A mine vehicle mixed marshalling scheduling method and system based on vehicle-road cloud cooperation

Abstract

The application discloses a mine area vehicle mixed marshalling scheduling method and system based on vehicle-road cloud cooperation, relates to vehicle scheduling, adopts fixed marshalling scheduling for manned vehicles, takes operation shift as a scheduling unit, calculates the number N of mine cars corresponding to each loading device and operation tasks, adopts dynamic marshalling scheduling for unmanned vehicles, takes single operation task as a scheduling unit, counts vehicle sequences of each loading point, selects an optimal loading point as a target loading point according to the idle state of the loading device and vehicle waiting time, allocates the target loading point and a corresponding planning path for the unmanned vehicle to be scheduled, takes the fixed marshalling return path and operation time window as constraint conditions, and when it is detected that the planning path of the unmanned vehicle conflicts with the fixed path of the manned vehicle, replans an alternative path for the unmanned vehicle or adjusts the corresponding task allocation to other loading points, so that the application improves the mixed marshalling scheduling efficiency.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of vehicle scheduling, in particular to a mine area vehicle mixed marshalling scheduling method and system based on vehicle-road cloud cooperation. BACKGROUND

[0002] With the rapid advancement of intelligent mine construction, the mine area transportation system is gradually developing towards unmanned and intelligent. In the current technological transformation period, open-pit mines and underground mines generally adopt a mixed marshalling operation mode of manned and unmanned vehicles. This mixed marshalling mode can not only take advantage of the automation of unmanned vehicles, but also retain the flexibility and experience advantage of manned vehicles, and is a necessary stage for the intelligent upgrading of mine area transportation systems.

[0003] However, the existing mine area mixed marshalling operation mainly relies on a single vehicle independent decision-making mode, which has serious technical defects. In the single vehicle independent decision-making mode, each vehicle only relies on the vehicle-mounted sensor to obtain local environmental information, lacking a global perspective and information sharing mechanism between vehicles. This information island phenomenon is particularly prominent in key areas such as intersections and single-lane roads:

[0004] In the intersection area, due to the limited sensing range of the vehicle-mounted sensor, the vehicle cannot obtain the information of other direction vehicles in advance, and when multiple vehicles enter the intersection at the same time, collision accidents are likely to occur. Especially the uncertainty of the reaction time of manned vehicles and the difference in decision-making logic of unmanned vehicles make the coordinated control of mixed marshalling at the intersection more difficult.

[0005] In the single-lane road scenario, when vehicles from both ends enter at the same time, due to the lack of a global scheduling mechanism, both vehicles proceed according to their own decision-making logic, and eventually meet in the narrow road to form an opposite deadlock. This deadlock not only seriously affects the transportation efficiency, but also brings additional safety risks in the process of reversing.

[0006] The existing technology has the following shortcomings in solving the above problems: first, the positioning and communication methods are single, open-pit mines mainly rely on GPS / Beidou positioning, underground mines rely on SLAM positioning, and there is a lack of multi-source fusion mechanism, which lacks reliability; second, the vehicle-road cooperation level is shallow, although some mine areas have deployed roadside equipment, they are only used for auxiliary sensing and have not achieved true vehicle-road cloud integration cooperation; third, the scheduling strategy is fixed, most systems use fixed marshalling and fixed route methods, which cannot be dynamically adjusted according to real-time road conditions, and are difficult to handle sudden situations such as intersection congestion and single-lane conflicts; fourth, the mixed marshalling cooperation mechanism is missing, the same control strategy is used for manned and unmanned vehicles, and the differences in response time, control accuracy, etc. between the two types of vehicles are not considered.

[0007] Therefore, there is an urgent need for a mine vehicle mixed marshalling scheduling method based on vehicle-road cloud cooperation, which realizes unified control of key areas such as intersections and single lanes by constructing a global information sharing mechanism, and fundamentally solves the vehicle conflict problem in mixed marshalling operation. SUMMARY

[0008] In view of the vehicle conflict at intersections or single lanes caused by the dependence of mixed marshalling of manned and unmanned vehicles on single vehicle independent decision, the application provides a mine vehicle mixed marshalling scheduling method and system based on vehicle-road cloud cooperation, and a mixed marshalling strategy based on vehicle-road cloud cooperation to solve the conflict of multiple vehicles at intersections or single lanes.

[0009] One aspect of the application provides a mine vehicle mixed marshalling scheduling method based on vehicle-road cloud cooperation, comprising: S1, constructing a global traffic map;

[0010] S2, differentiating scheduling according to the global traffic map: fixed marshalling scheduling for manned vehicles: taking operation shift as the scheduling unit, calculating the number N of mine cars corresponding to each loading device and the operation task; wherein the operation task includes loading point, unloading point and return path; dynamic marshalling scheduling for unmanned vehicles: taking single operation task as the scheduling unit, counting the vehicle sequence of each loading point, selecting the optimal loading point as the target loading point according to the idle state of the loading device and the vehicle waiting time, and assigning the target loading point and the corresponding planning path to the unmanned vehicle to be scheduled; wherein when there is an idle loading device, the loading device with the longest idle time is preferred; when all loading devices have vehicles queuing, the loading device with the shortest expected waiting time is selected;

[0011] The return path and operation time window of fixed marshalling are taken as constraint conditions; when it is detected that the planning path of the unmanned vehicle conflicts with the fixed path of the manned vehicle, a replacement path is planned for the unmanned vehicle or the corresponding task is adjusted to other loading points;

[0012] S3, scheduling and controlling the manned vehicles and unmanned vehicles according to the operation task, and uniformly controlling all vehicles in the target area based on the global traffic map: sending a pass instruction to the vehicle that arrives at the area first, and sending a stop and wait instruction to the vehicle that arrives later; wherein the target area includes: intersection, single lane;

[0013] Further, the global traffic map is constructed, including: obtaining state information of the manned vehicle, including: vehicle position, speed and attitude; wherein the position information is obtained by a GPS / Beidou positioning module in an open pit scene, and the position information is obtained by a UWB positioning module in an underground mine scene; obtaining state information and perception information of the unmanned vehicle; wherein the state information includes vehicle position, attitude, speed and control information obtained by a fusion positioning algorithm; the perception information includes surrounding obstacle category, position, speed, size and confidence detected by a vehicle-mounted sensor; obtaining road-side obstacle information detected by a road-side intelligent device, including: obstacle category, position, speed, size and confidence; fusing all state information of the manned vehicle, state information of the unmanned vehicle, perception information of the unmanned vehicle, and road-side obstacle information, to construct a global traffic map; the global traffic map includes: road network structure of the working area, global position coordinates and speed of all vehicles, vehicle position in a road segment, average driving speed of each road segment, and traffic state.

