Loader parking planning method and system based on efficiency and energy consumption

Abstract

The invention discloses a loader parking planning method and system based on efficiency and energy consumption, and the method comprises the steps: building a map in an operation region based on space positioning data and operation environment perception data, and recognizing the position coordinates of a truck, a loader and a material pile; based on the position coordinates of the truck, the loader and the material pile, the comprehensive efficiency of each operation mode is evaluated, and the operation mode with the highest comprehensive efficiency is selected for operation; vehicle state data, environment data, mechanical parameters and energy consumption auxiliary data of the loader and the truck are obtained, dynamic energy consumption models are constructed respectively, and loader energy consumption and truck energy consumption are obtained respectively; whether the truck moves or not is judged based on preset conditions, if yes, the path of the truck and the loader is jointly optimized, and if not, the loader runs according to the reference track of the current operation mode. The current optimal operation mode is selected based on the comprehensive efficiency, whether the truck actively cooperates with the loader or not is judged from the angle of energy consumption, the operation efficiency is improved, and the energy consumption is reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of loaders, and in particular to a loader parking planning method and system based on efficiency and energy consumption. Background Art

[0002] The statements in this section merely provide background information related to the present disclosure and do not necessarily constitute prior art.

[0003] With the rapid development of the global logistics industry, the demand for bulk material handling operations continues to grow. In this context, the efficiency of collaborative loading and unloading operations between loaders and trucks directly impacts the overall operational efficiency of the logistics chain. The introduction of autonomous driving technology has revolutionized bulk material handling operations, making loader operations more intelligent.

[0004] The inventors discovered that manual operation is inefficient, with the average effective operating time of each loader accounting for only about 60% of the total working time. Furthermore, fuel consumption remains high, with the energy cost of a single loading and unloading operation accounting for approximately 40% of total operating costs. During the collaborative operation between the loader and the truck, due to a lack of scientific path planning and operation scheduling, phenomena such as equipment waiting and idle operation often occur, resulting in reduced operating efficiency. In complex operating environments (such as mining areas and ports), single GPS positioning is susceptible to interference, while pure vision systems have poor stability in low light or dusty conditions, resulting in a high failure rate in the coordinated alignment between the loader and the truck. In a common operation method, the truck typically waits passively, while the loader, after loading the cargo, actively monitors the truck's position and plans and drives its trajectory based on the truck's current location. The truck remains stationary while the loader transports the cargo. This results in a long duration from the completion of the loading operation to the delivery of the cargo to the truck, which is not conducive to the loader's secondary operation of the cargo. Summary of the Invention

[0005] In order to overcome the shortcomings of the above-mentioned existing technologies, the present invention provides a loader parking planning method and system based on efficiency and energy consumption. The loader selects the current optimal operating mode based on comprehensive efficiency considerations, and judges whether the truck actively cooperates with the loader from the perspective of energy consumption, thereby optimizing the collaborative path of the loader and the truck, improving operating efficiency and reducing energy consumption.

[0006] The optimal operation mode is selected based on the considerations of load and energy consumption, and the optimal planning of the loader path is achieved based on the operation model considering multiple objectives, thereby improving the efficiency of loading and unloading operations.

[0007] To achieve the above objectives, one or more embodiments of the present invention provide the following technical solutions: In a first aspect, the present invention provides a loader parking planning method based on efficiency and energy consumption, comprising: Obtain spatial positioning data and working environment perception data of loaders and trucks; Building a map within the work area based on the spatial positioning data and the work environment perception data, and identifying the location coordinates of the truck, loader, and material pile; Based on the position coordinates of the truck, loader, and material pile, the comprehensive efficiency of each operation mode is evaluated, and the operation mode with the highest comprehensive efficiency is selected for operation; Obtain vehicle status data, environmental data, mechanical parameters, and energy consumption auxiliary data of the loader and truck, and build dynamic energy consumption models based on the vehicle status data, environmental data, mechanical parameters, and energy consumption auxiliary data to obtain the energy consumption of the loader and truck respectively; Based on preset conditions, it is determined whether the truck is moving. If it is moving, the paths of the truck and loader are jointly optimized. If it is not moving, the loader runs along the reference trajectory of the current operation mode.

