Coal mine underground unmanned vehicle work scheduling integrated management method and system

Abstract

The invention discloses a coal mine underground unmanned vehicle work scheduling integrated management method and system, and relates to the technical field of vehicle scheduling, and the method comprises the steps: evaluating the quality of each alternative path of a service vehicle, considering the quality condition of each alternative path of each vehicle, and providing a reliable basis for the subsequent path planning. The target path is determined in the plurality of alternative paths by combining the quality of the alternative paths and the path crossing degree, and the most reasonable target path is determined by comprehensively considering two factors of the path quality and the path crossing condition, so that the initial accuracy of path planning is ensured. A plurality of monitoring nodes are defined in a vehicle dispatch plan to monitor a plan deviation. The vehicle scheduling plan is adjusted by monitoring the plan deviation degree on the node, so that the accuracy and reliability of the comprehensive management of the work scheduling of the underground coal mine unmanned vehicle are improved, the complex and variable requirements of the underground coal mine unmanned vehicle are met, and the working efficiency of the unmanned vehicle is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of vehicle dispatching, and in particular to a comprehensive management method and system for dispatching unmanned vehicles in underground coal mines. Background Art

[0002] In traditional coal mining operations, manual dispatching suffers from low efficiency, slow response, and significant safety hazards, making it difficult to adapt to the efficient production requirements of modern coal mines. With the rapid development of unmanned driving technology, sensor technology, communication technology, and artificial intelligence, the use of unmanned vehicles in underground coal mines has become possible. These vehicles utilize high-precision positioning systems, lidar, cameras, and other sensors for environmental awareness. Combined with advanced control algorithms and path planning technology, they can autonomously navigate, avoid obstacles, and complete transportation tasks. Furthermore, intelligent dispatching systems can monitor vehicle status in real time, optimize transportation routes, and improve resource utilization. Therefore, developing a comprehensive management solution for dispatching unmanned vehicles in underground coal mines is crucial for improving coal mine production efficiency, reducing labor costs, and minimizing safety incidents.

[0003] In the existing technology, the scheduling of unmanned vehicles working in coal mines is set in advance. When deviations outside the scheduling plan occur, the scheduling plan cannot be modified in a timely and reasonable manner, resulting in poor reliability and accuracy of the comprehensive management of the scheduling of unmanned vehicles working in coal mines, which cannot meet the needs of the complex and changeable working environment of unmanned vehicles in coal mines.

[0004] Therefore, how to improve the reliability and accuracy of the comprehensive management of work scheduling of unmanned vehicles in coal mines is a technical problem that needs to be solved. Summary of the Invention

[0005] The purpose of the present invention is to solve the problem of poor reliability and accuracy in the prior art of comprehensive management of the work scheduling of unmanned vehicles in coal mines, and to propose a comprehensive management method for the work scheduling of unmanned vehicles in coal mines, which includes: Obtain road information, service vehicle information, and transportation task information in the coal mine, assign transportation tasks to multiple service vehicles, and plan multiple alternative routes for each service vehicle based on the transportation task information and road information; evaluating the quality of each alternative path for the in-service vehicle, determining a degree of path intersection based on the multiple alternative paths for all in-service vehicles, and determining a target path from the multiple alternative paths by combining the quality of the alternative paths and the degree of path intersection; Generate a transportation plan for each vehicle in service based on task assignment and target path. Combine the transportation plans of all vehicles in service to obtain a vehicle scheduling plan. Define multiple monitoring nodes in the vehicle scheduling plan to monitor plan deviations. During the execution of the vehicle scheduling plan, the vehicle scheduling plan is adjusted by monitoring the plan deviation at the node.

[0006] In some embodiments of the present application, transport tasks are assigned to multiple service vehicles, and multiple alternative routes are planned for each service vehicle according to transport task information and road information, including: Service vehicle information includes vehicle type, vehicle load, and vehicle range; transport mission information includes transport weight, transport priority, transport mileage, transport starting point, multiple transport transit points, and transport destination; Match vehicle type, vehicle load capacity, and vehicle range with the weight of the transported goods, transport priority, and transport mileage to determine the transport tasks corresponding to each service vehicle and divide the service vehicles into departure batches; Based on the transportation starting point, transportation end point and multiple transportation transit points, a path planning algorithm is used to obtain multiple alternative routes for each service vehicle.

