Data processing method and system applied to unmanned driving in mining area

Abstract

The invention discloses a data processing method and system applied to unmanned driving in a mining area, and a scheduling device can generate a first control instruction used for indicating a target vehicle to drive to a target area based on a current target scheduling task, the vehicle feature data of the vehicle in the mining area, and the environmental feature information of the mining area. And the second control instruction is used for controlling the target vehicle to execute the operation corresponding to the target scheduling task, so that the vehicle can realize unmanned and automatic operation based on the control instructions, and the running accuracy and the running safety of the unmanned vehicle in the mining area are ensured. And the actual mining area operation requirements are met.

Description

Technical Field

[0001] The present application relates to the field of unmanned driving technology, and in particular to a data processing method, related equipment and system applied to unmanned driving in mining areas. Background Art

[0002] Mine transportation requires experienced personnel to operate. The working environment in the mine is relatively harsh and there are certain safety hazards. Therefore, it is difficult to recruit professional staff. The application of unmanned vehicles in the mining industry has improved production efficiency, reduced production costs, and improved the harsh working environment in the mine.

[0003] However, due to the complex environment of mining areas and the large number of vehicles for different operations, the current unmanned driving scheduling methods are relatively simple and the degree of autonomous driving of mining vehicles is low, which cannot meet the actual application needs of unmanned driving in mining areas and reduces the accuracy of unmanned driving data processing. Summary of the invention

[0004] In order to achieve the above objectives, this application provides the following technical solutions:

[0005] A data processing method for unmanned driving in a mining area, applied to dispatching equipment, the method comprising:

[0006] In response to the target scheduling task, vehicle characteristic data of each vehicle in the mining area is obtained;

[0007] Determine the mining area environmental characteristic information corresponding to the target retrieval task;

[0008] Determining a target vehicle based on the vehicle characteristic data and the mining area environment characteristic information;

[0009] Based on the vehicle characteristic data of the target vehicle and the mining area environmental characteristic information, a first control instruction is generated, so that the target vehicle drives to a target area corresponding to the target scheduling task based on the first control instruction;

[0010] Based on the task characteristics of the target scheduling task, a second control instruction is generated so that the target vehicle performs a working operation corresponding to the target scheduling task in the target area based on the second control instruction.

[0011] Optionally, the determining of the mining area environmental characteristic information related to the target scheduling task includes:

[0012] Obtaining mining area map data of the operation area corresponding to the target scheduling task;

[0013] Obtaining real-time environmental data corresponding to the operation area;

[0014] Based on the mining area map data and the real-time environmental data, mining area environmental characteristic information corresponding to the target scheduling task is determined.

[0015] Optionally, the vehicle characteristic data and the mining area environment characteristic information are used to determine the target vehicle, including:

[0016] Based on the vehicle characteristic data, determining the current position information and vehicle operation status information of each vehicle;

[0017] Determine the driving path information of each vehicle based on the mining area environmental characteristic information and the current position information of each vehicle;

[0018] The target vehicle is determined based on the driving path information of each vehicle and the operating status information of each vehicle.

[0019] Optionally, the generating a first control instruction based on the vehicle characteristic data of the target vehicle and the mining area environment characteristic information includes:

[0020] Determining target driving parameters of the target vehicle based on the vehicle characteristic data of the target vehicle;

[0021] Based on the mining area environmental characteristic information, determining the driving path and obstacle avoidance information corresponding to the target vehicle;

[0022] A first control instruction for the target vehicle is generated according to the target driving parameter, the driving path, and the obstacle avoidance information.

[0023] Optionally, the generating a second control instruction based on the task feature of the target scheduling task includes:

[0024] Parsing the target scheduling task to obtain task content and a collaborative task vehicle corresponding to the task content;

[0025] Determining the operational characteristics of the collaborative task vehicle;

[0026] Based on the task content and the operating characteristics of the collaborative task vehicle, a second control instruction for the target vehicle is generated.

[0027] Optionally, the method further comprises:

[0028] Monitor the operating status of each object in the mining area and obtain monitoring information corresponding to each object, wherein the objects include at least vehicles and mining equipment;

[0029] Collect statistics on the operation data of each vehicle in the mining area and obtain statistical data of each vehicle;

[0030] Recording the operation data of each vehicle so as to enable data tracing based on the operation data of each vehicle;

[0031] The communication data is encrypted to obtain encrypted data, wherein the communication data at least includes the communication data between the dispatching device and the vehicle.