[0008] Further technical solution, the optimization function of the operation mode is:

[0009] in, Indicates the working time. Indicates the total distance traveled, Indicates fuel consumption, 、 、 Represents the weight coefficient.

[0010] Further technical solution, the dynamic energy consumption model of the loader is expressed as:

[0011] in, represents the energy consumption of the loader, Indicates the traction power, Indicates hydraulic power, Indicates auxiliary data of energy consumption.

[0012] In a further technical solution, the traction power is expressed as:

[0013] in, represents the rolling friction coefficient, Indicates the total mass of the loader, represents the acceleration due to gravity, Indicates real-time speed, represents the air density, The drag coefficient represents the air resistance of the vehicle's shape. Indicates the projected area of ​​the vehicle facing the airflow direction. represents the instantaneous acceleration of the loader, Indicates transmission efficiency.

[0014] Further technical solutions, the dynamic energy consumption model of the truck is expressed as:

[0015] in, represents the energy consumption of the truck, Indicates the traction power of the truck, Indicates auxiliary data of energy consumption.

[0016] Further technical solution, the preset conditions are:

[0017] in, represents the distance between the truck and the target location, represents the distance threshold, represents the angle between the truck and the target position, represents the angle threshold, represents the energy consumption of the truck, Indicates the energy consumption of the loader.

[0018] A further technical solution is to jointly optimize the paths of the truck and the loader based on a multi-objective optimization function, wherein the multi-objective optimization function is:

[0019] in, represents the energy consumption weight, Indicates energy consumption, represents the steering penalty weight, represents the dynamic steering penalty coefficient, represents the steering angular velocity constraint, represents the trajectory tracking weight, Indicates the actual position of the vehicle. represents the vehicle reference trajectory.

[0020] In a second aspect, the present invention provides a loader parking planning system based on efficiency and energy consumption, comprising: A data acquisition module is configured to: acquire spatial positioning data and working environment perception data of the loader and the truck; a location identification module configured to: construct a map within the work area based on the spatial positioning data and the work environment perception data, and identify the location coordinates of the truck, loader, and material pile; a mode selection module configured to: evaluate the comprehensive efficiency of each operation mode based on the position coordinates of the truck, loader, and material pile, and select the operation mode with the highest comprehensive efficiency for operation; an energy consumption calculation module configured to obtain vehicle status data, environmental data, mechanical parameters, and energy consumption auxiliary data of the loader and the truck, and construct dynamic energy consumption models based on the vehicle status data, environmental data, mechanical parameters, and energy consumption auxiliary data to obtain the energy consumption of the loader and the truck, respectively; The path optimization module is configured to: determine whether the truck is moving based on preset conditions, and if so, jointly optimize the paths of the truck and the loader; if not, the loader operates according to the reference trajectory of the current operation mode.

[0021] In a third aspect, the present invention provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps in a loader parking planning method based on efficiency and energy consumption as described in the first aspect.

[0022] In a fourth aspect, the present invention provides a computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, the steps in the loader parking planning method based on efficiency and energy consumption as described in the first aspect are implemented.

[0023] One or more of the above technical solutions have the following beneficial effects: The present invention is different from the existing loader operation mode. It is based on GPS and IMU identification and map construction, and can achieve accurate signal transmission and operation planning. While realizing autonomous control of the loader, it also performs trajectory planning and energy consumption calculation for the truck, providing a guarantee for improving the collaborative efficiency of the loader and truck.

[0024] The present invention selects the optimal operating mode by real-time evaluation of the comprehensive effectiveness of each operating mode, reduces ineffective movement paths, and improves operating efficiency; and constructs an energy consumption model that dynamically incorporates rolling resistance, air resistance, and slope resistance, optimizing driving strategies in combination with transmission efficiency.

