Intelligent path planning device and method for mine transportation vehicle

Abstract

The application relates to the technical field of vehicle path planning, in particular to a path intelligent planning device and method for a mine area transport vehicle. The method regards all loading areas, intersections and unloading areas as nodes, determines path data, vehicle data of the mine area transport vehicle, and lengths and slopes of paths between different nodes; calculates uphill energy consumption coefficients, downhill energy consumption coefficients and energy consumption items of the paths between different nodes, calculates a path cost and a path complexity of the mine area transport vehicle driving on the paths between different nodes; determines a path cost of the mine area transport vehicle driving on the paths between different nodes and a target function of the mine area transport vehicle, uses a particle swarm algorithm to minimize the target function, and obtains a path planning result of the mine area transport vehicle. The application can improve the efficiency and safety of mine area transport operations.

Description

Technical Field

[0001] This application relates to the field of vehicle route planning technology, specifically to an intelligent route planning device and method for mining area transport vehicles. Background Technology

[0002] Vehicle transportation efficiency is crucial in the mining process. Unmanned driving systems are typically used for path planning and to control mining vehicles to follow planned routes, enabling all-weather, high-efficiency operation of these vehicles within the mining area. Generally, particle swarm optimization (PSO) algorithms are used to pre-determine parameters within the scenario to find the optimal path.

[0003] However, excavation and blasting operations in mining areas can easily cause rockfalls and depressions on mining roads, affecting normal traffic flow. At the same time, multiple mining transport vehicles may share the same route in the mining area. Therefore, the complex working conditions in the mining area may cause the parameters in the preset scenario of the particle swarm optimization algorithm to be inconsistent with the actual needs of the vehicle transportation process, making the optimal path planning result unreasonable. Summary of the Invention

[0004] To address the aforementioned technical problems, this application provides an intelligent route planning device and method for mining area transport vehicles, the specific technical solution of which is as follows:

[0005] In a first aspect, one embodiment of this application provides a method for intelligent route planning for mining area transport vehicles, the method comprising the following steps:

[0006] Collect optical remote sensing image data and GIS data of the mining area, and take all loading areas, intersections and unloading areas as nodes. Based on all the collected data and nodes, determine the path data, vehicle data of mining area transport vehicles, and the length and slope of the path between different nodes.

[0007] Based on the vehicle data of the mining area transport vehicles and the length and slope of the paths between different nodes, the uphill energy consumption coefficient, downhill energy consumption coefficient, and energy consumption item of the mining area transport vehicles on the paths between different nodes are calculated respectively. Combining the vehicle data, the length and slope of the paths between different nodes, the cost of the mining area transport vehicles traveling on the paths between different nodes is calculated.

[0008] Based on the path data, calculate the path complexity of mining transport vehicles traveling between different nodes;

[0009] Based on the cost and path complexity of mining transport vehicles traveling between different nodes, the path cost and objective function of mining transport vehicles are determined. The particle swarm optimization algorithm is used to minimize the objective function and obtain the path planning results of mining transport vehicles.

[0010] Furthermore, the route data and vehicle data for mining area transport vehicles specifically include:

[0011] Based on the use of a geographic information system platform, a digital topographic model of the mining area is constructed, and path data is determined based on the digital topographic model;

[0012] Collect vehicle data for transport vehicles in the mining area.

[0013] Furthermore, the path data and the vehicle data of the mining area transport vehicles specifically include:

[0014] The path data includes elevation and width information at different locations along the path, as well as the location and width of obstacles along the path;

[0015] The vehicle data for mining area transport vehicles includes the real-time location, width, body length, driving speed, load weight, body weight, and operating power of the mining area transport vehicles.

[0016] Furthermore, the formula for calculating the uphill energy consumption coefficient is as follows:

[0017]

[0018] in, Indicates the first Energy consumption coefficient of mining area transport vehicles going uphill; Represents the logarithm with the natural constant as the base; The preset rolling resistance coefficient of mining area transport vehicles; node and The slope of the path between them; No. The weight of the vehicle body for transporting goods in the mining area; No. The load weight of a vehicle used for transporting goods in the mining area.

[0019] Furthermore, the formula for calculating the downhill energy consumption coefficient is as follows:

[0020]

[0021] in, Indicates the first Energy consumption coefficient of a mining area transport vehicle going downhill; This represents the maximum value function.