[0032] A data processing method for unmanned driving in a mining area, applied to a vehicle, comprising:

[0033] receiving a first control instruction sent by the scheduling device, and driving to a target area corresponding to a target scheduling task based on the first control instruction;

[0034] Receive a second control instruction sent by the scheduling device, and execute a job operation corresponding to the target scheduling task based on the second control instruction.

[0035] Optionally, the driving to a target area corresponding to a target scheduling task based on the first control instruction includes:

[0036] Parsing the first control instruction to obtain operating parameters and a driving path, so as to control the vehicle to travel along the driving path based on the operating parameters;

[0037] In response to the vehicle traveling along the driving path, collecting real-time environmental information on the driving path;

[0038] Based on the real-time environmental information, the driving path and the operating parameters are updated, wherein the updating of the driving path includes updating the driving path based on obstacles in the real-time environment; and the updating of the operating parameters includes updating the operating parameters based on the driving parameters of an associated vehicle corresponding to the vehicle.

[0039] Optionally, the performing a job operation corresponding to the target scheduling task based on the second control instruction includes:

[0040] Parsing the second control instruction to obtain the type of job to be executed;

[0041] Determining operation data based on the type of operation to be performed, information of the collaborative operation vehicle, and the operation environment;

[0042] A work operation corresponding to the second control instruction is performed based on the work operation data.

[0043] A data processing system for unmanned driving in mining areas, comprising:

[0044] dispatch equipment, vehicles, and manage equipment;

[0045] Wherein, the dispatching device is used to execute the data processing method applied to unmanned driving in a mining area as described in any one of the above-mentioned calling devices;

[0046] The vehicle is used to execute the data processing method for unmanned driving in a mining area as described in any one of the above-mentioned methods for vehicles;

[0047] The management device is used to manage and encrypt the communication data of the data processing system for unmanned driving in the mining area; and to manage each vehicle in the collaborative operation.

[0048] Compared with the prior art, the present application provides a data processing method and system for unmanned driving in mining areas. The scheduling equipment can generate a first control instruction for instructing the target vehicle to travel to the target area based on the current target scheduling task, the vehicle characteristic data of the mining area vehicle and the mining area environmental characteristic information, and a second control instruction for controlling the target vehicle to perform the operating operation corresponding to the target scheduling task, so that the vehicle can realize unmanned driving and automatic operating operation based on these control instructions, ensuring the operating accuracy and driving safety of the unmanned vehicles in the mining area, and meeting the actual mining area operation needs. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying any creative work.

[0050] Figure 1 A flowchart of a data processing method for unmanned driving in a mine area applied to dispatching equipment provided in an embodiment of the present application;

[0051] Figure 2 A flowchart of a data processing method for unmanned driving of a vehicle provided in an embodiment of the present application;

[0052] Figure 3 A schematic diagram of a mine production management system in an application scenario provided in an embodiment of the present application;

[0053] Figure 4 A schematic diagram of equipment arrangement for cooperative operation of an excavator provided in an embodiment of the present application;

[0054] Figure 5 A schematic diagram of a collaborative operation process provided in an embodiment of the present application. DETAILED DESCRIPTION

[0055] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0056] The terms "first" and "second" in this application are used to distinguish different objects, rather than to describe a specific order. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes a series of steps or units is not limited to the listed steps or units, but may include steps or units that are not listed.

[0057] In an embodiment of the present application, a data processing method for unmanned driving in mining areas is provided. The method is applied to the unmanned driving scenario of mining vehicles and can include the main processes of vehicle scheduling, control, and collaborative operation with other vehicles, thereby improving the efficiency and accuracy of unmanned vehicle control.

[0058] See also Figure 1 , is a flow chart of a data processing method for unmanned driving in a mine area applied to a dispatching device provided in an embodiment of the present application. The method may include the following steps:

[0059] S101. In response to a target scheduling task, obtain vehicle characteristic data of each vehicle in a mining area.