[0025] The present invention breaks the bottleneck of passive waiting of trucks, actively triggers the movement of trucks through preset conditions, optimizes the collaborative path of loaders and trucks on the basis of ensuring minimum energy consumption, reduces the waiting time during operation, and improves operation efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] The accompanying drawings, which constitute a part of the present invention, are used to provide a further understanding of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute improper limitations on the present invention.

[0027] Figure 1 This is a flow chart of a loader parking planning method based on efficiency and energy consumption according to an embodiment of the present invention; Figure 2 This is a flowchart of a loader parking planning method based on efficiency and energy consumption according to an embodiment of the present invention. DETAILED DESCRIPTION

[0028] It should be noted that the following detailed descriptions are exemplary and intended to provide further explanation of the present invention. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art to which the present invention belongs.

[0029] It should be noted that the terms used herein are only for describing specific embodiments and are not intended to limit the exemplary embodiments according to the present invention. As used herein, unless the context clearly indicates otherwise, the singular form is intended to include the plural form. In addition, it should be understood that when the terms "comprise" and / or "include" are used in this specification, they indicate the presence of features, steps, operations, devices, components and / or combinations thereof.

[0030] In the absence of conflict, the embodiments of the present invention and the features thereof may be combined with each other.

[0031] Explanation of terms: Loader: An engineering machine used for loading and unloading bulk materials.

[0032] GPS: Determines the precise location (longitude, latitude, altitude) and time of a receiving device (such as a vehicle, mobile phone, or measuring instrument) on the Earth's surface in real time by receiving radio signals from multiple satellites.

[0033] IMU: A motion sensing device that measures the three-axis angular velocity and three-axis acceleration of an object through inertial sensors. It can independently calculate the device's posture, speed, and position changes without relying on external signals.

[0034] Example 1 like Figure 1 As shown, this embodiment discloses a parking planning method for a loader based on efficiency and energy consumption, which includes the following steps: S1: Acquire spatial positioning data and working environment perception data of the loader and truck; In this embodiment, a global positioning system GPS, an inertial measurement unit IMU, a lidar, and a millimeter-wave radar are installed on the loader and the truck, respectively, and the spatial positioning data and working environment perception data of the loader and the truck are collected based on the above devices.

[0035] Spatial positioning data includes the real-time GPS coordinates of the loader and truck, vehicle IMU data (heading angle, pitch angle, roll angle) and wheel speed sensor pulse counts; working environment perception data includes lidar point cloud data (three-dimensional coordinate data of material piles and obstacles) and millimeter-wave radar obstacle contour data.

[0036] S2: Building a map in the work area based on the spatial positioning data and the work environment perception data, and identifying the location coordinates of the truck, loader, and material pile; Building a map and identifying entity locations based on spatial positioning data and work environment perception data is as follows: (1) Establish a unified coordinate system Define the work area and origin: Identify the boundaries of the work area based on the work environment perception data; select a fixed point on the edge of the work area as the coordinate origin, such as the entrance; determine the direction of the coordinate system to form a unified coordinate system.

[0037] Coordinate Conversion: The GPS coordinates of the loader and truck are converted to a unified coordinate system. The coordinates of the material pile and obstacles are also converted to a unified coordinate system. The coordinates of all entities—the loader, truck, material pile, and obstacles—are output in the coordinate system of the work area. The coordinates of the material pile and obstacles are derived from LiDAR point cloud data and millimeter-wave radar data and require conversion based on the real-time position of the loader and truck.

[0038] (2) Identify material piles and obstacles Preprocessing of the work environment perception data, including filtering and registration, is performed. Clustering algorithms are used to process point cloud data, identifying and separating different material piles and calculating the center of mass of each. Motion trajectory analysis is used to detect dynamic targets and mark the locations of fixed obstacles. The system outputs the positions of all material piles and obstacles in the work area coordinate system, distinguishing between dynamic and static objects.

[0039] (3) Constructing a raster map Determine the map boundaries (e.g., X-axis range, Y-axis range) and define grid properties (grid value 0 is the traversable area, grid value 1 is the material area, grid value 2 is the vehicle occupied area, and grid value 3 is the obstacle area). Map the identified entity locations to the grid and output a dynamic grid map.

