecting safety strategy, including: based on a vertical search algorithm, the maximum offset is twice the resolution, and real-time path intersection detection is introduced with a detection accuracy of ±0.1m; The hill-climbing optimization unit is used to execute the hill-climbing specific strategy, including: adopting a sector search area constraint, which in this embodiment of the invention can be set to a sector search area constraint of -30° to +30°, with the goal of minimizing energy consumption cost; The downhill safety unit is used to execute the downhill safety strategy, including: adopting a curve radius maximization strategy, in which the minimum radius constraint can be set to 60m in this embodiment of the invention.

[0038] Please see Figure 6 , Figure 6 A flowchart illustrating the process of generating a multi-path parallel module.

[0039] The dynamic path fusion processing module 40 is used to generate the final path by applying different fusion ratios to different partitions.

[0040] Climbing areas: Climbing-specific routes account for 60-80%, and non-intersecting safe routes account for 20-40%; Downhill areas: 20-40% of the downhill safe routes, and 60-80% of the safe routes without intersections; In gentle slope areas: slope-optimized paths and non-intersecting safe paths each account for 50%; Please see Figure 7 , Figure 7 This is a flowchart illustrating the method for merging partitioned paths for different terrain types.

[0041] (1) The fusion strategy for the climbing area is 70%×P_C+30%×P_B; Main focus: Hill Climbing Path P_C, accounting for 70%; Auxiliary safeguards: No-intersection safety path P_B, accounting for 30%). The design intent is to ensure climbing efficiency while maintaining basic path safety. (2) The downhill integration strategy is 30%×P_D+70%×P_B; Main focus: No-intersection safe path P_B, accounting for 70%; Safety Enhancement: Downhill safe routes P_D, accounting for 30%; The design intent is to prioritize safety on downhill sections; (3) The fusion strategy for the smooth area is 50%×P_A+50%×P_B; Balancing considerations: Slope optimization path P_A, accounting for 50%; Balancing considerations: No-intersection safe path P_B, accounting for 50%; The design intent is to balance slope optimization and path safety in gentle terrain; The fusion weight function is: w = 0.5 + 0.5 × cos(2π × p), where w is the relative position of the fused path and p is the relative position of the path.

[0042] Please see Figure 8 , Figure 8 A flowchart illustrating the methods for generating partitioned paths for different terrain types.

[0043] The post-processing optimization module 50 is used to perform slope adjustment, intersection elimination, and local smoothing.

[0044] The slope adjustment unit is used to compensate for elevation deviations according to the segment length ratio, thereby achieving slope consistency adjustment.

[0045] Please see Figure 9 , Figure 9 A flowchart illustrating the process of slope adjustment.

[0046] ΔZ_adjust,k = (S_target×L_k - ΔZ_actual,k) / n; k=1,2,...,n; Where ΔZ_adjust,k is the elevation adjustment amount that needs to be compensated for in the k-th segment. When the elevation value ΔZ_adjust,k is positive, filling is performed; when ΔZ_adjust,k is negative, excavation is performed. It should be noted that the unit of elevation value is meters. S_target is the target design slope, which is the expected slope value of the road design. L_k is the horizontal length of the k-th segment, which is the horizontal projected length of the current path segment, in meters. ΔZ_actual,k is the actual elevation difference of the k-th segment, which is the elevation difference between the start and end points of this segment in the original terrain. n is the total number of segments, meaning the path is divided into n segments.

[0047] The path verification unit is used to detect intersections in real time based on the vector cross product method and automatically replace them with safe path points.

[0048] Please see Figure 10 , Figure 10 A flowchart illustrating the process of eliminating intersections; A smoothing unit is used to apply a three-point moving average filter.

[0049] Please see Figure 11 , Figure 11 This is a flowchart illustrating the smoothing process. In the diagram, clean_path is the clean path after cross-cutting elimination, containing n path points; window_size=3 is the sliding window size, fixed at a 3-point moving average; smoothed_path is the final, completely smoothed path.