[0014] Further, the global traffic map is constructed, including: generating a road network structure of the traffic map based on a map of a mine area working area, the road network structure being composed of road segments and road junctions; establishing a connection relationship between the road segments and the road junctions; adding basic information to each road segment, including: road segment length, road segment speed limit and road segment driving direction; wherein the mine area working area includes an overall working area of an open pit or an overall roadway area of an underground mine; mapping position information of all vehicles to corresponding road segments according to real-time coordinates of the vehicles and start and end coordinates of the road segments, to determine a road segment to which each vehicle belongs; calculating driving direction of each vehicle in the belonging road segment and a proportion of a remaining mileage to a total length of the road segment according to state information of the vehicle; wherein the proportion is used to predict a time for the vehicle to reach an end point of the road segment; calculating average driving speed of a corresponding road segment according to speed information of all vehicles in each road segment; for a road segment without vehicles, taking the road segment speed limit of the corresponding road segment as the average driving speed; estimating a traffic state of the road segment according to the average driving speed of each road segment; wherein the traffic state includes smooth, congestion and impassable; integrating the road network structure, vehicle position mapping structure, road segment average driving speed and traffic state, to generate a global traffic map containing real-time traffic information.

[0015] Further, the manned vehicles are scheduled by fixed formation, including: obtaining loading point, manned vehicle and unloading point information participating in operation in this shift, establishing matching relationship between loading point and unloading point; according to the matching relationship between loading point and unloading point, combining with the connection relationship of road sections in the global traffic map, using A* algorithm to plan the round trip path between the corresponding loading point and unloading point with the shortest distance as the target; based on historical data, obtaining the loading time of each loading device loading a mine car and the unloading time of each mine car at the unloading point; according to the round trip path, calculating the loading path distance from the unloading point to the loading point, and the unloading path distance from the loading point to the unloading point; according to the loading time, unloading time, loading path distance, unloading path distance, average speed of mine car empty running and average speed of mine car full running, calculating the time of a mine car completing a cycle of operation; according to the ratio between the cycle time and the loading time, using the upward rounding method to calculate the number N of transport mine cars corresponding to each loading device; according to the number N of mine cars, assigning the manned vehicle to the corresponding loading device, generating the operation task including loading point, unloading point, loading path and unloading path, and sending it to the corresponding manned vehicle.

[0016] Further, the unmanned vehicles are scheduled by dynamic formation, including: obtaining loading point, unmanned vehicle and unloading point information participating in operation in this shift, establishing matching relationship between loading point and unloading point; statistics the vehicle sequence that has been sent to each loading point at the current time, obtaining the queuing information of each loading point; wherein, the vehicle sequence includes manned vehicles and unmanned vehicles; according to the position information of all vehicles in the vehicle sequence and the average speed of road section, calculating the time when each vehicle arrives at the loading point; combining the loading time and the loading completion time of the front vehicle, calculating the loading completion time of the last vehicle in the vehicle sequence as the last service time of the corresponding loading point; after the unmanned vehicle completes unloading, using A* algorithm to plan the planning path of the corresponding unmanned vehicle to all loading points; according to the length and average speed of each road section in the planning path, calculating the time when the corresponding unmanned vehicle arrives at each loading point; calculating the difference between the last service time of each loading point and the time when the unmanned vehicle to be scheduled arrives at the corresponding loading point; when the difference is negative, it means that the loading device is idle, and the loading point with the largest absolute value of the difference is selected as the target loading point; when the difference is positive, it means that the vehicle needs to be queued, and the loading point with the smallest difference is selected as the target loading point; according to the target loading point, generating the operation task including the target loading point and the planning path, and sending it to the unmanned vehicle to be scheduled; after the vehicle completes loading, the same scheduling algorithm is used to match the unloading point for the vehicle.

[0017] Further, the alternative path is planned for the unmanned vehicle or the corresponding task is assigned to other loading points again, including: extracting the round-trip path data of all manned vehicles, and constructing a road segment index table; the road segment index table includes road segment identification, road segment type and connection relationship; initializing a road segment occupancy space-time matrix, the rows of the matrix represent road segments, the columns represent time slices, and the matrix elements record the manned vehicle identification occupying the corresponding road segment; reading the position data and speed data of the manned vehicle at a preset period, determining the road segment where the manned vehicle is located through coordinate matching, and calculating the relative position of the manned vehicle on the current road segment; based on the current speed and the remaining distance of the manned vehicle, the time t1 when the manned vehicle leaves the current road segment is calculated; traversing the subsequent road segment sequence, according to the length of each road segment and the average driving speed of the road segment, the time sequence [t2, t3, …, tn] when the manned vehicle reaches each subsequent road segment is calculated; the time sequence [t1, t2, t3, …, tn] is converted into time slice index, the occupancy data of the corresponding road segment in the road segment occupancy space-time matrix is updated, and the historical occupancy record of the corresponding manned vehicle is cleared; obtaining the planned path of the unmanned vehicle, extracting the road segment sequence in the planned path and the corresponding expected passing time interval; the expected passing time interval is converted into a time slice index set, and each element of the occupancy data of the corresponding road segment in the road segment occupancy space-time matrix is compared; according to the comparison result, the alternative path is planned for the unmanned vehicle or the corresponding task is assigned to other loading points again;

[0018] Further, according to the comparison result, the alternative path is planned for the unmanned vehicle or the corresponding task is assigned to other loading points again, including: when it is detected that the time slice index set has a non-empty intersection, the type attribute of the conflict road segment is extracted; for a single lane road segment, the driving directions of the unmanned vehicle and the manned vehicle are compared: if the driving directions are opposite, it is marked as a conflict; if the driving directions are the same, it is judged whether the expected position distance of the two vehicles on the corresponding road segment meets the preset minimum safety distance requirement, and if not, it is marked as a conflict; for the intersection area, if the passing times of the two vehicles overlap, it is directly marked as a conflict; the road segment-time slice combination marked as a conflict is added to the forbidden set, and the A* algorithm is called to generate an alternative path for the corresponding unmanned vehicle to avoid the forbidden set; if the A* algorithm returns an empty set, it means that there is no feasible alternative path, and the current target loading point is deleted from the loading point list, and the dynamic marshalling scheduling method is executed again to match a new target loading point for the unmanned vehicle; a new planned path is generated according to the new target loading point, and the conflict detection is repeated.

[0019] In particular, the traditional mixed marshalling scheduling in the mining area has the problem of time and space blind area - the system can only obtain the current position of the vehicle and cannot predict the future time and space occupation of the vehicle. This limitation leads to the fact that when planning the path of the unmanned vehicle, the future driving trajectory of the manned vehicle cannot be considered, and the vehicle can only detect the conflict through the vehicle-mounted sensor during the actual driving process and then make an emergency avoidance. This processing method not only reduces the transportation efficiency, but also increases the collision risk, especially in critical areas such as one-way roads and intersections.

[0020] Based on the real-time speed and position of the manned vehicle, the application calculates the occupation time window [t1, t2, …, tn] of the manned vehicle in each road section in the future to form a complete time and space occupation chain. This prediction is not a simple linear extrapolation, but a dynamic prediction considering the actual average speed of each road section.

[0021] The unmanned vehicle converts the predicted passing time of the planned path into a time slice index and compares it with the matrix to detect potential conflicts in advance. When a time and space conflict is detected, the system provides a hierarchical solution strategy - it first tries to re-plan the path, and if there is no feasible path, it adjusts the task allocation. This flexibility ensures that a feasible solution can still be found under complex constraints, avoiding the scheduling deadlock in the traditional method.