[0007] In some embodiments of the present application, the quality of each alternative path of the service vehicle is evaluated, including: Road information includes road safety information, road traffic information, road environment adaptation information, road vehicle energy consumption information and road change information; Each alternative route is divided into sections, and the road safety information, road traffic information, road environment adaptation information, road vehicle energy consumption information and road change information corresponding to each section of the single alternative route are determined according to the sections; Parameters in road safety information, road traffic information, road environment adaptation information, road vehicle energy consumption information, and road change information are divided into static parameters and dynamic parameters, and a dynamic parameter curve is drawn showing the changes of dynamic parameters over time for each road section; On the dynamic parameter curve, the minimum point, the median point and the maximum point are counted, the curve between the minimum point and the median point is recorded as the lower curve, and the curve between the median point and the maximum point is recorded as the upper curve; Search for multiple points that appear frequently and are close to the minimum point in the lower curve, and record them as the first point. Search for multiple points that appear frequently and are close to the maximum point in the upper curve, and record them as the second point. The minimum slope value of the first point and the second point is used as the lower endpoint and the upper endpoint respectively, and the lower endpoint and the upper endpoint are used as the standard interval of the dynamic parameter; The quality of each alternative path for the service vehicle is determined according to standard intervals of static parameters and dynamic parameters.

[0008] In some embodiments of the present application, the degree of path intersection is determined based on multiple alternative paths of all vehicles in service, including: For the same departure batch of service vehicles, multiple alternative routes of multiple service vehicles are arranged and combined to obtain multiple path combinations. The driving and transportation conditions of each service vehicle in the departure batch are simulated according to the path combinations. The cross parameters generated during the simulation process are counted, and the path intersection degree is calculated based on the cross parameters under each path combination.

[0009] In some embodiments of the present application, the target path is determined from multiple candidate paths by combining the quality of the candidate paths and the degree of path intersection, including: For the same path combination, the quality of each alternative path is integrated to obtain the comprehensive quality; The comprehensive quality and the path intersection degree are standardized, and the entropy values ​​of the comprehensive quality and the path intersection degree are calculated respectively. The target weights of the comprehensive quality and the path intersection degree are determined according to the entropy values. The objective function of the comprehensive quality and the path intersection degree is constructed, and the target path is screened out through the objective function.

[0010] In some embodiments of the present application, the transportation plan of all vehicles in service is integrated to obtain a vehicle dispatch plan, including: Construct a time-space graph for each service vehicle according to the transportation plan of the service vehicle, and integrate the time-space graphs of all service vehicles into a global time-space graph; The global time-space graph is analyzed to detect multiple types of conflicts, and the transportation plan is optimized according to the multiple types of conflicts to obtain the vehicle scheduling plan.

[0011] In some embodiments of the present application, multiple monitoring nodes are defined in the vehicle scheduling plan to monitor the plan deviation, including: The monitoring nodes include a first monitoring point and a second monitoring point; Determine a comprehensive vehicle route map in the vehicle dispatch plan, and distinguish congested sections and non-congested sections in the comprehensive vehicle route map; For congested road sections, multiple first monitoring points are set up to predict the congestion situation and set the congestion standard value and transportation standard value corresponding to each monitoring point and each service vehicle on the congested road section; For non-congested road sections, multiple second monitoring points are evenly set up, and the transportation situation is predicted to set the transportation standard value corresponding to each service vehicle at each monitoring point on the non-congested road section.

[0012] In some embodiments of the present application, the vehicle dispatch plan is adjusted by monitoring the plan deviation at the node, including: For the first monitoring point, determining the actual congestion value and the actual transportation value of the road section where the service vehicle is currently located, and calculating the plan deviation based on the actual congestion value, the actual transportation value, the congestion standard value, and the transportation standard value; For the second monitoring point, the current actual transportation value of the service vehicle is determined, and the plan deviation is calculated based on the actual transportation value and the transportation standard value; Identify deviating vehicles by the planned deviation degree, and distinguish between vehicles that can change their routes and vehicles that cannot change their routes among the deviating vehicles; For vehicles that can change their routes, the routes are replanned and the cost of the modified routes is measured to determine whether to adjust the vehicle routes.

[0013] Correspondingly, the present application also provides a comprehensive management system for dispatching unmanned vehicles in coal mines, including: The first module is used to obtain road information, service vehicle information and transportation task information in the coal mine, assign transportation tasks to multiple service vehicles, and plan multiple alternative routes for each service vehicle based on the transportation task information and road information; The second module is configured to evaluate the quality of each alternative route for the in-service vehicle, determine the degree of route intersection based on the multiple alternative routes for all in-service vehicles, and determine a target route from the multiple alternative routes by combining the quality of the alternative routes and the degree of route intersection; The third module is used to generate a transportation plan for each vehicle in service based on the task assignment and target path. The transportation plans of all vehicles in service are combined to obtain a vehicle scheduling plan. Multiple monitoring nodes are defined in the vehicle scheduling plan to monitor plan deviations. The fourth module is used to adjust the vehicle scheduling plan by monitoring the plan deviation on the node during the execution of the vehicle scheduling plan.

