Multi-dimensional vehicle scheduling method and device, electronic equipment, storage medium and computer program product

Abstract

The invention discloses a multi-dimensional vehicle scheduling method and device, electronic equipment, a storage medium and a computer program product, and relates to the technical field of vehicle scheduling, and the method comprises the steps: collecting the basic information and state information of a transport vehicle and a fixed facility through an Internet of Things terminal and an industrial bus, building a mapping relation between an equipment ID and a digital twinborn body, and planning the shortest path to the target node for the abnormal transport vehicle with the information which does not influence the driving, otherwise, identifying the congestion of the normal transport vehicle and pushing the scheduling information. According to the invention, precision, dynamic and intelligent vehicle scheduling can be realized, and the transportation efficiency and safety are improved.

Description

Technical Field

[0001] The present invention relates to the field of vehicle dispatching technology. More specifically, the present invention relates to a multi-dimensional vehicle dispatching method and apparatus, electronic equipment, storage medium, and computer program product. Background Art

[0002] The dispatch of mining vehicles is crucial in open-pit mine operations, as their efficiency directly impacts the mine's production progress, operating costs, and operational safety. However, traditional methods for dispatching mining vehicles suffer from numerous limitations. First, information collection is incomplete, often failing to effectively capture both static attributes (location, energy consumption, etc.) and dynamic attributes (operating status, usage status) of transport vehicles and fixed facilities (such as loading and unloading points and maintenance stations). For example, each mining vehicle has unique performance parameters, such as load capacity, speed, fuel consumption, and current condition. Different vehicles will perform differently when faced with the same transport task, and the vehicle's condition directly determines its ability to participate in the transport operation. Vehicle failures directly impact the execution of transport plans. Second, the handling of faulty vehicles and vehicles with low fuel or battery levels relies on manual reporting, resulting in inefficient route planning, often relying on the driver's manual judgment. This can lead to vehicle breakdowns and disrupted transport continuity. Furthermore, congestion detection is inaccurate. Congestion is determined by simple vehicle counts, without dynamic analysis based on spatial distribution characteristics. This results in a high false alarm rate, increasing vehicle waiting times and operating costs. These multi-dimensional factors interact and influence each other, making the dispatch of mining vehicles in open-pit mines extremely complex. Traditional dispatch methods are no longer able to meet the requirements of efficient, safe, and economical production. A vehicle dispatch method that comprehensively considers these factors is urgently needed. Summary of the Invention

[0003] The present invention provides a multi-dimensional vehicle scheduling method and device, electronic equipment, storage medium, and computer program product, which can realize accurate and dynamic vehicle scheduling, improve transportation efficiency, reduce congestion misjudgment rate, ensure driving safety, and enhance the level of scheduling intelligence.

[0004] In order to achieve these purposes and other advantages according to the present invention, a multi-dimensional vehicle scheduling method is provided, comprising: Step 1: Collect basic information and status information through the integration of IoT terminals and industrial buses; The basic information includes static attributes of transport vehicles and fixed facilities, including loading and unloading points, maintenance stations, charging and swapping stations, and gas stations. The static attributes include the location and energy consumption of transport vehicles and the location of fixed facilities. The state information includes dynamic attributes of the transport vehicle and the fixed facility, and the dynamic attributes include the operating state of the transport vehicle and the use state of the fixed facility; At the same time, a mapping relationship between the unique device IDs of transport vehicles and fixed facilities and digital twins is established; Step 2: Obtain online transport vehicle information. When it is identified that there is information that does not affect the transport vehicle's driving, based on the static attributes and the dynamic attributes, with the available fixed facilities as the target nodes, calculate the shortest path for the transport vehicle to reach the fixed facilities; when it is identified that there is information that affects the transport vehicle's driving, analyze the spatial distribution of the online normal transport vehicle location point set. When the vehicle density value of a specific line exceeds the preset density threshold, push congestion warning and decentralized scheduling reminder information to the corresponding normal transport vehicle.

[0005] Preferably, the online transport vehicle information includes: vehicle long idle information, fault information, fuel or power information, and the information that does not affect driving of the transport vehicle includes: vehicle long idle, faults that do not affect driving, fuel or power not less than a threshold.

[0006] Preferably, when calculating the shortest path for the transport vehicle to reach the fixed facility, heavily loaded uphill sections with a gradient greater than a preset gradient threshold are excluded.

[0007] Preferably, the shortest path for the transport vehicle to reach the fixed facility is calculated using the Floyd-Warshall algorithm, including: 1) Loading and unloading points, maintenance stations, charging and swapping stations, and gas stations are considered path nodes, and the traversable road sections between the nodes are considered path edges. Each edge is assigned an initial weight, which is the actual length of the road section. If the road section is not a hardened road surface, the weight of the road section is adjusted to W = L ×1.3, if it is a hardened road surface, further judgment is made, if the slope of a certain road section α If the load is ≥5% and the transport vehicle is in heavy-load uphill transport mode, the weight of the road section will be modified: , Otherwise, the original length remains unchanged W = L ; in, W is the corrected road section weight, L is the original section length, α is the slope; 2) Initialize a distance matrix. Each element in the matrix represents the initial distance from the corresponding starting point to the end point. The distance between the same nodes is 0. The distance between nodes with direct links is the weight of the corresponding link. The distance between nodes without direct links is infinite. Starting from the first node, take each node as the intermediate node in turn and recalculate the shortest path from all starting points to the end points. If the path passing through the intermediate node is shorter than the previously recorded path, update the distance matrix; When the distance matrix no longer changes after three consecutive iterations, the calculation ends early; When k The first iteration and the k -3. k -2. k -When the distance matrix of 1 iteration is completely consistent, stop the calculation. k ≥3; 3) Extract the shortest path from the current location of the transport vehicle to the destination site from the final distance matrix; Automatically exclude slopes in the path α ≥8% and the transport vehicle is in heavy-load uphill transport mode; The final route information is sent to the onboard terminal instrument or mobile terminal APP of the transport vehicle.

[0008] Preferably, the transport path of the normal transport vehicle is dynamically planned, specifically including: a) Calculate a safety envelope based on the speed of a normal transport vehicle. The safety envelope is a strip extending forward from the normal transport vehicle for the next 10 seconds, backward for 10 meters, and to the left and right for 5 meters. b) Marking the abnormal transport vehicle as a virtual obstacle and calculating the virtual obstacle space. The virtual obstacle space is a long strip centered on the driving area, twice the length and width of the abnormal transport vehicle, and is calculated based on the abnormal transport vehicle's speed prediction for the next 30 seconds. The abnormal transport vehicle is a transport vehicle with information that does not affect driving. c) When the safety envelope of the normal transport vehicle overlaps with the spatial range of any virtual obstacle, the transport path of the normal transport vehicle is corrected and a detour transport path is calculated based on the A* algorithm. The A* algorithm uses the function f ( n ) = g ( n )+ h ( n ) Update the planned path in real time to form a detour path: in, n is the path node number, i is the road section number of the mining area, 1≤ i ≤m , L i For road sections i The actual length, w i For road weight, hardened road surface w i = 0.8, non-hardened road surface w i = 1.2, α i For road sections i The slope, d (n，终点) For nodes n Euclidean distance to the end point; The detour path must meet the following requirements: The minimum distance between the detour path and the virtual obstacle space is ≥10 meters, and f ( n )minimum.