[0040] (4) Dynamic map update The map is updated in real time based on the spatial positioning data and working environment perception data collected in real time.

[0041] The loader and truck need to sense each other's precise position and orientation in real time to determine the optimal collaborative path. By fusing data from sensors like GPS and IMU, the relative position and heading angle difference between the two vehicles are calculated on a map.

[0042] The relative position between the loader and the truck is expressed as:

[0043] in, Indicates the relative position difference between the loader and the truck in the x direction of the map, Indicates the x-coordinate of the loader on the map, represents the x-coordinate of the truck on the map, Indicates the relative position difference between the loader and the truck in the y direction of the map, Indicates the y coordinate of the loader on the map, Indicates the y coordinate of the truck on the map.

[0044] The relative heading angle difference is expressed as:

[0045] in, Represents the relative heading angle difference, Indicates the heading angle of the loader relative to the origin of the map coordinates. Indicates the heading angle of the truck relative to the map coordinate origin.

[0046] Meeting time estimates are expressed as:

[0047] in, Indicates meeting and time, Indicates the running speed of the loader. Indicates the truck's running speed. The meeting time is the minimum distance between the loader and the truck that can meet in a straight line.

[0048] The coordinates of the meeting point are expressed as:

[0049] .

[0050] S3: Based on the position coordinates of the truck, loader, and material pile, respectively, the comprehensive efficiency of each operation mode is evaluated, and the operation mode with the highest comprehensive efficiency is selected for operation; In this embodiment, the loader operation modes include parallel (V-type), vertical (T-type), right-angle (L-type) and straight-line (I-type) modes. The comprehensive efficiency includes three evaluation indicators: time, distance and energy consumption (fuel consumption). The highest comprehensive efficiency means that the sum of time, distance and energy consumption is the smallest.

[0051] The optimization function of the operation mode is:

[0052] in, Indicates the working time. Indicates the total distance traveled, Indicates fuel consumption, 、 、 Represents the weight coefficient.

[0053] S4: Obtain vehicle status data, environmental data, mechanical parameters, and energy consumption auxiliary data of the loader and truck, and construct dynamic energy consumption models based on the vehicle status data, environmental data, mechanical parameters, and energy consumption auxiliary data to obtain the energy consumption of the loader and the energy consumption of the truck respectively; In this embodiment, vehicle-mounted sensors such as IMU, pressure sensor, flow meter and other equipment are used to measure the vehicle status data of the loader and truck during operation: real-time speed , acceleration , load percentage , hydraulic system pressure and traffic ; Environmental data: air density and road slope; Mechanical parameters: Rolling friction coefficient of loaders and trucks measured by laboratory calibration or historical data fitting (no load / full load), transmission efficiency (varies with speed), pump station efficiency coefficient ; Energy consumption auxiliary data: Use current / voltage sensors to measure the power consumption of auxiliary systems (Such as air conditioning, lighting, etc.).

[0054] Calculates real-time power, including traction power and hydraulic power The traction power is used to optimize the driving speed and acceleration and reduce energy waste; the hydraulic power is used to dynamically adjust the load of the hydraulic system to avoid overload or inefficient operation.

[0055] Furthermore, the traction power model is traction power = (rolling resistance + air resistance + acceleration resistance) * transmission efficiency, and the expression is:

[0056] in, represents the rolling friction coefficient (in this embodiment, it is set to 0.04 when no-load and 0.08 when fully loaded), Indicates the total mass of the loader (including its own weight and load), represents the acceleration due to gravity (the gravity constant on the Earth's surface), Indicates the real-time speed (i.e. the instantaneous speed of the loader). represents the air density, The drag coefficient represents the air resistance of the vehicle's shape. Indicates the projected area of ​​the vehicle facing the airflow direction. Indicates the instantaneous acceleration of the loader (positive for acceleration, negative for deceleration), Indicates transmission efficiency (measured calibration curve, changes with speed).