[0014] Compared with the prior art, the present invention has the following beneficial effects: 1. Evaluate the quality of each alternative route for in-service vehicles, considering the quality of each alternative route for each vehicle to provide a reliable foundation for subsequent route planning. Determine the target route from multiple alternative routes by combining the quality of the alternative routes and the degree of path intersection. Combined consideration of both path quality and path intersections determines the most reasonable target route, ensuring the initial accuracy of route planning.

[0015] 2. Define multiple monitoring nodes in the vehicle dispatch plan to monitor plan deviations. These nodes monitor each vehicle's transport mission completion, known as plan deviations. By adjusting the vehicle dispatch plan based on plan deviations at the monitoring nodes, the system improves the accuracy and reliability of the integrated management of unmanned vehicle dispatch in underground coal mines, meeting the complex and ever-changing needs of unmanned vehicles in underground coal mines and enhancing their operational efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1This is a flow chart of a comprehensive management method for dispatching unmanned vehicles in coal mines proposed by the present invention; Figure 2 This is a structural diagram of a comprehensive management system for dispatching unmanned vehicles in coal mines proposed by the present invention. DETAILED DESCRIPTION

[0017] The technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments.

[0018] Reference Figure 1 A method for comprehensive management of work scheduling of unmanned vehicles in coal mines, comprising the following steps: Step S101, obtain road information, service vehicle information and transportation task information in the coal mine, assign transportation tasks to multiple service vehicles, and plan multiple alternative routes for each service vehicle according to the transportation task information and road information.

[0019] In this embodiment, in-service vehicle information includes vehicle type, vehicle load capacity, and vehicle range; transport task information includes transport weight, transport priority, transport distance, transport starting point, multiple transport routes, and transport destination. Vehicle type, vehicle load capacity, and vehicle range are matched with transport weight, transport priority, and transport distance to determine the transport task corresponding to each in-service vehicle and divide the in-service vehicle's departure batches. For example, vehicles with heavier loads are assigned to transport tasks with heavier loads, while vehicles with longer ranges are assigned to transport tasks with longer distances.

[0020] In some embodiments of the present application, transport tasks are assigned to multiple service vehicles, and multiple alternative routes are planned for each service vehicle according to transport task information and road information, including: Service vehicle information includes vehicle type, vehicle load, and vehicle range; transport mission information includes transport weight, transport priority, transport mileage, transport starting point, multiple transport transit points, and transport destination; Match vehicle type, vehicle load capacity, and vehicle range with the weight of the transported goods, transport priority, and transport mileage to determine the transport tasks corresponding to each service vehicle and divide the service vehicles into departure batches; Based on the transportation starting point, transportation end point and multiple transportation transit points, a path planning algorithm is used to obtain multiple alternative routes for each service vehicle.

[0021] In this embodiment, the service vehicle information: Vehicle type: such as mining trucks, explosion-proof electric vehicles, etc., must be adapted to the underground environment.

[0022] Vehicle load capacity: clearly state the maximum load capacity (e.g. 20 tons, 30 tons).

[0023] Vehicle range: battery capacity or range (e.g. 200 km).

[0024] Transport mission information: Transport weight: The actual weight of coal / materials to be transported.

[0025] Transport priority: high priority (such as emergency supplies), medium priority (routine transport), low priority (non-urgent tasks).

[0026] Transport mileage: the total distance from the starting point to the end point.

[0027] Path nodes: starting point, multiple transit points (such as transfer stations), and end point.

[0028] Matching logic: Load matching: The weight of the transported goods ≤ the vehicle load (e.g. 25 tons of cargo is assigned to a 30-ton vehicle).

[0029] Priority matching: High-priority tasks are assigned to vehicles in good condition and with sufficient range.

[0030] Mileage matching: transport mileage ≤ vehicle range (e.g., a 180-kilometer mission is assigned to a vehicle with a range of 200 kilometers).

[0031] Batch division: Divide batches by transport mileage, priority or time window (e.g. first batch: high-priority short-distance tasks; second batch: medium-priority long-distance tasks).

[0032] Input data: Road information: road topology, height limit, width limit, slope, turning radius, etc.

[0033] Dynamic information: real-time congestion, accident sections, and maintenance sections.

[0034] Path planning algorithm: A* algorithm: Based on heuristic search, suitable for static environments.

[0035] Dijkstra algorithm: Applicable to the shortest path without negative weight edges.

[0036] Hybrid algorithm: combines A* with dynamic weight adjustment to adapt to real-time changes.

[0037] Different strategies: Shortest path: prioritize time efficiency.

[0038] The safest route: Avoid high-risk sections (such as narrow areas or areas prone to landslides).

[0039] The path with the lowest energy consumption: choose a flat road with a small slope.

