A wind farm road construction earthwork intelligent deployment method, system and device

Abstract

The present application relates to the technical field of wind power plant construction, and particularly relates to a wind power plant road construction earthwork intelligent allocation method, system and equipment. The allocation method comprises the following steps: collecting wind power plant road earthwork engineering data to be built and preprocessing to obtain a standardized engineering data set; an earthwork intelligent allocation model based on A* algorithm and linear programming fusion is constructed, the model takes the transport volume priority as the core criterion, cancels the allocation priority mechanism, and realizes the optimal allocation by calculating the minimum transport volume of the global excavation pile number and the filling pile number; presetting the allocation limit condition, configuring the limit condition matrix, and based on the standardized engineering data set, the earthwork intelligent allocation model is used to generate the earthwork allocation scheme of the wind power plant road. The present application adapts to the topological characteristics of the wind power plant road, realizes the efficient and reasonable allocation of the wind power plant road earthwork through the global transport volume priority, and effectively controls the construction cost.

Description

Technical Field

[0001] This invention relates to the field of wind power plant construction technology, and in particular to a method, system and equipment for intelligent allocation of earthwork in wind farm road construction. Background Technology

[0002] In the field of wind farm construction, the design of on-site road engineering typically needs to be based on the wind turbine locations and access road layout. Since wind farm roads often exhibit a tree-like or network structure, earthwork allocation involves balancing excavation and filling between multiple roads, as well as comprehensive coordination of borrow pits and spoil heaps. Traditional highway subgrade earthwork allocation methods are generally designed for linear projects, employing the principles of segmented balancing and longitudinal transport, which are difficult to directly apply to the multi-point, multi-path collaborative allocation in the complex road networks of wind farms.

[0003] Currently, earthwork allocation among multiple roads mainly relies on manual experience to determine the inflow and outflow points and detour distances between the roads to plan allocation routes. However, this experience-based approach has two problems: First, when faced with tree-like or network-like road structures, manual judgment makes it difficult to simultaneously consider the overall cut-fill balance and the shortest transportation path, easily leading to unreasonable allocation route selection and increased secondary transportation. Second, the capacity and location of borrow pits and their relationship with the allocation of multiple roads are complex, and manual setting is cumbersome and inaccurate, failing to form a globally optimal engineering volume allocation plan. This results in increased earthwork transportation costs during construction, severely reducing construction efficiency and environmental quality. Summary of the Invention

[0004] To address the shortcomings of existing technologies, the purpose of this invention is to provide an intelligent earthwork allocation method for wind farm road construction, solving the problems of cumbersome steps and unreasonable transportation and allocation schemes in existing technologies, and providing a convenient and reasonable construction method for existing wind farm road construction projects.

[0005] To achieve the above objectives, the embodiments of the present invention provide the following technical solutions: In a first aspect, embodiments of the present invention provide an intelligent allocation method for earthwork in wind farm road construction, comprising: Collect and preprocess data on earthwork engineering for roads in the wind farm to be built to obtain a standardized engineering dataset. An intelligent earthwork allocation model based on the fusion of A* algorithm and linear programming is constructed. The model takes the transportation volume priority as the core criterion, eliminates the allocation priority mechanism, and achieves optimal allocation by calculating the minimum transportation volume of global excavation and filling station numbers. Preset allocation constraints, configure constraint matrix, and generate earthwork allocation schemes for wind farm roads using an intelligent earthwork allocation model based on standardized engineering datasets.

[0006] Secondly, embodiments of the present invention also provide a system for performing the intelligent allocation method for earthwork in wind farm road construction as described above, comprising: The data acquisition module is used to collect and preprocess data on the earthwork of the road to the wind farm to be built, and to obtain a standardized engineering dataset. The model building module is used to build an intelligent earthwork allocation model based on the fusion of A* algorithm and linear programming. This module includes a path search unit, a matrix construction unit, and an objective function and constraint setting unit. The scheme generation module is used to input standardized engineering datasets and generate globally optimal allocation schemes through model solving. The visualization module is used to visualize and display the allocation plan in the form of GIS maps and tables.

[0007] Thirdly, embodiments of the present invention also provide an electronic device, comprising: processor; Memory used to store processor-executable instructions; A computer program stored in the memory and capable of running on the processor; The processor is configured to invoke instructions stored in the memory to execute steps of the intelligent allocation method for earthwork in wind farm road construction as described above.

[0008] Fourthly, embodiments of the present invention also provide a computer-readable storage medium storing computer program instructions thereon, which, when executed by a processor, implement the steps of the intelligent allocation method for earthwork in wind farm road construction as described above.