[0057] Furthermore, the hydraulic power model is expressed as:

[0058] in, Indicates the efficiency coefficient of the pump station, reflecting the energy conversion efficiency of the hydraulic pump, including mechanical loss, volume loss, etc. Indicates the pressure difference between the pump outlet and inlet, representing the load size; Indicates hydraulic flow, that is, the volume flow of oil per unit time, It represents the load sensitivity coefficient, which characterizes the nonlinear effect of load on hydraulic power; Indicates the load rate, which is the percentage of the current load to the rated load.

[0059] Calculate the energy consumption of the loader throughout the entire process for path planning. The energy consumption formula is:

[0060] in, Indicates the energy consumption of the loader.

[0061] The calculation process of truck energy consumption is as follows: Before the truck moves, use MATLAB / Simulink or Python (Pyomo library) to perform discretized numerical integration of the energy consumption model. Energy consumption simulation based on the planned path and estimated load: (1) Input parameters: path terrain (slope ), distance, speed curve , the weight of the truck , the weight of the cargo provided by the loader (assuming it is fully loaded ).

[0062] (2) Segment calculation: Unloaded segment (towards the loading point): ; Load section: .

[0063] The energy consumption of a truck is mainly determined by traction energy consumption, auxiliary system energy consumption, and no-load / load status. The formula can be expressed as:

[0064] Traction power :

[0065] in, represents the energy consumption of the truck, is the rolling resistance coefficient, is the total mass of the truck (including load), is the slope angle, is the air resistance parameter, is the acceleration, Auxiliary power, including fixed power consumption of air conditioning, control system, etc.

[0066] Based on the above energy consumption calculation results, the driver or the automatic driving system is prompted in real time, such as the best time to shift gears (transmission efficiency Maximize), hydraulic system load threshold (avoid Nonlinear surge) etc.

[0067] S5: Determine whether the truck is moving based on preset conditions. If so, the paths of the truck and loader are jointly optimized. If not, the loader operates along the reference trajectory of the current operation mode. In this embodiment, before the truck moves, the necessity of moving the truck is determined, and the preset conditions are:

[0068] in, represents the distance between the truck and the target location, represents the distance threshold, represents the angle between the truck and the target position, represents the angle threshold, represents the energy consumption of the truck, Indicates the energy consumption of the loader.

[0069] If and only if any of the above preset conditions is met, it is determined that the truck needs to move, and the truck needs to move and the path optimization is performed. The path optimization is: according to the current operating mode of the loader (such as V-type), the target posture of the truck is calculated (near the V vertex) to generate the target path; when the truck executes the target path, the trajectory planning is performed according to the A* algorithm combined with heuristic search (such as Manhattan distance) to avoid obstacles. Collaborative control: The truck moves according to the target path, and the loader adjusts the unloading trajectory at the same time, and calibrates the posture in real time through GPS / IMU to ensure that the postures of the two vehicles are aligned at the meeting point. In other words, it ensures that the loader bucket and the truck's loading port can correspond to the loader's unloading. The above formula calculates the heading angles of the truck and loader relative to the coordinate origin. When meeting and unloading, the truck body needs to be at ninety degrees to the loader bucket.

[0070] It also provides visual feedback: the map and real-time path (such as blue route for trucks and red route for loaders) are displayed on the LCD screen.

[0071] The truck and loader paths are jointly optimized to minimize total energy consumption, steering penalty, and trajectory deviation (trajectory tracking), specifically: A multi-objective optimization function is constructed based on the loader's energy consumption, steering penalty, and trajectory tracking. The paths of the truck and loader are jointly optimized based on the multi-objective optimization function to obtain the optimized path. The loader's operating path is optimized and controlled from multiple objectives. The multi-objective optimization function is:

[0072]

[0073] in, represents the energy consumption weight, Indicates energy consumption, represents the steering penalty weight, represents the dynamic steering penalty coefficient, represents the steering angular velocity constraint, represents the trajectory tracking weight, Indicates the actual position of the vehicle. Represents the vehicle reference trajectory. The loader's reference trajectory is generated based on the selected operating mode. The optimal operating mode is selected based on the relative position of the material pile and the truck. Each mode corresponds to a specific geometric path. The actual operation may not be completely consistent with the reference trajectory.