[0009] Preferably, the spatial distribution of the online normal transport vehicle location point set is analyzed using the DBSCAN density clustering algorithm, including: Set the neighborhood radius and the minimum number of points. If the number of transport vehicles within the neighborhood radius of a transport vehicle's location point is greater than or equal to the minimum number of points, then mark the location point as a core point. The interconnected core points are merged to form clusters. When the number of transport vehicles in the cluster exceeds the threshold range, it is preliminarily determined to be a vehicle gathering, and congestion warnings and decentralized scheduling reminders are sent.

[0010] Preferably, for any two core points p and q, if the core point q is included in the neighborhood radius of the core point p, then p and q are determined to be directly connected; If there is a core point sequence, that is, there is a series of core points r1, r2, …r from the core point p to q. n , where each adjacent core point satisfies: the neighborhood radius of the previous core point contains the next core point, and the distance between the two does not exceed the neighborhood radius, then p and q are judged to be indirectly connected; All core points connected by direct or indirect connections are merged to form a cluster.

[0011] Preferably, after the preliminary determination that the vehicles are gathering, the method further includes: The DeepSORT tracking algorithm is integrated to continuously track the trajectories of transport vehicles within the cluster. If the number of transport vehicles in the cluster continues to exceed the threshold range within the set time window, it is confirmed as vehicle aggregation. Otherwise, it is determined to be instantaneous aggregation and the aggregation judgment is cancelled. Congestion warning and decentralized scheduling reminder information are generated based on the final aggregation judgment result.

[0012] Preferably, the congestion warning and decentralized dispatch reminder information is pushed to the corresponding normal transport vehicles through the vehicle terminal instrument or mobile terminal APP and displayed; Users provide confirmation information or report abnormalities through the vehicle terminal instrument or mobile terminal APP.

[0013] A multi-dimensional vehicle dispatching device, using the method described, comprises: An information collection and mapping module, which collects basic and status information through IoT terminals and industrial bus integration, and establishes a mapping relationship between the unique device IDs of transport vehicles and fixed facilities, such as loading and unloading points, maintenance stations, charging and swapping stations, and gas stations, and their digital twins; The transport vehicle path planning module obtains information about online transport vehicles and re-plans paths for abnormal transport vehicles that do not affect driving. The Floyd-Warshall algorithm is used to calculate the shortest path from the abnormal transport vehicle to available fixed facilities. The path calculation excludes heavily loaded uphill sections with slopes exceeding a preset threshold. A congestion detection and dispatch module, which is triggered when a transport vehicle has information that affects its travel. The congestion detection and dispatch module analyzes the spatial distribution of a set of normal transport vehicle locations online based on the DBSCAN density clustering algorithm. When the vehicle density on a specific route exceeds a preset threshold, it sends a congestion warning and decentralized dispatch reminder message. The information interaction module pushes the above reminder information through the vehicle terminal or mobile terminal APP, and receives confirmation or abnormal reporting information from the user.

[0014] Preferably, the transport vehicle path planning module includes: The abnormal transport vehicle path dynamic planning unit uses the Floyd-Warshall algorithm to calculate the shortest path and executes the following steps in sequence: 1) Loading and unloading points, maintenance stations, charging and swapping stations, and gas stations are considered path nodes, and the traversable road sections between the nodes are considered path edges. Each edge is assigned an initial weight, which is the actual length of the road section. If the road section is not a hardened road surface, the weight of the road section is adjusted to W = L ×1.3, if it is a hardened road surface, further judgment is made, if the slope of a certain road section α If the load is ≥5% and the transport vehicle is in heavy-load uphill transport mode, the weight of the road section will be modified: , Otherwise, the original length remains unchanged W = L ; in,W is the corrected road section weight, L is the original section length, α is the slope; 2) Initialize a distance matrix. Each element in the matrix represents the initial distance from the corresponding starting point to the end point. The distance between the same nodes is 0. The distance between nodes with direct links is the weight of the corresponding link. The distance between nodes without direct links is infinite. Starting from the first node, take each node as the intermediate node in turn and recalculate the shortest path from all starting points to the end points. If the path passing through the intermediate node is shorter than the previously recorded path, update the distance matrix; When the distance matrix no longer changes after three consecutive iterations, the calculation ends early; When k The first iteration and the k -3. k -2. k -When the distance matrix of 1 iteration is completely consistent, stop the calculation. k ≥3; 3) Extract the shortest path from the current location of the transport vehicle to the destination site from the final distance matrix; Automatically exclude slopes in the path α ≥8% and the transport vehicle is in heavy-load uphill transport mode; Send the final route information to the vehicle terminal of the abnormal transport vehicle; The normal transport vehicle path dynamic planning unit dynamically plans the transport path of the normal transport vehicle and executes the following steps in sequence: a) Calculate a safety envelope based on the speed of a normal transport vehicle. The safety envelope is a strip extending forward from the normal transport vehicle for the next 10 seconds, backward for 10 meters, and to the left and right for 5 meters. b) Marking the abnormal transport vehicle as a virtual obstacle and calculating the virtual obstacle space. The virtual obstacle space is a long strip centered on the abnormal transport vehicle's driving area, twice the length and width of the abnormal transport vehicle. The abnormal transport vehicle is one with a fault, a battery level less than 30%, or an oil level less than 30%. c) When the safety envelope of the normal transport vehicle overlaps with the spatial range of any virtual obstacle, the transport path of the normal transport vehicle is corrected and a detour transport path is calculated based on the A* algorithm. The A* algorithm uses the function f ( n ) = g ( n ) + h (n ) Update the planned path in real time to form a detour path: in, n is the path node number, i is the road section number of the mining area, 1≤ i ≤ m , L i For road sections i The actual length, w i For road weight, hardened road surface w i = 0.8, non-hardened road surface w i = 1.2, α i For road sections i The slope, d (n，终点) For nodes n Euclidean distance to the end point; The detour path must meet the following requirements: The minimum distance between the detour path and the virtual obstacle space is ≥10 meters, and f ( n )minimum.

[0015] Preferably, the congestion detection and scheduling module includes: The cluster analysis unit sets a neighborhood radius and a minimum number of points. It marks the location of any transport vehicle within the neighborhood radius where the number of transport vehicles is greater than or equal to the minimum number of points as a core point. It merges all core points connected directly or indirectly to form a cluster. When the number of transport vehicles in a cluster exceeds a threshold, it is preliminarily determined to be a vehicle cluster, and a congestion warning and decentralized scheduling reminder message are sent. The tracking and judgment unit is triggered when the number of transport vehicles in the cluster exceeds the threshold range. It integrates the DeepSORT tracking algorithm to continuously track the trajectories of the transport vehicles in the cluster. If the number of transport vehicles in the cluster continues to exceed the threshold within the set time window, it is judged as aggregation. Otherwise, it is judged as instantaneous aggregation and the judgment is cancelled. Congestion warning and decentralized scheduling reminder information are generated based on the final aggregation judgment result.

[0016] An electronic device includes: at least one processor, and a memory communicatively connected to the at least one processor, wherein the memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the described method.

