Scheduling optimization method of self-driving mine car based on real-time road condition

Abstract

The invention belongs to the technical field of vehicle scheduling, and particularly relates to a scheduling optimization method of an automatic driving mine car based on a real-time road condition, which comprises the following steps of: 1, counting vehicle information, and counting the types, the quantity, the working positions and the working types of working vehicles; 2, detecting equipment is installed at the corresponding position of the vehicle according to the type of the vehicle, and vehicle motion information is collected in real time; 3, planning a vehicle running route by taking the working position as a terminal point and the vehicle as a starting point; and step 4, acquiring obstacle information on the route, introducing real-time position information of other vehicles, and carrying out obstacle avoidance driving: integrating motion information, carrying out real-time monitoring feedback, and adjusting the motion state of the vehicle in time when deviation occurs, so that the vehicle runs through a reasonable route, the energy consumption of the vehicle is reduced, and the driving safety of the vehicle is improved. And according to the vehicle energy consumption, timely return flight and shift change are carried out, orderly work of the mine car is kept, and the work progress is prevented from being affected by shutdown.

Description

Technical Field

[0001] This invention relates to the field of vehicle scheduling technology, specifically to a scheduling optimization method for autonomous mining trucks based on real-time road conditions. Background Technology

[0002] Mines include coal mines, metallic mines, non-metallic mines, building material mines, and chemical mines, among others. Mine size (also known as production capacity) is usually expressed as annual or daily output. Annual output refers to the quantity of ore produced by the mine each year. Based on output size, mines are classified into three types: large, medium, and small. The size of a mine must be commensurate with its economically viable service life; only in this way can infrastructure costs be saved and costs reduced. In the mining process, extraction operations consume the most manpower, materials, and capital, but they also have the greatest potential for reducing mining costs. The main ways to reduce extraction costs are to improve labor productivity and product quality, and to reduce material consumption.

[0003] Mining cars are narrow-gauge railway vehicles used in mines to transport bulk materials such as coal, ore, and waste rock. They are generally pulled by locomotives or winches. Mining cars are classified into five main categories according to their structure and unloading method: stationary mining cars (material cars, flatbed cars), tipper mining cars, single-sided curved rail side-discharge mining cars, bottom (side) discharge mining cars, and shuttle mining cars.

[0004] Mining cars are vehicles pulled by locomotives or winches to transport coal, gangue, equipment, and personnel. They are mainly used for underground mine roadways, shafts, and surface rail transport, and are the most widely used and numerous type of transportation equipment in coal mines.

[0005] Autonomous driving technology is developing rapidly, and driverless operation has already been achieved in specific scenarios. Due to the special and complex nature of open-pit mine operations and management, the complexity of road conditions, and the relatively fixed transportation routes, mining areas require collaborative operation and management among different types of vehicles.

[0006] Currently, the scheduling and management of autonomous vehicles involves installing monitoring equipment along the vehicle's route. However, this method can only detect the vehicle's location and route, but cannot detect the vehicle's own status and therefore cannot select a suitable autonomous driving route based on the vehicle's status. To address this, this application proposes a scheduling optimization method for autonomous mining trucks based on real-time road conditions. Summary of the Invention

[0007] The purpose of this section is to outline some aspects of the embodiments of the present invention and to briefly describe some preferred embodiments. Simplifications or omissions may be made in this section, as well as in the abstract and title of this application, to avoid obscuring the purpose of these documents; however, such simplifications or omissions should not be construed as limiting the scope of the invention.

[0008] In view of the problems existing in the above and / or existing scheduling optimization methods for autonomous mining trucks, this invention is proposed.

[0009] Therefore, the purpose of this invention is to provide a scheduling optimization method for autonomous mining trucks based on real-time road conditions. This method calculates the remaining driving mileage of the vehicles, plans vehicle shift change routes, collects obstacle information on the route while the vehicles are driving on the route, and incorporates the real-time location information of other vehicles to perform obstacle avoidance driving. It integrates motion information, performs real-time monitoring and feedback, and adjusts the vehicle's motion state in a timely manner when deviation occurs, so that the vehicles can drive through reasonable routes, reduce vehicle energy consumption, and perform timely return and shift change based on vehicle energy consumption, so as to maintain the orderly operation of mining trucks and avoid downtime affecting the work progress.