[0074] The dynamic steering penalty coefficient is expressed as:

[0075] in, represents the basic coefficient (in this embodiment, it is set to 0.1 when no load is applied), Indicates load sensitivity (calibrated value is 1.2 in this embodiment), Indicates the weight of the material currently carried by the loader. Indicates the maximum design load capacity of the loader and is used to normalize the load ratio. Calibration data: Measured steering energy consumption increments under different loads.

[0076] The steering angular velocity constraint is expressed as:

[0077] in, represents the maximum steering angular velocity, represents the initial steering angular velocity, Indicates the critical value (80% of the rated load). The greater the load, the lower the permissible turning speed.

[0078] Energy consumption weight : The higher the load, the higher the priority of energy consumption optimization, expressed as:

[0079] Turn penalty weight :The closer the load is to the critical value When , the steering penalty weight increases significantly, which can be expressed as:

[0080] Trajectory tracking weight : Maintain basic tracking accuracy and avoid excessively sacrificing path accuracy

[0081] Steering angular velocity constraint, expressed as: .

[0082] Loader path planning is based on a multi-objective optimization function, achieved by dynamically weighing energy consumption, steering penalty, and trajectory tracking accuracy. First, an initial reference trajectory is generated based on the selected operating mode and parameterized into adjustable control points. The trajectory is then iteratively optimized using gradient descent or model predictive control (MPC), calculating three costs in real time: energy consumption (integral of traction and hydraulic power), steering penalty (load adaptation coefficient × squared angular velocity), and trajectory deviation (the error between the actual and reference positions). During the optimization process, weights are dynamically adjusted (e.g., increasing the steering penalty weight as the load increases), while rigid constraints are imposed on the steering angular velocity and obstacle avoidance conditions. Finally, a smooth B-spline path is output, which is converted into speed and steering commands. The overall goal is to minimize energy consumption, steering penalty, and trajectory tracking.

[0083] The truck's reference trajectory is from its current position to the loading point (either a straight line or a smooth curve), taking into account road network constraints. The loader's reference trajectory is selected based on the operating mode: V-shaped mode: from the material pile to the truck and back (efficient but with more turns); cross-shaped mode: a diagonal path (reducing turns but slightly longer distance). Time and space are discretized (e.g., 1-second intervals, 1-meter grids). Energy consumption and turning penalties are calculated for each state (position, speed), ultimately selecting the path combination with the lowest total cost. Model Predictive Control (MPC) uses a rolling-horizon optimization process to recalculate the optimal path for the next N seconds at each step and adjust the reference trajectory based on sensor feedback (e.g., GPS deviation).

[0084] Taking the loader selecting the V-type operation mode as an example, the path planned by the loader is:

[0085] The truck synchronization path (i.e. the planned path when the truck is moving) is:

[0086] in, Expressed as turning radius, it is related to the load.

[0087] Collaborative time optimization includes meeting time prediction, and speed planning.

[0088] Meeting and time forecast:

[0089] The predicted meeting time facilitates the dynamic coordination of loaders and trucks, ensuring that they arrive at the unloading point at the same time to avoid waiting or conflicts; it also facilitates energy-saving control. If the loader is predicted to be delayed, the truck can slow down to reduce idling energy consumption.

[0090] Speed ​​planning:

[0091] The planned path trajectory is displayed in real time, and the map rendering equation is: .

[0092] Energy consumption optimization: uniform motion Avoid acceleration and deceleration losses and reduce ; Turn to penalty optimization: linear change Make angular velocity Constant, minimized Existing algorithms and technologies such as parametric path planning (B-spline curves / Dubins paths) and model predictive control (MPC) can be used.