[0050] The 60 module for calculating quantities and costs is used to calculate earthwork volume and total lifecycle cost.

[0051] Earthwork volume calculation unit, used to obtain earthwork volume based on the volume surface method, with an accuracy of ±3%; The cost analysis unit is used to obtain the full-cycle cost by combining construction costs and operating costs over a preset period. In this embodiment of the invention, the preset period is 20 years.

[0052] in, For the entire lifecycle cost, For construction costs, The operating costs for year y. y is the discount factor for year y. The total lifecycle cost is the total economic investment over the 20 years from road construction to operation. Construction cost includes all expenses incurred before the road is completed, including but not limited to earthwork, pavement works, bridge and culvert works, and other ancillary facilities.

[0053] Please see Figure 12 , Figure 12This is a flowchart illustrating the process of calculating earthwork volume.

[0054] Please see Figure 3 , Figure 3 Another system block diagram of a road route planning system based on multiple search and dynamic cost function optimization provided in an embodiment of the present invention.

[0055] As a preferred embodiment of the present invention, the last road route planning system based on multiple search and dynamic cost function optimization also includes a CAD integration interface for generating standard format design files; the CAD integration interface includes: supporting Autodesk Civil 3D format output, automatically generating route plan, longitudinal profile, cross profile and outputting BIM model containing LOD300 level road information.

[0056] The technical solution of this invention was applied in a highway project (design length 18.6km), and the following effects were achieved, as shown in Table 1. Table 1 is a schematic table illustrating the effects of the technical solution of this invention: Table 1 Please see Figure 2 , Figure 2 This invention provides a flowchart illustrating a road route planning method based on multiple search and dynamic cost function optimization, the method comprising: Identify climbing, descending, and gentle slope areas based on digital terrain models; Establish a dynamic weighted multi-dimensional cost function model that includes elevation cost, earthwork cost, curve cost, and energy consumption cost; Simultaneously execute the slope optimization strategy, the non-crossing safety strategy, the climbing-specific strategy, and the downhill safety strategy; The final path is generated by applying differentiated fusion ratios to different partitions. Implement slope adjustment, intersection elimination, and local smoothing treatment; Calculate the earthwork volume and total lifecycle cost.

[0057] This invention discloses a road route planning system based on multiple search and dynamic cost function optimization. The system includes: a terrain feature partitioning module, a multi-dimensional cost function construction module, a multi-path parallel generation module, a dynamic path fusion processing module, a post-processing optimization module, and an engineering quantity and cost calculation module. The innovations are: (1) establishing a dynamic cost model with differentiated climbing / descending; (2) proposing a four-path parallel search and partition fusion mechanism; (3) integrating real-time cross detection and slope preservation algorithms; and (4) developing a full life cycle cost optimization model. Technical effects: slope control accuracy ±1%, elimination of path crossovers, reduction of earthwork volume by 25-30%, reduction of downhill accident risk by 35%, and improvement of design efficiency by 40%. It is particularly suitable for complex terrain scenarios such as mountain roads and mining roads.

[0058] It should be noted that the order of the above embodiments of the present invention is merely for descriptive purposes and does not represent the superiority or inferiority of the embodiments. The processes depicted in the accompanying drawings do not necessarily require a specific or sequential order to achieve the desired result. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0059] The various embodiments in this specification are described in a progressive manner. The same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on describing the differences from other embodiments.

Claims

1. A road route planning system based on multiple search and dynamic cost function optimization, characterized in that, The system includes the following modules: The terrain feature zoning module is used to identify climbing areas, downhill areas, and gentle slope areas based on the digital terrain model; The multi-dimensional cost function construction module is used to build a dynamically weighted multi-dimensional cost function model that includes elevation cost, earthwork cost, curve cost, and energy consumption cost. The multi-path parallel generation module is used to simultaneously execute slope optimization strategy, non-crossing safety strategy, climbing-specific strategy and downhill safety strategy; The dynamic path fusion processing module is used to generate the final path by applying different fusion ratios to different partitions. The post-processing optimization module is used to perform slope adjustment, intersection elimination, and local smoothing. The quantity and cost calculation module is used to calculate earthwork volume and total lifecycle cost.

