Mountain complex landform road line selection generation method and system

Abstract

The invention provides a mountain complex landform road line selection generation method and system, and relates to the technical field of geographic information technology and intelligent traffic, and the method comprises the steps: collecting landform data of a mountain park, and obtaining a DEM through a GIS technology; setting parameters of modeling software according to the topographic data and the DEM, and creating a road component library according to software parameters; according to the road component library, road line selection schemes are coded, the road line selection schemes are converted into particle position vector forms, and the position of each particle represents a possible road line selection scheme; and determining the scale of the particle swarm, randomly initializing the position vector and the velocity vector of each particle, and setting key parameters in a particle swarm optimization algorithm to construct a road model. According to the method, the road line selection scheme of the mountain complex landform is rapidly generated and visually displayed and analyzed, and scientific and effective support is provided for road planning and design.

Description

Technical Field

[0001] The present invention relates to the fields of geographic information technology and intelligent transportation technology, and in particular to a method and system for generating a road line selection in a mountainous area with complex landforms. Background Art

[0002] Road route selection in complex mountainous terrain is indeed a challenge that requires high technology and experience. The characteristics of this terrain usually include large undulating terrain, complex and changeable geological structure, and fragile ecological environment, all of which bring considerable difficulties to road route selection. First of all, the terrain factor is an important consideration that cannot be ignored. The mountainous terrain is complex, with undulating terrain, and mountains and valleys interlaced. This requires that when selecting the route, we must fully consider how to use the terrain and choose a relatively flat and stable route to reduce the amount of earth excavation and filling, and reduce the difficulty and cost of construction. At the same time, it is also necessary to consider the longitudinal and transverse slope design of the route to ensure the driving safety and comfort of the road. Secondly, the influence of geological conditions on road route selection is also particularly critical. The geological structure in mountainous areas is often complex and changeable, and there may be adverse geological phenomena such as faults, landslides, and mudslides. This requires detailed geological surveys during the route selection process to avoid these potential geological disaster areas and ensure the stability and safety of the road. At the same time, it is also necessary to select appropriate roadbed treatment methods and structural forms according to geological conditions to improve the bearing capacity and service life of the road. Furthermore, ecological environment protection is also an important factor that must be considered in road route selection. The ecological environment in mountainous areas is usually fragile, with high vegetation coverage and rich biodiversity. When selecting routes, damage to the ecological environment should be avoided as much as possible, and the felling of vegetation and interference with wild animals should be reduced. At the same time, drainage design and greening projects of roads need to be considered to reduce the impact of roads on the surrounding environment and achieve harmonious coexistence between road construction and the ecological environment.

[0003] In addition, construction cost and post-maintenance are also economic factors that cannot be ignored in road route selection. Construction conditions in mountainous areas are harsh, and material transportation and construction are difficult. This requires full consideration of factors such as construction cost and construction period when selecting routes, and the selection of economically reasonable route plans. At the same time, it is also necessary to consider the post-maintenance cost and service life of the road, and select materials and structural forms with good durability and low maintenance costs.

[0004] Traditional road route selection methods mainly rely on manual survey and design. This usually means that a lot of manpower, material resources and time resources are needed to carry out field surveys, data collection and analysis, and scheme design. However, due to the particularity of the complex topography of mountainous areas, this method often cannot guarantee the optimality of the route selection scheme. Summary of the invention

[0005] The technical problem to be solved by the present invention is to provide a method and system for generating road route selection in complex mountainous terrain, which can quickly generate road route selection plans for complex mountainous terrain, and perform visual display and analysis to provide scientific and effective support for road planning and design.

[0006] In order to solve the above technical problems, the technical solution of the present invention is as follows:

[0007] In a first aspect, a method for generating a road line selection in a mountainous complex terrain is provided, the method comprising:

[0008] Collect terrain data of the mountain park and obtain DEM using GIS technology;

[0009] According to the terrain data and DEM, set the parameter settings of the modeling software, and according to the software parameters, create a road component library;

[0010] According to the road component library, the road alignment scheme is encoded and converted into the position vector form of particles, and the position of each particle represents a possible road alignment scheme;

[0011] Determine the size of the particle swarm, randomly initialize the position vector and velocity vector of each particle, and set the key parameters in the particle swarm optimization algorithm to construct the road model;

[0012] A road model is constructed according to the position vector of the particle to construct a fitness function to obtain the fitness value of each particle;

[0013] According to the fitness function, the speed and position of the particles are updated, and the particle swarm optimization algorithm is used to perform iterative search to optimize the road selection scheme;

[0014] A detailed road model is constructed based on the optimized road route selection plan, and it is quickly visualized together with the terrain model and ecological model of the mountain park.

[0015] Preferably, the terrain data of the mountain park is collected and a digital elevation model is obtained using GIS technology, including:

[0016] Collect terrain data of the mountain park, including elevation data and slope data;

[0017] Obtain digital elevation models through geographic information systems.

[0018] Preferably, according to the terrain data and the digital elevation model, the parameter settings of the modeling software are set, and according to the software parameters, a road component library is created, including:

[0019] Set the parameter settings of the modeling software according to the terrain data and digital elevation model;

[0020] According to the parameter settings of the modeling software, a basic component library for park road line selection is created, including different types of road sections of asphalt pavement and cement pavement, and turning radius settings.