[0017] The storage medium stores a computer program, which implements the method when executed by a processor.

[0018] A computer program product comprises a computer program, wherein when the program is executed by a processor, the method described is implemented.

[0019] The present invention has at least the following beneficial effects: First, the multi-dimensional vehicle scheduling method of the present invention integrates the collection of basic information and status information of transport vehicles and fixed facilities through the Internet of Things terminal and the industrial bus. Combined with the mapping relationship between the device ID and the digital twin, the scheduling center can grasp the real status in real time, ensuring that abnormal transport vehicles can be identified and route planned in time. Based on the DBSCAN density clustering algorithm, the vehicle location point set is spatially analyzed, and the vehicle aggregation is preliminarily determined through core point identification and connected cluster merging. Then, combined with the DeepSORT tracking algorithm, the aggregation persistence is verified within the set time window, and the instantaneous vehicle meeting and real congestion are accurately distinguished. The misjudgment rate of congestion warning is significantly reduced, and the accuracy and efficiency of transportation scheduling are guaranteed.

[0020] Second, the multi-dimensional vehicle scheduling method of the present invention uses the Floyd-Warshall algorithm to calculate the path for abnormal transport vehicles. The road section weight is dynamically adjusted according to the road surface type and slope, so that the path cost is more in line with the actual energy consumption and risk. At the same time, the iteration termination condition is clarified, reducing the risk of abnormal vehicles breaking down midway. For normal transport vehicles, the safety boundary is clarified by defining the safety envelope space and the virtual obstacle space. When the two overlap, the detour path is calculated based on the A* algorithm, which effectively avoids the collision between normal vehicles and abnormal vehicles and improves transportation safety.

[0021] Third, the multi-dimensional vehicle dispatching device of the present invention realizes vehicle status monitoring, path planning, congestion handling, and information interaction through modular design. The information collection and mapping module collects data through the Internet of Things and the industrial bus to realize real-time synchronization of the physical and digital twins. The transport vehicle path planning module performs path planning for abnormal transport vehicles and obstacle avoidance path planning for normal transport vehicles to achieve accurate identification of congestion and decentralized scheduling. The information interaction module completes command push and feedback through the vehicle-mounted terminal or mobile terminal APP to form a closed-loop control.

[0022] Other advantages, objectives and features of the present invention will be reflected in part from the following description and will be understood by those skilled in the art through study and practice of the present invention. DETAILED DESCRIPTION

[0023] The present invention is further described in detail below with reference to examples so that those skilled in the art can implement the invention with reference to the description.

[0024] It should be understood that terms such as “having”, “including” and “comprising” used herein do not preclude the existence or addition of one or more other elements or combinations thereof.

[0025] It should be noted that the methods in the following embodiments are conventional methods unless otherwise specified, and therefore should not be construed as limiting the present invention.

[0026] In existing mining vehicle dispatching, information collection does not fully integrate the multi-dimensional data of transport vehicles and fixed facilities, and it is also impossible to integrate and improve the multi-dimensional data to solve the problems of abnormal vehicle path planning and congestion dynamic warning. The present invention provides a multi-dimensional vehicle dispatching method, which includes the following steps: The first step is information collection and mapping. Basic and status information is collected through the integration of IoT terminals and industrial buses. IoT terminals refer to sensors and positioning devices used to collect vehicle and facility data, such as on-board GPS and facility sensors. The industrial bus is the communication line connecting various devices and is used for data integration. The basic information includes the static attributes of transport vehicles and fixed facilities. Fixed facilities include loading and unloading points, maintenance stations, charging and swapping stations, and gas stations. Static attributes refer to information that does not change rapidly over time, including the location and energy consumption of transport vehicles and the location of fixed facilities. Status information includes dynamic attributes of transport vehicles and fixed facilities. Dynamic attributes refer to information that changes in real time, including the operating status of transport vehicles and the usage status of fixed facilities (such as whether a maintenance station is idle). After the collected data is integrated via the industrial bus, the system assigns a unique device ID to each vehicle and fixed facility. At the same time, a mapping relationship is established between the unique device IDs of transport vehicles and fixed facilities and the digital twin. Through virtual mapping of physical devices, the virtual model can reflect the status of physical devices in real time, enabling simulation and analysis.

[0027] The second step is abnormal vehicle path planning. Online transport vehicle information is obtained, specifically, information such as long idle time, fault information, and fuel or battery level information. When information that does not affect the transport vehicle's driving is identified, such as long idle time, continuous engine operation for more than 10 minutes, and a speed of ≤5 km / h, the user (driver) is reminded to pay attention. For example, when there is a fault that does not affect driving, or the battery level is ≥30% or the oil level is ≥30%, based on static and dynamic attributes, the Floyd-Warshall algorithm is used to calculate the shortest path to the transport vehicle, with available repair stations, charging and swapping stations, or gas stations as target nodes. Preferably, the path calculation excludes heavily loaded uphill sections with slope values ​​greater than a preset slope threshold (such as 8%). Such sections significantly increase energy consumption and the risk of failure and need to be avoided first.

[0028] Specifically, the shortest path for the transport vehicle to reach the fixed facility is calculated using the Floyd-Warshall algorithm, and the process is as follows: First, the path nodes and edges are determined. The current location of the abnormal transport vehicle, available maintenance stations, charging and swapping stations, gas stations, loading and unloading points and other fixed facilities are used as path nodes, and the passable road sections between the nodes are used as path edges. The weight of each edge is initially set to the actual length of the corresponding road section.

[0029] Next, a distance matrix is ​​initialized and iterative calculations are performed. Each element in the matrix represents the initial distance from one node to another. Distances between identical nodes are set to 0, distances between nodes with direct links are set to the weight of the corresponding edge (i.e., the actual length of the link), and distances between nodes without direct links are set to infinity. Each node is then used as an intermediate node to recalculate the shortest paths from all origins to destinations. If a path passing through an intermediate node is shorter than the currently recorded path, the corresponding distance value in the distance matrix is ​​updated. This process is repeated until all nodes are considered intermediate nodes, resulting in the shortest path distance between each node.

[0030] Finally, a path is generated, and the shortest path from the vehicle's current location to the target site is extracted from the final distance matrix. Road sections with a slope α ≥ 8% and where the vehicle is in a heavily loaded uphill state are automatically excluded, and the path information is sent to the on-board terminal of the abnormal transport vehicle.

[0031] When a transport vehicle is identified as having information that affects its driving, specifically, for example, a fault that affects its driving, or a battery level of less than 30% or an oil level of less than 30%, congestion may occur, requiring congestion detection and reminders. The vehicle positioning terminal uploads the location information of normal transport vehicles to the dispatch server in real time. The server runs the DBSCAN density clustering algorithm to analyze the spatial distribution of the online normal transport vehicle location point set, setting a neighborhood radius (such as 50 meters) and a minimum number of points (such as 5 vehicles). When the number of transport vehicles in the neighborhood of a certain location point meets the standard, it is marked as a core point. Connected core points form a cluster. When the vehicle density value of a specific route exceeds the preset density threshold, that is, the number of transport vehicles in the cluster exceeds the preset threshold (such as 10 vehicles), congestion warning and decentralized scheduling reminder information are pushed to the corresponding normal transport vehicles.