[0022] Further, S3, according to the operation task, the manned vehicle and the unmanned vehicle are controlled, including: according to the operation task generated by the fixed marshalling scheduling, the task information containing the loading point, the unloading point, the loading path and the unloading path is sent to the manned vehicle; according to the operation task generated by the dynamic marshalling scheduling, the task information containing the target loading point and the planned path is sent to the unmanned vehicle; real-time monitoring of the position information of all vehicles, judging whether the vehicle is close to the target area; the target area includes the intersection and the one-way lane; when detecting that the vehicle enters the preset range of the target area, the position, speed and driving direction of the corresponding vehicle are extracted; the expected time of the vehicle to reach the entrance of the target area is calculated; for the intersection area, the entrance is the boundary of the intersection; for the one-way lane, both ends of the one-way lane are defined as entrances, which are marked as the first entrance and the second entrance respectively; a target area occupation management table is established to record the occupation state and occupation vehicle information of each target area; for the one-way lane, the control is realized through the direction locking mechanism to avoid the conflict of opposite vehicles; for the intersection, the control is realized through the conflict detection and time and space reservation mechanism to solve the conflict of multi-direction vehicles; when the vehicle passes through the target area, the target area occupation management table is updated and the occupation record of the passed vehicle is cleared; the passing state of the vehicle in the target area is continuously monitored until all vehicles complete the passing.

[0023] Further, for one-way lane, the direction locking mechanism is used for management and control to avoid opposite vehicle conflict, including: setting the direction locking sign of one-way lane in the target area occupancy management table, recording the current allowed direction; when detecting that there are vehicles approaching at both ends of the one-way lane, the expected time of the vehicles reaching the respective entrances is extracted; the expected arrival time of the vehicles at both ends is compared to determine the end that arrives first; the direction locking sign of the one-way lane is set to the passing direction of the first arriving vehicle, and the passing instruction is sent to the vehicle in the passing direction, and the parking waiting instruction is sent to the opposite vehicle; when the vehicle in the passing direction starts to enter the one-way lane, the direction locking is continuously maintained, and the subsequent vehicles in the same direction are allowed to continuously pass until there is no waiting vehicle in the direction or the maximum continuous passing number is reached; when the one-way lane is empty and the direction switching condition is met, the current direction locking is released; it is checked whether there is a waiting vehicle in the opposite direction, and if there is, the direction locking is switched to the opposite direction to allow the opposite vehicle to pass;

[0024] In particular, the traditional mine one-way lane management simply regards the one-way lane as a one-way channel and only sets an entrance management at the starting point. However, in actual operation of the mine, the one-way lane is often a necessary channel connecting different operation areas, and vehicles may need to pass through at both ends. When the vehicles at both ends enter at the same time and meet in the narrow road, due to the large size of the mine car and the difficulty of reversing, it is easy to form an opposite deadlock. This deadlock seriously affects the transportation efficiency.

[0025] The present application can find the potential opposite conflict before the vehicle enters by setting the entrance detection at both ends. Once it is detected that there are vehicles approaching at both ends, the coordination mechanism is started immediately, the priority passing direction is determined by comparing the expected arrival time, and the direction locking sign is set.

[0026] The direction locking is not a simple one-car-one-lock, but supports the continuous passing of vehicles in the same direction. After a direction obtains the passing right, the subsequent vehicles in the direction can form a vehicle fleet to pass continuously until there is no waiting vehicle in the direction or the maximum continuous passing number is reached. This batch passing mode greatly improves the passing efficiency of the one-way lane.

[0027] Further, for the intersection, the conflict detection and space-time reservation mechanism is used for management, and the multi-direction vehicle conflict is solved, including: establishing an intersection conflict matrix, predefining the conflict relationship between each import path and export path according to the geometric structure and traffic rules of the intersection; when detecting that multiple vehicles approach the intersection at the same time, the import path, export path, expected arrival time and expected passing time of each vehicle are extracted; according to the intersection conflict matrix, it is judged whether there is a conflict between the driving paths of each vehicle; for vehicles without conflict, simultaneous passing is allowed; for vehicles with conflict, the passing priority is determined according to the expected arrival time, and the vehicle that arrives first obtains high priority; in the target area occupation management table, a space-time window for the high-priority vehicle to pass through the intersection is reserved, and the space-time window includes the time period from the vehicle entering the intersection to leaving the intersection and the occupied intersection area; the passing instruction is sent to the high-priority vehicle, and the stop and wait instruction is sent to the low-priority vehicle with conflict; after the high-priority vehicle passes through the intersection, the corresponding occupied space-time window is released, and the priority of the remaining vehicles is recalculated to allocate the passing right to the next vehicle; for vehicles that arrive at the same time and have path conflicts, the priority is determined according to the preset rules: manned vehicles have priority over unmanned vehicles, and fully loaded vehicles have priority over empty vehicles.

[0028] In particular, the traditional intersection management adopts a one-size-fits-all mode - either simply implementing first-come-first-served or all vehicles taking turns to pass through. This extensive management ignores a key fact that not all vehicle paths passing through the intersection will cause conflicts. For example, two vehicles that go east and then north and another two vehicles that go south and then west can pass through the intersection at the same time. The traditional method makes vehicles that can pass through in parallel also have to wait in line, which seriously wastes the passing capacity of the intersection and easily causes congestion in the dense traffic mine area scenario.

[0029] The present application locks the space-time resources required by the vehicle to pass through the intersection through the space-time window reservation mechanism, ensures the safety of the vehicle passing through, and also reserves the possibility of parallel passing for other non-conflicting paths. Once the vehicle passes through, the resources are immediately released for use by the next batch of vehicles. When multiple vehicles approach the intersection, the system does not make all vehicles wait in line, but quickly judges the path relationship through the conflict matrix. For vehicle groups with non-conflicting paths, simultaneous passing is directly allowed; only for vehicles with conflicts, the priority is sorted.

[0030] Compared with the prior art, the present application has the following advantages:

[0031] In view of the high intersection collision risk, frequent single-lane opposite deadlock and difficulty in mixed marshalling of manned and unmanned vehicles caused by the single-vehicle independent decision in the prior art of mine vehicle scheduling, the present application provides a mine vehicle mixed marshalling scheduling method based on vehicle-road cloud cooperation.

[0032] By constructing a global traffic map that fuses information from the vehicle end, roadside and cloud end, the real-time sharing of all vehicle states and road information in the mine area is realized; by using a differentiated scheduling strategy, fixed marshalling scheduling is implemented for manned vehicles and dynamic marshalling scheduling is implemented for unmanned vehicles, and the working tasks of the two types of vehicles are coordinated through a path conflict detection mechanism; at key target areas such as intersections and single-lane roads, unified control is carried out based on global information, the opposite deadlock of single-lane roads is avoided through a direction locking mechanism, and the conflict of multi-direction vehicles at intersections is solved through a conflict detection and space-time reservation mechanism.

