Power grid engineering construction machinery transportation path planning method and system and medium

Abstract

The invention discloses a path planning method and system for power grid engineering construction machinery transportation, and a medium. The method comprises the following steps: constructing a construction environment model and a grid adjacency list according to path planning data; processing the construction environment model and the grid adjacency list based on a path connectivity algorithm to obtain a weighted grid connectivity graph; analyzing the weighted grid connected graph based on a path analysis algorithm to obtain a transportation route combination graph; on the basis of a path selection algorithm, selecting a route communicating all construction nodes of the construction area in the transportation route combination diagram as a temporary transportation route; and detecting all the temporary transportation routes based on a route detection algorithm to obtain a target transportation route. Therefore, the shortest and most economical transportation planning path is found through the adaptive path algorithm and the deep reinforcement learning technology.

Description

Technical Field

[0001] The present invention relates to the technical field of path planning, and in particular to a path planning method, system and medium for transporting construction machinery for power grid projects. Background Art

[0002] Transport route planning for power grid construction machinery is a critical step in power infrastructure construction. Its core goal is to ensure the safe and efficient transportation of large-scale construction equipment, such as transformers, tower components, and heavy lifting machinery. Current route planning methods rely primarily on manual experience, determining transport routes through map selection and on-site surveys.

[0003] With the improvement of digitalization, technologies such as Dijkstra algorithm and A-star algorithm have also been widely used. At present, traditional path planning methods mostly use classical graph theory algorithms to solve the shortest path. By establishing a topological model of the road network, the path is optimized with travel distance or time as the weight. Although such methods can solve the basic path selection problem, they have two major limitations: (1) they do not consider the multi-dimensional constraints unique to engineering transportation, such as bridge load-bearing restrictions, turning radius requirements, and restrictions on the time of travel on special sections; (2) static models are difficult to cope with real-time traffic conditions, which can easily lead to a decrease in the actual traffic efficiency of the planned path. In summary, traditional methods are difficult to quickly generate the optimal path when facing complex terrain and multiple constraints. At the same time, when considering the special needs of mechanical transportation, such as load restrictions and terrain adaptability, they often lack sufficient flexibility and adaptability. Therefore, it is necessary to find the shortest and most economical construction path through an adaptive path algorithm and deep reinforcement learning technology. Summary of the Invention

[0004] The present invention provides a path planning method, system and medium for the transportation of power grid engineering construction machinery, which finds the shortest and most economical transportation planning path through an adaptive path algorithm and deep reinforcement learning technology.

[0005] In a first aspect, a path planning method for transporting construction machinery for a power grid project is provided, comprising the following steps: Obtain path planning data for the construction area; Constructing a construction environment model and a grid adjacency table based on the path planning data; Processing the construction environment model and the grid adjacency table based on a path connectivity algorithm to obtain a weighted grid connectivity graph; Analyzing the weighted grid connectivity graph based on a path analysis algorithm to obtain a transportation route combination graph; Selecting a route connecting all construction nodes in the construction area in the transportation route combination graph as a temporary transportation route based on a path selection algorithm; All the temporary transport routes are detected based on a path detection algorithm to obtain a target transport route.

[0006] In a second aspect, a path planning system for transporting power grid construction machinery is provided, comprising: A data acquisition module is used to obtain path planning data of the construction area; A data construction module, in communication with the data acquisition module, configured to construct a construction environment model and a grid adjacency table based on the path planning data; a path connectivity module, communicatively connected to the data construction module, for processing the construction environment model and the grid adjacency table based on a path connectivity algorithm to obtain a weighted grid connectivity graph; a path analysis module, communicatively connected to the path connectivity module, for analyzing the weighted grid connectivity graph based on a path analysis algorithm to obtain a transportation route combination graph; a path selection module, in communication with the path analysis module, configured to select, in the transport route combination graph, a route connecting all construction nodes in the construction area as a temporary transport route based on a path selection algorithm; and The path detection module is in communication with the path selection module and is used to detect all the temporary transportation routes based on a path detection algorithm to obtain a target transportation route.

[0007] In a third aspect, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the path planning method for the transportation of power grid construction machinery as described above is implemented.

