Intelligent control method of loading machine
By obtaining material weight data on the loader and formulating ideal driving routes and operating routes, establishing an energy consumption analysis model, and optimizing the driving routes and operating routes of the loader, the problems of complexity and low energy efficiency of the traditional loader control system are solved, and more efficient and safe loader operation is achieved, and energy consumption loss is reduced.
Patent Information
- Application Number
- CN202510245824.3
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-03-04
- Publication Date
- 2025-05-23
AI Technical Summary
The control system of traditional loaders is complex in operation and slow in action response, which affects operating efficiency and safety. At the same time, the energy efficiency is not high, the fuel consumption is large, and the operating costs are increased.
By obtaining the weight data and loading parameters in the bucket on the loader, calculating the load impact coefficient, and formulating an ideal driving route and operating path based on the starting point and target point of the material, calculating the theoretical energy consumption value, comparing the deviation between the actual energy consumption value and the theoretical energy consumption value, establishing an energy consumption analysis model, optimizing the driving route and operating path to reduce energy consumption loss.
The actual energy consumption value of the loader is closer to the theoretical energy consumption value, reducing energy consumption loss, improving operating efficiency and safety, and reducing operating costs.
Smart Images
Figure CN120026678A_ABST
Abstract
Description
Technical Field
[0001] The present invention relates to the technical field of intelligent control systems, and in particular to an intelligent control method for a loader. Background Art
[0002] Loaders are earthwork machines widely used in highway, railway, building, hydropower, port, mine and other construction projects. They are mainly used to load bulk materials such as soil, sand, lime, coal, etc., and can also do light digging of ore, hard soil, etc. By replacing different auxiliary working devices, they can also do bulldozing, lifting and loading and unloading of other materials such as wood; At present, the control systems of many traditional loaders are complex to operate, requiring operators to master multiple skills, such as manual adjustment, multiple levers and button combinations, which can easily lead to unsmooth operation, especially in busy or complex working environments, where the operator's learning curve is steep; and some old hydraulic system control and electrical control technologies are relatively backward, resulting in slow action response speed, especially in situations where a quick response is urgently needed, and the operation may be delayed, thus affecting work efficiency and safety; At the same time, in traditional control systems, components such as hydraulic systems and engines often require large power to support heavy-load operations, which is energy-inefficient. Long-term operations may result in high fuel consumption and increase operating costs. In view of the above-mentioned technical defects, a solution is now proposed. Summary of the invention
[0003] The purpose of the present invention is to calculate the load influence coefficient based on the material weight data in the bucket of the loader and the loading parameters, and formulate the ideal driving route and ideal operation path of the loader according to the starting point position and the target point position of the material to be loaded, and then calculate the theoretical energy consumption value, compare the actual energy consumption value with the theoretical energy consumption value to obtain the energy consumption deviation value, establish an energy consumption analysis model, and optimize the ideal driving route and ideal operation path of the loader based on the energy consumption analysis model to obtain the optimized loader driving route and optimized operation path, so that the actual energy consumption value of the loader is closer to the theoretical energy consumption value to reduce energy consumption loss.
[0004] In order to achieve the above object, the present invention adopts the following technical solution: an intelligent control method for a loader, comprising the following steps: Step 1: During the loading process of the loader, the weight data of the material in the bucket of the loader is obtained through a weighing sensor, and the loading parameters of the loader are obtained, and the load influence coefficient is calculated according to the material weight data and the loading parameters; Step 2: Obtain the starting point position and the target point position of the material to be loaded, and formulate the ideal driving route and the ideal operation path of the loader according to the starting point position and the target point position; Step 3: Obtain the rated parameters of the power system and hydraulic system of the loader, and calculate the theoretical energy consumption value in combination with the ideal driving route and ideal operation path of the loader; Step 4: Obtain the actual parameters of the power system and hydraulic system of the loader, calculate the actual energy consumption value according to the actual parameters, and compare the actual energy consumption value with the theoretical energy consumption value to obtain the energy consumption deviation value; Step 5: Combine the energy consumption deviation value and the load influence coefficient, establish an energy consumption analysis model according to the deep learning algorithm, and optimize the ideal driving route and ideal operation path of the loader based on the energy consumption analysis model to obtain the optimized loader driving route and optimized operation path, and send them to the overall control system.
[0005] Further, the overall control system includes a control unit and an execution unit, the execution unit is used to obtain an optimized loader travel route and an optimized operation path to generate a multi-level execution instruction, and send the multi-level execution instruction to the control unit; The control unit is used to control the power system and hydraulic system of the loader and execute the loading task according to the multi-level execution instructions.