[0033] The application effectively eliminates the information silos in the traditional single-vehicle decision-making mode, prevents the occurrence of intersection collision accidents and single-lane deadlock situations, fully utilizes the experience advantages of manned vehicles and the flexible scheduling advantages of unmanned vehicles, and improves the mixed marshalling operation efficiency; it is suitable for GPS / Beidou positioning environment in open-pit mines and UWB positioning environment in underground mines, and provides a complete vehicle scheduling solution for the intelligent upgrading of mine areas. BRIEF DESCRIPTION OF DRAWINGS

[0034] The application will be further described in the form of exemplary embodiments, which will be described in detail with reference to the accompanying drawings. These embodiments are not limiting, and in these embodiments, the same numbers represent the same structures, wherein:

[0035] Figure 1 is an exemplary module diagram of a mine area vehicle mixed marshalling scheduling system based on vehicle-road-cloud collaboration according to some embodiments of the application;

[0036] Figure 2 is an exemplary flowchart of a positioning algorithm fusion according to some embodiments of the application;

[0037] Figure 3 is a flowchart of a roadside device fusion perception algorithm according to some embodiments of the application;

[0038] Figure 4 is a flowchart of global traffic map construction according to some embodiments of the application;

[0039] Figure 5 is a traffic map road network schematic diagram according to some embodiments of the application;

[0040] Figure 6 is an intersection area schematic diagram according to some embodiments of the application;

[0041] Figure 7 is a single-lane area schematic diagram according to some embodiments of the application. DETAILED DESCRIPTION

[0042] The method and system provided by the embodiments of the present application are described in detail below with reference to the drawings.

[0043] As shown in the figure, the system comprises a cloud control platform, which is composed of a data fusion server, a traffic map construction server, an intelligent scheduling server, a UWB positioning server and a communication gateway. Figure 1

[0044] A roadside intelligent device, which is an intelligent roadside unit integrating various sensors (laser radar, 4D millimeter wave radar, camera), a communication module and a roadside computing unit.

[0045] A vehicle-mounted intelligent device, which is divided into two types: an unmanned vehicle-mounted intelligent device and a manned vehicle-mounted intelligent device. The unmanned vehicle-mounted intelligent device comprises vehicle-mounted sensors (laser radar, 4D millimeter wave radar, camera), a positioning module, a communication module and a vehicle-mounted domain controller. The manned vehicle-mounted intelligent device comprises a positioning module, a communication module, a human-machine interface and a vehicle-mounted controller.

[0046] The present application is a mixed marshalling dispatching system applicable to open-pit mines and underground mines. The vehicle autonomous positioning function is an important function at the vehicle end. In order to realize the mixed marshalling dispatching of the cloud control platform, the state information of all vehicles including manned vehicles and unmanned vehicles must be known. The positioning methods of manned vehicles and unmanned vehicles are different.

[0047] ​The manned vehicle-mounted intelligent device includes a positioning module, a communication module, a man-machine interaction interface, and a vehicle-mounted controller. For the open pit mine scene, the positioning module on the manned vehicle is a GPS / Beidou positioning module. The module receives signals of positioning satellites and can calculate the position and speed of the vehicle through self-solution. For the underground mine scene, the positioning module on the manned vehicle is a UWB module. The UWB module is only a positioning tag that communicates with a UWB base station. The UWB base station sends information of the vehicle-mounted UWB module to a cloud control platform, and the position information of the vehicle can be calculated through the solution of the cloud control platform. The communication module on the manned vehicle includes a 5G module for communication between the vehicle and the cloud control platform and a V2X module for communication between the vehicle and other vehicles and roadside devices, realizing vehicle-to-cloud (V2C), vehicle-to-vehicle (V2V), and vehicle-to-road (V2I) communication. The man-machine interaction interface on the manned vehicle is a touch screen with voice output function. The driver can input instructions through the touch screen, and the touch screen can also display information and give voice prompts to the driver. The vehicle-mounted controller receives positioning information of the positioning module, state information of the vehicle, and instruction information of the man-machine interaction module, and sends these information to the cloud control platform, other vehicles, and roadside devices. Meanwhile, the vehicle-mounted controller receives dispatching tasks and control commands from the cloud control platform, information from other vehicles and roadside devices, and displays these information to the driver through the man-machine interaction interface or gives voice prompts.

[0048] The unmanned vehicle-mounted intelligent device includes vehicle-mounted sensors (laser radar, 4D millimeter wave radar, camera), a positioning module, a communication module, and a vehicle-mounted domain controller. The vehicle-mounted sensors on the unmanned vehicle are used for surrounding environment perception and positioning. For the open pit mine scene, the positioning module on the unmanned vehicle is a differential GPS / Beidou positioning module. For the underground mine scene, the positioning module on the unmanned vehicle is a UWB module that is solved at the vehicle end. The communication module on the unmanned vehicle includes a 5G module for communication between the vehicle and the cloud control platform and a V2X module for communication between the vehicle and other vehicles and roadside devices, realizing vehicle-to-cloud (V2C), vehicle-to-vehicle (V2V), and vehicle-to-road (V2I) communication. The vehicle-mounted domain controller realizes all functions of unmanned driving.

[0049] The unmanned vehicle realizes autonomous positioning through a fusion positioning algorithm of differential GPS / Beidou (or UWB) + laser SLAM (simultaneous localization and mapping) + visual SLAM + IMU (inertial measurement unit) + wheel speed meter.

[0050] As Figure 2As shown, the fusion positioning algorithm adopts an extended Kalman filter (EKF) algorithm to fuse the data of differential GPS / Beidou (or UWB), laser SLAM, visual SLAM, IMU and wheel speed meter, so as to improve the accuracy and stability of vehicle positioning. The fusion positioning algorithm finally outputs the position of the unmanned vehicle and the attitude information of the vehicle.

[0051] As shown in FIG. 1, the vehicle-end autonomous perception function is consistent in the open-pit mine scene and the underground mine scene. Figure 3

[0052] For a manned vehicle, the vehicle does not have the function of perceiving the surrounding environment, and the vehicle only sends the state information of the vehicle to the cloud control platform, other vehicles and roadside devices through a communication module. The state information of the vehicle includes: the position and attitude of the vehicle, the speed of the vehicle.

[0053] For an unmanned vehicle, the vehicle has the function of perceiving the surrounding environment. The vehicle-end autonomous perception function collects the data of the laser radar, millimeter wave radar and camera, and detects the obstacles around the unmanned vehicle through a perception fusion algorithm.

[0054] The obstacle information output by the perception fusion algorithm includes:

[0055] (1) the category of the obstacle;

[0056] (2) the position of the obstacle relative to the vehicle;

[0057] (3) the speed of the obstacle relative to the vehicle;

[0058] (4) the length, width, height and orientation of the obstacle;

[0059] (5) the confidence of the obstacle.

[0060] After detecting the obstacle information, the unmanned vehicle sends the obstacle information and the state information of the vehicle to the cloud control platform, other vehicles and roadside devices through the communication module.

[0061] The state information of the vehicle includes: the position and attitude of the vehicle, the speed of the vehicle, the throttle, brake and steering wheel angle of the vehicle, and other control information.

[0062] The roadside perception function is consistent in the open-pit mine scene and the underground mine scene.

[0063] The roadside intelligent device is arranged at the key positions of the roadside of the open-pit mine or the roadway of the underground mine. The arrangement principle is: at the turning place, intersection place, and every 100 meters apart on the straight road, etc. The sensors in the roadside intelligent device can detect the obstacles on the road in real time and publish the obstacle information.

[0064] ​The detection of the obstacle on the road by the roadside intelligent device adopts a fusion perception algorithm similar to that in 2. The obstacle information output by the perception fusion algorithm includes:

[0065] (1) the category of the obstacle;

[0066] (2) the position of the obstacle relative to the roadside intelligent device;

[0067] (3) the speed of the obstacle relative to the roadside intelligent device;

[0068] (4) the length, width, height and orientation of the obstacle;

[0069] (5) the confidence of the obstacle.