[0008] Compared with existing technologies, the present invention has the following advantages: It constructs a construction environment model and a grid adjacency table based on the path planning data of the construction area; processes the construction environment model and the grid adjacency table using a path connectivity algorithm to obtain a weighted grid connectivity graph; analyzes the weighted grid connectivity graph using a path analysis algorithm to obtain a transportation route combination graph; selects routes from the transportation route combination graph that connect all construction nodes in the construction area using a path selection algorithm; and detects all these temporary transportation routes using a path detection algorithm to obtain the target transportation route. Therefore, the adaptive path algorithm and deep reinforcement learning technology can be used to find the shortest and most economical transportation planning path. BRIEF DESCRIPTION OF THE DRAWINGS

[0009] Figure 1 This is a flow chart of a method for path planning for transport of power grid construction machinery according to the present invention; Figure 2 It is a structural diagram of a path planning system for transporting power grid construction machinery according to the present invention. DETAILED DESCRIPTION

[0010] Reference will now be made in detail to specific embodiments of the present invention, examples of which are illustrated in the accompanying drawings. Although the present invention will be described in conjunction with specific embodiments, it will be understood that the present invention is not intended to be limited to those embodiments. On the contrary, it is intended to cover variations, modifications, and equivalents within the spirit and scope of the present invention as defined by the appended claims. It should be noted that the method steps described herein can be implemented by any functional block or functional arrangement, and any functional block or functional arrangement can be implemented as a physical entity or a logical entity, or a combination of the two.

[0011] In order to enable those skilled in the art to better understand the present invention, the present invention is further described in detail below with reference to the accompanying drawings and specific embodiments.

[0012] Note: The following example is only a specific example and is not intended to limit the embodiments of the present invention to the following specific steps, values, conditions, data, sequence, etc. Those skilled in the art can apply the concepts of the present invention to construct more embodiments not described in this specification by reading this specification.

[0013] See also Figure 1 As shown, an embodiment of the present invention provides a path planning method for transporting power grid construction machinery, comprising the following steps: Step S100, obtaining path planning data of the construction area; The path planning data mainly include the following: elevation data of the construction area, tower location data, existing road data, special area data (forestry and agricultural areas, water areas, etc.), maximum slope angle, preset grid side length, and the multiple of the increase in the cost of building temporary roads when passing through special areas. The specific format is shown in Table (1).

[0014] Table (1)

[0015] Step S200, constructing a construction environment model and a grid adjacency table based on the path planning data, includes: S210, converting the elevation data in the path planning data into an elevation data matrix; Typically, the elevation data (DEM data) for the designated construction area is processed. DEM data itself is a digital representation of the elevation of the terrain surface. First, the DEM data file is read, which may be in a common raster format (such as GeoTIFF). The file header information determines the number of rows and columns, resolution, and other parameters. Next, the elevation values ​​are read pixel by pixel in row and column order, and stored in a two-dimensional array, known as the elevation matrix. For example, if the DEM data represents a 100×100 area, the elevation value of each pixel is read and stored in a 100×100 two-dimensional array, elevationMatrix. Then, elevationMatrix[i][j] represents the elevation value at row i and column j.

[0016] S220, dividing the map data in the path planning data into grids based on a preset grid side length, and numbering the divided grids in sequence; Map data includes: DEM data, existing road data, and special area data.

[0017] Based on the preset grid side length, the entire construction area (map data) is divided by the grid side length, and the area is divided into grids of the same size. Each grid is numbered, and the numbering method can be based on the needs. It can start from the upper left corner and be in row-first or column-first order. According to the elevation data matrix, the average elevation, slope and other attributes of each grid are calculated. For example, for a grid, the average elevation is obtained by calculating the average of the multiple elevation values ​​it contains; the slope is calculated using the elevation difference and horizontal distance between adjacent grids. Therefore, the division process is completely dependent on the input map data to ensure that the grid division is consistent with the actual terrain and land feature distribution.

[0018] S230, performing grid attribute calculation and road marking on each numbered grid according to the elevation data matrix and the path planning data to obtain a construction environment model corresponding to the construction area; Combined with the input special area data (such as forestry areas, water areas, etc.) and existing road data, the grid is marked to determine whether it belongs to a special area or is a road, thereby completing the modeling of the environment represented by a grid set, that is, the digital construction environment model of the entire construction area.

[0019] The benefits of the construction environment model include: 1. Converting complex terrain and ground feature information into structured data for algorithm processing and path search. 2. Uniformly quantifying various constraints, such as slope, soil conditions, and special areas, to standardize and automate accessibility assessments. 3. Providing a data foundation for path planning algorithms, ensuring they can efficiently search for optimal paths in a digital environment.