[0040] Step S102 , evaluating the quality of each alternative path for the in-service vehicle, determining the degree of path intersection based on multiple alternative paths for all in-service vehicles, and determining a target path from the multiple alternative paths by combining the quality of the alternative paths and the degree of path intersection.

[0041] In this embodiment, the quality of the candidate paths is evaluated based solely on the road conditions, and then the target path is determined in combination with the intersection conditions of the vehicle paths.

[0042] In some embodiments of the present application, the quality of each alternative path of the service vehicle is evaluated, including: Road information includes road safety information, road traffic information, road environment adaptation information, road vehicle energy consumption information and road change information; Each alternative route is divided into sections, and the road safety information, road traffic information, road environment adaptation information, road vehicle energy consumption information and road change information corresponding to each section of the single alternative route are determined according to the sections; Parameters in road safety information, road traffic information, road environment adaptation information, road vehicle energy consumption information, and road change information are divided into static parameters and dynamic parameters, and a dynamic parameter curve is drawn showing the changes of dynamic parameters over time for each road section; On the dynamic parameter curve, the minimum point, the median point and the maximum point are counted, the curve between the minimum point and the median point is recorded as the lower curve, and the curve between the median point and the maximum point is recorded as the upper curve; Search for multiple points that appear frequently and are close to the minimum point in the lower curve, and record them as the first point. Search for multiple points that appear frequently and are close to the maximum point in the upper curve, and record them as the second point. The minimum slope value of the first point and the second point is used as the lower endpoint and the upper endpoint respectively, and the lower endpoint and the upper endpoint are used as the standard interval of the dynamic parameter; The quality of each alternative path for the service vehicle is determined according to standard intervals of static parameters and dynamic parameters.

[0043] In this embodiment, road safety information: Static parameters: road width, slope, turning radius, height limit, and width limit.

[0044] Dynamic parameters: real-time accident rate, landslide risk, and support stability.

[0045] Road traffic information: Static parameters: number of lanes, traffic direction restrictions.

[0046] Dynamic parameters: congestion index, maintenance section, and closed period.

[0047] Road environment adaptation information: Static parameters: humidity, temperature, and dust concentration threshold.

[0048] Dynamic parameters: real-time humidity, temperature, and dust concentration.

[0049] Road vehicle energy consumption information: Static parameters: energy consumption coefficient corresponding to road type (flat / slope).

[0050] Dynamic parameters: real-time vehicle energy consumption (kWh / km), load changes.

[0051] Road change information: Static parameters: historical change frequency (such as the number of maintenance times per month).

[0052] Dynamic parameters: real-time construction sections, temporary controls, implementation of landslide conditions, etc.

[0053] Because dynamic parameters have a lot of fluctuations, it is necessary to determine the standard interval or standard range of dynamic parameters to conduct quality assessment. The minimum slope value indicates the most stable point.

[0054] In some embodiments of the present application, the degree of path intersection is determined based on multiple alternative paths of all vehicles in service, including: For the same departure batch of service vehicles, multiple alternative routes of multiple service vehicles are arranged and combined to obtain multiple path combinations. The driving and transportation conditions of each service vehicle in the departure batch are simulated according to the path combinations. The cross parameters generated during the simulation process are counted, and the path intersection degree is calculated based on the cross parameters under each path combination.

[0055] In this embodiment, each of the multiple vehicles has multiple alternative routes, and the same departure batch means that the multiple vehicles have the same or similar departure time. The cross-parameters of the simulated transportation conditions of the vehicles under each route combination are counted.

[0056] Timeline Construction: A unified timeline is constructed for each path combination to simulate the driving conditions of vehicles in the same time period.

[0057] Driving simulation: The driving process of each vehicle under each path combination is simulated in turn, and the arrival time, departure time and driving status of the vehicle at key nodes are recorded.

[0058] Factors such as vehicle speed, acceleration, stopping and waiting are taken into account to ensure the authenticity of the simulation.

[0059] Cross-event identification: During the simulation, all intersection events are identified and recorded, i.e., situations where two or more vehicles arrive at the same key node at the same time (close enough, not necessarily crossing) or where their paths overlap.

[0060] Cross parameter calculation: For each intersection event, the intersection parameters are calculated, such as the intersection duration, the number of crossing vehicles, the length of the intersection section, etc.

[0061] Count key indicators such as the total number of intersections and average intersection duration under each path combination.

[0062] The formula for calculating the path intersection degree is as follows: ; in, For the The degree of path intersection under the path combination, is the number of crossover parameters, For the The combined weights of the cross parameters, For the The first path combination The size of the crossover parameter, 、 There are two influence weights, for The maximum value in for The median value in For the The first constant under the path combination, The maximum and median values ​​are combined to modify the sum of the cross-parameters. The first constant is used to balance the magnitude of the modification function. The cross-parameters are normalized to eliminate differences between different path combinations due to factors such as the number of vehicles and path length.