[0070] After the detection of the obstacle is completed, the roadside intelligent device sends the obstacle information to the surrounding autonomous vehicles through the V2X communication module, and sends the obstacle information to the cloud control platform through the 5G communication module or the wired network.

[0071] As shown in Figure 4 , the cloud control platform can receive the vehicle state information sent by each manned vehicle, the vehicle surrounding obstacle information and vehicle state information sent by each autonomous vehicle, and the road obstacle information sent by each roadside intelligent device.

[0072] The data fusion server of the cloud control platform fuses the information from the vehicle end and the roadside, and based on the fused data, the map construction server constructs the global traffic map in the working area. The information included in the map includes:

[0073] (1) the road network of the working area, including two basic elements of road segments and intersections and the connection relationship between the elements;

[0074] (2) the global position coordinates of the vehicle;

[0075] (3) the absolute speed of the vehicle;

[0076] (4) the driving direction of the vehicle;

[0077] (5) the confidence of the vehicle information;

[0078] (6) the proportion of the remaining mileage of the vehicle in the road segment;

[0079] (7) the average driving speed of each road segment;

[0080] (8) the traffic state information of each road segment.

[0081] As shown in Figure 5As shown, the map building server produces a road network of the traffic map based on the high-precision map of the working area, the road network is composed of two basic elements of road segments and intersections, and there is a connection relationship between the road segments and the intersections. As shown Figure 5 As shown, a, b, c, d, e, and f represent road segments, denoted as link_a~link_f; 1, 2, 3, 4, and 5 represent intersections, denoted as node_1~node_5. The intersection (node) serves to connect the road segments, and the map building server adds important basic information to the road segments (link), including: road segment length, road segment speed limit, and road segment driving direction.

[0082] After the map building server builds the road network, it maps the global vehicle target information into the road network, and the mapping basis is the real-time coordinates of the vehicle and the start and end coordinates of the road segment. Finally, the vehicle is attributed to the corresponding road segment. According to the state information of the vehicle, the driving direction of the vehicle in the road segment and the proportion of the remaining mileage of the vehicle in the road segment are calculated. Then, according to the vehicle speed information of each road segment, the average driving speed of the road segment is calculated. For road segments without vehicles, the average driving speed is the speed limit of the road segment. Finally, the map building server estimates the traffic state of each road segment according to the average speed of each road segment. The traffic state includes: smooth, congestion, and impassable (usually set by the operation and maintenance personnel).

[0083] The role of the intelligent scheduling server of the cloud control platform is to schedule and control manned mine cars and unmanned mine cars according to the global traffic map.

[0084] (1) Scheduling of manned mine cars

[0085] The mine production environment is composed of a number of loading points, a number of unloading points, and a number of transport mine cars. Each loading point has a loading device, and a loading device and a number of transport mine cars form a work group. The intelligent scheduling server adopts a fixed grouping method to schedule manned mine cars, i.e., fixed transport mine cars and fixed loading devices form a group for work. This is mainly due to the poor adaptability of human drivers to frequent changes in tasks, which leads to low work efficiency.

[0086] The intelligent scheduling server schedules manned mine cars in fixed groups with each work shift as the smallest unit. Before work, the operation and maintenance personnel selects the loading points, manned transport mine cars, and unloading points participating in the work of this shift through the scheduling configuration page of the cloud control platform, and matches the corresponding relationship between the loading points and the unloading points. The corresponding relationship between the loading points and the unloading points can be one-to-one, one-to-many, or many-to-one.

[0087] After receiving the configuration information from the operation and maintenance personnel, the intelligent scheduling server schedules the manned vehicles. The main steps are as follows:

[0088] Step one: planning path

[0089] The intelligent scheduling server plans the round trip path between the loading point and the unloading point according to the matching relationship between the loading point and the unloading point, and the connection relationship of the road section of the traffic map road network, using A* algorithm with the shortest distance as the target.

[0090] Step two: time factor calculation

[0091] First, according to the historical data, the loading time of each loading device loading a mine car is calculated , the unloading time of each mine car at the unloading point .

[0092] According to the round trip path of step one, the haul distance of the loading path from the unloading point to the loading point is calculated , and the haul distance of the unloading path from the loading point to the unloading point . Therefore, the time of a mine car completing a work cycle can be calculated:

[0093]

[0094] Among them, is the average speed of the empty mine car, is the average speed of the slow load mine car.

[0095] Step three: grouping calculation

[0096] According to the time factor calculated in step two, the number of transport mine cars matched with the loading device in a group can be calculated:

[0097]

[0098] Among them, ceil is the ceiling function, for example, if the calculation result is 4.3, the final value is 5. The purpose of such operation is to fully guarantee the utilization rate of the loading device, because once the loading device is idle, the production of the mine will be reduced, therefore the principle of fixed grouping is to avoid idle loading devices to improve production.

[0099] After calculating the number of mine cars needed in the group, the intelligent scheduling server will match the mine cars in the shift to the corresponding loading device according to the calculated number. And generate a work task for the mine car, which includes loading point, unloading point, loading path and unloading path information. Then, the cloud control platform sends the work task to the corresponding mine car, and displays it to the human driver through the human-machine interface on the mine car.

[0100] (2) Scheduling of unmanned mine cars

[0101] The intelligent scheduling server uses a dynamic grouping method to schedule unmanned mining trucks. This means the matching relationship between the mining trucks and loading equipment is not fixed but dynamically changing. The smallest scheduling unit in dynamic grouping is a task. After an unmanned mining truck completes unloading, the intelligent scheduling server calculates the next loading task for the truck until it reaches the loading point and completes loading. Then, the intelligent scheduling server calculates the next unloading task for the truck until it reaches the unloading point and completes unloading.

[0102] Before the operation, the maintenance personnel select the loading point, unmanned transport mining truck and unloading point for this shift through the scheduling and configuration page of the cloud control platform, and match the correspondence between the loading point and the unloading point. The correspondence between the loading point and the unloading point can be one-to-one, one-to-many or many-to-one.

[0103] After receiving the configuration information from the maintenance personnel, the intelligent dispatch server dispatches the autonomous vehicles. The main steps are as follows:

[0104] Step 1: Statistics of minecart sequences at loading and unloading points

[0105] Since the mining trucks are all dispatched by the intelligent scheduling server, the intelligent scheduling server can count the number of trucks that have been dispatched to the current location. The loading point and the first The vehicle sequence at each unloading point. (Using...) (i=1,2,..., )and (j=1, 2, ..., )express.

[0106] Step 2: Calculation of the last service time at the loading and unloading points

[0107] With the vehicle sequence calculated in step one, we can calculate the time when the loading equipment finishes loading the last vehicle in the vehicle sequence, and the time when the unloading point finishes unloading the last vehicle in the vehicle sequence.

[0108] Taking the loading point as an example, for the first... One loading point, The time of arrival at the loading point is:

[0109]

[0110] in:

[0111] : The current moment;

[0112] : The number of remaining road segments leading to the loading point;

[0113] : the length of the remaining kth road segment;

[0114] : the average speed of the remaining kth road segment;

[0115] : the estimated waiting time on the road for the arrival at the loading point;

[0116] the time when the loading is completed is:

[0117]

[0118] wherein:

[0119] the domain of is the same as that of the manned vehicle;

[0120] : the time when the loading of the previous mine vehicle is completed;

[0121] The reason for taking the max of and is that even if arrives at the loading point, if the loading of the previous vehicle has not been completed, the loading of will be performed after the loading of the previous vehicle is completed; if arrives at the loading point when the loading of the previous vehicle has been completed, the loading of will be performed immediately.