[0022] Furthermore, the formula for calculating the energy consumption item is:

[0023]

[0024] in, Indicates the first Mining area transport vehicles at the node and Energy consumption items along the path between; No. The operating power of the mining area transport vehicles; Represents a node and The length of the path between them; Indicates the first The speed of the vehicles transporting goods in the mining area.

[0025] Furthermore, the formula for calculating the cost of the mining area transport vehicles traveling between different nodes is as follows:

[0026]

[0027] in, Indicates the first Mining area transport vehicles at the node and The cost of traveling along the route between them; Represents a node Elevation data, Represents a node Elevation data; Indicates the first The safe braking speed for a mining transport vehicle going downhill.

[0028] Furthermore, the formula for calculating the path complexity is:

[0029]

[0030]

[0031]

[0032] in, Indicates the first Mining area transport vehicles at the node and The first complexity of the path between them; Indicates the first Mining area transport vehicles at the node and The second complexity of the path between; Indicates the first Mining area transport vehicles at the node and The path complexity of the paths between them; Represents an exponential function with the natural constant as its base; Indicates the first Mining area transport vehicles at the node and The maximum width of all obstacles on the path between them; Indicates the first Mining area transport vehicles at the node and The width of the path corresponding to the obstacle position at the maximum width in the path between them; This indicates the preset passable safe width coefficient; Indicates the first The width of a mining area transport vehicle; Represents a node and In the path between, the driving speed is less than the first All mining area transport vehicles occupy the node. and The ratio of the number of transport vehicles to all mining areas along the route; Indicates the first The length of the vehicle body of the mining area transport vehicle accounts for the node and The ratio of the lengths of the paths between them.

[0033] Furthermore, the method for obtaining the objective function of the mining area transport vehicles is as follows:

[0034] The positive correlation between the cost of mining transport vehicles traveling between different nodes and the path complexity is recorded as the path cost of mining transport vehicles traveling between different nodes.

[0035] The objective function of the mining area transport vehicle is the sum of the path costs of all routes taken by the vehicle from the starting point to the destination.

[0036] Secondly, another embodiment of this application provides a path intelligent planning device for mining area transport vehicles, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the steps of the above-described path intelligent planning method for mining area transport vehicles.

[0037] The embodiments of this application have at least the following beneficial effects:

[0038] This application considers that the energy and time consumed by mining transport vehicles differ when traveling on flat, uphill, and downhill sections. It calculates the uphill and downhill energy consumption coefficients for mining transport vehicles traveling on uphill and downhill sections respectively. Simultaneously, it calculates the energy consumption of mining transport vehicles traveling on downhill sections, obtaining the energy consumption items for the paths between different nodes. Combining vehicle data, the length and gradient of the paths between different nodes, and considering the specific conditions of mining transport vehicles under different loads and operating states in complex road conditions, the application calculates the cost of mining transport vehicles traveling between different nodes. Furthermore, based on the different... The complexity of paths travel between nodes is further analyzed to obtain the path complexity of mining transport vehicles traveling between different nodes. Based on the cost and path complexity of mining transport vehicles traveling between different nodes, the path cost and objective function of mining transport vehicles are determined. When the objective function is minimized, mining transport vehicles will make every effort to avoid choosing high-cost paths, avoid congested and obstructed paths, and improve the transportation efficiency and safety of mining transport vehicles. Finally, the path planning results of mining transport vehicles are obtained, which solves the problem that the particle swarm algorithm, which optimizes the best path based on static assumptions, is difficult to meet the needs of transportation path planning under complex mining conditions, and improves the efficiency and safety of mining transportation operations. Attached Figure Description

[0039] To more clearly illustrate the technical solutions and advantages in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0040] Figure 1 This is a flowchart illustrating the steps of an intelligent route planning method for mining area transport vehicles, provided as an embodiment of this application. Detailed Implementation

[0041] To further illustrate the technical means and effects adopted by this application in order to achieve the intended purpose of the invention, the following detailed description, in conjunction with the accompanying drawings and preferred embodiments, describes the specific implementation, structure, features and effects of a path intelligent planning device and method for mining area transport vehicles proposed in this application.

[0042] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains.

[0043] The following description, in conjunction with the accompanying drawings, details the specific scheme of the intelligent path planning device and method for mining area transport vehicles provided in this application.