[0060] The target scheduling task refers to the task that needs to be performed by an unmanned vehicle at present, such as loading and unloading of vehicles. The various vehicles in the mining area include vehicles that can be dispatched in the current mining area, and may also include vehicles that are currently performing corresponding tasks. Vehicle characteristic data includes vehicle location, vehicle driving characteristic data, vehicle energy consumption data, etc. By analyzing the vehicle characteristic data of the vehicles that can be dispatched in the mining area, candidate vehicles that can perform the current target scheduling task can be determined; by analyzing the vehicle characteristic data of the vehicles that are currently performing the corresponding tasks, it can be used for subsequent route planning processing to ensure that it does not affect the driving of the vehicles that are currently performing the tasks.

[0061] S102. Determine the mining area environmental characteristic information corresponding to the target scheduling task.

[0062] According to the task area corresponding to the target scheduling task, the mining area environmental characteristic information that can reach the task area is determined. The mining area environmental characteristic information may include route information, obstacle information, and relevant information of existing running vehicles in the current mining area, so that a more accurate driving path can be determined based on the current environment during route planning.

[0063] In one implementation of the present application, determining the mining area environmental characteristic information corresponding to the target scheduling task includes: obtaining mining area map data of the operating area corresponding to the target scheduling task; obtaining real-time environmental data corresponding to the operating area; and determining the mining area environmental characteristic information corresponding to the target scheduling task based on the mining area map data and the real-time environmental data. In this implementation, preliminary information of the current geographical environment can be obtained based on the mining area map data, and then the mining area environmental characteristic information can be determined based on the real-time environmental data and the preliminary information determined based on the map. Furthermore, the mining area environmental characteristic information can also be determined based on the real-time tracking information of the vehicle to meet the subsequent processing requirements of the path of the unmanned vehicle.

[0064] S103: Determine the target vehicle based on the vehicle characteristic data and the mining area environment characteristic information.

[0065] Through the vehicle characteristic data, the candidate unmanned vehicles that can meet the current target scheduling task can be analyzed. For example, the candidate unmanned vehicles can be determined based on the loading capacity, fuel consumption data, and the remaining fuel in the tank of each vehicle. Through the mining area environmental characteristic information, the optimal path from each candidate unmanned vehicle to the target scheduling task execution area can be analyzed, and then the vehicle with the shortest path can be selected as the target vehicle. Correspondingly, the path information of each dispatchable vehicle can be determined based on the mining area environmental characteristic information, and then the target vehicle can be determined based on the driving characteristics and energy consumption data of each vehicle.

[0066] In one implementation of the present application, the target vehicle is determined based on vehicle characteristic data and mining environment characteristic information, including: determining the current position information and vehicle operating status information of each vehicle based on the vehicle characteristic data; determining the driving path information of each vehicle based on the mining environment characteristic information and the current position information of each vehicle; and determining the target vehicle based on the driving path information and the operating status information of each vehicle. In this implementation, the current position and operating status of the vehicle are determined based on the vehicle characteristic data, and the unmanned vehicle closest to the target scheduling task can be determined as a candidate vehicle, and then the target vehicle is determined based on the operating status information and the driving path information. In combination with the driving path and the operating status, the most qualified vehicle can be determined from different dimensions. Specifically, the path with the least obstacles in the driving path can also be selected as the optimal path, and the vehicle corresponding to the optimal path can be selected as the target vehicle.

[0067] S104: Generate a first control instruction based on the vehicle characteristic data of the target vehicle and the mining area environment characteristic information.

[0068] S105: Generate a second control instruction based on the task characteristics of the target scheduling task.

[0069] The target vehicle can travel to the target area corresponding to the target scheduling task based on the first control instruction. When the target vehicle reaches the target area, the target vehicle can perform the operation corresponding to the target scheduling task based on the second control instruction.

[0070] Specifically, the first control instruction is a related instruction for controlling the driving of the target vehicle, such as controlling the driving path of the target vehicle, and controlling the target vehicle to drive according to corresponding operating parameters, such as controlling the target vehicle to drive to the target area at a specified speed and path. The second control instruction is an instruction for controlling the operation of the target vehicle, such as controlling the loading and unloading operation of the target vehicle, and coordinating with other loading and unloading vehicles.