[0020] Feasibility of direct judgment without construction environment model: It cannot be directly judged. The reasons are as follows: 1. Without a construction environment model, it is impossible to convert continuous terrain data (such as DEM data) into discrete, calculable units, making it difficult to quantify the slope of each location, special area attributes, etc. 2. The lack of a unified modeling framework makes it impossible to systematically integrate multi-dimensional constraints, such as maximum slope and special area access restrictions, resulting in a lack of consistency and accuracy in accessibility judgments. 3. The path planning algorithm needs to search based on a structured construction environment model. Without modeling, the algorithm cannot be effectively executed.

[0021] S240: Determine whether each grid in the construction environment model has path accessibility based on the grid accessibility judgment factor, and construct an adjacency table based on the connection relationship between each grid with path accessibility and its adjacent multiple accessible grids.

[0022] Determining the accessibility of a grid: 1. Considering terrain factors: If a grid's slope exceeds the maximum slope angle, the grid is considered impassable. For example, if the maximum slope angle is set to 15° and the calculated slope of a grid is 20°, the grid is impassable. 2. Considering special areas: If a grid falls within a special, impassable area (such as areas with high water content, soft and slippery soil, or rocky, difficult-to-excavate areas, as mentioned in the construction documents), the grid is considered impassable. 3. Considering existing roads and tower locations: If a grid lies on an existing road or overlaps with a tower, it is considered impassable. The cost of constructing temporary roads is also considered. For example, if the cost of constructing a grid through a special area is increased excessively, exceeding a certain threshold, the grid may be considered impassable. Based on these criteria, each grid is assigned an accessibility flag (e.g., 0 for impassable, 1 for impassable). An adjacency table is constructed based on the grid and its adjacent grids, recording the connections between the accessible grids and providing a data foundation for subsequent path search.

[0023] Step S300, processing the construction environment model and the grid adjacency table based on a path connectivity algorithm to obtain a weighted grid connectivity graph, including: S310, searching for a connection route between all construction nodes within a range from a starting point to an end point of a transportation route in the construction environment model and the grid adjacency table based on an A-star algorithm and by considering the transportation route construction cost and a heuristic function; S320 , using all construction nodes as vertices, the connection routes as edges, and the connection cost of each edge as a weight to construct a weighted grid connection graph.

[0024] Specifically, in this embodiment, the A-star algorithm is based on the construction environment model and the adjacency table, and inputs the starting position information of the transportation route (such as the material station) and the ending position information of the transportation route (such as the tower position).

[0025] The A-star algorithm searches for the optimal route from a starting point to a destination in an adjacency table by comprehensively considering actual costs (such as grid movement distance and construction costs) and heuristic functions (such as the estimated distance to the destination). During the search, the movement cost of each grid (such as the cost increase when passing through a special area) is quantified as a weight, and the weight of the edges between adjacent grids is determined by this cost.

[0026] The weights in this algorithm can be quantified using an algorithm based on fuzzy logic. First, multiple factors influencing the cost of movement must be identified, such as terrain slope, special zone type, and soil conditions. For each factor, a corresponding input fuzzy variable is assigned. For example, the fuzzy variable for slope can be set to "flat," "steep," or "precipitous," while the fuzzy variable for special zone type can be set to "normal area," "river area," or "swamp area." Furthermore, a corresponding membership function is defined for each fuzzy variable to determine the degree to which a given actual value belongs to that fuzzy variable.

[0027] Next, a fuzzy logic rule base is constructed based on expert experience or historical data. For example, if the slope is "steep" and the area is "swampy," the movement cost weight is "very high"; if the slope is "flat" and the area is "normal," the movement cost weight is "low," and so on. During the calculation, the actual environmental factors of the current grid (such as the actual slope value and the type of area) are input into the fuzzy logic controller. Through fuzzy reasoning (such as the Mamdani method), combining membership functions with fuzzy logic rules, a fuzzy output result regarding the movement cost weight is obtained. Finally, the fuzzy output result is defuzzified (such as using the center of gravity method) to convert the fuzzy value into a precise weight value. For example, suppose a grid has a slope of 30°. The membership function calculates its membership for the "steeper" fuzzy variable as 0.7 and its membership for the "steep" fuzzy variable as 0.3. This grid is located in a river region, so its membership for the "river region" fuzzy variable is 1. According to fuzzy logic rules, "steeper and river region" is assigned a "high" weight, while "steep and river region" is assigned a "very high" weight. After defuzzification using Mamdani reasoning and the centroid method, the final quantized moving cost weight for this grid is 0.8 (a hypothetical value, provided only for illustrative purposes). The edge weight between adjacent grids is determined based on the moving cost weights of the two connected grids, as well as factors such as the distance between the grids. For example, edge weight = (grid A weight + grid B weight) / 2 × distance weight coefficient (the distance weight coefficient is set based on actual conditions to adjust the influence of distance on edge weight).