[0063] In some embodiments of the present application, the target path is determined from multiple candidate paths by combining the quality of the candidate paths and the degree of path intersection, including: For the same path combination, the quality of each alternative path is integrated to obtain the comprehensive quality; The comprehensive quality and the path intersection degree are standardized, and the entropy values ​​of the comprehensive quality and the path intersection degree are calculated respectively. The target weights of the comprehensive quality and the path intersection degree are determined according to the entropy values. The objective function of the comprehensive quality and the path intersection degree is constructed, and the target path is screened out through the objective function.

[0064] In this embodiment, the entropy values ​​of the comprehensive quality and the path intersection degree are calculated respectively. The entropy value reflects the degree of dispersion of the indicator. The greater the dispersion, the smaller the entropy value, and the more important the indicator is in decision making.

[0065] ; in, is the maximization objective function, 、 are the target weights of comprehensive quality and path intersection degree respectively, 、 are the comprehensive quality and the path intersection degree, This is equivalent to the path intersection degree appearing as a penalty item, thereby comprehensively considering both the comprehensive quality and the path intersection degree.

[0066] Step S103: Generate a transportation plan for each vehicle in service based on the task allocation and the target path, and obtain a vehicle scheduling plan by integrating the transportation plans of all vehicles in service. Define multiple monitoring nodes in the vehicle scheduling plan to monitor the plan deviation.

[0067] In this embodiment, the transportation plans of all vehicles are integrated to obtain an overall vehicle scheduling plan, and two types of monitoring nodes are set. The first monitoring point is used to monitor the congestion and transportation completion status of congested sections, and the second monitoring point is used to monitor the transportation completion status of non-congested sections.

[0068] In some embodiments of the present application, the transportation plan of all vehicles in service is integrated to obtain a vehicle dispatch plan, including: Construct a time-space graph for each service vehicle according to the transportation plan of the service vehicle, and integrate the time-space graphs of all service vehicles into a global time-space graph; The global time-space graph is analyzed to detect multiple types of conflicts, and the transportation plan is optimized according to the multiple types of conflicts to obtain the vehicle scheduling plan.

[0069] In this embodiment, the transportation plan for each active vehicle is analyzed, including information such as the mission's starting point, destination, waypoints, estimated departure time, arrival time, driving route, and mission priority. Based on the transportation plan, a time-space graph is drawn for each active vehicle, with time on the horizontal axis and space (e.g., underground tunnels, working faces) on the vertical axis. The graph depicts the vehicle's position and travel direction at different points in time. The time-space graphs for all active vehicles are layered to form a global time-space graph, which visually displays the positions and driving status of all vehicles at different points in time. Key nodes, such as intersections, narrow sections, and loading and unloading points, are marked in the global time-space graph. These nodes serve as key areas for conflict detection. Multiple types of conflicts are defined, including spatial conflicts (e.g., two vehicles meeting at a narrow section), temporal conflicts (e.g., two vehicles arriving at the same intersection at the same time), and mission conflicts (e.g., two vehicles assigned to the same mission but overlapping in time). A conflict detection algorithm is used to scan the global time-space graph, detecting and recording all conflict events, including conflict type, time of occurrence, location, and vehicles involved. Based on the conflict detection results, optimization strategies are developed, such as adjusting vehicle departure times and reallocating tasks, to eliminate or reduce conflicts. Based on the optimized transportation plan, a vehicle dispatch plan is compiled, specifying each vehicle's departure time, route, task schedule, and precautions. The dispatch plan is published to the unmanned vehicle dispatch system, guiding vehicles in their transportation tasks and monitoring their driving status in real time to ensure smooth execution.

[0070] In some embodiments of the present application, multiple monitoring nodes are defined in the vehicle scheduling plan to monitor the plan deviation, including: The monitoring nodes include a first monitoring point and a second monitoring point; Determine a comprehensive vehicle route map in the vehicle dispatch plan, and distinguish congested sections and non-congested sections in the comprehensive vehicle route map; For congested road sections, multiple first monitoring points are set up to predict the congestion situation and set the congestion standard value and transportation standard value corresponding to each monitoring point and each service vehicle on the congested road section; For non-congested road sections, multiple second monitoring points are evenly set up, and the transportation situation is predicted to set the transportation standard value corresponding to each service vehicle at each monitoring point on the non-congested road section.

[0071] In this embodiment, a comprehensive vehicle path map is constructed based on the vehicle dispatch plan, in which the driving paths, key nodes (such as intersections, loading and unloading points, etc.) and task areas of all vehicles are marked.

[0072] Definition of congested road sections: According to the scheduling plan, determine the road sections prone to congestion, such as narrow alleys and multi-vehicle intersections.

[0073] Definition of non-congested road sections: road sections other than congested road sections.