[0044] Please see Figure 1 The diagram illustrates a flowchart of a method for intelligent route planning for mining area transport vehicles according to an embodiment of this application. The method includes the following steps:

[0045] Step S001: Collect optical remote sensing image data and GIS data of the mining area, and take all loading areas, intersections and unloading areas as nodes. Based on all the collected data and nodes, determine the path data, vehicle data of mining area transport vehicles, and the length and slope of the path between different nodes.

[0046] Drones equipped with optical remote sensing and GIS mapping equipment were used to collect optical remote sensing image data and GIS data of the mining area, respectively.

[0047] Preprocessing was performed on both optical remote sensing image data and GIS data.

[0048] Preprocessing optical remote sensing image data and GIS data separately is a well-known technique and will not be elaborated further. Preferably, as an embodiment of this application, atmospheric correction and geometric correction are performed on the optical remote sensing image data to eliminate the influence of ground object interference and lens distortion, and the GIS data is projected onto the WGS84 coordinate system to ensure the consistency of spatial coordinate data.

[0049] The geographic information system platform is used to process optical remote sensing image data and GIS data to construct a digital topographic model of the mining area.

[0050] The use of a geographic information system platform to construct a terrain digital model is a well-known technology and will not be elaborated further. The terrain digital model of the mining area is used to analyze and plan the transportation operations of mining vehicles. In this embodiment, the GIS platform in the geographic information system platform is used to construct the terrain digital model of the mining area.

[0051] Based on the terrain digital model, path data is determined, which includes elevation information and width at different locations along the path, as well as the location and width of obstacles along the path.

[0052] Based on the sensors installed on the mining area transport vehicles, vehicle data of the mining area transport vehicles is collected. The vehicle data of the mining area transport vehicles includes the real-time location, width, body length, driving speed, load weight, body weight and operating power of the mining area transport vehicles.

[0053] The real-time location is represented by latitude and longitude coordinates.

[0054] Based on the terrain digital model, all loading areas, intersections, and unloading areas in the mining area are identified. All loading areas, intersections, and unloading areas are treated as nodes. Based on the terrain digital model, the length and slope of the path between different nodes are calculated.

[0055] Calculating the length and slope of the path between different nodes is a well-known technique and will not be elaborated further. Preferably, as an embodiment of this application, the length of the path between different nodes is calculated using the Haversine formula.

[0056] At this point, we have obtained path data, vehicle data for transporting vehicles in the mining area, and the length and gradient of the paths between different nodes.

[0057] Step S002: Based on the vehicle data of the mining area transport vehicles and the length and slope of the path between different nodes, calculate the uphill energy consumption coefficient, downhill energy consumption coefficient, and energy consumption item of the mining area transport vehicles on the path between different nodes. Combine the vehicle data, the length and slope of the path between different nodes, and calculate the cost of the mining area transport vehicles traveling on the path between different nodes.

[0058] Excavation and blasting operations in mining areas can easily cause rockfalls and depressions on roads, affecting normal traffic flow. Furthermore, multiple mining transport vehicles may share the same route. Therefore, the complex working conditions in mining areas may cause the parameters in the pre-set scenario of the particle swarm optimization algorithm to not match the actual needs of vehicle transportation, resulting in unreasonable optimal path planning results. To address the problem that the particle swarm optimization algorithm, based on static assumptions, cannot meet the needs of transportation path planning under complex mining conditions, this paper proposes a path planning method for mining transport vehicles based on real-time road conditions.

[0059] The graph structure of the mining area transportation route is constructed by connecting different nodes with edges. The weights of the edges between different nodes are calculated as follows.

[0060] First, the energy and time consumed by mining transport vehicles differ depending on whether they are traveling on flat, uphill, or downhill sections. Specifically, on flat sections, the vehicles maintain a constant and stable speed. On uphill sections, the vehicles need to increase their power output based on the gradient and load, consuming additional energy to maintain normal speed. On downhill sections, when the vehicle's downhill potential energy is less than its rolling resistance, the engine continues to operate to keep the vehicle moving forward. When the vehicle's downhill potential energy exceeds its rolling resistance, the engine does not consume energy, and the vehicle must brake to slow down and maintain a safe downhill speed.