[0071] In one implementation of the embodiment of the present application, the generation of the first control instruction based on the vehicle characteristic data of the target vehicle and the mining environment characteristic information includes: determining the target driving parameters of the target vehicle based on the vehicle characteristic data of the target vehicle; determining the driving path and obstacle avoidance information corresponding to the target vehicle based on the mining environment characteristic information; generating the first control instruction for the target vehicle according to the target driving parameters, the driving path and the obstacle avoidance information. Correspondingly, the generation of the second control instruction based on the task characteristics of the target scheduling task includes: parsing the target scheduling task to obtain the task content and the collaborative task vehicle corresponding to the task content; determining the operating characteristics of the collaborative task vehicle; generating the second control instruction for the target vehicle based on the task content and the operating characteristics of the collaborative task vehicle.

[0072] In the embodiment of the present application, during the execution of the data processing method, the scheduling device can also monitor the operating status of each object in the mining area to obtain monitoring information corresponding to each object, wherein the objects include at least vehicles and mining equipment; collect statistics on the operating data of each vehicle in the mining area to obtain statistical data of each vehicle; record the operating data of each vehicle to enable data tracing based on the operating data of each vehicle; encrypt the communication data to obtain encrypted data, wherein the communication data includes at least the communication data between the scheduling device and the vehicle.

[0073] It should be noted that the above functions can be executed directly through the scheduling device, and further can also reduce the occupation of data processing resources of the scheduling device. The above functions can be executed through other devices in the unmanned driving system, such as data monitoring equipment, data statistics equipment or data storage equipment in the unmanned driving system.

[0074] In another implementation of the embodiment of the present application, a data processing method for unmanned driving of a vehicle is also provided. Figure 2 , the method may include the following steps:

[0075] S201, receiving a first control instruction sent by a scheduling device, and driving to a target area corresponding to a target scheduling task based on the first control instruction;

[0076] S202: Receive a second control instruction sent by the scheduling device, and execute a job operation corresponding to the target scheduling task based on the second control instruction.

[0077] In one implementation of the embodiment of the present application, the driving to the target area corresponding to the target scheduling task based on the first control instruction includes: parsing the first control instruction to obtain operating parameters and driving path, so that the vehicle is controlled to drive according to the driving path based on the operating parameters; in response to the vehicle driving according to the driving path, collecting real-time environmental information in the driving path; based on the real-time environmental information, updating the driving path and the operating parameters, wherein the updating of the driving path includes updating the driving path based on obstacles in the real-time environment; updating the operating parameters includes updating the operating parameters based on the driving parameters of the associated vehicle corresponding to the vehicle. In this implementation, after the target vehicle receives the first instruction, it can drive according to the path and operating parameters corresponding to the first instruction, and during the driving process, it can also adjust the relevant parameters according to the operating parameters of the associated vehicle (such as the front vehicle) to achieve safe vehicle following processing, and can also avoid risks according to the real-time obstacle status, and adjust the path to ensure that the vehicle drives accurately and efficiently during the unmanned driving process.

[0078] Correspondingly, the execution of the operation corresponding to the target scheduling task based on the second control instruction includes: parsing the second control instruction to obtain the type of operation to be executed; determining the operation data based on the type of operation to be executed, the collaborative operation vehicle information and the operation environment; and executing the operation corresponding to the second control instruction based on the operation data. In this embodiment, the collaborative operation can be realized according to the second control instruction, and the safest operation parameters can be determined based on the current operation environment to complete the corresponding operation.

[0079] In order to ensure the accurate operation of unmanned vehicles in the unmanned driving system in the mine area, the unmanned driving system may also include a management device, which can manage and encrypt the communication data of the data processing system of the mine area unmanned driving application; manage each vehicle in the collaborative operation. Furthermore, the management device can also realize data management and analysis, emergency state processing, etc.

[0080] It should be noted that in the embodiment of the present application, the dispatching device, the management device and the unmanned vehicle can be connected to each other based on the 5G communication network, which can greatly improve the bandwidth, rate and reduce the delay. The 5G network plays a key role as a communication bridge for quickly applying the calculation results of the simulation model to the actual production operation.