[0074] Congested road monitoring: A plurality of first monitoring points are set up on the congested road section for real-time monitoring of vehicle driving status and congestion situation.

[0075] Number of monitoring points: The number of monitoring points is determined based on the length, complexity and historical congestion frequency of the congested road section to ensure full coverage of the congested road section.

[0076] Congestion standard value setting: Predict congestion conditions and set congestion standard values ​​corresponding to each monitoring point and each service vehicle on the congested road section.

[0077] Definition of congestion standard value: If the passing time of multiple vehicles at a monitoring point exceeds a predetermined threshold and the congestion value is determined, it is judged as congestion.

[0078] Transport standard value setting: At the same time, the transportation standard values ​​corresponding to each service vehicle at each monitoring point on the congested road section are set, such as the scheduled time for the vehicle to arrive at the monitoring point, energy consumption, etc.

[0079] Non-congested road section monitoring: Multiple second monitoring points are evenly set up on non-congested road sections to monitor vehicle transportation conditions in real time.

[0080] Number of monitoring points: Evenly distribute monitoring points according to the length of non-congested road sections and transportation demand to ensure the continuity and effectiveness of monitoring.

[0081] Step S104: During the execution of the vehicle dispatch plan, the vehicle dispatch plan is adjusted by monitoring the plan deviation on the node.

[0082] In some embodiments of the present application, the vehicle dispatch plan is adjusted by monitoring the plan deviation at the node, including: For the first monitoring point, determining the actual congestion value and the actual transportation value of the road section where the service vehicle is currently located, and calculating the plan deviation based on the actual congestion value, the actual transportation value, the congestion standard value, and the transportation standard value; For the second monitoring point, the current actual transportation value of the service vehicle is determined, and the plan deviation is calculated based on the actual transportation value and the transportation standard value; Identify deviating vehicles by the planned deviation degree, and distinguish between vehicles that can change their routes and vehicles that cannot change their routes among the deviating vehicles; For vehicles that can change their routes, the routes are replanned and the cost of the modified routes is measured to determine whether to adjust the vehicle routes.

[0083] In this embodiment, the actual value of congestion: the data such as the passing time and queue length of vehicles on the congested road section are obtained in real time through sensors or monitoring systems. The actual value of transportation: the actual time, speed and other transportation parameters of the vehicle arriving at the monitoring point are recorded. The deviated vehicles are identified by the plan deviation. Vehicles with a high plan deviation indicate that they do not meet the plan requirements and need to modify the route to catch up. Vehicles that can be rerouted: have not entered the congested road section or the congested road section can be detoured (such as through an alternative path or adjusting the order of tasks). Vehicles that cannot be rerouted: have entered the congested road section and cannot detour (such as waiting in line, unique path).

[0084] Path planning: Based on real-time road conditions (such as congestion prediction and path length) and vehicle status (such as battery level and load), a path planning algorithm (such as A* and Dijkstra) is called to generate candidate paths.

[0085] Cost Assessment: Time cost: The difference between the estimated time of the new path and the original path.

[0086] Energy cost: the difference between the energy consumption of the new path and the original path.

[0087] Mission impact: The chain reaction (e.g., delay, resource usage) of route adjustments on other vehicles or missions.

[0088] Adjustment decisions: If the comprehensive cost (time, energy consumption, and task impact) of the new path is lower than the threshold (e.g., the total cost is reduced by 10%), the path is adjusted; otherwise, the original path is maintained.

[0089] The formula for the planned deviation of the first monitoring point is as follows: ; in, For the The first monitoring point The plan deviation of each vehicle, is the number of transport parameters, For the Conversion factors for transport parameters, 、 Respectively The first monitoring point The first vehicle The transport standard value and actual value of each transport parameter, For the The congestion conversion coefficient at the first monitoring point, 、 Respectively The first monitoring point The congestion standard value and actual congestion value of the road section where each vehicle is located, For the The second constant under the first monitoring point, It represents the correction of congestion deviation to the sum of transportation deviations. The second constant is to balance the size of the correction function.

[0090] Correspondingly, this application also provides a comprehensive management system for dispatching unmanned vehicles in coal mines, such as Figure 2 Shown, including, The first module is used to obtain road information, service vehicle information and transportation task information in the coal mine, assign transportation tasks to multiple service vehicles, and plan multiple alternative routes for each service vehicle based on the transportation task information and road information; The second module is configured to evaluate the quality of each alternative route for the in-service vehicle, determine the degree of route intersection based on the multiple alternative routes for all in-service vehicles, and determine a target route from the multiple alternative routes by combining the quality of the alternative routes and the degree of route intersection; The third module is used to generate a transportation plan for each vehicle in service based on the task assignment and target path. The transportation plans of all vehicles in service are combined to obtain a vehicle scheduling plan. Multiple monitoring nodes are defined in the vehicle scheduling plan to monitor plan deviations. The fourth module is used to adjust the vehicle scheduling plan by monitoring the plan deviation on the node during the execution of the vehicle scheduling plan.