[0009] One or more technical solutions provided in the embodiments of the present invention have at least the following technical effects or advantages: The allocation method of this invention prioritizes transport volume, eliminating the need to set a specific order for road entry and exit points. Instead, it treats all cut and fill station numbers as equal nodes, with the model uniformly calculating the minimum transport volume. This mechanism avoids path selection bias caused by manually setting priorities. Secondly, the method integrates the A* algorithm and linear programming: the A* algorithm searches for the shortest transport distance from any cut station to any fill station in the tree-like or network-like road structure of the wind farm, replacing the tedious step of manually estimating detour distances; linear programming uses the minimum global total transport volume as the objective function, simultaneously allocating earthwork flows between all roads and between roads and borrow pits. Compared to the limitations of human experience in handling the coordination relationships of multiple roads and multiple borrow pits simultaneously, this invention integrates the A* algorithm and linear programming, enabling the model to automatically find the optimal allocation combination in complex topologies. Furthermore, the method pre-sets allocation constraints and configures a constraint matrix. Economic distance constraints and capacity constraints are encoded as matrix elements, automatically eliminating infeasible solutions during the solution process to ensure that the output conforms to engineering specifications and avoids the inefficiency and oversights of manual verification. In summary, this method, by eliminating the allocation priority mechanism, introducing the A* algorithm and integrating it with linear programming, and setting a constraint matrix, transforms the originally tedious process relying on manual experience into automated global optimization calculations, reducing secondary transportation and improving construction efficiency.

[0010] Advantages of additional aspects of the invention will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. Attached Figure Description

[0011] To more clearly illustrate the technical solutions 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. The drawings described below are only some embodiments recorded in the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort. In addition, the dimensions or spacing between the components are exaggerated to show the position of each component, and the schematic diagrams are for illustrative purposes only.

[0012] Figure 1 This is a flowchart of an intelligent allocation method for earthwork in wind farm road construction, as described in an embodiment of this application. Figure 2 This is a flowchart of steps S2 and 3 in an intelligent allocation method for earthwork in wind farm road construction according to an embodiment of this application.

[0013] Figure 3 This is a schematic diagram of the road topology for an intelligent allocation method of earthwork for wind farm road construction, as described in an embodiment of this application. Detailed Implementation

[0014] To more clearly illustrate the technical solutions of the embodiments in this specification, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are merely some examples or embodiments of this specification. For those skilled in the art, these drawings can be applied to other similar scenarios without creative effort. Unless obvious from the linguistic context or otherwise specified, the same reference numerals in the drawings represent the same structures or operations.

[0015] Generally speaking, the terms "comprising" and "including" only indicate that the steps and elements are explicitly identified, and these steps and elements do not constitute an exclusive list. The method or apparatus may also include other steps or elements.

[0016] Terminology Explanation: A* Algorithm: A heuristic path search algorithm that finds the shortest path from a starting point to a target point in a graph by evaluating a function.

[0017] GIS: Geographic Information System, a technology platform used for collecting, storing, analyzing and managing spatial data.

[0018] DEM: Digital Elevation Model, is a digital representation of the elevation of the Earth's surface, used as a base map in GIS to show the topographic undulations of wind farms.

[0019] Python: A programming language used in this scheme to build linear programming models using its pullp library.

[0020] Pulse is a linear programming modeling library for Python, used to define decision variables, objective functions, and constraints, and to call a solver to solve them.

[0021] CBC: An open-source linear programming solver that can be called by the pulp library to solve mixed-integer linear programming problems.

[0022] Gurobi: A commercial high-performance mathematical programming solver that can be used to solve problems such as linear programming and integer programming.

[0023] Folium: A lightweight GIS front-end library for Python, based on Leaflet.js for map visualization. In this solution, it is used to establish the linkage between GIS and tables.

[0024] ArcGIS: A professional geographic information system software platform that provides functions such as topology analysis and map drawing.

[0025] CAD: Computer-Aided Design, in this context referring to software used to draw road engineering quantity drawings.

[0026] Example 1 In one typical embodiment of this disclosure, a method for intelligent allocation of earthwork for wind farm road construction is provided, comprising: Data on earthwork for the roads of the wind farm to be built was collected and preprocessed to obtain a standardized engineering dataset. The purpose of this data collection and preprocessing step is to convert the original road design data, earthwork balance data, etc., into a structured format that can be directly used by subsequent models.

[0027] An intelligent earthwork allocation model based on the fusion of A* algorithm and linear programming is constructed. This model takes the transportation volume priority as the core criterion, eliminates the allocation priority mechanism, and achieves optimal allocation by calculating the minimum transportation volume of global excavation and filling station numbers.

[0028] Traditional methods rely on manual experience to determine the inbound and outbound routes and detour distances between multiple roads, and the existence of a priority mechanism leads to unreasonable route selection. This step eliminates the priority mechanism, no longer pre-setting which road prioritizes transport to another. Instead, all cut and fill station numbers are treated as equal nodes, and the A* algorithm searches for the shortest path. Linear programming is then used to solve for the global minimum transport volume, resolving the problems of unreasonable route selection and the inability to form an optimal engineering quantity allocation plan.