[0010] To address the aforementioned technical problems, according to one aspect of the present invention, the present invention provides the following technical solution:

[0011] A scheduling optimization method for autonomous mining trucks based on real-time road conditions includes the following steps:

[0012] Step 1: Collect vehicle information, including the type, quantity, work location, and work type of the work vehicles;

[0013] Step 2: Install detection equipment at the appropriate location on the vehicle according to its type to collect vehicle movement information in real time;

[0014] Step 3: Plan the vehicle's route, starting from the work location and ending at the vehicle itself;

[0015] Step 4: Collect obstacle information on the route and incorporate the real-time location information of other vehicles to perform obstacle avoidance maneuvers;

[0016] Step 5: Input the vehicle's remaining energy consumption, calculate the vehicle's remaining driving range, and plan the vehicle shift change route;

[0017] Step Six: Integrate motion information, conduct real-time monitoring and feedback, and adjust the vehicle's motion status promptly when deviation occurs.

[0018] As a preferred embodiment of the scheduling optimization method for autonomous mining trucks based on real-time road conditions described in this invention, in step one, the vehicle types include transport vehicles, loaders, excavators, and trailers, and the work types include transportation, shoveling, excavation, and towing.

[0019] As a preferred embodiment of the scheduling optimization method for autonomous mining trucks based on real-time road conditions described in this invention, in step two, the detection equipment includes an image acquisition probe, a vehicle positioning module, a vehicle body radar, and a speed detection module.

[0020] As a preferred embodiment of the scheduling optimization method for autonomous mining trucks based on real-time road conditions described in this invention, in step two, the detection equipment is installed at different positions on the vehicle according to different vehicle types, the image acquisition probe avoids the vehicle's movable arm, the vehicle body radar is installed at the corner of the vehicle body's circumference, and the speed detection module is installed on the wheels.

[0021] As a preferred embodiment of the scheduling optimization method for autonomous mining trucks based on real-time road conditions described in this invention, in step three, when planning the vehicle's route, wide and flat roads are prioritized, and visual detection is performed using an image acquisition probe to collect real-time environmental information during vehicle movement.

[0022] As a preferred embodiment of the scheduling optimization method for autonomous mining trucks based on real-time road conditions described in this invention, in step four, obstacles are divided into static obstacles and dynamic obstacles, and obstacles are identified by vehicle radar and image acquisition probes.

[0023] As a preferred embodiment of the scheduling optimization method for autonomous mining trucks based on real-time road conditions described in this invention, in step five, the remaining energy consumption of the vehicle is divided into fuel and electricity. Based on the real-time distance between the vehicle and the garage, the vehicle's energy consumption is taken into account to calculate the remaining energy consumption required for the vehicle's return trip in real time.

[0024] As a preferred embodiment of the scheduling optimization method for autonomous mining trucks based on real-time road conditions described in this invention, in step six, motion information is integrated based on the vehicle's autonomous driving system, in conjunction with motion information, obstacle avoidance routes, and shift change routes, to achieve vehicle scheduling and driving.

[0025] As a preferred embodiment of the scheduling optimization method for autonomous mining trucks based on real-time road conditions described in this invention, in step four, the detected obstacles are evaluated and predicted to determine their collision relationship with the unmanned vehicle. The parameters for evaluation include slope, route width, obstacle width, and obstacle height.

[0026] As a preferred embodiment of the scheduling optimization method for autonomous mining trucks based on real-time road conditions described in this invention, the fuel vehicle's range is calculated using the formula: range = remaining fuel in the tank ÷ fuel consumption.

[0027] Compared with existing technologies, this invention uses the vehicle itself as a monitoring point, setting up detection equipment on the moving vehicle to monitor its movement status in real time, plan the vehicle's working route, and calculate the remaining driving range based on the vehicle type, considering the remaining energy consumption of the vehicle, and planning the vehicle shift change route. When the vehicle is traveling on the route, it collects obstacle information on the route and incorporates the real-time location information of other vehicles to avoid obstacles. It integrates movement information and provides real-time monitoring and feedback. When deviation occurs, it promptly adjusts the vehicle's movement status to ensure the vehicle travels along a reasonable route, reducing vehicle energy consumption. Based on vehicle energy consumption, it promptly returns to base for shift change, maintaining the orderly operation of the mining trucks and avoiding downtime that could affect the work progress. Attached Figure Description

[0028] To more clearly illustrate the technical solutions of the embodiments of the present invention, the present invention will be described in detail below with reference to the accompanying drawings and detailed embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort. Wherein:

[0029] Figure 1 This is a schematic diagram of the process flow of the present invention. Detailed Implementation

[0030] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0031] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of the invention. Therefore, the invention is not limited to the specific embodiments disclosed below.

[0032] Secondly, the present invention is described in detail with reference to the schematic diagrams. When detailing the embodiments of the present invention, for ease of explanation, the cross-sectional views illustrating the device structure may be partially enlarged, not according to the usual scale. Furthermore, the schematic diagrams are merely examples and should not limit the scope of protection of the present invention. In addition, actual fabrication should include three-dimensional spatial dimensions of length, width, and depth.