[0032] In the above technical solution, the static and dynamic information of transport vehicles and fixed facilities is integrated through IoT terminals and industrial buses, and a mapping between device IDs and digital twins is established. This allows comprehensive and accurate data to be obtained during scheduling, avoiding scheduling deviations caused by scattered and inaccurate information in the past. For vehicles with faults, insufficient power or oil that do not affect driving, the Floyd-Warshall algorithm is used to calculate the shortest path, which deliberately excludes heavy-loaded uphill sections with slopes exceeding the preset threshold. This can reduce the possibility of problems for these vehicles on high-energy-consuming and high-risk sections, allowing them to arrive at maintenance or supply stations more safely and reducing the probability of breakdowns and delays in transportation. DBSCAN density clustering is used to analyze the location distribution of normal vehicles. When the vehicle density exceeds the threshold, congestion warnings and dispersion reminders are pushed in a timely manner, which can detect and guide congestion as early as possible, reduce waiting time caused by vehicle congestion, make overall transportation smoother, and improve overall scheduling efficiency and safety.

[0033] Furthermore, the aforementioned Floyd-Warshall algorithm does not fully consider the impact of road type and slope on transportation. Simply calculating the path based on length may cause abnormal vehicles to travel on high-energy-consuming and high-risk sections of road, such as heavily loaded uphill sections. Furthermore, the iteration termination conditions are unclear, affecting the efficiency and safety of abnormal vehicles reaching the target station. In another technical solution, the improved Floyd-Warshall algorithm is used to calculate the shortest path, including: 1) Loading and unloading points, maintenance stations, charging and swapping stations, and gas stations are considered path nodes. In the path node setting, charging and swapping stations and gas stations are selected based on vehicle type (new energy vehicles correspond to charging and swapping stations, and gasoline vehicles correspond to gas stations) to avoid interference from irrelevant nodes. The traversable road sections between nodes are considered path edges, and each edge is assigned an initial weight equal to the actual length of the road section. If there are multiple traversable road sections (such as different branches), each road section corresponds to a path edge, and each is assigned an initial weight equal to the actual length. If there is only one traversable road section, the road surface is unique and corresponds to one edge. The weights are modified to reflect the actual driving costs (energy consumption, risk, loss) of the road section. If the road section is a non-hardened road surface with high driving resistance, fast vehicle wear and high energy consumption, the weight of the road section is adjusted to W = L ×1.3, give priority to hardened road surfaces to reduce parts wear and increased energy consumption caused by vehicle bumps.

[0034] If it is a hardened road surface, further judgment is made if the slope of a certain road section is α ≥5% and the transport vehicle is in a heavy-load uphill transport mode. The greater the slope, the more power consumption and the higher the risk of failure. The slope impact is quantified by the proportional coefficient (α / 5%), so that the weight increases with the increase of the slope. The flat road section is given priority to reduce the risk of mid-way breakdown. The weight of the road section is adjusted: , Otherwise, the original length remains unchanged W = L ; in, W is the corrected road section weight, L is the original section length, α is the slope; 2) Initialize a distance matrix and update the path with each node as the intermediate node. Stop when the matrix remains unchanged after three consecutive iterations. Specifically: Each element in the matrix represents the initial distance from the corresponding starting point to the end point. The distance between the same nodes is 0, the distance between nodes with direct connection is the weight of the corresponding road segment, and the distance between nodes without direct connection is infinite. Starting from the first node, each node t As an intermediate node, recalculate all starting points a To the end b If the path through the intermediate node is shorter than the previously recorded path, the distance matrix is ​​updated. a → t → b The total weight is less than the current a → b The weight of , then update the matrix a arrive b The distance is a → t → b The total weight of When the distance matrix does not change after three consecutive iterations, it means that the shortest paths between all nodes have been found and the calculation ends early; When k The first iteration and the k -3. k -2. k -When the distance matrix of 1 iteration is completely consistent, stop the calculation. k ≥3; 3) Extract the shortest path from the current location of the transport vehicle to the destination site from the final distance matrix; Automatically exclude slopes in the path α ≥8% and the transport vehicle is in heavy-load uphill transport mode; The final route information is sent to the onboard terminal instrument or mobile terminal APP of the transport vehicle.

[0035] In the above technical solution, path planning not only takes into account the impact of road type and slope on energy consumption, but also ensures computing efficiency through iterative termination conditions, reduces the risk of breakdowns along the way, and ensures that they arrive at maintenance stations or supply points more safely. At the same time, clear iterative termination conditions reduce the waste of computing resources and can respond quickly when multiple transport vehicles are dispatched at the same time, thereby improving the practicality and reliability of path planning as a whole.

[0036] When a temporary obstacle (abnormal transport vehicle) appears during normal transport, there is a risk of collision if the route adjustment is not triggered automatically according to the obstacle in time. In another technical solution, the transport route of the normal transport vehicle is dynamically planned, specifically including: a) Calculate a safety envelope based on the speed of a normal transport vehicle, i.e., a dynamic safety zone centered on the vehicle. This area adjusts in real time based on vehicle type and speed. The safety envelope is a strip extending forward from the normal transport vehicle for the next 10 seconds, backward for 10 meters, and to the left and right for 5 meters. b) Mark abnormal transport vehicles as virtual obstacles and calculate a virtual obstacle space, adjusting it in real time based on vehicle type and speed. This virtual obstacle space is converted into a virtual no-go zone to prevent normal transport vehicles from approaching. The predicted driving area for the next 30 seconds is calculated based on the abnormal transport vehicle's speed. The virtual obstacle space is a long strip centered on the driving area, with a length and width twice that of the abnormal transport vehicle. An abnormal transport vehicle is one with a fault that does not affect driving, a battery level less than 30%, or an oil level less than 30%. c) When the safety envelope of the normal transport vehicle overlaps with the spatial range of any virtual obstacle, the transport path of the normal transport vehicle is corrected and a detour transport path is calculated based on the A* algorithm. The A* algorithm uses the function f ( n ) = g ( n )+ h ( n ) Update the planned path in real time to form a detour path, f ( n ) is from the starting point a’ go through n To the end b’ The estimated total cost, g ( n ) is from the starting point a’ To the current node n The actual cost, h ( n ) is a slave node n To the end b’ the estimated cost; There are a lot of unhardened roads (dirt roads, gravel roads) in the mining area. w i Weighting allows the algorithm to prioritize low-resistance hardened roads, reducing vehicle wear and energy consumption. The slope (especially heavy-loaded uphill and downhill) directly affects power consumption. The greater the absolute value of the slope, the higher the energy consumption. The actual cost of steep slopes is magnified, guiding the algorithm to avoid high-energy steep slopes. In addition, most mining paths are non-straight (limited by terrain and facilities). The actual path length is magnified to be closer to the actual path, adapting to the real-time requirements of mining scheduling. n is the path node number, i is the road section number of the mining area, 1≤ i ≤ m , L i For road sections i The actual length, w i For road weight, hardened road surface w i = 0.8, non-hardened road surface w i = 1.2, α i For road sections i The slope, d (n，终点) For nodes n Euclidean distance to the end point.