[0091] Compared with the prior art, the present invention has the following beneficial effects: 1. Evaluate the quality of each alternative route for in-service vehicles, considering the quality of each alternative route for each vehicle to provide a reliable foundation for subsequent route planning. Determine the target route from multiple alternative routes by combining the quality of the alternative routes and the degree of path intersection. Combined consideration of both path quality and path intersections determines the most reasonable target route, ensuring the initial accuracy of route planning.

[0092] 2. Define multiple monitoring nodes in the vehicle dispatch plan to monitor plan deviations. These nodes monitor each vehicle's transport mission completion, known as plan deviations. By adjusting the vehicle dispatch plan based on plan deviations at the monitoring nodes, the system improves the accuracy and reliability of the integrated management of unmanned vehicle dispatch in underground coal mines, meeting the complex and ever-changing needs of unmanned vehicles in underground coal mines and enhancing their operational efficiency.

[0093] Through the above description of the embodiments, those skilled in the art will clearly understand that the present invention can be implemented via hardware or via software combined with a necessary general-purpose hardware platform. Based on this understanding, the technical solution of the present invention can be embodied in the form of a software product. This software product can be stored on a non-volatile storage medium (such as a CD-ROM, USB flash drive, or external hard drive) and includes instructions for enabling a computer device (such as a personal computer, server, or network device) to execute the methods described in various implementation scenarios of the present invention.

[0094] Those skilled in the art will understand that the accompanying drawings are merely schematic diagrams of a preferred implementation scenario, and the modules or processes in the accompanying drawings are not necessarily required to implement the present invention.

[0095] Those skilled in the art will appreciate that the modules in the system of the implementation scenario can be distributed in the system of the implementation scenario according to the implementation scenario description, or can be modified accordingly and located in one or more systems different from the implementation scenario. The modules of the above implementation scenario can be combined into one module or further divided into multiple submodules.

[0096] The above description is only a preferred specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any technician familiar with the technical field, within the technical scope disclosed by the present invention, who makes equivalent replacements or changes based on the technical solution and inventive concept of the present invention, should be covered by the scope of protection of the present invention.

Claims

1. A comprehensive management method for dispatching unmanned vehicles in coal mines, characterized in that: include, Obtain road information, service vehicle information, and transportation task information in the coal mine, assign transportation tasks to multiple service vehicles, and plan multiple alternative routes for each service vehicle based on the transportation task information and road information; evaluating the quality of each alternative path for the in-service vehicle, determining a degree of path intersection based on the multiple alternative paths for all in-service vehicles, and determining a target path from the multiple alternative paths by combining the quality of the alternative paths and the degree of path intersection; Generate a transportation plan for each vehicle in service based on task assignment and target path. Combine the transportation plans of all vehicles in service to obtain a vehicle scheduling plan. Define multiple monitoring nodes in the vehicle scheduling plan to monitor plan deviations. During the execution of the vehicle scheduling plan, the vehicle scheduling plan is adjusted by monitoring the plan deviation at the node.

2. The method for comprehensive management of work scheduling of unmanned vehicles in coal mines according to claim 1, characterized in that: Assign the transport task to multiple service vehicles, and plan multiple alternative routes for each service vehicle according to the transport task information and road information, including: Service vehicle information includes vehicle type, vehicle load, and vehicle range; transport mission information includes transport weight, transport priority, transport mileage, transport starting point, multiple transport transit points, and transport destination; Match vehicle type, vehicle load capacity, and vehicle range with the weight of the transported goods, transport priority, and transport mileage to determine the transport tasks corresponding to each service vehicle and divide the service vehicles into departure batches; Based on the transportation starting point, transportation end point and multiple transportation transit points, a path planning algorithm is used to obtain multiple alternative routes for each service vehicle.

3. The method for comprehensive management of work scheduling of unmanned vehicles in coal mines according to claim 2, characterized in that: Evaluate the quality of each alternative path for the service vehicle, including, Road information includes road safety information, road traffic information, road environment adaptation information, road vehicle energy consumption information and road change information; Each alternative route is divided into sections, and the road safety information, road traffic information, road environment adaptation information, road vehicle energy consumption information and road change information corresponding to each section of the single alternative route are determined according to the sections; Parameters in road safety information, road traffic information, road environment adaptation information, road vehicle energy consumption information, and road change information are divided into static parameters and dynamic parameters, and a dynamic parameter curve is drawn showing the changes of dynamic parameters over time for each road section; On the dynamic parameter curve, the minimum point, the median point and the maximum point are counted, the curve between the minimum point and the median point is recorded as the lower curve, and the curve between the median point and the maximum point is recorded as the upper curve; Search for multiple points that appear frequently and are close to the minimum point in the lower curve, and record them as the first point. Search for multiple points that appear frequently and are close to the maximum point in the upper curve, and record them as the second point. The minimum slope value of the first point and the second point is used as the lower endpoint and the upper endpoint respectively, and the lower endpoint and the upper endpoint are used as the standard interval of the dynamic parameter; The quality of each alternative path for the service vehicle is determined according to standard intervals of static parameters and dynamic parameters.