[0122] According to this formula, the time when the loading of the last vehicle in the vehicle sequence is completed , that is, the last service time of the nth loading point

[0123] Using the same method, the last service time of the mth unloading point can be calculated.

[0124] Step three: calculation of the time when the mine vehicle arrives at the loading point

[0125] The time when the mine vehicle arrives at the loading point in this step refers to the time when the mine vehicle arrives at the loading point after waiting for scheduling after unloading is completed. After a mine vehicle completes unloading, the intelligent scheduling server will plan a path for the mine vehicle to all loading points. The planning algorithm is the A* algorithm with the shortest path as the target. After planning is completed, the sequence of road segments to each loading point can be expressed as:

[0126]

[0127] The time when the vehicle arrives at each loading point can be expressed as:

[0128]

[0129] wherein:

[0130] : length of the kth road segment to the nth loading point;

[0131] : average vehicle speed of the kth road segment to the nth loading point;

[0132] : estimated waiting time of the process to the nth loading point on the road;

[0133] Step four: loading point matching

[0134] According to the above calculation, the intelligent scheduling server finally selects the loading point to which the mine car needs to go, and the selection basis is:

[0135]

[0136] When the difference is greater than 0, , the mine car does not need to queue, and the difference represents the idle time of the loading device. The first priority of the scheduling is to adjust the mine car to the most idle loading device.

[0137] , the mine car needs to queue, and the absolute value of the difference represents the waiting time of the mine car. In this case, the mine car will be adjusted to the loading device with the shortest waiting time.

[0138] The fixed formation round trip path and the operation time window are taken as constraint conditions; when it is detected that the planned path of the unmanned vehicle conflicts with the fixed path of the manned vehicle, an alternative path is planned for the unmanned vehicle or the corresponding task is adjusted and allocated to other loading points;

[0139] Specifically, the round trip path data of all manned vehicles is extracted, and a road segment index table is constructed. The road segment index table includes road segment identification, road segment type and connection relationship;

[0140] The road segment occupancy space-time matrix is initialized, the rows of the matrix represent the road segments, the columns represent the time slices, and the matrix elements record the manned vehicle identification occupying the corresponding road segment;

[0141] The position data and speed data of the manned vehicle are read at a preset period, the road segment where the manned vehicle is located is determined through coordinate matching, and the relative position of the manned vehicle in the current road segment is calculated;

[0142] Calculate the time t1 when the manned vehicle leaves the current road segment based on the current speed and remaining mileage of the manned vehicle; traverse the sequence of subsequent road segments, and calculate the sequence of times [t2, t3, …, tn] when the manned vehicle reaches each subsequent road segment based on the length of each road segment and the average driving speed on the road segment;

[0143] Convert the sequence of times [t1, t2, t3, …, tn] into time slice indexes, update the occupancy data of the corresponding road segment in the road segment occupancy space-time matrix at the corresponding time slice, and clear the historical occupancy record of the corresponding manned vehicle;

[0144] Obtain the planned path of the unmanned vehicle, extract the sequence of road segments in the planned path and the corresponding expected passing time interval;

[0145] Convert the expected passing time interval into a set of time slice indexes, and perform element-by-element comparison with the occupancy data of the corresponding road segment in the road segment occupancy space-time matrix;

[0146] According to the comparison result, re-plan an alternative path for the unmanned vehicle or adjust the corresponding task allocation to other loading points, including: when a non-empty intersection is detected in the set of time slice indexes, extract the type attribute of the conflict road segment; for single-lane road segments, compare the driving directions of the unmanned vehicle and the manned vehicle: if the driving directions are opposite, mark it as a conflict; if the driving directions are the same, determine whether the expected position distance of the two vehicles on the corresponding road segment meets the preset minimum safety distance requirement, if not, mark it as a conflict; for intersection areas, if there is an overlap in the passing times of the two vehicles, directly mark it as a conflict; add the road segment-time slice combination marked as a conflict to the forbidden set, and call the A* algorithm to generate an alternative path for the corresponding unmanned vehicle that avoids the forbidden set; if the A* algorithm returns an empty set, indicating that there is no feasible alternative path, delete the current target loading point from the loading point list and re-execute the dynamic marshalling scheduling method to match a new target loading point for the unmanned vehicle; generate a new planned path based on the new target loading point and repeat the conflict detection.

[0147] Based on this scheduling principle, both the idle time of the loading equipment and the waiting time of the mine car are minimized, and the efficiency of the entire operation system can be optimized.

[0148] Step five: unloading point matching

[0149] For one-to-one and many-to-one correspondence between loading points and unloading points, the unloading point matched with the loading point is unique, and the intelligent scheduling server does not need to match the unloading point, but only needs to plan the shortest driving path for the mine car.

[0150] For the one-to-many correspondence between the loading point and the unloading point, the intelligent scheduling server matches the unloading point for the mine car using the same scheduling algorithm as steps three and four.

[0151] After matching the loading point or the unloading point, the scheduling server generates a work task for the unmanned mine car, and the work task includes loading point or unloading point information and driving path information. Then, the cloud control platform sends the work task to the corresponding unmanned mine car, and the mine car performs corresponding work according to the task.

[0152] As shown in Figure 6 The special area mainly refers to the intersection and single-lane area. In these two special areas, when multiple vehicles enter the area, there will be a risk of collision and congestion. The intelligent scheduling server has the same control method for manned mine cars and unmanned mine cars. According to the global traffic map, it determines which vehicles are approaching the area, and uses the first-come-first-served principle to send communication instructions to the vehicles that first arrive in the area, and sends parking waiting instructions to the vehicles that arrive later in the area, thereby realizing the control of the vehicles.

[0153] Specifically, according to the work task generated by the fixed marshalling scheduling, the task information including the loading point, the unloading point, the loading path and the unloading path is sent to the manned vehicle;

[0154] According to the work task generated by the dynamic marshalling scheduling, the task information including the target loading point and the planned path is sent to the unmanned vehicle;

[0155] Real-time monitoring of the position information of all vehicles to determine whether the vehicle is approaching the target area; the target area includes the intersection and the single-lane;

[0156] When it is detected that the vehicle enters the preset range of the target area, the position, speed and driving direction of the corresponding vehicle are extracted;

[0157] The estimated time for the vehicle to arrive at the entrance of the target area is calculated; for the intersection area, the entrance is the boundary of the intersection; for the single-lane, both ends of the single-lane are defined as entrances, which are marked as the first entrance and the second entrance, respectively;

[0158] A target area occupation management table is established to record the occupation state and occupation vehicle information of each target area;