[0037] The detour path must meet the following requirements: When the A* algorithm searches for a path, it adds a distance check to each node to ensure that the minimum distance between the detour path and the virtual obstacle space is ≥10 meters. For all candidate paths that meet the requirements, the cost function of the A* algorithm is used. f ( n ) = g ( n )+ h ( n ) calculate the total cost, and make f ( n )minimum.

[0038] In the above technical solution, a safety boundary is defined by clarifying the safety envelope space and virtual obstacle space to avoid close contact between normal vehicles and abnormal vehicles. The path is corrected in real time through the A* algorithm to ensure safe and efficient detours. The dynamic response mechanism does not require human intervention, which improves the safety and smoothness of normal vehicle transportation in complex scenarios.

[0039] To adapt to complex environments such as mining areas and avoid misidentifying scattered vehicles as core points or missing actual clusters, another technical solution first sets a neighborhood radius and a minimum number of points based on scene characteristics such as mining road width and vehicle size. Then, the number of vehicles within the neighborhood radius of each normal transport vehicle location is counted. If the number is greater than or equal to the minimum number of points, the location is marked as a core point. Connected core points are merged to form clusters. For example, if the neighborhood of vehicle A includes vehicle B, and the neighborhood of vehicle B includes vehicle C, then vehicles A, B, and C are merged into one cluster. When the number of transport vehicles in the cluster exceeds the threshold range, it is preliminarily determined to be a vehicle gathering, and congestion warnings and decentralized scheduling reminders are sent.

[0040] In the above technical solution, the spatial aggregation status of vehicles can be accurately identified to avoid misjudgment caused by relying solely on total statistics. For example, it can distinguish between situations where vehicles are concentrated and dispersed, making congestion warnings more accurate and reducing ineffective scheduling. In this way, users (drivers) can receive reliable diversion reminders in a timely manner, avoid waiting caused by crowding, and make overall transportation smoother.

[0041] To further clarify the scope of clusters and avoid splitting core points that should belong to the same cluster area into different clusters or incorrectly merging unrelated core points, which would affect the accurate judgment of the vehicle cluster range and lead to congestion warning deviations, another technical solution is to use a method where, for any two core points p and q, if the neighborhood radius of core point p contains core point q, then p and q are considered to be directly connected. If there is a core point sequence, that is, there is a series of core points r1, r2, …r from the core point p to q. n , where each adjacent core point (such as r1, r2, …r n ) all satisfy: the neighborhood radius of the previous core point contains the next core point, and the distance between the two does not exceed the neighborhood radius, then p and q are judged to be indirectly connected; All core points connected by direct or indirect connections are merged to form clusters to clarify the boundaries of the clustering area.

[0042] In the above technical solution, by clarifying the core point connectivity rules, the scope of the cluster can be accurately defined, avoiding cluster splitting or mismerging due to ambiguous connectivity standards. This makes the division of vehicle gathering areas more in line with the actual spatial distribution, ensuring that congestion warnings can accurately reflect the actual gathering situation, making decentralized scheduling reminders more targeted, reducing ineffective scheduling caused by inaccurate judgment of the gathering range, and improving the traffic efficiency of transportation routes.

[0043] To further improve the accuracy of analysis and judgment, for example, clusters formed by temporary vehicle confluences (such as meeting or brief stops) meet the density requirements for clustering, but are transient. Directly determining this as congestion and dispatching it would result in ineffective operations, increased driver burden, and transportation disruption. In another technical solution, after initially determining that a vehicle cluster is present, the following steps are also included: The DeepSORT tracking algorithm is integrated to continuously track the trajectories of transport vehicles within the cluster, obtain the position, speed and other data of each transport vehicle in the cluster in real time, continuously record its trajectory, and count the number of vehicles in the cluster every 30 seconds. If the number of transport vehicles in the cluster continues to exceed the threshold range within the set time window (such as 3 minutes), it is confirmed to be vehicle gathering. Conversely, if the statistical number at a certain moment is lower than the threshold, it is determined to be instantaneous gathering and the gathering judgment is cancelled. Congestion warning and decentralized scheduling reminder information are generated based on the final gathering judgment result.

[0044] In the above technical solution, through continuous tracking in the time dimension, it is possible to accurately distinguish between real congestion and instantaneous gathering, and avoid misjudgment caused by temporary vehicle intersections. The congestion warnings and dispatch reminders pushed in this way are more in line with the actual situation, reducing unnecessary detours or diversion operations for drivers, reducing transportation interference, making dispatch more accurate and efficient, and ensuring smooth traffic on the line.

[0045] To improve the timeliness of information transmission and interaction, another technical solution pushes congestion warning and decentralized dispatch reminder information to corresponding normal transport vehicles through the vehicle terminal instrument or mobile terminal app and displays them. For example, the vehicle terminal instrument displays text scrolling and icons flashing, and the mobile terminal app pops up a prompt box accompanied by vibration. After checking, the user (driver) can confirm the information through the vehicle terminal instrument or mobile terminal APP. If there is no congestion or other abnormalities on the scene, the abnormality report information can be submitted and transmitted back to the dispatch system in real time.

[0046] In the above technical solution, multi-mode push ensures that users (drivers) can see reminders in time to avoid missing information; the information reception status is fed back to the dispatch system, and when anomalies are encountered, it can be quickly verified and adjusted, reducing invalid operations caused by information asymmetry. The present invention also provides a multi-dimensional vehicle dispatch device, which adopts the method described above, and the device includes: An information collection and mapping module collects basic information and status information through IoT terminals (such as vehicle-mounted GPS and facility sensors) integrated with the industrial bus. The basic information includes static attributes of transport vehicles and loading and unloading points, maintenance stations, charging and swapping stations, and gas stations, including the location of transport vehicles, energy consumption, and the location of fixed facilities. The status information includes dynamic attributes of transport vehicles and loading and unloading points, maintenance stations, charging and swapping stations, and gas stations, including the operating status of transport vehicles and the usage status of fixed facilities (such as whether the maintenance station is idle). A mapping relationship between the unique device ID of transport vehicles and fixed facilities and digital twins is established; The transport vehicle path planning module obtains online transport vehicle information, specifically, information such as vehicle long idle speed information, fault information, fuel level or battery level information, and information that does not affect the driving of the transport vehicle, including: vehicle long idle speed, faults that do not affect driving, fuel level or battery level not less than a threshold. Specifically, long idle vehicles can be reminded to pay attention to the user (driver). Abnormal transport vehicles with faults or battery level less than 30% or oil level less than 30% will be re-routed. The Floyd-Warshall algorithm is used to calculate the shortest path from the abnormal transport vehicle to the available repair station, charging and swapping station or gas station, with available maintenance stations, charging and swapping stations or gas stations as target nodes. The path calculation excludes heavy-load uphill sections with a slope exceeding a preset threshold. A congestion detection and dispatch module, which is triggered when a transport vehicle has information that affects its travel. The congestion detection and dispatch module analyzes the spatial distribution of a set of normal transport vehicle locations online based on the DBSCAN density clustering algorithm. When the vehicle density on a specific route exceeds a preset threshold, it sends a congestion warning and decentralized dispatch reminder message. The information interaction module pushes the above reminder information through the vehicle terminal or mobile terminal APP, and receives confirmation or abnormal reporting information from the user to form information interaction.