[0029] The system pre-defines allocation constraints, configures a constraint matrix, and generates earthwork allocation schemes for wind farm roads using an intelligent earthwork allocation model based on a standardized engineering dataset. The introduction of the constraint matrix allows the model to automatically eliminate allocation combinations that do not meet constraints such as excavation surplus, filling gaps, borrow pit capacity, spoil pit capacity, and economic haulage distance, avoiding invalid calculations. By integrating the A* algorithm with linear programming, and in conjunction with the constraint matrix, the model can automatically output the allocation scheme with the minimum global haulage volume while satisfying actual engineering constraints. This replaces the tedious and inaccurate manual setting process, thereby reducing secondary transportation and improving construction efficiency.

[0030] This allocation method enables the systematic and intelligent allocation of earthwork for wind farm road construction, generating a visualized allocation plan. This improves allocation efficiency, enhances the rationality of planning schemes, and provides efficient earthwork allocation technology for the entire wind farm construction cycle. Specifically, the intelligent earthwork allocation method of this application overcomes the inherent shortcomings of complex and inefficient multi-road collaborative earthwork allocation. It coordinates all roads and borrow pits in the wind farm, presets allocation constraints, constructs an intelligent earthwork allocation model, and generates a rational and economical earthwork allocation plan for the entire wind farm construction process. This achieves efficient and precise earthwork allocation and cost control, reduces secondary transportation, and, combined with visualization, allows for precise adaptation of global earthwork project resource allocation and scheduling to the on-site construction organization design. It is an intelligent solution for earthwork allocation in the complex road network of large-scale wind farms.

[0031] In other specific examples disclosed herein, the earthwork engineering data for the wind farm roads to be constructed specifically includes road network topology data, node stationing data, cut and fill volume data, loose earthwork coefficient data, and pre-selected borrow and spoil sites data. This data forms the foundation for the earthwork allocation of the wind farm. The road network topology data describes the connectivity between nodes and edges; the node stationing data provides the location identifier for each road segment; the cut and fill volume data provides the cut and fill volumes for each road segment; the loose earthwork coefficient data is used for subsequent volume conversion; and the pre-selected borrow and spoil sites data provides the site capacity and connection stationings. The completeness of the above data ensures that the model accurately reflects the actual site conditions.

[0032] In some specific examples disclosed herein, preprocessing includes: analyzing the topology and node station data of wind farm roads, dividing the roads and borrow pits into several road segments and connecting nodes. Next, the cut and fill volumes of each road segment are analyzed, and the excavation and filling volumes of each road segment are obtained after lateral excavation and filling, and the economic haul distance is calculated. Then, a road network connection table is constructed based on the road network topology data, clarifying the intersection relationships and intersection station numbers of each road segment. Afterwards, the loose volume coefficient is calculated based on the volume ratio of earth and rock in each road segment and borrow pit under natural and compacted conditions. Finally, spoil ground sites are pre-selected, and the capacity, connecting station numbers, and detour distances of each borrow pit are statistically analyzed. Preprocessing transforms the original design data into a model-recognizable partition matrix and connection relationships, while simultaneously calculating the economic haul distance as a subsequent constraint. Through road segment division and parameter calculation, the complexity of the model is reduced, allowing the subsequent A* algorithm to search paths on a finer-grained grid, improving the accuracy of the shortest path calculation.

[0033] In other specific examples of this disclosure, the construction of the intelligent earthwork allocation model based on the fusion of the A* algorithm and linear programming includes: First, based on the road topology and node stationings, the wind farm roads and borrow pits are subdivided into m+n equal-length road segments, establishing a partition matrix of dimension (m+p)×(n+q). Here, m represents the number of road excavation stationings, p represents the number of borrow pits, n represents the number of road embankment stationings, and q represents the number of spoil pits. Borrow pits are assimilated into road segments, simplifying the topology. Then, based on the partition matrix, a haul distance matrix, a allocation matrix, and a loosening coefficient matrix are constructed. The haul distance matrix uses the path search function of the A* algorithm to calculate the haul distance, while the A* algorithm estimates the haul distance using a heuristic function. Next, earthwork allocation is divided into three modes: inter-road transport, transport between roads and borrow pits, and transport between roads and spoil pits. These three transport modes are merged, and a linear programming model is constructed by minimizing the objective function. By mapping the complex road network of a wind farm into a partition matrix, the problem of finding the shortest path between multiple points, which was originally impossible to calculate directly, is transformed into matrix operations. The A* algorithm ensures the efficiency of distance calculation, while linear programming ensures the global optimal solution.

[0034] In some specific examples disclosed herein, the transport distance matrix is ​​constructed based on the A* algorithm to build a path search module. It treats the spoil heap as the fill and excavation surplus earthwork at road connection stations, uses road node stations as coordinate points and road connection relationships as feasible paths, and calculates the shortest transport distance from the global excavation station to each connectable fill station. The allocation quantity matrix includes allocation quantities between different roads, allocation quantities from road excavation surplus to spoil heaps, and allocation quantities from borrow heaps to road fill. Allocation types that do not meet engineering requirements are discarded, thus the row and column vector dimensions of the matrix are correspondingly reduced. The loose volume coefficient matrix uses row vectors for the excavation surplus part and column vectors for the fill part, with matrix elements representing the loose volume coefficient of earthwork during the allocation process. Since excavation and spoil are usually measured in natural volume, while fill and borrow are measured in compacted volume, the loose volume coefficient matrix is ​​used to unify the volume under different conditions to natural volume, avoiding measurement errors. The three matrices provide key parameters for transport distance, allocation quantity, and volume conversion, respectively, providing accurate data support for the linear programming objective function.