[0061] Based on the vehicle data of the mining area transport vehicles and the length and gradient of the paths between different nodes, the uphill energy consumption coefficient, downhill energy consumption coefficient, and energy consumption item of the mining area transport vehicles along the paths between different nodes are calculated. The calculation formula is as follows:

[0062]

[0063]

[0064]

[0065] in, Indicates the first Mining area transport vehicles at the node and Energy consumption items along the path between; Indicates the first Energy consumption coefficient of mining area transport vehicles going uphill; Indicates the first Energy consumption coefficient of a mining area transport vehicle going downhill; No. The operating power of the mining area transport vehicles; node and The slope of the path between them; No. The weight of the vehicle body for transporting goods in the mining area; No. The load weight of each mining area transport vehicle; The preset rolling resistance coefficient of the mining area transport vehicle is set according to the actual roughness of the path between nodes. In this embodiment, the rolling resistance coefficient is set to 0.025. Represents the logarithm with the natural constant as the base; This function represents the maximum value and its function is to find the maximum value among all the values ​​separated by commas within the parentheses. Represents a node and The length of the path between them; Indicates the first The speed of the vehicles transporting goods in the mining area.

[0066] The purpose of logarithms is to scale down the energy consumption and climbing energy consumption coefficients of mining transport vehicles to a normal level, preventing them from having an excessive impact on the cost indicators of mining transport vehicle operation. The energy consumption term represents the energy consumption of mining transport vehicles traveling on a road segment. The uphill and downhill energy consumption coefficients of mining transport vehicles are used to evaluate the additional energy cost of driving on a flat road surface. Specifically, on uphill sections, the additional energy required for climbing is determined based on the vehicle's load and the slope of the route; on downhill sections, the downhill energy consumption coefficient is determined based on the impact of the slope on the vehicle's downhill potential energy. If the vehicle's downhill potential energy is greater than its rolling resistance, the downhill energy consumption coefficient is 0, and the vehicle does not require additional energy consumption.

[0067] Based on the uphill energy consumption coefficient, downhill energy consumption coefficient, and vehicle data of mining area transport vehicles, the energy consumption items of mining area transport vehicles on the path between different nodes, as well as the length and gradient of the path between different nodes, the cost of mining area transport vehicles traveling on the path between different nodes is calculated.

[0068]

[0069] in, Indicates the first Mining area transport vehicles at the node and The cost of traveling along the route between them; Represents a node Elevation data, Represents a node Elevation data; Indicates the first The safe braking speed of a mining transport vehicle when going downhill is set according to the load capacity of the mining transport vehicle. The larger the load capacity of the mining transport vehicle, the smaller the value of the safe braking speed. The value of the safe braking speed should be greater than or equal to 10 and less than or equal to 20. In this embodiment, the value of the safe braking speed is 15.

[0070] The process of obtaining the cost value of mining transport vehicles traveling between different nodes fully considers the travel cost of mining transport vehicles under different loads and operating states under complex road conditions.

[0071] At this point, the cost of all mining area transport vehicles traveling between different nodes is obtained.

[0072] Step S003: Calculate the path complexity of the mining area transport vehicles traveling between different nodes based on the path data.

[0073] Furthermore, the complexity of the routes traveled by mining transport vehicles between different nodes is further analyzed.

[0074] Based on the path data, calculate the path complexity of mining transport vehicles traveling between different nodes.

[0075]

[0076]

[0077]

[0078] in, Indicates the first Mining area transport vehicles at the node and The first complexity of the path between them; Indicates the first Mining area transport vehicles at the node and The second complexity of the path between; Indicates the first Mining area transport vehicles at the node and The path complexity of the paths between them; Represents an exponential function with the natural constant as its base; Indicates the first Mining area transport vehicles at the node and The maximum width of all obstacles on the path between them; Indicates the first Mining area transport vehicles at the node and The width of the path corresponding to the obstacle position at the maximum width in the path between them; This represents the preset passable safety width coefficient. The value of the passable safety width coefficient should be greater than 1 and less than or equal to 2. In this embodiment, the value of the passable safety width coefficient is 1.25. Indicates the first The width of a mining area transport vehicle; Represents a node and In the path between, the driving speed is less than the first All mining area transport vehicles occupy the node. and The ratio of the number of transport vehicles to all mining areas along the route; Indicates the first The length of the vehicle body of the mining area transport vehicle accounts for the node and The ratio of the lengths of the paths between them.

[0079] It is understandable that the number of mining transport vehicles traveling on the inter-node paths is determined by the real-time location of the mining transport vehicles.

[0080] The path complexity, constructed based on the congestion risk and obstacles along the routes of mining transport vehicles, can clearly evaluate the status of the routes and can be used to adjust the cost of mining transport vehicles traveling on those routes.