[0159] For single-lane, the direction locking mechanism is used to control and avoid the conflict of opposite vehicles, including: setting the direction locking sign of single-lane in the target area occupancy management table, recording the current allowed direction; when detecting that there are vehicles approaching from both ends of the single-lane, extracting the expected time of the vehicles reaching the respective entrances; comparing the expected arrival time of the vehicles from both ends to determine the end that arrives first; setting the direction locking sign of the single-lane to the passing direction of the first arriving vehicle, sending the passing instruction to the vehicle in the passing direction and the parking waiting instruction to the opposite vehicle; when the vehicle in the passing direction starts to enter the single-lane, the direction locking is continuously maintained to allow the subsequent vehicles in the same direction to continuously pass until there is no waiting vehicle in the direction or the maximum number of continuous passing is reached; when the single-lane is empty and the direction switching condition is met, the current direction locking is released; checking whether there is a waiting vehicle in the opposite direction, if yes, switching the direction locking to the opposite direction to allow the opposite vehicle to pass;

[0160] For the intersection, the conflict detection and space-time reservation mechanism is used to control and solve the conflict of multi-direction vehicles, including: establishing an intersection conflict matrix, predefining the conflict relationship between each entrance path and exit path according to the geometric structure and traffic rules of the intersection; when detecting that multiple vehicles approach the intersection at the same time, extracting the entrance, exit, expected arrival time and expected passing time of each vehicle; according to the intersection conflict matrix, judging whether there is a conflict between the driving paths of each vehicle; for vehicles without conflict, allowing them to pass at the same time; for vehicles with conflict, determining the passing priority according to the expected arrival time, and the first arriving vehicle obtains high priority; in the target area occupancy management table, reserving the space-time window for the high priority vehicle to pass through the intersection, which includes the time period from the vehicle entering the intersection to leaving the intersection and the occupied intersection area; sending the passing instruction to the high priority vehicle and the parking waiting instruction to the low priority vehicle with conflict; when the high priority vehicle passes through the intersection, releasing the corresponding occupied space-time window and recalculating the priority of the remaining vehicles to allocate the passing right to the next vehicle; for vehicles that arrive at the same time and have path conflict, the priority is determined according to the preset rules: manned vehicles have priority over unmanned vehicles, and fully loaded vehicles have priority over empty vehicles. When the vehicle passes through the target area, the target area occupancy management table is updated to clear the occupancy record of the passed vehicle;

[0161] The passing state of the vehicle in the target area is continuously monitored until all vehicles complete the passing.

[0162] For manned mine cars, when the mine car receives a task, it is displayed to the driver through the man-machine interaction interface, and the driver performs the task of the current shift according to the task. The control of the cloud control platform to the mine car will be notified to the driver in the form of voice prompt. When the driver drives the mine car to approach the special area, the driver performs the passing or parking operation according to the voice prompt.

[0163] For the unmanned mine car, when the mine car receives a task, the mine car extracts the driving path of the task from the high-precision map according to the driving path information in the task, and then drives along the path, and then performs loading or unloading operation. When the unmanned mine car approaches a special area, it is automatically judged whether a passing instruction of the cloud control platform is received. If the passing instruction is received, the driving continues; if a parking instruction is received or no instruction is received, the unmanned mine car stops and waits.

[0164] The above description of the application and its embodiments is illustrative and not restrictive, and the application can be implemented in other specific forms without departing from the spirit or essential characteristics thereof. The embodiments shown in the drawings are only one of the embodiments of the application, and the actual structure is not limited thereto. Therefore, if a person skilled in the art is inspired by it, without departing from the spirit of the application, similar structural forms and embodiments can be designed without creative design, which should belong to the protection scope of the application. In addition, the inclusion of a word does not exclude other elements or steps, and a word before an element does not exclude the inclusion of multiple elements. The words first, second, etc. are used to indicate names, not any specific order.

Claims

1. A method for hybrid train scheduling in mining areas based on vehicle-road-cloud collaboration, characterized in that, include: S1, construct a global traffic map; S2 performs differentiated scheduling of mixed formations based on the global traffic map: For manned vehicles, a fixed-group scheduling method is adopted: the scheduling unit is the work shift, and the number of mining cars N and the work tasks corresponding to each loading equipment are calculated; among them, the work tasks include loading points, unloading points and round-trip routes; Dynamic grouping and scheduling are adopted for unmanned vehicles: the scheduling unit is a single task, the vehicle sequence of each loading point is counted, and the optimal loading point is selected as the target loading point based on the idle status of the loading equipment and the waiting time of the vehicles. The target loading point and the corresponding planned path are assigned to the unmanned vehicles to be scheduled. Among them, when there is an idle loading equipment, the loading equipment with the longest idle time is selected first; when all loading equipment has vehicles queuing, the loading equipment with the shortest expected waiting time is selected. The round-trip route and operation time window of the fixed formation are used as constraints; when a conflict is detected between the planned route of the unmanned vehicle and the fixed route of the manned vehicle, an alternative route is replanned for the unmanned vehicle or the corresponding task is reassigned to other loading points. S3 dispatches and controls manned and unmanned vehicles according to the task, and manages all vehicles in the target area based on the global traffic map: it sends passage instructions to vehicles that arrive at the area first, and sends stop and wait instructions to vehicles that arrive later; the target area includes: intersections and one-way lanes. S3, scheduling and controlling manned and unmanned vehicles according to the task, including: Based on the job tasks generated by the fixed formation scheduling, task information including loading point, unloading point, loading path and unloading path is sent to manned vehicles. Based on the job tasks generated by dynamic grouping and scheduling, send task information containing the target loading point and the planned path to the autonomous vehicle; Real-time monitoring of the location information of all vehicles to determine whether a vehicle is approaching the target area; the target area includes intersections and one-way lanes. When a vehicle is detected to have entered a preset range of the target area, the position, speed and direction of travel of the corresponding vehicle are extracted. Calculate the estimated time when the vehicle arrives at the entrance of the target area; for intersection areas, the entrance is the intersection boundary; for one-way lanes, both ends of the one-way lane are defined as entrances, and marked as the first entrance and the second entrance, respectively. Establish a target area occupancy management table to record the occupancy status and vehicle information for each target area; For one-way lanes, a direction locking mechanism is used for control to avoid conflicts between oncoming vehicles; For intersections, conflict detection and spatiotemporal reservation mechanisms are used for control to resolve multi-directional vehicle conflicts; Once a vehicle passes through the target area, update the target area occupancy management table and clear the occupancy record of the vehicle that has passed through. Continuously monitor the traffic status of vehicles within the target area until all vehicles have passed through.

2. The method for hybrid grouping and scheduling of mining vehicles based on vehicle-road-cloud collaboration according to claim 1, characterized in that: S1, construct a global traffic map, including: The system acquires the status information of manned vehicles, including vehicle position, speed, and attitude. In open-pit mines, location information is acquired via GPS / BeiDou positioning modules, while in underground mines, location information is acquired via UWB positioning modules. The system acquires state and perception information of the autonomous vehicle. The state information includes the vehicle's position, attitude, speed, and control information obtained through a fusion localization algorithm. The perception information includes the category, position, speed, size, and confidence level of surrounding obstacles detected by onboard sensors. Acquire information on roadside obstacles detected by roadside intelligent devices, including: obstacle type, location, speed, size, and confidence level; By integrating the status information of all manned vehicles, the status information of unmanned vehicles, the perception information of unmanned vehicles, and the information of roadside obstacles, a global traffic map is constructed. The global traffic map includes: the road network structure of the work area, the global position coordinates and speed of all vehicles, the position of vehicles in road segments, the average driving speed of each road segment, and the traffic status.