[0028] Finally, the A-star algorithm finds all the paths connecting towers and roads. These paths are used as edges, and the tower and road nodes are used as vertices to construct a weighted grid connectivity graph. The weight of each edge in the graph is the total cost of the path (taking into account factors such as distance, terrain costs, and environmental impacts). Ultimately, a weighted grid connectivity graph is formed that includes all key nodes (towers and roads).

[0029] Step S400: Analyze the weighted grid connectivity graph based on a path analysis algorithm to obtain a transportation route combination graph.

[0030] There are two methods in step S400, which are as follows: In one embodiment, a pheromone matrix is ​​initialized on the weighted grid connectivity graph based on an ant colony algorithm, and ants are deployed at the starting point of a transportation route on the weighted grid connectivity graph; the ants are controlled to select the next construction node on the weighted grid connectivity graph based on pheromone concentration and heuristic information, all transportation routes from the starting point to the end point of the transportation route are constructed, and the total cost of each transportation route is calculated; the pheromone is updated according to the total cost of the transportation route, and the ant transportation route construction and pheromone update are continuously repeated until a termination condition is met to obtain a transportation route combination graph containing multiple transportation routes.

[0031] Specifically, in this embodiment, during the path selection analysis phase, if an ant colony algorithm is used, the pheromone matrix is ​​first initialized, with a size corresponding to the weighted grid connectivity graph. Ants begin searching on the weighted grid connectivity graph, each selecting the next node based on pheromone concentration and heuristic information (such as the inverse of the distance between two points), and constructing a path from the starting point (tower or road) to the end point (another tower or road). After all ants have completed path construction, the total cost of each transport route is calculated (taking into account factors such as temporary road construction costs and environmental impacts). Pheromones are updated based on the total cost of the transport route, increasing pheromones on short paths and evaporating pheromones on long paths. The pheromone update formula is as follows: τ_{ij}(t + 1) = (1 - ρ)τ_{ij}(t)+Δτ_{ij} Among them, τ_{ij}(t) represents the pheromone concentration from node i to j at time t, ρ is the pheromone volatility rate, and Δτ_{ij} is the pheromone increment from node i to j in this iteration.

[0032] The process of ant transport route construction and pheromone update is repeated until the termination condition is met (such as the number of iterations reaches the upper limit or the minimum cost path combination that meets the requirements is found), and finally a transport route combination graph connecting all towers and roads is obtained.

[0033] In another embodiment, an improved Dijkstra algorithm is used to solve the transportation route planning problem for power grid projects. On a weighted grid connectivity graph, a starting node (such as a tower site or road node) is first determined. A distance array is initialized, with the distance from the starting node to itself set to 0 and the distances to all other nodes set to infinity. A priority queue is maintained, containing all construction nodes in the weighted grid connectivity graph and the distances from the starting point of the transportation route to the construction node. Starting from the starting node, the node with the smallest distance is extracted from the priority queue each time, and its adjacent nodes are traversed. The new distance from the current node to the adjacent node is calculated. If the new distance is less than the currently recorded distance of the adjacent node, the distance to the adjacent node and the predecessor node (recorded path) are updated. Special constraints for power grid project transportation (such as bridge load limits and turning radius requirements) are considered. If the adjacent node does not meet these constraints, the distance update is not performed. This process is repeated until the priority queue is empty, at which point the distance array stores the shortest distance from the starting node to each other node (considering the constraints). By backtracking the predecessor node, the shortest path from the starting node to other nodes is found, and then based on the shortest paths of all tower locations and road nodes, a transportation route combination graph connecting all tower locations and roads is combined.

[0034] Step S500, selecting a route connecting all construction nodes in the construction area in the transportation route combination graph as a temporary transportation route based on a path selection algorithm; There are two methods in step S500, which are as follows: In one embodiment, all edges in the transport route combination graph are sorted from small to large according to the edge weights; all vertices in the transport route combination graph are initialized and set-finded; each edge is selected from the sorted edge list in turn, and for each current edge currently selected, a union-find operation is performed to determine whether the two endpoints of the current edge are in the same set; if they are not in the same set, the current edge is added to the minimum spanning tree edge set, and the sets where the two endpoints of the current edge are located are merged; the minimum spanning tree edge set is iteratively updated until the number of selected edges is equal to the number of vertices minus 1, and the final minimum spanning tree edge set is used as a temporary transport route connecting all construction nodes in the construction area.