[0047] In the above technical solution, through the full-process design of information collection and mapping module, transport vehicle path planning module, congestion detection and scheduling module, and information interaction module, multi-dimensional integration of vehicle status, facility information, and road condition data is achieved. Abnormal vehicle path planning ensures that it arrives at the supply / repair station efficiently, reducing the risk of breakdown. Congestion detection combines spatial density and time continuity to reduce the misjudgment rate. The information interaction closed loop ensures the implementation of scheduling instructions, thereby improving the overall safety, efficiency and intelligence level of transportation in complex scenarios such as mining areas.

[0048] In another technical solution, the transport vehicle path planning module includes: The abnormal transport vehicle path dynamic planning unit uses the Floyd-Warshall algorithm to calculate the shortest path and executes the following steps in sequence: 1) Loading and unloading points, maintenance stations, charging and swapping stations, and gas stations are considered path nodes, and the traversable road sections between the nodes are considered path edges. Each edge is assigned an initial weight, which is the actual length of the road section. If the road section is not a hardened road surface, the weight of the road section is adjusted to W = L ×1.3, give priority to hardened roads to reduce parts wear and increased energy consumption caused by vehicle bumps; If it is a hardened road surface, further judgment is made if the slope of a certain road section is α If the load is ≥5% and the transport vehicle is in heavy-load uphill transport mode, the weight of the road section will be modified: , Otherwise, the original length remains unchanged W = L , give priority to flat roads to reduce the risk of breakdown; in, W is the corrected road section weight, L is the original section length, α is the slope; 2) Initialize a distance matrix. Each element in the matrix represents the initial distance from the corresponding starting point to the end point. The distance between the same nodes is 0. The distance between nodes with direct links is the weight of the corresponding link. The distance between nodes without direct links is infinite. Starting from the first node, update the path with each node as the intermediate point. Specifically, take each node as the intermediate node and recalculate the shortest path from all starting points to the end point. If the path passing through the intermediate node is shorter than the previously recorded path, update the distance matrix. When the distance matrix no longer changes after three consecutive iterations, the calculation ends early; When k The first iteration and the k -3. k -2. k -When the distance matrix of 1 iteration is completely consistent, stop the calculation. k ≥3; 3) Extract the shortest path from the current location of the transport vehicle to the destination site from the final distance matrix; Automatically exclude slopes in the path α ≥8% and the transport vehicle is in heavy-load uphill transport mode; Send the final route information to the vehicle terminal of the abnormal transport vehicle; The normal transport vehicle path dynamic planning unit dynamically plans the transport path of the normal transport vehicle and executes the following steps in sequence: a) A safety envelope is calculated based on the speed of normal transport vehicles and adjusted in real time based on vehicle type and speed. The safety envelope is a strip extending forward from the normal transport vehicle for the next 10 seconds, backward for 10 meters, and to the left and right for 5 meters. b) Mark abnormal transport vehicles as virtual obstacles and calculate a virtual obstacle space, adjusting it in real time based on vehicle type and speed. The virtual obstacle space is calculated based on the predicted speed of the abnormal transport vehicle for the next 30 seconds. The virtual obstacle space is a long strip centered on the driving area, with a length and width twice that of the abnormal transport vehicle. The abnormal transport vehicle is one with a fault, a battery level less than 30%, or an oil level less than 30%. c) When the safety envelope of the normal transport vehicle overlaps with the spatial range of any virtual obstacle, the transport path of the normal transport vehicle is corrected and a detour transport path is calculated based on the A* algorithm. The A* algorithm uses the function f ( n ) = g ( n ) + h ( n ) Update the planned path in real time to form a detour path: in, n is the path node number, i is the road section number of the mining area, 1≤ i ≤ m , L i For road sections i The actual length, w i For road weight, hardened road surface w i = 0.8, non-hardened road surface w i = 1.2, α i For road sections i The slope, d (n，终点) For nodes n Euclidean distance to the end point; The detour path must meet the following requirements: The minimum distance between the detour path and the virtual obstacle space is ≥10 meters, and f ( n )minimum.

[0049] In the above technical solution, the dynamic planning unit for abnormal transport vehicle paths takes into account the impact of road type and slope on energy consumption, and reduces the risk of abnormal transport vehicles breaking down midway through weight correction and exclusion of high-risk sections. The dynamic planning unit for normal transport vehicle paths combines dynamic safety boundaries with the A* algorithm to achieve active obstacle avoidance for abnormal transport vehicles, ensuring safe and efficient detours.

[0050] In another technical solution, the congestion detection and scheduling module includes: The cluster analysis unit sets the neighborhood radius and minimum point number range based on the scene characteristics such as the width of the mining area road and the size of the vehicle. It marks the location point of any transport vehicle within the neighborhood radius where the number of transport vehicles is greater than or equal to the minimum point range as a core point, and merges all core points that are directly or indirectly connected to form a cluster. Specifically, points in each other's neighborhood are judged to be directly connected, and points connected by the core point sequence are judged to be indirectly connected. When the number of transport vehicles contained in the cluster exceeds the threshold range, it is preliminarily judged to be a vehicle gathering, and congestion warning and decentralized scheduling reminder information are sent; The tracking and judgment unit is triggered when the number of transport vehicles in the cluster exceeds the threshold range. It integrates the DeepSORT tracking algorithm to continuously track the trajectories of the transport vehicles in the cluster and sets a time window range. If the number of transport vehicles in the cluster continuously exceeds the threshold within the set time window range, it is judged as aggregation. Otherwise, it is judged as instantaneous aggregation and the judgment is cancelled. Congestion warning and decentralized scheduling reminder information are generated based on the final aggregation judgment result.

[0051] In the above technical solution, the configuration of neighborhood radius and time window adapts to the traffic capacity of different road sections, solving the problem of misjudging instantaneous oncoming vehicles when traditional congestion detection relies solely on spatial density. DeepSORT tracking improves the trajectory association accuracy in complex scenarios, can accurately identify the spatial aggregation status of vehicles, and improve road traffic efficiency.

[0052] The present invention also provides an electronic device comprising: at least one processor; and a memory communicatively connected to the at least one processor, wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to cause the at least one processor to perform the method described herein. The electronic device may be any terminal device, such as a mobile phone, a laptop computer, a desktop computer, a tablet computer, a PDA (Personal Digital Assistant), a POS (Point of Sales), or an in-vehicle computer.

[0053] The present invention also provides a storage medium having a computer program stored thereon, which, when executed by a processor, implements the method described. Through the description of the above embodiments, those skilled in the art can clearly understand that the present invention can be implemented by means of software plus necessary general-purpose hardware, and of course can also be implemented by dedicated hardware including application-specific integrated circuits, dedicated CPUs, dedicated memories, dedicated components, etc. In general, all functions performed by a computer program can be easily implemented by corresponding hardware, and the specific hardware structures used to implement the same function can also be diverse, such as analog circuits, digital circuits, or dedicated circuits. However, for the present invention, software program implementation is a better implementation method in most cases. Based on this understanding, the technical solution of the present invention is essentially or the part that contributes to the prior art can be embodied in the form of a software product, which is stored in a readable storage medium, such as a computer floppy disk, USB flash drive, mobile hard disk, read-only memory (ROM), random access memory (RAM), magnetic disk or optical disk, etc., and includes a number of instructions for causing a computer device (which can be a personal computer, server, or network device, etc.) to execute the method described in each embodiment of the present invention.