[0021] Preferably, according to the road component library, the road route selection scheme is encoded, and the road route selection scheme is converted into a particle position vector form, and the position of each particle represents a possible road route selection scheme, including:

[0022] According to the road component library, a road centerline represented by a series of coordinate points is constructed. Each coordinate point is used as a dimension in the position vector. The width and slope parameters of the road are added to the position vector with corresponding codes to encode the road line selection scheme.

[0023] According to the coded road selection scheme, the road selection scheme is converted into the position vector form of particles. The position of each particle represents a possible road selection scheme. For a road centerline composed of n nodes, the position vector is expressed as: (x1, y1, z1, w1, θ1, x2, y2, z2, w2, θ2, …, x n ,y n ,z n ,w n ,θ n ), where (x i ,y i ,z i ) The coordinates of the i-th node, w i is the width of the road at the node, θ i is the slope of the road at the node.

[0024] Preferably, the size of the particle swarm is determined, and the position vector and velocity vector of each particle are randomly initialized, and key parameters in the particle swarm optimization algorithm are set to construct a road model, including:

[0025] Initialize particle swarm parameters and determine the size of the particle swarm;

[0026] Initialize the position vector and velocity vector of each particle;

[0027] Set the inertia weight and initialize the inertia weight by Determine the way to reduce the number of iterations, where k is the current number of iterations, iter max is the maximum number of iterations, w is the inertia weight, w max To initialize the inertia weight w max =0.9, w min =0.4;

[0028] Set the learning factors c1 and c2, c1 and c2 are set to 2;

[0029] The road model is constructed in SubassemblyComposer according to the parameter settings of position vector, velocity vector, inertia weight and learning factor.

[0030] Preferably, a road model is constructed according to the position vector of the particle to construct a fitness function, including:

[0031] By analyzing the amount of excavation and filling of the road model in mountainous terrain, combined with the information of material prices and construction unit prices, C cost =C w +C t +C f Calculate the engineering cost of road construction, C w is the excavation cost, C t is the filling cost, C f is the construction cost;

[0032] By analyzing the intersection of road selection schemes and ecologically sensitive areas, the ecological benefit value is quantified through the SBE landscape evaluation method;

[0033] According to the road type, slope and climate conditions of the mountainous area in the road selection plan, estimate the subsequent maintenance cost of the road during its use;

[0034] According to the project cost, ecological benefits and later maintenance costs, Construct a fitness function, where α, β, and γ are weight coefficients, and E eco is the ecological benefit value, C maintain It is the subsequent maintenance cost.

[0035] Preferably, according to the fitness function, the speed and position of the particles are updated, and a particle swarm optimization algorithm is used to perform an iterative search to optimize the road selection scheme, including:

[0036] For each particle i, The new velocity vector is is the velocity of particle i in the kth iteration, r1 and r2 are random numbers in the interval [0,1], pbest i is the individual optimal position of particle i, gbest is the global optimal position;

[0037] According to the velocity vector, Update the particle's position vector;

[0038] According to the updated particle position vector, the road model is reconstructed, and the fitness value of each particle is calculated according to the fitness function;

[0039] Compare the new fitness value of each particle with the individual optimal fitness value to update the individual optimal position and the individual optimal fitness value;

[0040] Compare the individual optimal fitness values ​​of all particles, find the global optimal fitness value and the corresponding global optimal position, and determine whether the maximum number of iterations has been reached to optimize the road selection plan and obtain the global optimal road selection plan.

[0041] In a second aspect, a road route selection generation system for complex mountainous terrain includes:

[0042] The acquisition module is used to collect the terrain data of the mountain park and obtain DEM using GIS technology;

[0043] The analysis module is used to set the parameter settings of the modeling software according to the terrain data and DEM, and to create a road component library according to the software parameters; encode the road route selection scheme according to the road component library, and convert the road route selection scheme into the position vector form of particles, and the position of each particle represents a possible road route selection scheme; determine the size of the particle swarm, and randomly initialize the position vector and velocity vector of each particle, and set the key parameters in the particle swarm optimization algorithm to build a road model;

[0044] The processing module is used to construct a road model based on the position vector of the particle to construct a fitness function to obtain the fitness value of each particle; according to the fitness function, the speed and position of the particle are updated, and the particle swarm optimization algorithm is used for iterative search to optimize the road selection scheme; a detailed road model is constructed based on the optimized road selection scheme, and it is quickly visualized and generated together with the terrain model and ecological model of the mountain park.

[0045] According to a third aspect, a computing device includes:

[0046] one or more processors;

[0047] The storage device is used to store one or more programs. When the one or more programs are executed by the one or more processors, the one or more processors implement the method described.

[0048] In a fourth aspect, a computer-readable storage medium stores a program, and when the program is executed by a processor, the method described is implemented.

[0049] The above solution of the present invention includes at least the following beneficial effects:

[0050] The present invention can significantly reduce manual operation and calculation time through automated data collection, processing and analysis processes, thereby accelerating the generation of road route selection plans, helping project teams to complete more work in a shorter time, and improving overall planning efficiency. Taking into account multiple goals such as project cost, ecological benefits and post-maintenance, the particle swarm optimization algorithm is used to find the optimal road route selection plan. It can intuitively demonstrate the effect of the road route selection plan in complex mountainous landforms, which helps all stakeholders to better understand and evaluate the plan, reduce communication barriers, and improve the smoothness of project progress. Based on high-precision terrain data and professional modeling technology, high-quality road route selection plans can be generated, which not only meets the actual requirements of the project, but also protects the ecological environment to the greatest extent, achieving a win-win situation of economic and social benefits.