[0035] Specifically, in this embodiment, Data preparation: Obtain graph data containing all tower locations and road nodes. The edges in the graph are weighted, and the weights can be a combination of factors such as temporary road construction costs and distance. For example, tower locations and road nodes are abstracted as graph vertices, and possible connections between nodes are abstracted as edges. The weight of each edge is calculated based on the actual situation. Edge sorting: Sort all edges in the graph by weight, from smallest to largest. This step typically uses an efficient sorting algorithm, such as quick sort or merge sort, with a typical time complexity of O(mlogm), where m is the number of edges. For example, suppose there is an edge connecting tower A and tower B with a weight of 10; another edge connecting tower A and road C with a weight of 15 will be ranked higher after sorting.

[0036] Initialize a union-find set: Create a separate set for each vertex, which is used to determine whether adding edges will form a loop. Union-find sets can efficiently merge and search sets, with a time complexity close to constant time O(1). For example, create a set for each tower location and road node, each containing only one element.

[0037] Edge selection: From the sorted edge list, remove each edge in turn. For the current edge, determine whether its two endpoints are in the same set (using a union-find operation). If they are not in the same set, add this edge to the edge set of the minimum spanning tree and merge the sets of the two endpoints (using a union-find operation). If they are in the same set, skip this edge because adding it would form a loop. For example, the current edge connects tower D and road E. Using a union-find operation, determine that they are not in the same set. Add this edge to the minimum spanning tree edge set, and then merge the sets of D and E. Termination condition judgment: Repeat the edge selection steps until the number of selected edges is equal to the number of vertices minus 1 (because the minimum spanning tree of a connected graph containing n vertices has n-1 edges). At this time, the resulting edge set is the minimal connected subgraph connecting all tower sites and roads. In other words, the minimum spanning tree serves as a temporary transportation route connecting all construction nodes in the construction area.

[0038] In another embodiment, an initial population is generated in the transport route combination graph based on a genetic algorithm, and each individual in the initial population represents each initial route; individuals for the next generation of inheritance are selected based on the fitness value of each initial route, and genetic recombination and mutation operations are performed on the selected individuals to obtain a new generation of individuals; individuals are updated iteratively until the optimal individual population obtained when a preset stopping condition is reached serves as a temporary transport route connecting all construction nodes in the construction area.

[0039] Specifically, in this embodiment, Encoding: The path problem connecting all towers and roads is converted into an individual form suitable for genetic algorithms. The order in which all towers and road nodes are visited is usually considered a "chromosome." For example, if there are towers T1, T2, and T3 and roads R1 and R2, a possible chromosome is represented as [T1, R1, T2, R2, T3], which represents a visit order. Initial population generation: Randomly generate a certain number of initial solutions (individuals) (i.e., population size, such as 100 individuals) as the first generation of the population. For example, by randomly shuffling the node order, multiple different chromosomes are generated as individuals in the initial population. Fitness evaluation: Calculate the fitness value of each individual (i.e., each route). Since the goal is to find the shortest route, fitness can be defined as the inverse of the total route length (calculated based on the distance between nodes). Alternatively, a comprehensive cost function can be constructed by incorporating factors such as temporary road construction costs. Fitness is the inverse of this comprehensive cost, with the goal of maximizing the fitness value. For example, the distances between adjacent nodes on chromosome [T1, R1, T2, R2, T3] are calculated and summed to obtain the total route length, and the inverse of this sum is then calculated as the fitness value.

[0040] Selection operation: Selecting outstanding individuals to enter the next generation based on their fitness ratio. Common selection methods, such as roulette wheel selection, have a probability of selection proportional to their fitness. For example, suppose there are three individuals with fitness levels of 0.3, 0.5, and 0.2, respectively. Through roulette wheel selection, the individual with a fitness level of 0.5 has a relatively high probability of being selected. Crossover: Performs genetic recombination (crossover) on two or more selected individuals (parents) to create a new solution (offspring). For example, using a partial mapping crossover method, two crossover points are randomly selected. The genetic segments between the two parent individuals are then swapped. Conflict resolution is then performed to ensure that the offspring individuals follow a legal path (i.e., each node appears only once). For example, if the parents P1 = [1, 2, 3, 4, 5] and P2 = [5, 4, 3, 2, 1], and crossover points 2 and 4 are randomly selected, the offspring individuals obtained after swapping the middle segments may be [1, 4, 3, 2, 5]. Conflict resolution is then performed to ensure that the offspring individuals follow a legal path.