[0035] In other specific examples of this disclosure, the minimization objective function is: ; in, This represents the total earthwork transport volume for the wind farm. The distance from the excavated portion to the filled portion is the transportation distance. Detour distance needs to be added. ; For the allocation of resources between roads, , These are the amounts allocated between roads and borrow / disposal sites; This is the loose square coefficient matrix; is the constraint matrix; w represents excavation, t represents filling, q represents soil extraction, and z represents waste soil.

[0036] The function consists of the sum of three parts: the first part is the transport volume generated by inter-road allocation, which is the transport distance from excavation station w to fill station t multiplied by the allocation amount, then multiplied by the loosening coefficient and the elements of the constraint matrix; the second part is the transport volume of excavated material allocated to spoil pits; and the third part is the transport volume of borrow pits allocated to fill gaps in roads. All possible allocation patterns are unified into a single expression, and a linear programming solver is used to find the combination of decision variables that minimizes f. This step quantifies the earthwork allocation problem into a computable minimization problem, replacing manual experience-based judgment and ensuring the mathematical optimality of the output scheme.

[0037] In some specific examples disclosed herein, the preset allocation constraints include: constraints on surplus excavation (total outflow not exceeding its own surplus excavation volume); constraints on fill gaps (total inflow not exceeding its own fill gap volume); constraints on borrow pit capacity (total output not exceeding its own capacity); constraints on spoil pit capacity (total input not exceeding its own capacity); and constraints on economic transport distance (the allocation distance between roads, the spoil pit distance, and the borrow pit distance are all not greater than the economic transport distance). These constraints reflect the basic principles of earthwork balance and the requirements of engineering economics. Elements in the constraint matrix are 0 or 1, where 1 indicates that all constraints are met and earthwork transport is permitted, and 0 indicates that at least one constraint is not met and earthwork transport is prohibited. Multiplying by the matrix in the objective function is equivalent to forcing the transport volume of allocation paths that do not meet the constraints to zero.

[0038] Furthermore, the method also includes visualizing the generated allocation plan in GIS and tabular form: mapping and associating the allocation data output by the intelligent earthwork allocation model with spatial data to construct a linked data pool between GIS and tables. The GIS side loads the wind farm DEM base map, draws roads and borrow / disposal sites in layers, and then draws allocation paths using different styles of arrows according to the allocation mode; for example, black solid arrows are used for road allocation, green solid arrows for borrow / disposal filling, and orange solid arrows for disposal, with allocation volume and transport distance marked next to the arrows. The table side extracts fields from the data pool and automatically generates simplified allocation details and constraint verification display information. Simultaneously, an interactive statistical query function is developed to complete the full-process visualization implementation. Through visualization, the abstract allocation plan is presented intuitively in graphical and tabular form, facilitating understanding and execution by construction personnel; the linkage mechanism between GIS and tables supports bidirectional querying and editing, improving the operability and review efficiency of the plan.

[0039] To make the technical solutions provided by the embodiments of the present invention clearer, an example is given to illustrate the intelligent allocation method for earthwork in wind farm road construction provided by the present invention. However, this should not be construed as limiting the scope of the above-mentioned subject matter of the present invention to the following embodiments. All technologies implemented based on the content of the present invention fall within the scope of the present invention.

[0040] like Figure 1 As shown, a method for intelligent allocation of earthwork in wind farm road construction includes the following steps: S1: Collect and preprocess the earthwork data of the road to the wind farm to be built to obtain a standardized engineering dataset.

[0041] In this embodiment, the earthwork engineering data for the wind farm road refers to the data used for balancing and allocating earthwork along the road after the wind turbine locations and access roads have been determined and the on-site road design is completed. This includes road route data, earthwork balance data, wind turbine foundation coordinates, loosening coefficients, and other basic data. The standardized engineering dataset refers to a structured engineering dataset compiled by preprocessing the basic data of the wind farm road, taking into account the characteristics of wind power engineering and earthwork allocation logic. Data preprocessing includes a series of standardization processes such as topology analysis and road segment division, economic transport distance calculation, excavation and filling calculation, connection table construction, loosening coefficient conversion, and take-off and spoil disposal site parameter statistics. The specific implementation process of this step is as follows: Specifically, step S1 includes: S11, analyze the topology and node chainage data of the wind farm roads, divide the roads and spoil heaps into several road segments and connecting nodes, and associate each road segment with a unique chainage interval and node identifier.

[0042] Specifically, the topology of wind farm roads refers to the connection relationships and geometric structure between nodes (such as intersections, road start and end points, and connectors) and edges (such as road segments and borrow pits) in the road network. Figure 3 As shown. Roads include three types: main roads, branch roads, and connecting roads. Connecting points include three types: points above borrow pits, points below spoil pits, and start and end points of connecting lines. When analyzing the topology of wind farm roads, the topology analysis function in ArcGIS can be used to generate a data file of the road network's positional relationships.