[0033] To make the objectives, technical solutions, and advantages of the present invention clearer, the embodiments of the present invention will be described in further detail below with reference to the accompanying drawings.

[0034] This invention provides a scheduling optimization method for autonomous mining trucks based on real-time road conditions. It calculates the remaining driving range of each vehicle, plans shift change routes, and collects obstacle information along the route while the vehicle is traveling. This information is combined with the real-time location information of other vehicles for obstacle avoidance. The method integrates motion information and provides real-time monitoring and feedback. If deviation occurs, the method promptly adjusts the vehicle's motion state to ensure it travels along a reasonable route, reducing energy consumption. Based on energy consumption, the method performs timely return trips for shift changes, maintaining orderly operation of the mining trucks and avoiding downtime that could disrupt the work process. Please refer to [link / reference]. Figure 1 It includes the following methods and steps:

[0035] Step 1: Collect vehicle information, including the type, quantity, work location, and work type of the working vehicles. In Step 1, vehicle types include transport vehicles, loaders, excavators, and trailers, and work types include transportation, shoveling, excavation, and towing.

[0036] Because transport vehicles, loaders, excavators, and trailers consume different amounts of electricity during operation due to differences in vehicle type and load, different statistical methods are used for different types of vehicles when making vehicle statistics. For fuel-powered vehicles, fuel consumption is measured, while for electric vehicles, electricity consumption is measured.

[0037] Step 2: Install detection equipment at the corresponding location on the vehicle according to the vehicle type to collect vehicle motion information in real time; In Step 2, the detection equipment includes an image acquisition probe, a vehicle positioning module, a vehicle body radar, and a speed detection module; In Step 2, depending on the vehicle type, the detection equipment is installed at different locations on the vehicle, the image acquisition probe avoids the vehicle's movable arm, the vehicle body radar is installed at the corner of the vehicle's circumference, and the speed detection module is installed on the wheels.

[0038] Since loaders and excavators have external booms that can obstruct the view during operation, image acquisition probes need to be positioned away from the vehicle's boom. Commonly used radar and sensors include laser sensors, vision sensors, infrared sensors, and ultrasonic sensors.

[0039] Step 3: Using the work location as the endpoint and the vehicle itself as the starting point, plan the vehicle's route. In step 3, when planning the vehicle's route, prioritize wide and flat roads, and use image acquisition probes for visual detection to collect real-time environmental information during vehicle movement.

[0040] In the route planning process, GPS positioning devices are used to locate the vehicle's position, and sensors on the vehicle capture environmental information for visual detection and recognition. The route is then selected based on the visually captured environmental information.

[0041] Step 4: Collect obstacle information on the route and incorporate the real-time location information of other vehicles to perform obstacle avoidance driving; in Step 4, obstacles are divided into static obstacles and dynamic obstacles, which are identified by vehicle radar and image acquisition probes; in Step 4, the detected obstacles are rated and predicted to determine the collision relationship with the autonomous vehicle. The parameters for rating include slope, route width, obstacle width, and obstacle height;

[0042] When avoiding obstacles, a personalized learning system is used to analyze the captured obstacles and determine their mass using the vehicle's radar. The system identifies and classifies obstacles, such as lightweight obstacles like foam, sponge, and plastic, which can be driven over directly, while heavy obstacles like metal, stone, and wood require obstacle avoidance.

[0043] Step 5: Input the vehicle's remaining energy consumption, calculate the vehicle's remaining driving range, and plan the vehicle shift change route; In Step 5, the vehicle's remaining energy consumption is divided into fuel and electricity. Based on the real-time distance between the vehicle and the garage, input the vehicle's energy consumption and calculate the vehicle's real-time required return energy consumption.

[0044] Among them, the range calculation of fuel vehicles adopts the formula method: range = remaining fuel in the tank ÷ fuel consumption. The range calculation of electric vehicles is relatively complicated. Range (km) = rated battery capacity (Ah) × system efficiency (0.8~0.9) × voltage (V) ÷ motor power (KW), which involves battery energy, power consumption, and driving conditions, and reserves sufficient power for return trips and shift changes.

[0045] Step Six: Integrate motion information and provide real-time monitoring and feedback. When deviation occurs, adjust the vehicle's motion status in a timely manner. In Step Six, the integration of motion information is based on the vehicle's autonomous driving system, combined with motion information, obstacle avoidance routes, and shift change routes to achieve vehicle dispatching and driving.