[0041] Mutation: Mutate individuals with a certain probability (e.g., set to 0.05), randomly changing certain genes in the individuals to increase exploration of the solution space and prevent the algorithm from falling into local optima. For example, by randomly swapping the positions of two nodes in an individual, the individual [1, 2, 3, 4, 5] might become [1, 4, 3, 2, 5] after mutation.

[0042] Iterative process: Repeat the above steps, that is, continuously perform fitness evaluation, selection, crossover and mutation operations until the preset stopping condition is reached, such as reaching the maximum number of iterations (such as 500 times) or the fitness value no longer improves significantly (such as the fitness change is less than a certain threshold for 50 consecutive generations).

[0043] Output result: The optimal individual population finally obtained serves as the temporary transportation route connecting all construction nodes in the construction area.

[0044] Step S600, detecting all the temporary transport routes based on a path detection algorithm to obtain a target transport route, including: Selecting a temporary transport route from all the temporary transport routes, dividing the selected temporary transport route at preset intervals starting from the transport route starting point, and calculating the slope of each divided route segment; If the slope of each route segment is less than or equal to the preset limit slope, and the length of each route segment is less than or equal to the preset length, then the selected temporary transport route is determined to be the target transport route; Otherwise, the next temporary transport route is selected and judged whether it is the target transport route.

[0045] Specifically, in this embodiment, each route in the temporary transport route is first traversed. Each route is divided into 50-meter segments, starting from the starting point. The slope of each segment is calculated. This slope calculation uses the elevation difference and horizontal distance between the two ends of the segment, using the formula (slope = fraction{elevation difference}{horizontal distance} × 100%). This determines whether each segment exceeds 50 meters in length. If so, the slope of any 50-meter segment (e.g., the slope of the first 50 meters from the starting point, the slope of the second 50 meters, etc.) and the maximum slope of the road (the maximum slope calculated for all segments exceeding 50 meters) are recorded. If the slopes of all segments of the route meet the requirements (e.g., the maximum slope does not exceed the specified value, and the continuous uphill slope does not exceed the corresponding slope limit of 50 meters), the path information is exported to complete the route planning. Otherwise, the transport routes that do not meet the requirements are deleted, and the route selection module is returned to reselect the transport routes until the route meets the slope requirements.

[0046] In summary, the present invention has the following beneficial effects: Multi-constraint precise adaptation: Deeply integrates the bridge load-bearing thresholds, turning radius restrictions, and traffic time constraints of special road sections unique to power grid projects. Through raster modeling, geological parameters such as soil softness and terrain slope are quantified into calculable indicators. Compared with traditional algorithms, this improves the professional adaptability of path planning under complex working conditions.

[0047] Multi-algorithm collaborative optimization: Integrates the dual solution strategies of minimum spanning tree and genetic algorithm. The former ensures optimal connectivity between all tower locations and roads, while the latter simulates biological evolution to find the minimum cost Hamiltonian circuit. The two work together to reduce the total path cost compared to a single algorithm, while taking into account both project accessibility and economy.

[0048] See also Figure 2 As shown, an embodiment of the present invention provides a path planning system for transporting power grid construction machinery, including: A data acquisition module is used to obtain path planning data of the construction area; A data construction module, in communication with the data acquisition module, configured to construct a construction environment model and a grid adjacency table based on the path planning data; a path connectivity module, communicatively connected to the data construction module, for processing the construction environment model and the grid adjacency table based on a path connectivity algorithm to obtain a weighted grid connectivity graph; a path analysis module, communicatively connected to the path connectivity module, for analyzing the weighted grid connectivity graph based on a path analysis algorithm to obtain a transportation route combination graph; a path selection module, in communication with the path analysis module, configured to select, in the transport route combination graph, a route connecting all construction nodes in the construction area as a temporary transport route based on a path selection algorithm; and The path detection module is in communication with the path selection module and is used to detect all the temporary transportation routes based on a path detection algorithm to obtain a target transportation route.

[0049] Specifically, this embodiment corresponds one-to-one to the above method embodiment, and the functions of each module have been described in detail in the corresponding method embodiment, so they will not be repeated here.

[0050] Based on the same inventive concept, an embodiment of the present application further provides a computer-readable storage medium on which a computer program is stored. When the computer program is executed by a processor, all or part of the method steps of the above method are implemented.