[0051] The present invention uses the data provided by the survey unit and the surface element function in the 3D software to analyze the contour lines and project red lines to form a parameterized and lossless mountain model. This model not only accurately reflects the geometric characteristics of the mountain, but also contains rich terrain information. The significance of the parametric information model is that the designer can conveniently adjust and modify the mountain model through professional research and judgment. The importance of the lossless information model is that it can completely retain the original data and avoid information loss during data conversion and processing. These two characteristics are of great significance for subsequent mountain analysis and planning design, and can provide more accurate and comprehensive data support.

[0052] The present invention can analyze the optimal route selection, and it is an efficient and parameterized method to assemble the road. The first of the road parametric modeling is that the plane selection and section design of the road can be designed independently without interfering with each other, and after determining one of them, the unit module can be quickly replaced to form an overall linkage scheme modification. Secondly, in the plane selection, the designer can make judgments and adjustments based on the immediate feedback of the software, and at the same time, the elevation has two-dimensional processing methods of cross section and longitudinal section. The cross section is programmed for the road practice and the lateral drainage slope. The longitudinal section can be adjusted for each section of the slope change point, so that the reasonable slope required by the first party is always within the specified range. Finally, during the writing process, the designer can also give the road foundation, surface layer, bonding layer and other structural code information, so as to form a complete road model in the 3D software. This parameterized road model has high flexibility, and its material information can be given as needed in subsequent work. This not only makes the road model more accurate in the calculation of engineering quantity, but also can be used for effect rendering and engineering animation simulation. In this way, we can better evaluate the feasibility and visual effect of road design and provide strong support for decision-making.

[0053] In addition, the road information model also helps improve work efficiency. Since the model is built based on parameters, designers can easily modify and adjust it without having to start modeling from scratch. This greatly reduces duplication of work and time costs, and improves design efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] Figure 1 It is a flow chart of a method for generating a road line selection in a mountainous complex terrain provided by an embodiment of the present invention.

[0055] Figure 2 It is a schematic diagram of a road route selection generation system for complex mountainous terrain provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0056] The exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although the exemplary embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in a form and should not be limited by the embodiments set forth herein. On the contrary, these embodiments are provided in order to enable a more thorough understanding of the present disclosure and to fully convey the scope of the present disclosure to those skilled in the art.

[0057] like Figure 1 As shown, an embodiment of the present invention provides a method for generating a road line selection in a mountainous complex terrain, the method comprising the following steps:

[0058] Step 11, collect terrain data of the mountain park and obtain DEM using GIS technology;

[0059] Step 12, setting the parameter settings of the modeling software according to the terrain data and the DEM, and creating a road component library according to the software parameters;

[0060] Step 13, encoding the road alignment scheme according to the road component library, and converting the road alignment scheme into a particle position vector form, where the position of each particle represents a possible road alignment scheme;

[0061] Step 14, determine the size of the particle swarm, randomly initialize the position vector and velocity vector of each particle, and set the key parameters in the particle swarm optimization algorithm to construct a road model;

[0062] Step 15, constructing a road model according to the position vector of the particle to construct a fitness function to obtain the fitness value of each particle;

[0063] Step 16, according to the fitness function, the speed and position of the particles are updated, and the particle swarm optimization algorithm is used to perform iterative search to optimize the road line selection scheme;

[0064] Step 17, construct a detailed road model based on the optimized road selection plan, and quickly visualize it with the terrain model and ecological model of the mountain park.

[0065] In the embodiment of the present invention, the terrain data of the mountain park is collected and the DEM (digital elevation model) is obtained by using GIS technology, which ensures the accuracy and comprehensiveness of the data and provides a solid foundation for the subsequent road selection. By setting the parameters of the modeling software according to the terrain data and DEM, a road component library adapted to the specific terrain can be created, which improves the flexibility and efficiency of modeling. By encoding the road selection scheme and converting it into the position vector form of particles, a variety of possible road selection schemes can be represented, which increases the diversity and choice of the scheme. Determining the scale of the particle swarm and initializing the position vector and velocity vector of the particles, setting key parameters, it is helpful to build a road model that can globally search for the optimal solution, and improve the optimization ability. Constructing a fitness function to obtain the fitness value of each particle can quantitatively evaluate the advantages and disadvantages of different road selection schemes, providing an objective basis for decision-making. Using the particle swarm optimization algorithm for iterative search, it can quickly converge to the optimal solution, improving the optimization efficiency and effect of the road selection scheme. A detailed road model is constructed according to the optimized road selection scheme, and it is quickly visualized with the terrain model, ecological model, etc. of the mountain park, so that all stakeholders can intuitively understand and evaluate the scheme, which promotes project communication and decision-making.

[0066] In a preferred embodiment of the present invention, the above step 11, collecting terrain data of the mountain park and obtaining a digital elevation model using GIS technology, includes:

[0067] Collect terrain data of the mountain park, including elevation data and slope data;

[0068] Obtain digital elevation models through geographic information systems.