[0043] For example, wind farm roads and borrow pits are divided into: R0 road: R0K0+000, R0K0+020, R0K0+035.90-R7K0+000, R0K0+040, R0K0+051.34; R1 road: R1K0+000, R1K0+020, R1K0+040, R1K0+060, R1K0+067.23-R13K0+000, R1K0+080, R1K0+095.61; Connector L0: L0K0+000-R6K1+154.82, L0K0+358.54-R14K0+456.23; Spoil disposal site: ZT1-R18K0+186.98 ( K0+180~K0+200), ZT2-R16K0+113.50( (K0+100~K0+120); Borrow pit: QT1-R4K0+550.19 ( K0+540~K0+560, 1120m), QT2-R13K0+260.72( K0+260~K0+280, 1350m); S12, analyze the cut and fill volume of each road segment, and after lateral excavation and filling, obtain parameters such as the remaining excavation volume and fill volume of each road segment, and calculate the economic transport distance L. e .

[0044] In this embodiment, the cut and fill volume data for each road segment needs to be obtained from CAD engineering drawings or engineering quantity tables. After lateral earthwork utilization, the excavation surplus or fill deficit of each road segment is obtained; the off-road borrowing soil unit price C is obtained based on market research and standard analysis. b Long-distance freight unit price C t Based on basic data such as the free transport distance L0, the economic transport distance L is calculated using this formula. e .

[0045] ; S13. Based on the road network topology data, construct a road network connection table to clarify the intersection relationships of each road segment and parameters such as the station number corresponding to the intersection point.

[0046] Specifically, the overall layout map of the wind farm area is analyzed to obtain information such as the intersection relationships of each road segment and the corresponding station numbers of the intersection points in the road network connection table. For example, the connecting roads of R2 are R6, R11, and L2, with connection station numbers of R2K0+354.76-R6K0+000, R2K0+552.26-R11K1+227.65, and R2K0+768.54-L2K0+000, respectively. The connection types are fork, intersection, and connection, respectively. Among them, the main road R0 is the default access road to the wind farm.

[0047] S14. Based on the volume ratio of earth and rock in each road section and borrow pit under natural and compacted conditions, the loose volume coefficient data of earth and rock are calculated.

[0048] Specifically, the natural compacted volume of earth and stone before and after transportation was collected and recorded at each section of the road and in the borrow pits. Compacted volume of fill The ratio is used to obtain the loosening coefficient K, where the K value for earthwork is >1 and the K value for stonework is <1.

[0049] S15, pre-select spoil disposal sites, and statistically obtain parameters such as the capacity of each spoil disposal site, connection chainage, and detour distance.

[0050] Specifically, borrow pits are influenced by many factors, such as land area, land use type, geological conditions, slope ratio, catchment area, and the importance level of structures at the bottom of the ditch. Therefore, borrow pit sites with reasonable capacity and haul distance need to be pre-selected manually, and a chainage number closest to the nearest road should be chosen as the connecting chainage, such as ZT3-R8K0+433.23. K0+420~K0+440), QT3-R12K0+662.30( K0+660~K0+680, 1710m), where 1710m is the distance from the borrow pit to the road R12.

[0051] S2. Construct an intelligent earthwork allocation model based on the fusion of A* algorithm and linear programming. The model takes the transportation volume priority as the core criterion, cancels the allocation priority mechanism, and achieves optimal allocation by calculating the minimum transportation volume of global excavation and filling station numbers.

[0052] In this embodiment, the intelligent earthwork allocation model replaces the traditional allocation priority principle with the heuristic search algorithm A* algorithm to calculate the shortest transportation distance of earthwork in each road segment, and constructs a linear programming model by combining allocation amount and loosening coefficient. Finally, constraints are set to limit the calculation range of the model, and the linear programming model is solved to obtain the global minimum transportation volume and the optimal allocation path.

[0053] Specifically, such as Figure 2 As shown, step S2 includes: S21, based on the road topology and node stationings, the wind farm roads and borrow pits are subdivided into m+n equal-length road segments, establishing a dimension of... The partition matrix.

[0054] In this embodiment, m represents the number of road excavation station numbers, p represents the number of borrow pits, n represents the number of road embankment station numbers, and q represents the number of spoil disposal sites. The average haul distance is factored in during actual transport. The concept simplifies design calculations by merging the chainages of continuous cut and fill sections and taking the average chainage as the center chainage of the road segment, along with the average haulage distance. The distance is calculated based on the distance from the center of the cut section to the center of the fill section. For example, if earth is transported from R9K0+080 (K0+020~K0+140) to K0+190 (K0+140~R9K0+240), the average transport distance is... =190-80=110m.

[0055] S22, construct the transport distance matrix (the transport distance is calculated using the path search function of the A* algorithm), the allocation matrix, and the loose square coefficient matrix based on the partition matrix.