[0093] After loading materials, the loader needs to deliver them to the truck. However, due to a lack of close positional connection between the loader and the truck, the truck passively waits rather than actively coordinating with the loader. Furthermore, the loader lacks consideration for optimal path planning, resulting in reduced coordination efficiency and often consuming more time and fuel. To ensure coordination between the loader and the truck after loading, the present invention installs GPS and IMU on the loader and truck, respectively. After completing a task, the loader needs to adjust its posture during reverse to maintain vertical alignment with the truck in order to deliver the materials to the truck. Therefore, after loading the materials, the loader uses the GPS and IMU to identify the material pile and communicate with the truck. By determining its position relative to the materials and the truck, the loader selects the most efficient (effective) operating mode among common operating modes, such as V, T, L, and I. Simultaneously, trajectory planning is performed for the loader and truck. For example, in a V-shaped operation method, after the loader loads the materials, the truck can travel to the loader's initial position (the apex of the V) and adjust its posture to facilitate unloading by the loader. By simulating operations, the system calculates and fits the operating methods of the loader and truck, constructing a map of the operating area to determine which operating method minimizes the distance and fuel consumption for the loader and truck. As the two vehicles operate independently, appropriate routes are planned to ensure minimal energy consumption for both vehicles. After the truck's sensors identify and plan the route, a local map is displayed on the LCD screens installed on the loader and the autonomous driving monitoring system. The map also shows the loader and truck's routes. GPS is used to determine the final destination of the loader and truck's movement, ensuring that the loader or truck can adjust its loading posture during movement, allowing them to replenish materials according to the planned route.

[0094] Example 2 This embodiment discloses a loader parking planning system based on efficiency and energy consumption, including: A data acquisition module is configured to: acquire spatial positioning data and working environment perception data of the loader and the truck; a location identification module configured to: construct a map within the work area based on the spatial positioning data and the work environment perception data, and identify the location coordinates of the truck, loader, and material pile; a mode selection module configured to: evaluate the comprehensive efficiency of each operation mode based on the position coordinates of the truck, loader, and material pile, and select the operation mode with the highest comprehensive efficiency for operation; an energy consumption calculation module configured to obtain vehicle status data, environmental data, mechanical parameters, and energy consumption auxiliary data of the loader and the truck, and construct dynamic energy consumption models based on the vehicle status data, environmental data, mechanical parameters, and energy consumption auxiliary data to obtain the energy consumption of the loader and the truck, respectively; The path optimization module is configured to: determine whether the truck is moving based on preset conditions, and if so, jointly optimize the paths of the truck and the loader; if not, the loader operates according to the reference trajectory of the current operation mode.

[0095] Example 3 The purpose of this embodiment is to provide a computing device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the method of embodiment 1 when executing the program.

[0096] Example 4 The purpose of this embodiment is to provide a computer-readable storage medium, a computer-readable storage medium having a computer program stored thereon, which performs the steps of the method of embodiment 1 when executed by a processor.

[0097] The steps involved in the apparatuses of Examples 3 and 4 above correspond to those of Method Example 1. For detailed implementation, please refer to the relevant description of Example 1. The term "computer-readable storage medium" should be understood to mean a single medium or multiple media containing one or more instruction sets; it should also be understood to include any medium capable of storing, encoding, or carrying an instruction set for execution by a processor and causing the processor to perform any of the methods of the present invention.

[0098] Those skilled in the art will appreciate that the modules or steps of the present invention described above can be implemented using a general-purpose computer device. Alternatively, they can be implemented using program code executable by a computing device, which can then be stored in a storage device and executed by the computing device. Alternatively, they can be fabricated into separate integrated circuit modules, or multiple modules or steps can be fabricated into a single integrated circuit module for implementation. The present invention is not limited to any specific combination of hardware and software.

[0099] The foregoing description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Those skilled in the art will readily appreciate that various modifications and variations of the present invention are possible. Any modifications, equivalent substitutions, or improvements made within the spirit and principles of the present invention are intended to be within the scope of protection of the present invention.

[0100] Although the above describes the specific embodiments of the present invention in conjunction with the accompanying drawings, it is not intended to limit the scope of protection of the present invention. Those skilled in the art should understand that various modifications or variations that can be made by those skilled in the art on the basis of the technical solution of the present invention without any creative work are still within the scope of protection of the present invention.