[0006] Furthermore, the specific process of calculating the load influence coefficient is as follows: S101, obtaining material weight data Mk in a bucket on a loader through a weighing sensor; S102, obtaining loading parameters of the loader from a database, wherein the loading parameters include a rated working load Mi, a maximum bucket unloading height Hi, and a maximum heap load capacity Vi; S103. Calculate the load influence coefficient Yk according to the following formula: , where e1, e2 and e3 are preset proportional coefficients. The load influence coefficient Yk is used to reflect the influence of the current material weight in the bucket on the loading performance of the loader during the loading task. The larger the load influence coefficient Yk, the greater the influence of the material weight in the bucket on the loading performance of the loader during the loading task. Conversely, the smaller the load influence coefficient Yk, the smaller the influence of the material weight in the bucket on the loading performance of the loader during the loading task.
[0007] Furthermore, the specific process of obtaining the ideal driving route and ideal operation path of the loader is as follows: S201, acquiring point cloud data of the loader operating area environment, preprocessing the point cloud data of the loader operating area environment to obtain preprocessed point cloud data, and constructing a three-dimensional environment model of the loader operation according to the preprocessed point cloud data; S202, obtaining the starting point position and the target point position of the material to be loaded, and obtaining the starting point coordinates and the target point coordinates in the three-dimensional environment model; S203, using an ant colony algorithm to calculate the shortest path between the starting point coordinates and the target point coordinates as the ideal driving route for the loader; S204, using the three-dimensional environment model to generate multiple candidate shoveling points at the starting point, calculating and recording the shoveling parameters of the candidate shoveling points, and evaluating the candidate shoveling points according to the shoveling parameters of the candidate shoveling points to obtain the optimal shoveling point; S205, obtaining the initial point position of the bucket with the center position of the bucket teeth as a reference point, obtaining the initial point coordinates and the optimal digging point coordinates, so as to generate an ideal operation path.
[0008] Furthermore, the specific process of calculating the theoretical energy consumption value is as follows: S301, obtaining the rated driving speed va of the loader and the ideal driving route of the loader, obtaining the path length Ls, and then calculating the theoretical driving time ts of the loader according to the speed formula: ts=Ls / va; S302, obtaining the rated power Ps of the motor of the loader, and calculating the power energy consumption value Ws of the loader according to the following formula: ; S303, obtaining the rated output power Wt of the loader hydraulic system, and obtaining the ideal operation path, disassembling the ideal operation path to obtain the total path length Lt, and calculating the hydraulic energy consumption value Wt of the loader according to the following formula: , where η is the rated working efficiency of the hydraulic system; S304. It can be known that the theoretical energy consumption value Wz=Ws+Wt.
[0009] Furthermore, the specific process of obtaining the energy consumption deviation value is as follows: S401, obtain the actual parameters of the power system and hydraulic system of the loader, the actual parameters including the actual fuel consumption rate u and the real-time engine speed vi, and calculate the actual energy consumption value Wp according to the following formula: , where ti is the actual working time of the loader; S402: It can be known that the energy consumption deviation value ΔW = Wz-Wp.
[0010] Furthermore, the specific process of obtaining the optimized loader driving route and optimized operation path is as follows: S501, acquiring historical operation data of the loader, the historical operation data including historical driving routes and historical operation paths of the loader and historical energy consumption data of the loader, and constructing an initial energy consumption analysis model for analyzing the direction of energy consumption deviation based on the historical operation data and in combination with coefficients for adjusting various types of the historical operation data; S502, using the historical operation data as training samples and the load influence coefficient corresponding to the historical operation data as labels, training the initial energy consumption analysis model to obtain an energy consumption analysis model; S503, performing energy consumption analysis on the loading process of the loader through the energy consumption prediction model, obtaining multiple path optimization schemes, and determining an energy consumption deviation value corresponding to each of the path optimization schemes; S504. Obtain the actual energy consumption value of each of the path optimization schemes, and determine the optimal path optimization scheme in combination with the energy consumption deviation value of each of the path optimization schemes. Based on the optimal path optimization scheme, optimize the ideal driving route and the ideal operating path of the loader to obtain the optimized loader driving route and the optimized operating path.