[0056] In this embodiment, the optimal allocation scheme is based on a partition matrix, and the constructed matrix serves as the objective function, which is the factor affecting the total earthwork transportation volume of the wind farm. The key element is the distance matrix L, which is a direct reflection of the rationality of the allocation path.

[0057] Specifically, the distance matrix L is used to construct the path search module using the A* algorithm, where the core formula is: ,in, Estimate the transport distance for each available relocation route. The cost of the distance traveled from the initial search segment to the current segment. This estimates the transport distance from the current road segment to the target road segment. Undoubtedly, This is a key factor and also the heuristic function of the A* algorithm. For the distance matrix in matrix form, this embodiment uses the Manhattan distance as the heuristic function of the A* algorithm. , where d is the average transport distance between adjacent road segments.

[0058] The allocation matrix V contains , , , , , Six elements, among which , , This allocation type is clearly unsuitable for engineering needs and is therefore discarded. Thus, the row vector dimension of all matrices is less than m+p and the column vector dimension is less than n+q. To allocate resources between different roads. The amount of soil allocated from borrow pits to fill gaps in road construction. This refers to the amount of excavated soil from roads that is allocated to spoil pits.

[0059] In this embodiment, the fill, pile utilization, and borrowing volumes in the basic data are all assumed to be compacted volumes, while the excavation, transportation, and disposal volumes are all assumed to be natural volumes. Therefore, it is necessary to construct a loose volume coefficient matrix K based on the project's geotechnical engineering parameters, specify the loose volume coefficients for earthwork and rockwork respectively, and convert the earthwork and rockwork allocation volume into natural volume volumes to correct the allocation volume matrix. The loose volume coefficient K represents the natural compacted volume before and after transportation. Compacted volume of fill The ratio is such that the vector dimension is consistent with the allocation matrix.

[0060] S23, construct a linear programming model based on minimizing the objective function.

[0061] ; in, This represents the total earthwork transport volume for the wind farm. The distance from the excavated portion to the filled portion is the transportation distance. Detour distance needs to be added. (Distance between the borrow pit and the chainage number). For the allocation of resources between roads, , These are the amounts allocated between roads and borrow / disposal sites; This is the loose square coefficient matrix; is the constraint matrix; w represents excavation, t represents filling, q represents soil extraction, and z represents waste soil.

[0062] S3 presets allocation constraints, configures the constraint matrix, and generates an earthwork allocation scheme for wind farm roads using an intelligent earthwork allocation model based on a standardized engineering dataset.

[0063] Specifically, such as Figure 2 As shown, step S3 includes: S31, based on the earthwork balance principle and project requirements, preset allocation constraints with the aim of reducing the model's calculation range and complexity.

[0064] Specifically, the allocation restrictions include the following constraints: Surplus Excavation Constraint: Total excavation volume (road allocation + spoil) ≤ self-excavation surplus, i.e. ; Filling constraint: Total transfer volume (road allocation + soil borrowing for filling) ≤ self-filling volume, i.e. ; Borrow site capacity constraint: Total output (road filling) ≤ its own capacity, i.e. ; Spoil pit capacity constraint: Total input (road allocation) ≤ its own capacity, i.e. ; Economic transport distance constraint: the inter-road dispatch distance ≤ the economic transport distance, i.e. .

[0065] S32, configure the constraint condition matrix according to the engineering allocation constraints.

[0066] In this embodiment, the matrix elements in the constraint matrix H are 0 or 1. 1 indicates that all constraints are met and soil transportation is allowed, while 0 indicates that at least one constraint is not met and soil transportation is prohibited. S33 solves the linear programming model with the constraint matrix configured, and outputs the earthwork allocation scheme between the wind farm road and the borrow pit.

[0067] The intelligent earthwork allocation scheme generated in this application can be used in engineering applications by inputting the objective function and engineering allocation constraints into the Python Pulse library, defining decision variables, and calling the open-source solver CBC or the commercial solver Gurobi to solve the model and output the allocation scheme.

[0068] S4 will visualize the allocation plan in the form of GIS + tables, showing the allocation details of different roads and borrow pits.

[0069] In this embodiment, GIS refers to Geographic Information System technology, and the linkage between GIS and the table is based on the same set of standardized engineering datasets.

[0070] Specifically, the above allocation plan is organized into structured data, including fields such as allocation source, allocation destination, allocation volume, transportation distance, total transportation volume, and loose coefficient, forming an allocation plan data table.

[0071] Furthermore, the allocation data (including allocation volume, transport distance, and loosening coefficient for the three types of transportation modes) output by the intelligent earthwork allocation model are mapped and associated with spatial data (road station numbers and locations of borrow pits and spoil heaps) to construct a linked data pool of GIS and tables.

[0072] Furthermore, the wind farm DEM base map is loaded onto the GIS platform, and the vector maps of roads and borrow pits are overlaid onto the DEM base map in layers. Then, according to the transportation mode, different styles of arrows are used to draw the allocation path. Black solid arrows are used for road allocation, green solid arrows are used for borrow pit filling, and orange solid arrows are used for waste disposal. Core parameter labels are embedded next to the arrows, such as "R1K0+060~R1K0+140→R5K0+120~R5K0+200: 4000m³, 850m".