4. The method for comprehensive management of work scheduling of unmanned vehicles in coal mines according to claim 2, characterized in that: Determine the degree of path intersection based on multiple alternative paths for all vehicles in service, including, For the same departure batch of service vehicles, multiple alternative routes of multiple service vehicles are arranged and combined to obtain multiple path combinations. The driving and transportation conditions of each service vehicle in the departure batch are simulated according to the path combinations. The cross parameters generated during the simulation process are counted, and the path intersection degree is calculated based on the cross parameters under each path combination.

5. The method for comprehensive management of work scheduling of unmanned vehicles in underground coal mines according to claim 3 or 4, characterized in that: The quality of alternative paths and the degree of path intersection are combined to determine the target path among multiple alternative paths, including: For the same path combination, the quality of each alternative path is integrated to obtain the comprehensive quality; The comprehensive quality and the path intersection degree are standardized, and the entropy values ​​of the comprehensive quality and the path intersection degree are calculated respectively. The target weights of the comprehensive quality and the path intersection degree are determined according to the entropy values. The objective function of the comprehensive quality and the path intersection degree is constructed, and the target path is screened out through the objective function.

6. The method for comprehensive management of work scheduling of unmanned vehicles in underground coal mines according to claim 1, characterized in that: Integrate the transportation plan of all vehicles in service to obtain the vehicle dispatch plan, including: Construct a time-space graph for each service vehicle according to the transportation plan of the service vehicle, and integrate the time-space graphs of all service vehicles into a global time-space graph; The global time-space graph is analyzed to detect multiple types of conflicts, and the transportation plan is optimized according to the multiple types of conflicts to obtain the vehicle scheduling plan.

7. The method for comprehensive management of work scheduling of unmanned vehicles in underground coal mines according to claim 1, characterized in that: Define multiple monitoring nodes in the vehicle scheduling plan to monitor plan deviations, including: The monitoring nodes include a first monitoring point and a second monitoring point; Determine a comprehensive vehicle route map in the vehicle dispatch plan, and distinguish congested sections and non-congested sections in the comprehensive vehicle route map; For congested road sections, multiple first monitoring points are set up to predict the congestion situation and set the congestion standard value and transportation standard value corresponding to each monitoring point and each service vehicle on the congested road section; For non-congested road sections, multiple second monitoring points are evenly set up, and the transportation situation is predicted to set the transportation standard value corresponding to each service vehicle at each monitoring point on the non-congested road section.

8. The method for comprehensive management of work scheduling of unmanned vehicles in underground coal mines according to claim 7, characterized in that: Adjust the vehicle dispatch plan by monitoring the plan deviation at the node, including: For the first monitoring point, determining the actual congestion value and the actual transportation value of the road section where the service vehicle is currently located, and calculating the plan deviation based on the actual congestion value, the actual transportation value, the congestion standard value, and the transportation standard value; For the second monitoring point, the current actual transportation value of the service vehicle is determined, and the plan deviation is calculated based on the actual transportation value and the transportation standard value; Identify deviating vehicles by the planned deviation degree, and distinguish between vehicles that can change their routes and vehicles that cannot change their routes among the deviating vehicles; For vehicles that can change their routes, the routes are replanned and the cost of the modified routes is measured to determine whether to adjust the vehicle routes.

9. A comprehensive management system for dispatching unmanned vehicles in coal mines, characterized in that: include, The first module is used to obtain road information, service vehicle information and transportation task information in the coal mine, assign transportation tasks to multiple service vehicles, and plan multiple alternative routes for each service vehicle based on the transportation task information and road information; The second module is configured to evaluate the quality of each alternative path for the in-service vehicle, determine the degree of path intersection based on the multiple alternative paths for all in-service vehicles, and determine a target path from the multiple alternative paths by combining the quality of the alternative paths and the degree of path intersection; The third module is used to generate a transportation plan for each vehicle in service based on the task assignment and target path. The transportation plans of all vehicles in service are combined to obtain a vehicle scheduling plan. Multiple monitoring nodes are defined in the vehicle scheduling plan to monitor plan deviations. The fourth module is used to adjust the vehicle scheduling plan by monitoring the plan deviation on the node during the execution of the vehicle scheduling plan.

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