Claims

1. A parking planning method for a loader based on efficiency and energy consumption, characterized in that: include: Obtain spatial positioning data and working environment perception data of loaders and trucks; Building a map within the work area based on the spatial positioning data and the work environment perception data, and identifying the location coordinates of the truck, loader, and material pile; Based on the position coordinates of the truck, loader, and material pile, the comprehensive efficiency of each operation mode is evaluated, and the operation mode with the highest comprehensive efficiency is selected for operation; Obtain vehicle status data, environmental data, mechanical parameters, and energy consumption auxiliary data of the loader and truck, and build dynamic energy consumption models based on the vehicle status data, environmental data, mechanical parameters, and energy consumption auxiliary data to obtain the energy consumption of the loader and truck respectively; Based on preset conditions, it is determined whether the truck is moving. If it is moving, the paths of the truck and loader are jointly optimized. If it is not moving, the loader runs along the reference trajectory of the current operation mode.

2. A parking planning method for a loader based on efficiency and energy consumption as claimed in claim 1, characterized in that: The optimization function of the operation mode is: in, Indicates the working time. Indicates the total distance traveled, Indicates fuel consumption, 、 、 Represents the weight coefficient.

3. The method for planning parking for a loader based on efficiency and energy consumption according to claim 1, wherein: The dynamic energy consumption model of the loader is expressed as: in, represents the energy consumption of the loader, Indicates the traction power, Indicates hydraulic power, Indicates auxiliary data of energy consumption.

4. A parking planning method for a loader based on efficiency and energy consumption as claimed in claim 3, characterized in that: The traction power is expressed as: in, represents the rolling friction coefficient, Indicates the total mass of the loader, represents the acceleration due to gravity, Indicates real-time speed, represents the air density, The drag coefficient represents the air resistance of the vehicle's shape. Indicates the projected area of ​​the vehicle facing the airflow direction. represents the instantaneous acceleration of the loader, Indicates transmission efficiency.

5. The method for planning parking for a loader based on efficiency and energy consumption according to claim 1, wherein: The dynamic energy consumption model of a truck is expressed as: in, represents the energy consumption of the truck, Indicates the traction power of the truck, Indicates auxiliary data of energy consumption.

6. A parking planning method for a loader based on efficiency and energy consumption as claimed in claim 1, characterized in that: The preset conditions are: in, represents the distance between the truck and the target location, represents the distance threshold, represents the angle between the truck and the target position, represents the angle threshold, represents the energy consumption of the truck, Indicates the energy consumption of the loader.

7. The method for planning parking for a loader based on efficiency and energy consumption according to claim 1, wherein: The paths of the truck and loader are jointly optimized based on a multi-objective optimization function: in, represents the energy consumption weight, Indicates energy consumption, represents the steering penalty weight, represents the dynamic steering penalty coefficient, represents the steering angular velocity constraint, represents the trajectory tracking weight, Indicates the actual position of the vehicle. represents the vehicle reference trajectory.

8. A loader parking planning system based on efficiency and energy consumption, characterized in that: include: A data acquisition module is configured to: acquire spatial positioning data and working environment perception data of the loader and the truck; a location identification module configured to: construct a map within the work area based on the spatial positioning data and the work environment perception data, and identify the location coordinates of the truck, loader, and material pile; a mode selection module configured to: evaluate the comprehensive efficiency of each operation mode based on the position coordinates of the truck, loader, and material pile, and select the operation mode with the highest comprehensive efficiency for operation; an energy consumption calculation module configured to obtain vehicle status data, environmental data, mechanical parameters, and energy consumption auxiliary data of the loader and the truck, and construct dynamic energy consumption models based on the vehicle status data, environmental data, mechanical parameters, and energy consumption auxiliary data to obtain the energy consumption of the loader and the truck, respectively; The path optimization module is configured to: determine whether the truck is moving based on preset conditions, and if so, jointly optimize the paths of the truck and the loader; if not, the loader operates according to the reference trajectory of the current operation mode.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the steps of a loader parking planning method based on efficiency and energy consumption as described in any one of claims 1 to 7 are implemented.

10. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the program, the steps of the loader parking planning method based on efficiency and energy consumption as described in any one of claims 1 to 7 are implemented.

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