[0073] Furthermore, the table extracts fields from the data pool and automatically generates simplified structured tables such as allocation details and constraint checks. It uses the lightweight Python GIS front-end library Folium to establish a linkage mechanism with GIS. When an allocation data is selected in the table, the corresponding allocation path is automatically highlighted on the GIS side. When the allocation path parameters are edited on the GIS side, the corresponding fields on the table are updated synchronously.

[0074] Furthermore, an interactive statistical query function was developed concurrently, allowing users to view detailed data of objects by clicking on the allocation path and configure parameter calculation modules to support statistical calculations of similar GIS element parameters. The statistical results of GIS parameters are then displayed in a table, completing the full-process visualization implementation.

[0075] In summary, this application comprehensively manages the earthwork and rock excavation for all roads and borrow pits in a wind farm. Utilizing standardized engineering datasets and pre-setting allocation constraints, it generates a full-process earthwork allocation plan for wind farm construction based on an intelligent earthwork allocation model. Combined with visualization, this improves the efficiency of overall earthwork resource scheduling and plan decision-making, and enhances the accuracy of on-site construction schedule planning. Secondly, it assimilates borrow pits into road segments and, based on a dimension of... The partition matrix is ​​used to construct matrices for transport distance, allocation volume, loose material coefficient, and constraints. This helps simplify the topology of wind farm roads and borrow pits, reduces the complexity of the intelligent earthwork allocation model, and improves system allocation efficiency. Simultaneously, the path search characteristics of the A* algorithm are introduced to construct the transport distance matrix. Ignoring earthwork allocation priority, allocation schemes are formulated based on transport volume priority, improving the rationality and systematic nature of the schemes.

[0076] Example 2 Based on the intelligent allocation method for earthwork in wind farm road construction described in Example 1, this example provides an intelligent allocation system for earthwork in wind farm road construction, including a data acquisition module, a model building module, a scheme generation module, and a visualization module.

[0077] The data acquisition module is used to collect and preprocess data on the earthwork of the road to the wind farm to be built, and to obtain a standardized engineering dataset.

[0078] The model building module is used to build an intelligent earthwork allocation model based on the fusion of A* algorithm and linear programming. This module further includes a path search unit, a matrix construction unit, and an objective function and constraint setting unit.

[0079] The path search unit is responsible for calling the A* algorithm to calculate the distance, the matrix construction unit is responsible for generating the partition matrix and three sub-matrices, and the objective function and constraint setting unit is responsible for establishing the linear programming model.

[0080] The scheme generation module is used to input standardized engineering datasets and generate globally optimal allocation schemes through model solving.

[0081] The visualization module is used to visualize and display allocation plans in the form of GIS maps and tables.

[0082] This system facilitates the integration of methods into functional modules, making software implementation and deployment easier.

[0083] Example 3 Based on the intelligent earthwork allocation method for wind farm road construction described in Embodiment 1, this embodiment provides an electronic device, including a processor, a memory for storing processor-executable instructions, and a computer program stored in the memory and executable on the processor. The processor is configured to invoke the instructions stored in the memory to execute the steps of the intelligent earthwork allocation method for wind farm road construction described in Embodiment 1.

[0084] The essence of this device is to load the aforementioned method into a general-purpose or special-purpose computing device in the form of a computer program, so that it can automatically perform the calculation and output of earthwork allocation scheme.

[0085] Example 4 Based on the intelligent allocation method for earthwork in wind farm road construction described in Embodiment 1, this embodiment provides a computer-readable storage medium storing computer program instructions. When these instructions are executed by a processor, they implement the steps of the intelligent allocation method for earthwork in wind farm road construction described in Embodiment 1.

[0086] The storage medium can be ROM, RAM, hard disk, solid-state drive, USB flash drive, optical disk, etc., which facilitates the copying, distribution and deployment of the method. Users can install the program in the storage medium onto any compatible computing device to run.

[0087] This disclosure is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this disclosure. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create a machine for implementing the flowchart illustrations. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0088] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0089] The above-described embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application, and should all be included within the protection scope of this application.

Claims

1. A method for intelligent allocation of earthwork in wind farm road construction, characterized in that, include: Collect and preprocess data on earthwork engineering for roads in the wind farm to be built to obtain a standardized engineering dataset. An intelligent earthwork allocation model based on the fusion of A* algorithm and linear programming is constructed. The model takes the transportation volume priority as the core criterion, eliminates the allocation priority mechanism, and achieves optimal allocation by calculating the minimum transportation volume of global excavation and filling station numbers. Preset allocation constraints, configure constraint matrix, and generate earthwork allocation schemes for wind farm roads using an intelligent earthwork allocation model based on standardized engineering datasets.

2. The intelligent allocation method for earthwork in wind farm road construction as described in claim 1, characterized in that, The earthwork engineering data for the wind farm to be built includes road network topology data, node station data, cut and fill volume data, loose earthwork coefficient data, and pre-selected sites for borrow and spoil.