[0081] Specifically, in the embodiment of the present application, mining area vehicles mainly refer to unmanned vehicles in mining areas, which may include mining trucks, excavators (referred to as excavators), loaders, etc. Under 5G network conditions, by modifying the existing mining trucks (referred to as mining trucks) with wire control, deploying the automatic driving system, and deploying the collaborative operation system for the excavator, the mining, transportation, and rowing of mining areas with collaborative operation of excavators and trucks, unmanned transportation of mining trucks, and automatic unloading can be realized. Through the intelligent cluster scheduling platform management of the dispatching equipment, it can be seamlessly connected with the complex production operations in the mining area, and at the same time has the function of remote remote control and takeover of the vehicle. The unmanned mining truck receives the planned path data from the cluster control center corresponding to the dispatching equipment, and can autonomously drive to the designated location to perform mining operations. And according to the received maximum vehicle speed, maximum speed, maximum throttle opening and other information, the vehicle fuel economy can be improved to the best level. Unmanned mining trucks and semi-autonomous excavators can perform intelligent digging card collaborative operations, complete the loading of earthwork, ore and other materials more efficiently and smoothly, and start and leave the loading point autonomously, and enter the lane along the planned path. The unmanned mining truck has an advanced intelligent autonomous obstacle avoidance function. The intelligent perception system can accurately identify the size and type of obstacles, so as to take the best obstacle avoidance path and pass safely at low speed. The multi-sensor fusion method can operate normally at night and in rainy and snowy weather. In the embodiment of the present application, the dispatching equipment can also monitor the operation of the vehicle to ensure the safety of the unmanned driving system. For example, when an unmanned mining truck fails, the fault information can be fed back to the fleet control center corresponding to the dispatching equipment in a timely manner. The technicians complete the manual takeover of the faulty vehicle through remote operation to eliminate safety hazards.

[0082] The data processing system applied to unmanned driving in mining areas, referred to as unmanned driving system, includes at least dispatching equipment, vehicles and management equipment, among which the vehicles are unmanned vehicles, and the communication network is mainly based on 5G communication network. Make full use of relevant technologies in the fields of smart mines, autonomous driving, and 5G+V2X network communication technology to ensure that the system can be easily upgraded and expanded without replacing existing facilities and losing initial investment, so as to maximize the overall life cycle of the system and ensure that the system can play a long-term and stable production role. In this system, data security can be ensured by encrypting data during data transmission and performing identity authentication. Specifically, make full use of the functions provided by the software security assurance module to ensure the security of software access, hardware use, vehicle driving, collaborative operations and other parts of the entire system. Make full use of identity recognition and authentication, access control and decision-making, information encryption and permissions to ensure comprehensive and systematic security mechanisms and measures. When performing dispatching tasks, the dispatching equipment can also design and develop software systems based on specific needs and actual work to ensure a practical, efficient and convenient user experience of the system. Make full use of existing resources and coordinate construction activities of different departments. The unmanned driving system can also be optimized, maintained and adjusted according to real-time needs and development requirements. The system can use open and standardized technologies to build a parallel smart mining system, ensuring that the system is easy to expand and enrich business processes. It also fully considers the integration function with the original system, and can safely open API system calls or integrate third-party APIs. It fully considers future development and migration, and has good system flexibility. For details, see Figure 3 , which is a schematic diagram of a mine production management system in an application scenario provided by an embodiment of the present application, wherein the central end includes a dispatching device, which can specifically realize unmanned intelligent dispatching and supervision, and intelligent driving-vehicle-road system services. Specifically, the dispatching device can correspond to intelligent transportation dispatching, equipment detection safety management, manned vehicle anti-collision warning, data analysis management, simulation platform and emergency takeover platform; the network end includes 4G / 5G / Mesh / V2X communication, satellite navigation, positioning system, etc., through which the positioning of the vehicle and the communication connection between various devices are realized, and further data interaction and command transmission can be realized. The equipment end mainly includes unmanned vehicles, such as semi-autonomous systems of excavators / electric shovels, unmanned vehicles, vehicle-road cooperative equipment, semi-autonomous systems of bulldozers / loaders, etc.