[0069] In an embodiment of the present invention, the elevation data and slope data are collected by professional means to ensure the accuracy and detail of these data. The elevation data and slope data can fully reflect the terrain characteristics of the mountain park, including the undulating mountains, slope changes, etc., so that planners can have a deeper understanding of the terrain conditions; the digital elevation model (DEM) built based on accurate terrain data can provide decision makers with an intuitive three-dimensional view, helping them to better understand the complexity and challenges of the terrain. Using GIS technology to acquire and process data can automatically generate digital elevation models, greatly improving the efficiency of data processing. Accurate terrain data and digital elevation models help identify and assess potential geological risks and ecologically sensitive areas. Taking these factors into account in the project planning stage can reduce risks and costs in future implementation processes. By sharing these precise terrain data and digital elevation models, different departments can communicate and collaborate more effectively.

[0070] In a specific embodiment of the present invention, step 11 is specifically implemented as follows: collect terrain data of the mountain park, including elevation data, slope data, etc., and obtain a high-precision digital elevation model (DEM) through a geographic information system (GIS). Secondly, obtain information such as vegetation distribution, wildlife habitats, and water distribution in the park through historical data, and mark ecologically sensitive areas. At the same time, organize engineering cost data related to road construction, including construction costs under different terrain conditions (such as excavation and filling costs, material transportation costs, etc.), as well as later maintenance cost data (such as maintenance costs of different road surface types in mountainous environments, maintenance costs of road facilities, etc.).

[0071] In a preferred embodiment of the present invention, the above step 12, setting the parameters of the modeling software according to the terrain data and the digital elevation model, and creating a road component library according to the software parameters, includes:

[0072] Set the parameter settings of the modeling software according to the terrain data and digital elevation model;

[0073] According to the parameter settings of the modeling software, a basic component library for park road line selection is created, including different types of road sections of asphalt pavement and cement pavement, and turning radius settings.

[0074] In an embodiment of the present invention, by setting the modeling software parameters according to specific terrain data and digital elevation models, a high degree of customization of road design can be achieved. Creating a road component library means that designers can directly use pre-defined components (such as different types of road sections and turning radius settings such as asphalt pavement and cement pavement), thereby avoiding the tedious process of designing from scratch. The elements in the component library are carefully designed and optimized, so the use of these components can ensure the quality and consistency of the road design. This reduces the risk of design errors and improves the quality of the overall project. If the road design needs to be modified or maintained, using the component library can be more convenient and quick.

[0075] In a specific embodiment of the present invention, step 12 is specifically implemented as follows:

[0076] Step 1: Import the terrain model of the mountain park in SubassemblyComposer, set the basic parameters such as modeling unit (m), coordinate system, etc. to ensure the accuracy and usability of the model.

[0077] Step 2: Create a basic component library for park road line selection in the modeling software, including different types of road sections such as asphalt pavement, cement pavement, turning radius settings, etc., so that the road model can be quickly constructed based on the parameters generated by the algorithm.

[0078] In a preferred embodiment of the present invention, the above step 13, encoding the road route selection scheme according to the road component library, converting the road route selection scheme into the position vector form of particles, wherein the position of each particle represents a possible road route selection scheme, includes:

[0079] According to the road component library, a road centerline represented by a series of coordinate points is constructed. Each coordinate point is used as a dimension in the position vector. The width and slope parameters of the road are added to the position vector with corresponding codes to encode the road line selection scheme.

[0080] According to the coded road selection scheme, the road selection scheme is converted into the position vector form of particles. The position of each particle represents a possible road selection scheme. For a road centerline composed of n nodes, the position vector is expressed as: (x1, y1, z1, w1, θ1, x2, y2, z2, w2, θ2, …, x n ,y n ,z n ,w n ,θ n ), where (x i ,y i ,z i ) The coordinates of the i-th node, w i is the width of the road at the node, θi is the slope of the road at the node.

[0081] In an embodiment of the present invention, by expressing the road centerline as a series of coordinate points and adding the road width and slope coding in the position vector, each road route selection scheme can be described very clearly and accurately. This digital expression is convenient for storage, transmission and analysis, and greatly improves the accuracy and operability of the scheme expression. The road route selection scheme is converted into the position vector form of particles, so that the optimization algorithm (such as particle swarm optimization algorithm) can be conveniently applied to the road route selection problem. The position of each particle represents a possible road route selection scheme. Through the iterative search of the optimization algorithm, the optimal or approximately optimal road route selection scheme can be quickly found. The present invention encodes the road route selection scheme into a position vector, so that the design scheme can be automatically generated and optimized, greatly improving the design efficiency. Since the position of each particle represents a possible road route selection scheme, by adjusting the position and speed of the particle, the design space can be fully explored and more possible excellent schemes can be found. This encoding method has good flexibility and scalability. By adjusting the elements and encoding methods in the position vector, different design requirements and constraints can be easily adapted.