[0054] The present invention also provides a computer program product comprising a computer program that, when executed by a processor, implements the method described. A computer program (also referred to or described as a program, software, software application, module, software module, script, or code) can be written in any programming language, including compiled or interpreted languages ​​or declarative or procedural languages, and can be deployed in any form, including as a standalone program or as a module, component, subroutine, or other unit suitable for use in a computing environment. A computer program may, but need not, correspond to a file in a file system. A program may be stored as part of a file that stores other programs or data, for example, one or more scripts in a markup language document; in a single file dedicated to the related program; or in multiple collaborative files, for example, files storing one or more modules, subroutines, or code portions. A computer program can be deployed to execute on one or more computers, the computers being located in one location or distributed across multiple locations and interconnected via a communication network. The processes and logic flows described in this specification can be performed by one or more programmable computers that execute one or more computer programs to perform functions by operating on input data and generating output.

[0055] The number of devices and processing scales described herein are intended to simplify the description of the present invention. Applications, modifications, and variations of the present invention will be readily apparent to those skilled in the art.

[0056] Although the embodiments of the present invention have been disclosed above, they are not limited to the applications listed in the description and implementation methods. They can be fully applied to various fields suitable for the present invention. For those familiar with the art, additional modifications can be easily implemented. Therefore, without departing from the general concept defined by the claims and the scope of equivalents, the present invention is not limited to the specific details and examples shown and described herein.

Claims

1. A multi-dimensional vehicle scheduling method, characterized in that: include: Step 1: Collect basic information and status information through the integration of IoT terminals and industrial buses; The basic information includes static attributes of transport vehicles and fixed facilities, including loading and unloading points, maintenance stations, charging and swapping stations, and gas stations. The static attributes include the location and energy consumption of transport vehicles and the location of fixed facilities. The state information includes dynamic attributes of the transport vehicle and the fixed facility, and the dynamic attributes include the operating state of the transport vehicle and the use state of the fixed facility; At the same time, a mapping relationship between the unique device IDs of transport vehicles and fixed facilities and digital twins is established; Step 2: Obtain online transport vehicle information. When it is identified that there is information that does not affect the transport vehicle's driving, based on the static attributes and the dynamic attributes, with the available fixed facilities as the target nodes, calculate the shortest path for the transport vehicle to reach the fixed facilities; when it is identified that there is information that affects the transport vehicle's driving, analyze the spatial distribution of the online normal transport vehicle location point set. When the vehicle density value of a specific line exceeds the preset density threshold, push congestion warning and decentralized scheduling reminder information to the corresponding normal transport vehicle.

2. The multi-dimensional vehicle dispatching method according to claim 1, characterized in that: Online transport vehicle information includes: vehicle long idle information, fault information, fuel or power information. Information that does not affect driving of the transport vehicle includes: vehicle long idle information, faults that do not affect driving, fuel or power information not less than a threshold.

3. The multi-dimensional vehicle dispatching method according to claim 1, characterized in that: When calculating the shortest path for the transport vehicle to reach the fixed facility, sections of heavy-loaded uphill roads with a slope value greater than a preset slope threshold are excluded.

4. The multi-dimensional vehicle scheduling method according to claim 1, characterized in that: The shortest path for the transport vehicle to reach the fixed facility is calculated using the Floyd-Warshall algorithm, which includes: 1) Loading and unloading points, maintenance stations, charging and swapping stations, and gas stations are considered path nodes, and the traversable road sections between the nodes are considered path edges. Each edge is assigned an initial weight, which is the actual length of the road section. If the road section is not a hardened road surface, the weight of the road section is adjusted to W = L ×1.3, if it is a hardened road surface, further judgment is made, if the slope of a certain road section α If the load is ≥5% and the transport vehicle is in heavy-load uphill transport mode, the weight of the road section will be modified: Otherwise, the original length remains unchanged W = L ; in, W is the corrected road section weight, L is the original section length, α is the slope; 2) Initialize a distance matrix. Each element in the matrix represents the initial distance from the corresponding starting point to the end point. The distance between the same nodes is 0. The distance between nodes with direct links is the weight of the corresponding link. The distance between nodes without direct links is infinite. Starting from the first node, take each node as the intermediate node in turn and recalculate the shortest path from all starting points to the end points. If the path passing through the intermediate node is shorter than the previously recorded path, update the distance matrix; When the distance matrix no longer changes after three consecutive iterations, the calculation ends early; When k The first iteration and the k -3. k -2. k -When the distance matrix of 1 iteration is completely consistent, stop the calculation. k ≥3; 3) Extract the shortest path from the current location of the transport vehicle to the destination site from the final distance matrix; Automatically exclude slopes in the path α ≥8% and the transport vehicle is in heavy-load uphill transport mode; The final route information is sent to the onboard terminal instrument or mobile terminal APP of the transport vehicle.

5. The multi-dimensional vehicle dispatching method according to claim 4, characterized in that: Dynamically plan the transport routes of normal transport vehicles, including: a) Calculate a safety envelope based on the speed of a normal transport vehicle. The safety envelope is a strip extending forward from the normal transport vehicle for the next 10 seconds, backward for 10 meters, and to the left and right for 5 meters. b) Marking the abnormal transport vehicle as a virtual obstacle and calculating the virtual obstacle space. The virtual obstacle space is a long strip centered on the driving area, twice the length and width of the abnormal transport vehicle, and is calculated based on the abnormal transport vehicle's speed prediction for the next 30 seconds. The abnormal transport vehicle is a transport vehicle with information that does not affect driving. c) When the safety envelope of the normal transport vehicle overlaps with the spatial range of any virtual obstacle, the transport path of the normal transport vehicle is corrected and a detour transport path is calculated based on the A* algorithm. The A* algorithm uses the function f ( n ) = g ( n )+ h ( n ) Update the planned path in real time to form a detour path: in, n is the path node number, i is the road section number of the mining area, 1≤ i ≤ m , L i For road sections i The actual length, w i For road weight, hardened road surface w i = 0.8, non-hardened road surface w i = 1.2, α i For road sections i The slope, d (n，终点) For nodes n Euclidean distance to the end point; The detour path must meet the following requirements: The minimum distance between the detour path and the virtual obstacle space is ≥10 meters, and f ( n )minimum.

6. The multi-dimensional vehicle dispatching method according to claim 1, characterized in that: Analyze the spatial distribution of the online normal transport vehicle location point set using the DBSCAN density clustering algorithm, including: Set the neighborhood radius and the minimum number of points. If the number of transport vehicles within the neighborhood radius of a transport vehicle's location point is greater than or equal to the minimum number of points, then mark the location point as a core point. The interconnected core points are merged to form clusters. When the number of transport vehicles in the cluster exceeds the threshold range, it is preliminarily determined to be a vehicle gathering, and congestion warnings and decentralized scheduling reminders are sent.