[0083] Under the 5G network conditions, by modifying the existing mining trucks with wire control, deploying the automatic driving system, and deploying the collaborative operation system for the excavator, the full process automation of mining, transportation, and discharge in the mine area, such as the collaborative operation of the excavator and the truck, the unmanned transportation of the mining truck, and the automatic unloading, can be realized. Through the management of the intelligent cluster scheduling platform corresponding to the scheduling equipment, it can be seamlessly connected with the complex production operations in the mining area, and at the same time has the function of remote remote control and takeover of the vehicle. The unmanned mining truck remote driving system is a human-vehicle collaborative driving system composed of unmanned mining trucks, 5G networks, intelligent scheduling and management systems, remote monitoring and emergency takeover systems, intelligent cockpits, and large-screen monitoring. Among them, positioning data, vehicle perception data, and control data all need to be transmitted through the 5G wireless communication network; in order to meet the system's strict requirements for communication indicators such as latency, rate, and reliability, the 5G network is the best choice to meet such communication requirements. The characteristics of 5G low latency, large bandwidth, and high reliability can be used to achieve real-time monitoring and remote control of vehicles, and improve the safety of unmanned mining truck operation. At the same time, we will combine the application of industrial Internet in mines, deploy MEC resource pool, and sink it to the mining area through local UPF, which can greatly shorten the network access delay and provide more powerful and reliable network protection for autonomous driving and remote driving.

[0084] Specifically, the cloud-based intelligent dispatching and management system corresponding to the dispatching equipment can communicate with the unmanned mining vehicles and the collaborative operation system, support the 5G network communication protocol, and can complete the intelligent dispatching of mining area transportation, equipment monitoring, data management and analysis management, and terrain data management functions through uninterrupted calculation, planning, dispatching, and management. See Table 1, which shows the main functional description of the dispatching equipment.

[0085] Table 1

[0086]

[0087]

[0088] The unmanned vehicle has the function of automatic driving (uninterrupted cycle operation in the loading / unloading area), can receive cloud-based intelligent scheduling or manual scheduling tasks at any time, and can optimize the path according to its own status and road conditions. Specifically, see Table 2, which shows the main functions of the unmanned vehicle (such as an unmanned mining truck, excavator, etc.) in the embodiment of this application.

[0089] Table 2

[0090]

[0091]

[0092] The management device in the embodiment of the present application can realize remote monitoring and emergency takeover, and can also realize the coordinated management of vehicles. Remote monitoring and emergency takeover is a centralized solution for remote operation, daily monitoring and emergency takeover in mines. It can take over the vehicle with one click in an emergency, and the vehicle is controlled by the driver at the remote control end, which is the safety redundancy of unmanned mine trucks. See Table 3 for a description of the main functions of remote monitoring and emergency takeover in the embodiment of the present application.

[0093] Table 3

[0094]

[0095]

[0096] See also Figure 4 , which is a schematic diagram of the equipment layout for the collaborative operation of an excavator provided in an embodiment of the present application. For example, each excavator will be paired with 4 mining trucks to complete the excavation and mining tasks by loading materials in an orderly manner. The independently developed collaborative loading subsystem can efficiently, safely and controllably dispatch mining trucks and excavators to cooperate with each other and complete mining tasks. The excavator is equipped with core components such as controllers and dynamic angle sensors to achieve bucket positioning and output of loading point positions. The loading point can be randomly adjusted according to changes in the working position. After the unmanned vehicle reaches the loading area and obtains the loading point coordinate instructions, it can reverse into position and reach the designated position.

[0097] See also Figure 5 , is a schematic diagram of a collaborative operation process provided by an embodiment of the present application. Specifically, the relevant functions in the collaborative operation are shown in Table 4.

[0098] Table 4

[0099]

[0100]

[0101] In an embodiment of the present application, the scheduling device can generate a first control instruction for instructing the target vehicle to travel to the target area, and a second control instruction for controlling the target vehicle to perform the operating operation corresponding to the target scheduling task based on the current target scheduling task, the vehicle characteristic data of the mining vehicle and the mining environment characteristic information, thereby enabling the vehicle to achieve unmanned driving and automatic operating operations based on these control instructions, ensuring the operating accuracy and driving safety of unmanned vehicles in the mining area, and meeting the actual mining operation needs.

[0102] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

[0103] In this specification, each embodiment is described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the embodiments can be referred to each other. For the device disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant parts can be referred to the method part.