[0051] The present invention may implement all or part of the above-described method processes by instructing related hardware through a computer program. The computer program may be stored in a computer-readable storage medium. When executed by a processor, the computer program may implement the steps of each of the above-described method embodiments. The computer program includes computer program code, which may be in source code form, object code form, an executable file, or some intermediate form. Computer-readable media may include any entity or device capable of carrying computer program code, recording media, USB flash drives, removable hard drives, magnetic disks, optical disks, computer memory, read-only memory (ROM), random access memory (RAM), electric carrier signals, telecommunications signals, and software distribution media. It should be noted that the content of a computer-readable medium may be appropriately expanded or reduced based on the requirements of legislation and patent practice within a jurisdiction. For example, in some jurisdictions, based on legislation and patent practice, computer-readable media do not include electric carrier signals or telecommunications signals.

[0052] Based on the same inventive concept, an embodiment of the present application also provides an electronic device, including a memory and a processor, wherein the memory stores a computer program running on the processor, and when the processor executes the computer program, all or part of the method steps in the above method are implemented.

[0053] The processor may be a central processing unit (CPU), other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA), other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor. The processor is the control center of a computer device, connecting all parts of the entire computer device using various interfaces and circuits.

[0054] The memory can be used to store computer programs and / or modules. The processor implements the various functions of the computer device by running or executing the computer programs and / or modules stored in the memory and accessing the data stored in the memory. The memory may primarily include a program storage area and a data storage area. The program storage area may store an operating system and at least one application required for a function (e.g., a sound playback function, an image playback function, etc.); the data storage area may store data generated based on the use of the mobile phone (e.g., audio data, video data, etc.). Furthermore, the memory may include high-speed random access memory and non-volatile memory, such as a hard disk, internal memory, a plug-in hard disk, a SmartMedia Card (SMC), a Secure Digital (SD) card, a flash card, at least one disk storage device, a flash memory device, or other volatile solid-state storage device.

[0055] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, servers, or computer program products. Thus, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage and optical storage) containing computer-usable program code.

[0056] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), servers, and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowcharts and / or block diagrams, as well as combinations of processes and / or blocks in the flowcharts 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, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowcharts and / or block diagrams. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0057] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0058] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0059] Obviously, those skilled in the art may make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if such changes and modifications fall within the scope of the claims and their equivalents, the present invention is intended to include such changes and modifications.

Claims

1. A path planning method for transporting power grid construction machinery, characterized in that: include: Obtain path planning data for the construction area; Constructing a construction environment model and a grid adjacency table based on the path planning data; Processing the construction environment model and the grid adjacency table based on a path connectivity algorithm to obtain a weighted grid connectivity graph; Analyzing the weighted grid connectivity graph based on a path analysis algorithm to obtain a transportation route combination graph; Selecting a route connecting all construction nodes in the construction area in the transportation route combination graph as a temporary transportation route based on a path selection algorithm; All the temporary transport routes are detected based on a path detection algorithm to obtain a target transport route.

2. The path planning method for transporting power grid construction machinery according to claim 1, characterized in that: The step of constructing a construction environment model and a grid adjacency table based on the path planning data includes: Converting the elevation data in the path planning data into an elevation data matrix; Dividing the map data in the path planning data into grids based on a preset grid side length, and numbering the divided grids in sequence; Calculate grid attributes and mark roads for each numbered grid according to the elevation data matrix and the path planning data to obtain a construction environment model corresponding to the construction area; Based on the grid accessibility judgment factor, it is judged whether each grid in the construction environment model has path accessibility, and an adjacency table is constructed based on the connection relationship between each grid with path accessibility and its adjacent multiple accessible grids.

3. The path planning method for transporting power grid construction machinery according to claim 1, characterized in that: The process of processing the construction environment model and the grid adjacency table based on a path connectivity algorithm to obtain a weighted grid connectivity graph includes: Based on the A-star algorithm and by considering the construction cost of the transportation route and the heuristic function, searching for a connection route between all construction nodes within the range from the starting point to the end point of the transportation route in the construction environment model and the grid adjacency table; All construction nodes are used as vertices, the connection routes are used as edges, and the connection cost of each edge is quantified as a weight to construct a weighted grid connection graph.

4. The path planning method for transporting power grid construction machinery according to claim 1, wherein: The weighted grid connectivity graph is analyzed based on a path analysis algorithm to obtain a transportation route combination graph, including: Initializing a pheromone matrix on the weighted grid connectivity graph based on an ant colony algorithm, and deploying ants at the starting point of a transportation route on the weighted grid connectivity graph; Control ants to select the next construction node on the weighted grid connectivity graph based on pheromone concentration and heuristic information, construct all transportation routes from the starting point to the end point of the transportation route, and calculate the total cost of each transportation route; The pheromone is updated according to the total cost of the transportation route, and the ant transportation route construction and pheromone update are repeated until the termination condition is met to obtain a transportation route combination graph containing multiple transportation routes.