[0082] In a specific embodiment of the present invention, step 13 is specifically implemented as follows: Encode the road line selection scheme into a particle position vector. Specifically, construct a road centerline represented by a series of coordinate points, and each coordinate point is used as a dimension in the position vector. At the same time, considering parameters such as the width and slope of the road, add corresponding codes to the position vector. For example, for a road centerline consisting of \(n\) nodes, the position vector can be represented as (x1, y1, z1, w1, θ1, x2, y2, z2, w2, θ2, …, x n ,y n ,z n ,w n ,θ n ), where (x i ,y i ,z i ) The coordinates of the i-th node, w i is the width of the road at the node, θ i is the slope of the road at the node.

[0083] In a preferred embodiment of the present invention, the above step 14, determining the size of the particle swarm, and randomly initializing the position vector and velocity vector of each particle, setting the key parameters in the particle swarm optimization algorithm to construct the road model, includes:

[0084] Initialize particle swarm parameters and determine the size of the particle swarm;

[0085] Initialize the position vector and velocity vector of each particle;

[0086] Set the inertia weight and initialize the inertia weight by Determine the way to reduce the number of iterations, where k is the current number of iterations, iter max is the maximum number of iterations, w is the inertia weight, w max To initialize the inertia weight w max =0.9, w min =0.4;

[0087] Set learning factors c1 and c2, where c1 and c2 are set to 2;

[0088] The road model is constructed in SubassemblyComposer according to the parameter settings of position vector, velocity vector, inertia weight and learning factor.

[0089] In an embodiment of the present invention, by determining a suitable particle swarm size, the diversity of the search space can be ensured, thereby increasing the possibility of finding a global optimal solution. The appropriate particle swarm size can also control the computational complexity while maintaining the search effect, thereby improving the operation efficiency of the algorithm. Randomly initializing the position and velocity of the particles helps the algorithm to explore extensively in the search space and avoid falling into the local optimum too early. Different initial positions and velocity vectors mean that particles can start searching from multiple different starting points, increasing the chance of finding different local optimal solutions. The setting of the inertia weight helps to balance the global search and local search capabilities of the algorithm. By adjusting the inertia weight in a decaying manner according to the number of iterations, global exploration can be emphasized in the early stage of the search, while local fine search can be strengthened in the later stage. As the number of iterations increases, the decreasing strategy of the inertia weight enables the algorithm to adaptively adjust the search strategy and improve the search efficiency. c1 and c2 represent the influence of individual experience and group experience on particle movement, respectively. Setting them to appropriate values ​​(such as 2) can balance individual learning and social learning and avoid over-reliance on single-sided information. The appropriate setting of the learning factor can accelerate the convergence speed of the algorithm while maintaining the diversity of the search and preventing premature convergence to a non-global optimal solution. By building a road model in SubassemblyComposer through the particle swarm optimization algorithm, the road design scheme can be intuitively displayed and optimized. Using intelligent optimization algorithms to assist road design can greatly improve design efficiency and reduce the workload of manual adjustments. By iteratively searching for the optimal solution through the algorithm, a more reasonable and efficient road design scheme can be found under the condition of meeting the design constraints.

[0090] In a specific embodiment of the present invention, step 14 is specifically implemented as follows:

[0091] Step 1: Initialize the particle swarm parameters and determine the size m of the particle swarm. Specifically, set m=50.

[0092] Step 2: Randomly initialize the position vector and velocity vector of each particle. The initialization of the position vector should be within the effective range of the mountain park, and the initialization of the velocity vector should be set to a suitable range based on experience. Specifically, if the mountain park is large and the search space for road selection is wide, in order to allow particles to quickly traverse a large area in the initial iteration to find potential optimal solution areas, a larger speed range, such as (-2, 2), can be appropriately selected. On the contrary, if the park is small and the search space is limited, a smaller speed range, such as (-0.5, 0.5), should be selected to avoid particles from excessively crossing the effective search area and improve search efficiency. For complex mountain terrain road selection problems, there is a high possibility that there are multiple local optimal solutions. If the speed range is too large, particles may quickly skip some areas where local optimal solutions are located and miss the global optimal solution. At this time, a smaller speed range, such as (-1, 1) or (-0.5, 0.5), helps particles explore local areas more carefully and increases the probability of finding the global optimal solution.

[0093] Step 3: Set the inertia weight w, for example, the initial value is set to 0.9, and determine how it decays with the number of iterations, such as linear decay, through Determine the way to reduce the number of iterations, where k is the current number of iterations, iter max is the maximum number of iterations, w is the inertia weight, w max To initialize the inertia weight w max =0.9, w min =0.4.

[0094] Step 5: Build the road model in SubassemblyComposer based on the particle position vector (road line selection scheme).

[0095] In a preferred embodiment of the present invention, the above step 15, constructing a road model according to the position vector of the particle to construct a fitness function, includes:

[0096] By analyzing the amount of excavation and filling of the road model in mountainous terrain, combined with the information of material prices and construction unit prices, C cost =C w +C t +C f Calculate the engineering cost of road construction, C w is the excavation cost, C t is the filling cost, C f is the construction cost;

[0097] By analyzing the intersection of road selection schemes and ecologically sensitive areas, the ecological benefit value is quantified through the SBE landscape evaluation method;

[0098] According to the road type, slope and climate conditions of the mountainous area in the road selection plan, estimate the subsequent maintenance cost of the road during its use;

[0099] According to the project cost, ecological benefits and later maintenance costs, Construct a fitness function, where α, β, and γ are weight coefficients, and E eco is the ecological benefit value, C maintain It is the subsequent maintenance cost.