[0104] The above description of the disclosed embodiments enables those skilled in the art to implement or use the present application. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application will not be limited to the embodiments shown herein, but will conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A data processing method for unmanned driving in mining areas, characterized in that: Applied to a scheduling device, the method comprises: In response to the target scheduling task, vehicle characteristic data of each vehicle in the mining area is obtained; Determine the mining area environmental characteristic information corresponding to the target retrieval task; Determining a target vehicle based on the vehicle characteristic data and the mining area environment characteristic information; Based on the vehicle characteristic data of the target vehicle and the mining area environmental characteristic information, a first control instruction is generated, so that the target vehicle drives to a target area corresponding to the target scheduling task based on the first control instruction; Based on the task characteristics of the target scheduling task, a second control instruction is generated so that the target vehicle performs a working operation corresponding to the target scheduling task in the target area based on the second control instruction.

2. The method according to claim 1, characterized in that The determination of the mining area environmental characteristic information related to the target scheduling task includes: Obtaining mining area map data of the operation area corresponding to the target scheduling task; Obtaining real-time environmental data corresponding to the operation area; Based on the mining area map data and the real-time environmental data, mining area environmental characteristic information corresponding to the target scheduling task is determined.

3. The method according to claim 1, characterized in that The vehicle characteristic data and the mining area environment characteristic information are used to determine the target vehicle, including: Based on the vehicle characteristic data, determining the current position information and vehicle operation status information of each vehicle; Determine the driving path information of each vehicle based on the mining area environmental characteristic information and the current position information of each vehicle; The target vehicle is determined based on the driving path information of each vehicle and the operating status information of each vehicle.

4. The method according to claim 1, characterized in that: The generating of the first control instruction based on the vehicle characteristic data of the target vehicle and the mining area environment characteristic information comprises: Determining target driving parameters of the target vehicle based on the vehicle characteristic data of the target vehicle; Based on the mining area environmental characteristic information, determining the driving path and obstacle avoidance information corresponding to the target vehicle; A first control instruction for the target vehicle is generated according to the target driving parameter, the driving path, and the obstacle avoidance information.

5. The method according to claim 1, characterized in that The generating a second control instruction based on the task feature of the target scheduling task includes: Parsing the target scheduling task to obtain task content and a collaborative task vehicle corresponding to the task content; Determining the operational characteristics of the collaborative task vehicle; Based on the task content and the operating characteristics of the collaborative task vehicle, a second control instruction for the target vehicle is generated.

6. The method according to claim 1, characterized in that The method further comprises: Monitor the operating status of each object in the mining area and obtain monitoring information corresponding to each object, wherein the objects include at least vehicles and mining equipment; Collect statistics on the operation data of each vehicle in the mining area and obtain statistical data of each vehicle; Recording the operation data of each vehicle so as to enable data tracing based on the operation data of each vehicle; The communication data is encrypted to obtain encrypted data, wherein the communication data at least includes the communication data between the dispatching device and the vehicle.

7. A data processing method for unmanned driving in mining areas, characterized in that: Applied to a vehicle, the method comprises: receiving a first control instruction sent by the scheduling device, and driving to a target area corresponding to a target scheduling task based on the first control instruction; Receive a second control instruction sent by the scheduling device, and execute a job operation corresponding to the target scheduling task based on the second control instruction.

8. The method according to claim 7, characterized in that The driving to a target area corresponding to a target scheduling task based on the first control instruction includes: Parsing the first control instruction to obtain operating parameters and a driving path, so as to control the vehicle to travel along the driving path based on the operating parameters; In response to the vehicle traveling along the driving path, collecting real-time environmental information on the driving path; Based on the real-time environmental information, the driving path and the operating parameters are updated, wherein the updating of the driving path includes updating the driving path based on obstacles in the real-time environment; and the updating of the operating parameters includes updating the operating parameters based on the driving parameters of an associated vehicle corresponding to the vehicle.

9. The method according to claim 7, characterized in that: The performing of the operation corresponding to the target scheduling task based on the second control instruction includes: Parsing the second control instruction to obtain the type of job to be executed; Determining operation data based on the type of operation to be performed, information of the collaborative operation vehicle, and the operation environment; A work operation corresponding to the second control instruction is performed based on the work operation data.

10. A data processing system for unmanned driving in mining areas, characterized in that: include: dispatch equipment, vehicles and manage equipment; Wherein, the scheduling device is used to execute the data processing method applied to unmanned driving in a mining area according to any one of claims 1 to 6; The vehicle is used to execute the data processing method for unmanned driving in a mining area as described in any one of claims 7 to 9; The management device is used to manage and encrypt the communication data of the data processing system for unmanned driving in the mining area; Manage the vehicles working together.

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