2. The road route planning system based on multiple search and dynamic cost function optimization according to claim 1, characterized in that, The terrain feature zoning processing module includes: The DEM data import unit is used to input digital terrain model (DTM) data and establish an elevation database. The terrain feature analysis unit is used to obtain the slope of each terrain unit based on the elevation database. The slope zoning coding unit is used to divide different terrain units into climbing areas, downhill areas, and gentle areas according to the slope of different terrain units; the output terrain zoning coding matrix is ​​stored as a terrain feature database.

3. The road route planning system based on multiple search and dynamic cost function optimization according to claim 2, characterized in that, The terrain feature zoning processing module further includes: using terrain units with a slope of ≥5% as climbing areas; using terrain units with a slope of ≤-4% as downhill areas; and using terrain units with a slope between -4% and 5% as flat areas.

4. The road route planning system based on multiple search and dynamic cost function optimization according to claim 1, characterized in that, The multi-dimensional cost function construction module includes: The cost components in the multi-dimensional cost function construction module include: elevation cost, earthwork cost, curve cost, and energy consumption cost. The elevation cost component adopts a differentiated model for climbing or descending slopes. When the climbing slope is >7%, the weighting coefficient is 1.5, and when the descending slope is <-5%, the weighting coefficient is 1.

8. The earthwork cost component adopts a quadratic model; The cost component of the curve adopts the inverse radius model; The energy cost component adopts a directional correlation model, with the cost for climbing sections being 2.5 times the product of slope and length, and the cost for descending sections being 0.8 times the product of the absolute value of slope and length.

5. The road route planning system based on multiple search and dynamic cost function optimization according to claim 1, characterized in that, The multi-path parallel generation module includes: A slope-priority unit is used to execute the slope optimization strategy, including: a circular local search algorithm; A non-intersecting path unit, used to execute the non-intersecting safety policy, including: a vertical direction search algorithm; The hill-climbing optimization unit is used to execute the hill-climbing specific strategy, including: using a sector search area constraint; A downhill safety unit is used to execute the downhill safety strategy, including: employing a curve radius maximization strategy.

6. The road route planning system based on multiple search and dynamic cost function optimization according to claim 1, characterized in that, The dynamic path fusion processing module includes: Climbing areas: Climbing-specific routes account for 60-80%, and non-intersecting safe routes account for 20-40%; Downhill areas: 20-40% of the downhill safe routes, and 60-80% of the safe routes without intersections; In gentle slope areas: slope-optimized paths and non-intersecting safe paths each account for 50%; The fusion weight function is: w = 0.5 + 0.5 × cos(2π × p), where p is the relative position of the path.

7. The road route planning system based on multiple search and dynamic cost function optimization according to claim 1, characterized in that, The post-processing optimization module includes: The slope adjustment unit is used to compensate for elevation deviations proportionally to the segment length. ΔZ_adjust,k = (S_target×L_k - ΔZ_actual,k) / n; k=1,2,...,n; The path verification unit is used to detect intersections in real time based on the vector cross product method and automatically replace them with safe path points. A smoothing unit is used to apply a three-point moving average filter.

8. The road route planning system based on multiple search and dynamic cost function optimization according to claim 1, characterized in that, The quantity and cost calculation module includes: Earthwork volume calculation unit, used to obtain earthwork volume based on the volume surface method; The cost analysis unit is used to obtain the full life cycle cost by combining construction costs and operating costs for a predetermined number of years.

9. The road route planning system based on multiple search and dynamic cost function optimization according to claim 1, characterized in that, The system also includes: a CAD integration interface; Supports Autodesk Civil 3D format output; Automatically generate route plan, longitudinal profile, and cross profile; The output BIM model includes road information at the LOD300 level.

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