3. The method for hybrid grouping and scheduling of mining vehicles based on vehicle-road-cloud collaboration according to claim 2, characterized in that: Fixed-formation scheduling is adopted for manned vehicles, including: Obtain information on loading points, manned vehicles, and unloading points involved in the current shift's operations, and establish a matching relationship between loading and unloading points; Based on the matching relationship between loading and unloading points, and combined with the road segment connection relationship in the global traffic map, the A* algorithm is used to plan the round-trip path between the corresponding loading and unloading points with the goal of minimizing the distance. Based on historical data, obtain the loading time of each loading device for one mine car and the unloading time of each mine car at the unloading point; Based on the round-trip route, calculate the loading path distance from the unloading point to the loading point, and the unloading path distance from the loading point to the unloading point. Calculate the time it takes for a mine car to complete one work cycle based on loading time, unloading time, loading route distance, unloading route distance, average speed of mine car when unloaded and average speed of mine car when fully loaded. The number of transport cars N corresponding to each loading device is calculated by rounding up based on the ratio between the operation cycle time and the loading time. Based on the number of mining trucks N, manned vehicles are assigned to the corresponding loading equipment, and a job task containing loading point, unloading point, loading path and unloading path is generated and sent to the corresponding manned vehicle.

4. The method for hybrid grouping and scheduling of mining vehicles based on vehicle-road-cloud collaboration according to claim 2, characterized in that: Dynamic grouping and scheduling of driverless vehicles includes: Obtain information on loading points, unmanned vehicles, and unloading points involved in this shift's operations, and establish a matching relationship between loading and unloading points; The system calculates the vehicle sequence that has been dispatched to each loading point at the current moment and obtains the queuing information for each loading point; the vehicle sequence includes both manned and unmanned vehicles. Based on the location information of all vehicles in the vehicle sequence and the average speed of the road segment, calculate the arrival time of each vehicle at the loading point; combine the loading time and the time when the previous vehicle finished loading to calculate the time when the last vehicle in the vehicle sequence finished loading, which is taken as the last service time of the corresponding loading point; After the autonomous vehicles have finished unloading, the A* algorithm is used to plan the corresponding autonomous vehicles to all loading points. Based on the length of each segment and the average speed of the planned route, calculate the time when the corresponding unmanned vehicle arrives at each loading point. Calculate the difference between the last service time of each loading point and the time when the unmanned vehicle to be dispatched arrives at the corresponding loading point; When the difference is negative, it indicates that the loading equipment is idle, and the loading point with the largest absolute value of the difference is selected as the target loading point; When the difference is positive, it means that the vehicles need to queue, and the loading point with the smallest difference is selected as the target loading point; Based on the target loading point, a task containing the target loading point and the planned path is generated and sent to the unmanned vehicles to be scheduled. Once the vehicle has finished loading, the same scheduling algorithm is used to match the vehicle with an unloading point.

5. The method for hybrid train scheduling in mining areas based on vehicle-road-cloud collaboration according to claim 2, characterized in that: Replanning alternative routes for autonomous vehicles or adjusting corresponding task assignments to other loading points, including: Extract all round-trip route data of manned vehicles and construct a road segment index table; the road segment index table includes road segment identifier, road segment type and connection relationship; Initialize the road segment occupancy spatiotemporal matrix. The rows of the matrix represent road segments, the columns represent time slices, and the matrix elements record the identifiers of manned vehicles occupying the corresponding road segments. The system reads the location and speed data of manned vehicles at preset intervals, determines the road segment where the manned vehicle is located through coordinate matching, and calculates the relative position of the manned vehicle in the current road segment. Based on the current speed and remaining mileage of the manned vehicle, calculate the time t1 when the manned vehicle leaves the current road segment; traverse the subsequent road segment sequence, and calculate the time sequence [t2, t3, ..., tn] when the manned vehicle arrives at each subsequent road segment according to the length of each road segment and the average driving speed of each road segment; Convert the time series [t1, t2, t3, ..., tn] into time slice indexes, update the occupancy data of the corresponding road segment in the corresponding time slice in the road segment occupancy spatiotemporal matrix, and clear the historical occupancy records of the corresponding manned vehicles; Obtain the planned path of the autonomous vehicle, and extract the road segment sequence and the corresponding estimated travel time interval in the planned path; The expected travel time interval is converted into a time slice index set and compared element by element with the occupancy data of the corresponding road segment in the road segment occupancy spatiotemporal matrix; Based on the comparison results, alternative routes are replanned for autonomous vehicles or corresponding tasks are reassigned to other loading points.

6. The method for hybrid grouping and scheduling of mining vehicles based on vehicle-road-cloud collaboration according to claim 1, characterized in that: For one-way lanes, a direction locking mechanism is used for control to avoid collisions between oncoming vehicles, including: In the target area occupancy management table, set a direction lock sign for one-way lanes and record the currently permitted direction of passage; When vehicles are detected approaching from both ends of a single lane at the same time, the estimated arrival times of the vehicles at both ends at their respective entrances are extracted. Compare the estimated arrival times of the vehicles at both ends to determine which end will arrive first; Set the direction lock sign of the one-way lane to the direction of travel of the first arriving vehicle, send a passage instruction to vehicles in the direction of travel, and send a stop and wait instruction to vehicles in the opposite direction. Once vehicles in the direction of travel begin to enter a single lane, the direction is locked, allowing subsequent vehicles in the same direction to pass continuously until there are no waiting vehicles in that direction or the preset maximum number of vehicles that can pass continuously is reached. When a single lane is cleared and the conditions for changing direction are met, the current direction lock is released; check if there are waiting vehicles in the opposite direction. If so, switch the direction lock to the opposite direction and allow oncoming vehicles to pass.

7. The method for hybrid grouping and scheduling of mining vehicles based on vehicle-road-cloud collaboration according to claim 6, characterized in that: For intersections, conflict detection and spatiotemporal reservation mechanisms are used for management to resolve multi-directional vehicle conflicts, including: Establish an intersection conflict matrix and predefine the conflict relationships between each approach lane to exit lane based on the intersection's geometry and traffic rules. When multiple vehicles are detected approaching an intersection at the same time, extract the entrance lane, exit lane, estimated arrival time, and estimated passage time for each vehicle; Based on the intersection conflict matrix, determine whether there is a conflict between the travel paths of each vehicle; for vehicles that do not have a conflict, they are allowed to pass at the same time. For vehicles in conflict, the passage priority is determined based on the estimated arrival time, with the vehicle that arrives first receiving higher priority; In the target area occupancy management table, a time and space window is reserved for high-priority vehicles to pass through the intersection. The time and space window includes the time period from when the vehicle enters the intersection to when it leaves the intersection, as well as the intersection area it occupies. Send passage instructions to high-priority vehicles and stop and wait instructions to low-priority vehicles that are in conflict. Once a high-priority vehicle has passed through the intersection, the corresponding time and space window is released, the priority of the remaining vehicles is recalculated, and the right of way is allocated to the next vehicle. For vehicles that arrive at the same time and have conflicting routes, priority is determined according to preset rules: manned vehicles take priority over unmanned vehicles, and fully loaded vehicles take priority over empty vehicles.

8. A system based on the vehicle-road-cloud collaborative method for hybrid grouping and scheduling of mining vehicles as described in any one of claims 1 to 7.

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