[0081] Thus, the path complexity of the mining area transport vehicles traveling between different nodes is obtained.

[0082] Step S004: Based on the cost and path complexity of the mining transport vehicle traveling between different nodes, determine the path cost and objective function of the mining transport vehicle. Use the particle swarm optimization algorithm to minimize the objective function and obtain the path planning result of the mining transport vehicle.

[0083] The positive correlation between the cost of mining transport vehicles traveling between different nodes and the path complexity is denoted as the path cost of mining transport vehicles traveling between different nodes.

[0084] It is understood that a positive correlation is applied to cost and path complexity, meaning that the cost and path complexity are positively correlated with the path cost, respectively. It is also understood that the positive correlation in this application refers to the relationship between the independent and dependent variables, where the independent variables are cost and path complexity, and the dependent variable is path cost. The positive correlation means that the dependent variable increases (decreases) as the independent variables increase (decreases), and can be an additive, multiplicative, or similar relationship.

[0085] Preferably, as an embodiment of this application, the product of the cost of the mining transport vehicle traveling between different nodes and the path complexity is denoted as the path cost of the mining transport vehicle traveling between different nodes.

[0086] When there are obstacles or congestion risks on the path between different nodes, the path cost increases, and the weight of the edges between the corresponding nodes also increases. When there are no obstacles and no congestion risks on the path between different nodes, the path cost is 1, which is the cost for mining transport vehicles to travel normally on the path.

[0087] It is important to understand that the path cost of a mining transport vehicle traveling between different nodes is the weight of the edge between the different nodes corresponding to the mining transport vehicle.

[0088] The sum of the path costs of all routes taken by mining transport vehicles from the starting point to the destination is used as the objective function of the mining transport vehicles. Each mining transport vehicle is treated as a particle swarm, and the particle swarm optimization algorithm is used to find the optimal value that minimizes the objective function. The path planning results of the mining transport vehicles are then obtained and displayed as a sequence of different nodes arranged in the order of path planning.

[0089] Understandably, when the objective function is minimized, mining transport vehicles will make every effort to avoid choosing high-cost routes and bypass congested or obstructed paths, thereby improving the transportation efficiency and safety of mining vehicles. This embodiment utilizes the learning factor of the particle swarm optimization algorithm. The value of is set to 2; the solution process of the particle swarm algorithm is a well-known technique and will not be described in detail here.

[0090] The formula for updating the inertia weight in the particle swarm optimization algorithm is as follows:

[0091]

[0092] in, Indicates the first Inertia weights in the next iteration; Indicates the first Inertia weights in the next iteration; Represents the maximum value function; This represents the total number of iterations using the particle swarm optimization algorithm. In this embodiment... The value is 200; This represents the preset minimum inertia weight. The value of the minimum inertia weight should be greater than 0 and less than or equal to 0.9. In this embodiment, the value of the minimum inertia weight is 0.2.

[0093] In this embodiment, the inertia weight is set to 0.9 in the first iteration. As the iteration progresses, the inertia weight gradually decreases. The larger value of the inertia weight in the first iteration can improve the ability to explore the solution space globally during the iteration process, while the gradually decreasing inertia weight is beneficial for finely searching for the optimal solution locally.

[0094] At this point, the route planning results for the mining area transport vehicles are obtained.

[0095] This application also proposes an intelligent route planning device for mining area transport vehicles, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the steps described above. Since a method for intelligent route planning for mining area transport vehicles has been described in detail above, it will not be repeated here.

[0096] It should be noted that the order of the embodiments described above is merely for descriptive purposes and does not represent the superiority or inferiority of the embodiments. Furthermore, specific embodiments of this specification have been described above. Additionally, the processes depicted in the accompanying drawings do not necessarily require a specific or sequential order to achieve the desired results. In some implementations, multitasking and parallel processing are possible or may be advantageous.

[0097] The various embodiments in this specification are described in a progressive manner. The same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on describing the differences from other embodiments.