[0046] In practical use, detection equipment is installed at appropriate locations on the vehicle, depending on the vehicle type, to collect vehicle movement information in real time and perform motion detection. Unlike installing monitoring equipment on the route, installing detection equipment on the vehicle allows for accurate analysis and identification of its own operating status. The vehicle's route is planned with the working position as the endpoint and the vehicle itself as the starting point. When planning the route, wide and flat roads are prioritized, and visual detection is performed using image acquisition probes to collect real-time environmental information during vehicle movement, enabling the vehicle to drive automatically along the route. During movement, obstacle information along the route is collected and incorporated into the system. The system monitors the real-time location of other vehicles to guide obstacle avoidance. During obstacle avoidance, obstacles are identified and categorized. Lightweight obstacles such as foam, sponge, and plastic can be directly driven over, while heavy obstacles such as metal, stone, and wood require obstacle avoidance. Furthermore, the system incorporates the remaining energy consumption of each vehicle. Different vehicle types and loads result in varying actual energy consumption during operation. The system calculates the remaining mileage of each vehicle, plans shift change routes, and ensures vehicles travel along reasonable routes to reduce energy consumption. Based on energy consumption, timely return trips and shift changes are implemented to maintain orderly operation of the mining trucks and prevent downtime from affecting the work progress.

[0047] Although the present invention has been described above with reference to embodiments, various modifications can be made and components can be replaced with equivalents without departing from the scope of the invention. In particular, as long as there is no structural conflict, the features in the disclosed embodiments can be combined with each other in any manner. The lack of an exhaustive description of these combinations in this specification is merely for the sake of brevity and resource conservation. Therefore, the present invention is not limited to the specific embodiments disclosed herein, but includes all technical solutions falling within the scope of the claims.

Claims

1. A scheduling optimization method for an automatic driving mine car based on real-time road conditions, characterized in that: The method comprises the following steps: Step 1: Collect vehicle information, including the types, quantity, working location and working type of working vehicles; Step 2: Install detection equipment at the corresponding position of the vehicle according to the vehicle type to collect vehicle movement information in real time; Step 3: Plan the vehicle route with the work location as the end point and the vehicle itself as the starting point; Step 4: Collect obstacle information on the route and bring in the real-time location information of other vehicles to avoid obstacles; Step 5: Input the remaining energy consumption of the vehicle, calculate the remaining mileage of the vehicle, and plan the vehicle shift route; Step 6: Integrate motion information, conduct real-time monitoring and feedback, and adjust the vehicle's motion status in a timely manner when deviation occurs.

2. The scheduling optimization method of an automatic driving mine car based on real-time road conditions according to claim 1 is characterized in that: In the step 1, the vehicle types include transport trucks, forklifts, excavators and trailers, and the work types include transportation, shoveling, excavation and towing.

3. The scheduling optimization method of an automatic driving mine car based on real-time road conditions according to claim 2 is characterized in that: In the step 2, the detection equipment includes an image acquisition probe, a vehicle positioning module, a vehicle body radar and a speed detection module.

4. The scheduling optimization method of an automatic driving mine car based on real-time road conditions according to claim 3 is characterized in that: In the step 2, the detection equipment is installed at different positions of the vehicle according to different vehicle types, the image acquisition probe avoids the vehicle movable arm, the body radar is installed at the corner position of the body circumference, and the speed detection module is installed on the wheel.

5. The method for optimizing the scheduling of an automatic driving mine car based on real-time road conditions according to claim 4 is characterized in that: In the step three, when planning the vehicle's route, priority is given to wide and flat roads, and visual detection is performed through an image acquisition probe to collect real-time environmental information when the vehicle is moving.

6. The method for optimizing the scheduling of an automatic driving mine car based on real-time road conditions according to claim 5, characterized in that: In the step 4, obstacles are divided into static obstacles and dynamic obstacles, and obstacles are collected and identified by the vehicle body radar and image acquisition probe.

7. The scheduling optimization method of an automatic driving mine car based on real-time road conditions according to claim 1 is characterized in that: In step five, the remaining energy consumption of the vehicle is divided into fuel and electricity. The vehicle energy consumption is taken into account according to the real-time distance between the vehicle and the garage to calculate the remaining energy consumption required by the vehicle for returning in real time.

8. The method for optimizing the scheduling of an automatic driving mine car based on real-time road conditions according to claim 1, characterized in that: In step six, the integrated motion information is based on the vehicle's automatic driving system, and the motion information, obstacle avoidance route and shift change route are coordinated to achieve vehicle scheduling.

9. The method for optimizing the scheduling of an automatic driving mine car based on real-time road conditions according to claim 1, characterized in that: In the step 4, the detected obstacles are rated and predicted for likelihood, and the collision relationship with the unmanned vehicle is determined. During the rating, the parameters include slope, route width, obstacle width, and obstacle height.

10. The method for optimizing the scheduling of an automatic driving mine car based on real-time road conditions according to claim 1, characterized in that: The fuel vehicle's cruising range is calculated using the formula: cruising range = remaining fuel in the tank / fuel consumption.

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