7. The multi-dimensional vehicle dispatching method according to claim 6, characterized in that: For any two core points p and q, if the core point q is included in the neighborhood radius of the core point p, then p and q are considered to be directly connected; If there is a core point sequence, that is, there is a series of core points r1, r2, ... r from the core point p to q. n , where each adjacent core point satisfies: the neighborhood radius of the previous core point contains the next core point, and the distance between the two does not exceed the neighborhood radius, then p and q are judged to be indirectly connected; All core points connected by direct or indirect connections are merged to form a cluster.

8. The multi-dimensional vehicle dispatching method according to claim 6, characterized in that: After the initial determination of a vehicle gathering, it also includes: The DeepSORT tracking algorithm is integrated to continuously track the trajectories of transport vehicles within the cluster. If the number of transport vehicles in the cluster continues to exceed the threshold range within the set time window, it is confirmed as vehicle aggregation. Otherwise, it is determined to be instantaneous aggregation and the aggregation judgment is cancelled. Congestion warning and decentralized scheduling reminder information are generated based on the final aggregation judgment result.

9. The multi-dimensional vehicle dispatching method according to claim 1, characterized in that: Push congestion warning and decentralized dispatch reminder information to corresponding normal transport vehicles through vehicle terminal instruments or mobile terminal APP and display them; Users provide confirmation information or report abnormalities through the vehicle terminal instrument or mobile terminal APP.

10. Multi-dimensional vehicle dispatching device, characterized in that: The method according to any one of claims 1 to 9, wherein the device comprises: An information collection and mapping module, which collects basic and status information through IoT terminals and industrial bus integration, and establishes a mapping relationship between the unique device IDs of transport vehicles and fixed facilities, such as loading and unloading points, maintenance stations, charging and swapping stations, and gas stations, and their digital twins; The transport vehicle path planning module obtains information about online transport vehicles and re-plans paths for abnormal transport vehicles that do not affect driving. The Floyd-Warshall algorithm is used to calculate the shortest path from the abnormal transport vehicle to available fixed facilities. The path calculation excludes heavily loaded uphill sections with slopes exceeding a preset threshold. A congestion detection and dispatch module, which is triggered when a transport vehicle has information that affects its travel. The congestion detection and dispatch module analyzes the spatial distribution of a set of normal transport vehicle locations online based on the DBSCAN density clustering algorithm. When the vehicle density on a specific route exceeds a preset threshold, it sends a congestion warning and decentralized dispatch reminder message. The information interaction module pushes the above reminder information through the vehicle terminal or mobile terminal APP, and receives confirmation or abnormal reporting information from the user.

11. The multi-dimensional vehicle dispatching device according to claim 10, characterized in that: The transport vehicle path planning module includes: The abnormal transport vehicle path dynamic planning unit uses the Floyd-Warshall algorithm to calculate the shortest path and executes the following steps in sequence: 1) Loading and unloading points, maintenance stations, charging and swapping stations, and gas stations are considered path nodes, and the traversable road sections between the nodes are considered path edges. Each edge is assigned an initial weight, which is the actual length of the road section. If the road section is not a hardened road surface, the weight of the road section is adjusted to W = L ×1.3, if it is a hardened road surface, further judgment is made, if the slope of a certain road section α If the load is ≥5% and the transport vehicle is in heavy-load uphill transport mode, the weight of the road section will be modified: Otherwise, the original length remains unchanged W = L ; in, W is the corrected road section weight, L is the original section length, α is the slope; 2) Initialize a distance matrix. Each element in the matrix represents the initial distance from the corresponding starting point to the end point. The distance between the same nodes is 0. The distance between nodes with direct links is the weight of the corresponding link. The distance between nodes without direct links is infinite. Starting from the first node, take each node as the intermediate node in turn and recalculate the shortest path from all starting points to the end points. If the path passing through the intermediate node is shorter than the previously recorded path, update the distance matrix; When the distance matrix no longer changes after three consecutive iterations, the calculation ends early; When k The first iteration and the k -3. k -2. k -When the distance matrix of 1 iteration is completely consistent, stop the calculation. k ≥3; 3) Extract the shortest path from the current location of the transport vehicle to the destination site from the final distance matrix; Automatically exclude slopes in the path α ≥8% and the transport vehicle is in heavy-load uphill transport mode; Send the final route information to the vehicle terminal of the abnormal transport vehicle; The normal transport vehicle path dynamic planning unit dynamically plans the transport path of the normal transport vehicle and executes the following steps in sequence: a) Calculate a safety envelope based on the speed of a normal transport vehicle. The safety envelope is a strip extending forward from the normal transport vehicle for the next 10 seconds, backward for 10 meters, and to the left and right for 5 meters. b) Marking the abnormal transport vehicle as a virtual obstacle and calculating the virtual obstacle space. The virtual obstacle space is a long strip centered on the abnormal transport vehicle's driving area, twice the length and width of the abnormal transport vehicle. The abnormal transport vehicle is one with a fault, a battery level less than 30%, or an oil level less than 30%. c) When the safety envelope of the normal transport vehicle overlaps with the spatial range of any virtual obstacle, the transport path of the normal transport vehicle is corrected and a detour transport path is calculated based on the A* algorithm. The A* algorithm uses the function f ( n ) = g ( n ) + h ( n ) Update the planned path in real time to form a detour path: in, n is the path node number, i is the road section number of the mining area, 1≤ i ≤ m , L i For road sections i The actual length, w i For road weight, hardened road surface w i = 0.8, non-hardened road surface w i = 1.2, α i For road sections i The slope, d (n，终点) For nodes n Euclidean distance to the end point; The detour path must meet the following requirements: The minimum distance between the detour path and the virtual obstacle space is ≥10 meters, and f ( n )minimum.

12. The multi-dimensional vehicle dispatching device according to claim 10, characterized in that: The congestion detection and scheduling module includes: The cluster analysis unit sets a neighborhood radius and a minimum number of points. It marks the location of any transport vehicle within the neighborhood radius where the number of transport vehicles is greater than or equal to the minimum number of points as a core point. It merges all core points connected directly or indirectly to form a cluster. When the number of transport vehicles in a cluster exceeds a threshold, it is preliminarily determined to be a vehicle cluster, and a congestion warning and decentralized scheduling reminder message are sent. The tracking and judgment unit is triggered when the number of transport vehicles in the cluster exceeds the threshold range. It integrates the DeepSORT tracking algorithm to continuously track the trajectories of the transport vehicles in the cluster. If the number of transport vehicles in the cluster continues to exceed the threshold within the set time window, it is judged as aggregation. Otherwise, it is judged as instantaneous aggregation and the judgment is cancelled. Congestion warning and decentralized scheduling reminder information are generated based on the final aggregation judgment result.

13. An electronic device, characterized in that include: At least one processor, and a memory communicatively connected to the at least one processor, wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to cause the at least one processor to perform the method according to any one of claims 1 to 9.

14. A storage medium having a computer program stored thereon, characterized in that When the program is executed by a processor, the method according to any one of claims 1 to 9 is implemented.

15. Computer program product comprising a computer program, characterized in that When the program is executed by a processor, the method according to any one of claims 1 to 9 is implemented.

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