[0100] In an embodiment of the present invention, by accurately calculating the cost of excavation and filling, combined with the material price and the unit price of construction, the engineering cost of road construction can be more accurately estimated, which helps to have a clear control of the cost at the beginning of the project, so as to make a more reasonable budget planning. The introduction of the SBE landscape evaluation method can scientifically quantify the intersection of the road selection scheme and the ecologically sensitive area, and obtain the ecological benefit value. This not only helps to protect the ecological environment, but also avoids potential ecological damage in the planning stage. The later maintenance cost is estimated according to the road type, slope and climatic conditions, which makes the project planning more comprehensive, and can take into account the continuous investment in the road use process in advance, avoiding the economic pressure caused by excessive maintenance costs in the future. By constructing a fitness function based on the comprehensive engineering cost, ecological benefit and later maintenance cost, the advantages and disadvantages of the road selection scheme can be more comprehensively evaluated. This comprehensive evaluation method helps to find a solution that is both economical and environmentally friendly with relatively low maintenance costs. The α, β, and γ weight coefficients in the fitness function can be adjusted according to the actual situation, which provides great flexibility, allowing decision makers to customize the evaluation criteria according to different project needs and priorities.

[0101] In a specific embodiment of the present invention, step 15 is specifically implemented as follows:

[0102] Step 1: Calculate the construction cost of the road by analyzing the amount of excavation and filling of the road model in the mountainous terrain and combining local material prices, construction unit prices and other information. Specifically, the excavation cost C w Equal to the excavation volume calculated in SubassemblyComposer × unit excavation price calculation; fill cost C t The cost of filling materials and transportation costs should also be considered. In addition, the construction cost of road ancillary facilities (such as drainage facilities, guardrails, etc.) f , and the project cost Ccost is obtained comprehensively.

[0103] Step 2: Analyze the intersection of the road selection scheme and ecologically sensitive areas. When the road passes through densely vegetated areas, wildlife habitats, or is close to water protection areas, the ecological benefit loss value is calculated based on the pre-set ecological damage weight. The longer the length of the road passing through the forest area, the greater the ecological benefit loss.

[0104] Step 3: Quantify the ecological benefit value E through SBE landscape evaluation method eco ,In order to facilitate unification with other objectives, the ecological benefit value is expressed here by the inverse of the loss value or the value after a certain transformation, so that the larger the value, the better the ecological benefit.

[0105] Step 4: Estimate the subsequent maintenance cost of the road during use based on the road type (such as asphalt pavement, cement pavement, etc.) and slope conditions in the road selection plan, as well as the climate conditions in the mountainous area (such as rainfall, temperature changes, etc.). Specifically, sections with larger slopes may be more prone to road wear and soil erosion, and have higher maintenance costs; in rainy areas, the maintenance cost of drainage facilities also needs to be considered. Calculate the subsequent maintenance cost Cmaintain.

[0106] Step 5: Fitness function construction: Considering the engineering cost, ecological benefits and later maintenance costs, the fitness function is constructed: Construct a fitness function, where α, β, and γ are weight coefficients, and E eco is the ecological benefit value, c maintain is the later maintenance cost; it is determined according to the importance the project places on the three major goals. For example, if the ecological benefit requirement is high, the value of β can be relatively large.

[0107] In a preferred embodiment of the present invention, the above step 16, according to the fitness function, updates the speed and position of the particles, and uses the particle swarm optimization algorithm to perform iterative search to optimize the road line selection scheme, including:

[0108] For each particle i, The new velocity vector is is the velocity of particle i in the kth iteration, r1 and r2 are random numbers in the interval [0,1], pbest i is the individual optimal position of particle i, gbest is the global optimal position;

[0109] According to the velocity vector, Update the particle's position vector;

[0110] According to the updated particle position vector, the road model is reconstructed, and the fitness value of each particle is calculated according to the fitness function;

[0111] Compare the new fitness value of each particle with the individual optimal fitness value to update the individual optimal position and the individual optimal fitness value;

[0112] Compare the individual optimal fitness values ​​of all particles, find the global optimal fitness value and the corresponding global optimal position, and determine whether the maximum number of iterations has been reached to optimize the road selection plan and obtain the global optimal road selection plan.

[0113] In an embodiment of the present invention, the particle swarm optimization algorithm can perform efficient search in a complex search space by simulating the behavior of bird flocks foraging. By continuously updating the speed and position of particles, the algorithm can quickly find areas with higher fitness, thereby improving search efficiency. The algorithm not only focuses on the optimal solution of individual particles, but also guides the particle swarm to converge to the global optimal solution by sharing the global optimal position. This mechanism helps to avoid falling into local optimality and enhance the global optimization ability. During the iteration process, particles will dynamically adjust their own speed and position according to individual optimal and global optimal information, thereby achieving self-optimization and group collaborative optimization. The particle swarm optimization algorithm is an optimization method based on swarm intelligence with a high degree of automation. By setting appropriate parameters and fitness functions, the algorithm can automatically perform iterative search to reduce the burden of manual intervention. The algorithm is applicable to various types of road selection problems, and can adapt to different optimization objectives by adjusting parameters and fitness functions. In addition, with the increase of computing resources, the algorithm can be easily extended to larger-scale optimization problems. The road selection scheme obtained by the particle swarm optimization algorithm can be intuitively displayed on the map, which is convenient for decision makers to understand and evaluate.