3. The intelligent allocation method for earthwork in wind farm road construction as described in claim 1, characterized in that, The preprocessing specifically includes: analyzing the topology and node station data of the wind farm roads, dividing the roads and borrow pits into several road segments and connecting nodes; analyzing the cut and fill volume of each road segment, obtaining the excavation surplus and fill deficit of each road segment after lateral excavation and filling, and calculating the economic haul distance; constructing a road network connection table based on the road network topology data; calculating the loose volume coefficient based on the volume ratio of earth and rock in each road segment and borrow pit under natural and compacted conditions; pre-selecting spoil disposal sites, and statistically analyzing the capacity, connecting station number, and detour distance of each borrow pit.

4. The intelligent allocation method for earthwork in wind farm road construction as described in claim 1, characterized in that, The construction of the intelligent earthwork allocation model based on the fusion of A* algorithm and linear programming includes: subdividing the wind farm roads and borrow pits into m+n equal-length road segments according to the road topology and node stationing, and establishing a dimension of... The partition matrix is ​​used to construct a haul distance matrix, an allocation matrix, and a loosening coefficient matrix. The haul distance matrix is ​​calculated using the path search function of the A* algorithm. The earthwork allocation is divided into three modes: inter-road haul, haul between roads and borrow pits, and haul between roads and spoil pits. The three haul modes are merged and a linear programming model is constructed by minimizing the objective function.

5. The intelligent allocation method for earthwork in wind farm road construction as described in claim 4, characterized in that, The transport distance matrix is ​​based on the A* algorithm to construct a path search module. It takes the spoil heap as the filling and excavation surplus earthwork at the road connection station, uses the road node station as the coordinate point and the road connection relationship as the feasible path, and calculates the shortest transport distance from the global excavation station to each connectable filling station. The allocation quantity matrix includes the allocation quantity between different roads, the allocation quantity of road excavation surplus to spoil heap, and the allocation quantity of borrow heap to road filling. The loosening coefficient matrix uses row vectors for the excavation surplus part and column vectors for the filling part, and the matrix elements are the loosening coefficients of earthwork in the allocation process.

6. The intelligent allocation method for earthwork in wind farm road construction as described in claim 4, characterized in that, The minimization objective function is: in, This represents the total earthwork transport volume for the wind farm. The distance from the excavated portion to the filled portion is the transportation distance. Detour distance needs to be added. ; For the allocation of resources between roads, , These are the amounts allocated between roads and borrow / disposal sites; This is the loose square coefficient matrix; is the constraint matrix; w represents excavation, t represents filling, q represents soil extraction, and z represents waste soil.

7. The intelligent allocation method for earthwork in wind farm road construction as described in claim 1, characterized in that, The preset allocation constraints include: surplus portion constraint, the total amount allocated out shall not exceed its own surplus portion; filling portion constraint, the total amount allocated in shall not exceed its own filling portion; borrow pit capacity constraint, the total output shall not exceed its own capacity; spoil pit capacity constraint, the total input shall not exceed its own capacity; economic transport distance constraint, the allocation distance between roads, the spoil pit disposal distance, and the borrow pit extraction distance shall all not exceed the economic transport distance; the matrix elements in the constraint matrix are 0 or 1, where 1 indicates that all constraints are met and soil transportation is allowed, and 0 indicates that at least one constraint is not met and soil transportation is prohibited. The allocation method also includes visualizing the generated allocation plan in the form of GIS and tables: mapping and associating the allocation data output by the intelligent earthwork allocation model with spatial data to build a linked data pool between GIS and tables; loading the wind farm DEM base map on the GIS side, drawing roads and spoil heaps in layers, and then drawing allocation paths with different styles of arrows according to the allocation mode; extracting fields from the data pool on the table side to automatically generate simplified allocation details and constraint verification display information; and simultaneously developing interactive statistical query functions to complete the full-process visualization implementation.

8. A system for implementing the intelligent allocation method for earthwork in wind farm road construction as described in any one of claims 1-7, characterized in that, include: The data acquisition module is used to collect and preprocess data on the earthwork of the road to the wind farm to be built, and to obtain a standardized engineering dataset. The model building module is used to build an intelligent earthwork allocation model based on the fusion of A* algorithm and linear programming. This module includes a path search unit, a matrix construction unit, and an objective function and constraint setting unit. The scheme generation module is used to input standardized engineering datasets and generate globally optimal allocation schemes through model solving. The visualization module is used to visualize and display the allocation plan in the form of GIS maps and tables.

9. An electronic device, characterized in that, include: processor; Memory used to store processor-executable instructions; A computer program stored in the memory and capable of running on the processor; The processor is configured to invoke instructions stored in the memory to execute the steps of implementing the intelligent allocation method for earthwork in wind farm road construction as described in any one of claims 1-7.

10. A computer-readable storage medium having computer program instructions stored thereon, characterized in that, When the computer program instructions are executed by the processor, they implement the steps of the intelligent allocation method for earthwork in wind farm road construction as described in any one of claims 1-7.

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