[0098] The above-described embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them; modifications to the technical solutions described in the foregoing embodiments, or equivalent substitutions of some of the technical features, do not cause the essence of the corresponding technical solutions to deviate from the 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 route planning for mining area transport vehicles, characterized in that, The method includes the following steps: Collect optical remote sensing image data and GIS data of the mining area, and take all loading areas, intersections and unloading areas as nodes. Based on all the collected data and nodes, determine the path data, vehicle data of mining area transport vehicles, and the length and slope of the path between different nodes. Based on the vehicle data of the mining area transport vehicles and the length and slope of the paths between different nodes, the uphill energy consumption coefficient, downhill energy consumption coefficient, and energy consumption item of the mining area transport vehicles on the paths between different nodes are calculated respectively. Combining the vehicle data, the length and slope of the paths between different nodes, the cost of the mining area transport vehicles traveling on the paths between different nodes is calculated. Based on the path data, calculate the path complexity of mining transport vehicles traveling between different nodes; Based on the cost and path complexity of mining transport vehicles traveling between different nodes, the path cost and objective function of mining transport vehicles are determined. The particle swarm optimization algorithm is used to minimize the objective function and obtain the path planning results of mining transport vehicles. The formula for calculating the cost of the mining area transport vehicles traveling between different nodes is as follows: in, Indicates the first Mining area transport vehicles at the node and The cost of traveling along the route between them; Represents a node Elevation data, Represents a node Elevation data; Indicates the first The safe braking speed of a mining transport vehicle when going downhill; Represents a node and The length of the path between them; Indicates the first The speed of the vehicles transporting goods in the mining area; Indicates the first Energy consumption coefficient of mining area transport vehicles going uphill; Indicates the first Energy consumption coefficient of a mining area transport vehicle going downhill; Indicates the first Mining area transport vehicles at the node and Energy consumption items along the path between; The formula for calculating the path complexity is: in, Indicates the first Mining area transport vehicles at the node and The first complexity of the path between them; Indicates the first Mining area transport vehicles at the node and The second complexity of the path between; Indicates the first Mining area transport vehicles at the node and The path complexity of the paths between them; Represents an exponential function with the natural constant as its base; Indicates the first Mining area transport vehicles at the node and The maximum width of all obstacles on the path between them; Indicates the first Mining area transport vehicles at the node and The width of the path corresponding to the obstacle position at the maximum width in the path between them; This indicates the preset passable safe width coefficient; Indicates the first The width of a mining area transport vehicle; Represents a node and In the path between, the driving speed is less than the first All mining area transport vehicles occupy the node. and The ratio of the number of transport vehicles to all mining areas along the route; Indicates the first The length of the vehicle body of the mining area transport vehicle accounts for the node and The ratio of the lengths of the paths between them.

2. The intelligent route planning method for mining area transport vehicles according to claim 1, characterized in that, The route data and vehicle data for mining area transport vehicles specifically include: Based on the use of a geographic information system platform, a digital topographic model of the mining area is constructed, and path data is determined based on the digital topographic model; Collect vehicle data for transport vehicles in the mining area.

3. The intelligent route planning method for mining area transport vehicles according to claim 1, characterized in that, The route data and the vehicle data of the mining area transport vehicles specifically include: The path data includes elevation and width information at different locations along the path, as well as the location and width of obstacles along the path; The vehicle data for mining area transport vehicles includes the real-time location, width, body length, driving speed, load weight, body weight, and operating power of the vehicles.

4. The intelligent route planning method for mining area transport vehicles according to claim 3, characterized in that, The formula for calculating the uphill energy consumption coefficient is: in, Represents the logarithm with the natural constant as the base; The preset rolling resistance coefficient of mining area transport vehicles; node and The slope of the path between them; No. The weight of the vehicle body for transporting goods in the mining area; No. The load weight of a vehicle used for transporting goods in the mining area.

5. The intelligent route planning method for mining area transport vehicles according to claim 4, characterized in that, The formula for calculating the downhill energy consumption coefficient is: in, This represents the maximum value function.

6. The intelligent route planning method for mining area transport vehicles according to claim 5, characterized in that, The formula for calculating the energy consumption item is: in, No. The operating power of a mining area transport vehicle.

7. The intelligent route planning method for mining area transport vehicles according to claim 1, characterized in that, The method for obtaining the objective function of the mining area transport vehicles is as follows: The positive correlation between the cost of mining transport vehicles traveling between different nodes and the path complexity is recorded as the path cost of mining transport vehicles traveling between different nodes. The objective function of the mining area transport vehicle is the sum of the path costs of all routes taken by the vehicle from the starting point to the destination.

8. A path intelligent planning device for mining area transport vehicles, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the intelligent path planning method for mining area transport vehicles as described in any one of claims 1 to 7.

Images