[0114] In a specific embodiment of the present invention, step 16 is specifically implemented as follows:

[0115] Step 1: For each particle i, calculate the new velocity vector according to the velocity update formula in, is the velocity of particle i in the kth iteration, r1 and r2 are random numbers in the interval [0,1], pbest i is the individual optimal position of particle i, and gbest is the global optimal position.

[0116] Step 2: Then update the particle's position vector according to the position update formula.

[0117] Step 3: Based on the updated particle position vector, rebuild the road model in SubassemblyComposer and calculate its fitness value.

[0118] Step 4: Compare the new fitness value of each particle with the individual optimal fitness value. If the new value is better, update the individual optimal position pbesti and the individual optimal fitness value.

[0119] Step 5: Compare the individual optimal fitness values ​​of all particles to find the global optimal fitness value and the corresponding global optimal position gbest.

[0120] Step 6: Determine whether the maximum number of iterations has been reached. In this case, the maximum number of iterations is set to 200. If it has been reached, the algorithm terminates and outputs the road selection scheme corresponding to the global optimal position; if it has not been reached, it returns to the particle position update step and continues to the next iteration.

[0121] In a preferred embodiment of the present invention, the above step 17 includes:

[0122] Construct a detailed road model based on the parameters of the optimal solution, including the road geometry, pavement material, and ancillary facilities;

[0123] The road model is integrated with the terrain model and ecological model of the mountain park to intuitively present the position of the road line in the complex mountain landforms, its relationship with ecologically sensitive areas, and the overall landscape effect, so as to achieve rapid visualization of road line selection in complex mountain landforms.

[0124] In an embodiment of the present invention, by constructing a detailed road model, including the geometric shape of the road, road surface material, ancillary facilities, etc., the actual appearance of the road can be accurately displayed. The integration with the terrain model and the ecological model allows the position of the road line in the complex mountain landform, the relationship with the ecologically sensitive areas, and the overall landscape effect to be intuitively presented. Visual generation enables decision makers to quickly understand and evaluate different road line selection schemes, thereby improving decision-making efficiency. Through intuitive display, decision makers can more easily identify potential problems and risks and make corresponding adjustments. Visualization helps to identify the relationship between road line selection and ecologically sensitive areas, thereby avoiding damage to the ecological environment in the planning stage, helping to protect natural resources and the ecological environment and achieve sustainable development.

[0125] In a specific embodiment of the present invention, step 17 is specifically implemented as follows:

[0126] Step 1: After the algorithm is completed, the globally optimal road line selection scheme is obtained, and its corresponding particle position vector contains key parameters such as road centerline coordinates, width, slope, etc.

[0127] Step 2: Visualize the display in Subassembly Composer and build a detailed road model based on the parameters of the optimal solution, including the road geometry, road surface material, and ancillary facilities. At the same time, integrate the road model with the terrain model and ecological model of the mountain park to intuitively present the location of the road line in the complex mountain landform, the relationship with the ecologically sensitive area, and the overall landscape effect, providing a comprehensive visualization basis for road construction decisions.

[0128] like Figure 2 As shown, an embodiment of the present invention further provides a road line selection generation system 20 for mountainous complex terrain, comprising:

[0129] The acquisition module 21 is used to collect the terrain data of the mountain park and obtain the DEM using GIS technology;

[0130] The analysis module 22 is used to set the parameter settings of the modeling software according to the terrain data and the DEM, and to create a road component library according to the software parameters; to encode the road route selection scheme according to the road component library, and to convert the road route selection scheme into the position vector form of particles, where the position of each particle represents a possible road route selection scheme; to determine the size of the particle swarm, and to randomly initialize the position vector and velocity vector of each particle, and to set the key parameters in the particle swarm optimization algorithm to construct a road model;

[0131] The processing module 23 is used to construct a road model according to the position vector of the particle to construct a fitness function to obtain the fitness value of each particle; according to the fitness function, the speed and position of the particle are updated, and the particle swarm optimization algorithm is used to perform iterative search to optimize the road line selection scheme; a detailed road model is constructed according to the optimized road line selection scheme, and it is quickly visualized and generated together with the terrain model, ecological model, etc. of the mountain park.

[0132] The above is a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A method for generating a road line selection in a mountainous complex terrain, characterized in that: The method comprises: Collect terrain data of the mountain park and obtain DEM using GIS technology; According to the terrain data and DEM, set the parameter settings of the modeling software, and according to the software parameters, create a road component library; According to the road component library, the road alignment scheme is encoded and converted into the position vector form of particles, and the position of each particle represents a possible road alignment scheme; Determine the size of the particle swarm, randomly initialize the position vector and velocity vector of each particle, and set the key parameters in the particle swarm optimization algorithm to construct the road model; A road model is constructed according to the position vector of the particle to construct a fitness function to obtain the fitness value of each particle; According to the fitness function, the speed and position of the particles are updated, and the particle swarm optimization algorithm is used to perform iterative search to optimize the road selection scheme; A detailed road model is constructed based on the optimized road route selection plan, and it is quickly visualized together with the terrain model and ecological model of the mountain park.