5. The path planning method for transporting power grid construction machinery according to claim 1, characterized in that: The weighted grid connectivity graph is analyzed based on a path analysis algorithm to obtain a transportation route combination graph, including: Determine a transportation route starting point on a weighted grid connectivity graph, and initialize a distance array on the weighted grid connectivity graph based on a Dijkstra algorithm; Setting a priority queue, wherein the priority queue includes all construction nodes in the weighted grid connectivity graph and the distance from the starting point of the transportation route to the construction node; Starting from the starting point of the transportation route, each time the construction node with the smallest distance is taken from the priority queue and defined as the current node. The new distance from the current node to its adjacent node is calculated. If the new distance is less than the currently recorded distance of the adjacent node and meets the construction transportation constraints, the distance of the adjacent node and the predecessor node are updated. The predecessor node is defined as the adjacent node whose updated distance originates from the current node. Otherwise, no distance update is performed. The construction node selection and distance update calculation are iterated until the construction node in the priority queue is empty. At this time, the distance array stores the shortest distance from the starting point of the transportation route to each construction node. By backtracking the predecessor node, the shortest path from the starting point of the transportation route to each construction node is found, and a transportation route combination graph containing multiple transportation routes is obtained.

6. The path planning method for transporting power grid construction machinery according to claim 1, characterized in that: The method of selecting a route connecting all construction nodes in the construction area as a temporary transportation route in the transportation route combination graph based on the path selection algorithm includes: Sort all edges in the transport route combination graph in ascending order according to edge weights; Initialize and query all vertices in the transport route combination graph; Select each edge from the sorted edge list in turn. For each edge currently selected, use a union-find operation to determine whether the two endpoints of the current edge are in the same set. If they are not in the same set, add the current edge to the minimum spanning tree edge set and merge the sets where the two endpoints of the current edge are located. The minimum spanning tree edge set is iteratively updated until the number of selected edges is equal to the number of vertices minus 1. The final minimum spanning tree edge set is used as the temporary transportation route connecting all construction nodes in the construction area.

7. The path planning method for transporting power grid construction machinery according to claim 1, characterized in that: The method of selecting a route connecting all construction nodes in the construction area as a temporary transportation route in the transportation route combination graph based on the path selection algorithm includes: generating an initial population in the transport route combination graph based on a genetic algorithm, wherein each individual in the initial population represents each initial route; According to the fitness value of each initial route, individuals for the next generation of inheritance are selected, and gene recombination and mutation operations are performed on the selected individuals to obtain a new generation of individuals; Individual updates are performed iteratively until the optimal individual population obtained when the preset stopping condition is reached is used as the temporary transportation route connecting all construction nodes in the construction area.

8. The path planning method for transporting power grid construction machinery according to claim 1, characterized in that: The method of detecting all the temporary transport routes based on the path detection algorithm to obtain the target transport route includes: Selecting a temporary transport route from all the temporary transport routes, dividing the selected temporary transport route at preset intervals starting from the transport route starting point, and calculating the slope of each divided route segment; If the slope of each route segment is less than or equal to the preset limit slope, and the length of each route segment is less than or equal to the preset length, then the selected temporary transport route is determined to be the target transport route; Otherwise, the next temporary transport route is selected and judged whether it is the target transport route.

9. A path planning system for transporting power grid construction machinery, characterized in that: include: A data acquisition module is used to obtain path planning data of the construction area; A data construction module, in communication with the data acquisition module, configured to construct a construction environment model and a grid adjacency table based on the path planning data; a path connectivity module, communicatively connected to the data construction module, for processing the construction environment model and the grid adjacency table based on a path connectivity algorithm to obtain a weighted grid connectivity graph; a path analysis module, communicatively connected to the path connectivity module, for analyzing the weighted grid connectivity graph based on a path analysis algorithm to obtain a transportation route combination graph; A path selection module, in communication with the path analysis module, configured to select, in the transport route combination graph, a route connecting all construction nodes in the construction area as a temporary transport route based on a path selection algorithm; as well as, The path detection module is in communication with the path selection module and is used to detect all the temporary transportation routes based on a path detection algorithm to obtain a target transportation route.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the path planning method for transportation of power grid construction machinery is implemented as described in any one of claims 1 to 8.

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