2. The method for generating road line selection in mountainous complex terrain according to claim 1, characterized in that: Collect terrain data of the mountain park and use GIS technology to obtain digital elevation models, including: Collect terrain data of the mountain park, including elevation data and slope data; Obtain digital elevation models through geographic information systems.

3. The method for generating road line selection in mountainous complex terrain according to claim 2, characterized in that: According to the terrain data and digital elevation model, set the parameter settings of the modeling software, and according to the software parameters, create a road component library, including: Set the parameter settings of the modeling software according to the terrain data and digital elevation model; According to the parameter settings of the modeling software, a basic component library for park road line selection is created, including different types of road sections of asphalt pavement and cement pavement, and turning radius settings.

4. The method for generating road line selection in mountainous complex terrain according to claim 3, characterized in that: According to the road component library, the road alignment scheme is encoded and converted into the position vector form of particles. The position of each particle represents a possible road alignment scheme, including: According to the road component library, a road centerline represented by a series of coordinate points is constructed. Each coordinate point is used as a dimension in the position vector. The width and slope parameters of the road are added to the position vector with corresponding codes to encode the road line selection scheme. According to the coded road selection scheme, the road selection scheme is converted into the position vector form of particles. The position of each particle represents a possible road selection scheme. For a road centerline composed of n nodes, the position vector is expressed as: (x1, y1, z1, w1, θ1, x2, y2, z2, w2, θ2, …, x n ,y n ,z n ,w n ,θ n ), where (x i ,y i ,z i ) The coordinates of the i-th node, w i is the width of the road at the node, θ i is the slope of the road at the node.

5. The method for generating road line selection in mountainous complex terrain according to claim 4, characterized in that: Determine the size of the particle swarm, randomly initialize the position vector and velocity vector of each particle, and set the key parameters in the particle swarm optimization algorithm to build a road model, including: Initialize particle swarm parameters and determine the size of the particle swarm; Initialize the position vector and velocity vector of each particle; Set the inertia weight and initialize the inertia weight by Determine the way to reduce the number of iterations, where k is the current number of iterations, iter max is the maximum number of iterations, w is the inertia weight, w max To initialize the inertia weight w max =0.9, w min =0.4; Set the learning factors c1 and c2, c1 and c2 are set to 2; The road model is constructed according to the parameter settings of the position vector, velocity vector, inertia weight and learning factor.

6. The method for generating road line selection in mountainous complex terrain according to claim 5, characterized in that: A road model is constructed based on the particle's position vector to construct a fitness function, including: By analyzing the amount of excavation and filling of the road model in mountainous terrain, combined with the information of material prices and construction unit prices, C cost =C w +C t +C f Calculate the construction cost of road construction, C w is the excavation cost, C t is the filling cost, C f is the construction cost; By analyzing the intersection of road selection schemes and ecologically sensitive areas, the ecological benefit value is quantified through the SBE landscape evaluation method; According to the road type, slope and climate conditions of the mountainous area in the road selection plan, estimate the subsequent maintenance cost of the road during its use; According to the project cost, ecological benefits and later maintenance costs, Construct a fitness function, where α, β, and γ are weight coefficients, and E eco is the ecological benefit value, C maintain It is the subsequent maintenance cost.

7. The method for generating road line selection in mountainous complex terrain according to claim 6, characterized in that: According to the fitness function, the speed and position of the particles are updated, and the particle swarm optimization algorithm is used for iterative search to optimize the road selection scheme, including: For each particle i, The new velocity vector is is the velocity of particle i in the kth iteration, r1 and r2 are random numbers in the interval [0,1], pbest i is the individual optimal position of particle i, gbest is the global optimal position; According to the velocity vector, Update the particle's position vector; According to the updated particle position vector, the road model is reconstructed, and the fitness value of each particle is calculated according to the fitness function; Compare the new fitness value of each particle with the individual optimal fitness value to update the individual optimal position and the individual optimal fitness value; Compare the individual optimal fitness values ​​of all particles, find the global optimal fitness value and the corresponding global optimal position, and determine whether the maximum number of iterations has been reached to optimize the road selection plan and obtain the global optimal road selection plan.

8. A road selection generation system for complex mountainous terrain, characterized in that: include: The acquisition module is used to collect the terrain data of the mountain park and obtain DEM using GIS technology; The analysis module is used to set the parameter settings of the modeling software according to the terrain data and DEM, and to create a road component library according to the software parameters; encode the road route selection scheme according to the road component library, and convert the road route selection scheme into the position vector form of particles, and the position of each particle represents a possible road route selection scheme; determine the size of the particle swarm, and randomly initialize the position vector and velocity vector of each particle, and set the key parameters in the particle swarm optimization algorithm to build a road model; The processing module is used to construct a road model based on the position vector of the particle to construct a fitness function to obtain the fitness value of each particle; according to the fitness function, the speed and position of the particle are updated, and the particle swarm optimization algorithm is used for iterative search to optimize the road selection scheme; a detailed road model is constructed based on the optimized road selection scheme, and it is quickly visualized and generated together with the terrain model and ecological model of the mountain park.

9. A computing device, characterized in that include: one or more processors; A storage device for storing one or more programs, when the one or more programs are executed by the one or more processors, the one or more processors implement the method as claimed in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a program, which, when executed by a processor, implements the method according to any one of claims 1 to 7.

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