[0011] In summary, due to the adoption of the above technical solution, the beneficial effects of the present invention are: The intelligent control method of the loader calculates the load influence coefficient based on the material weight data in the bucket of the loader and the loading parameters, and formulates the ideal driving route and ideal operation path of the loader according to the starting point position and the target point position of the material to be loaded, and then calculates the theoretical energy consumption value in combination with the ideal driving route and the ideal operation path of the loader, compares the actual energy consumption value with the theoretical energy consumption value to obtain the energy consumption deviation value, establishes an energy consumption analysis model according to the deep learning algorithm, and optimizes the ideal driving route and the ideal operation path of the loader based on the energy consumption analysis model to obtain the optimized loader driving route and the optimized operation path, so that the actual energy consumption value of the loader is closer to the theoretical energy consumption value to reduce energy consumption loss. BRIEF DESCRIPTION OF THE DRAWINGS
[0012] Figure 1 A schematic diagram of the overall method flow of the present invention is shown. DETAILED DESCRIPTION
[0013] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention. Example
[0014] like Figure 1 As shown, an intelligent control method for a loader comprises the following steps: Step 1: During the loading process of the loader, the material weight data in the bucket of the loader is obtained through the weighing sensor, and the loading parameters of the loader are obtained, and the load influence coefficient is calculated according to the material weight data and the loading parameters; The specific process of calculating the load influence coefficient is as follows: S101, obtaining material weight data Mk in a bucket on a loader through a weighing sensor; S102, obtaining loading parameters of the loader from a database, the loading parameters including a rated working load Mi, a maximum bucket unloading height Hi, and a maximum heap load capacity Vi; S103. Calculate the load influence coefficient Yk according to the following formula: , where e1, e2 and e3 are preset proportional coefficients. The load influence coefficient Yk is used to reflect the influence of the current material weight in the bucket on the loading performance of the loader during the loading task. The larger the load influence coefficient Yk, the greater the influence of the material weight in the bucket on the loading performance of the loader during the loading task. Conversely, the smaller the load influence coefficient Yk, the smaller the influence of the material weight in the bucket on the loading performance of the loader during the loading task.
[0015] Step 2: Obtain the starting point position and the target point position of the material to be loaded, and formulate the ideal driving route and the ideal operation path of the loader according to the starting point position and the target point position; The specific process of obtaining the ideal driving route and ideal operation path of the loader is as follows: S201, acquiring point cloud data of the loader operating area environment, preprocessing the point cloud data of the loader operating area environment to obtain preprocessed point cloud data, and constructing a three-dimensional environment model of the loader operation according to the preprocessed point cloud data; S202, obtaining the starting point position and the target point position of the material to be loaded, and obtaining the starting point coordinates and the target point coordinates in the three-dimensional environment model; S203, using an ant colony algorithm to calculate the shortest path between the starting point coordinates and the target point coordinates as the ideal driving route for the loader; S204, using the three-dimensional environment model to generate multiple candidate shoveling points at the starting point, calculating and recording the shoveling parameters of the candidate shoveling points, and evaluating the candidate shoveling points according to the shoveling parameters of the candidate shoveling points to obtain the optimal shoveling point; S205, obtaining the initial point position of the bucket with the center position of the bucket teeth as a reference point, obtaining the initial point coordinates and the optimal digging point coordinates, so as to generate an ideal operation path.
[0016] Step 3: Obtain the rated parameters of the power system and hydraulic system of the loader, and calculate the theoretical energy consumption value in combination with the ideal driving route and ideal operation path of the loader; The specific process of calculating the theoretical energy consumption value is as follows: S301, obtaining the rated driving speed va of the loader and the ideal driving route of the loader, obtaining the path length Ls, and then calculating the theoretical driving time ts of the loader according to the speed formula: ts=Ls / va; S302, obtaining the rated power Ps of the motor of the loader, and calculating the power energy consumption value Ws of the loader according to the following formula: ; S303, obtaining the rated output power Wt of the loader hydraulic system, and obtaining the ideal operation path, disassembling the ideal operation path to obtain the total path length Lt, and calculating the hydraulic energy consumption value Wt of the loader according to the following formula: , where η is the rated working efficiency of the hydraulic system; S304. It can be known that the theoretical energy consumption value Wz=Ws+Wt.
[0017] Step 4: Obtain the actual parameters of the power system and hydraulic system of the loader, calculate the actual energy consumption value according to the actual parameters, and compare the actual energy consumption value with the theoretical energy consumption value to obtain the energy consumption deviation value; The specific process of obtaining the energy consumption deviation value is as follows: S401, obtain the actual parameters of the power system and hydraulic system of the loader, including the actual fuel consumption rate u and the real-time engine speed vi, and calculate the actual energy consumption value Wp according to the following formula: , where ti is the actual working time of the loader; S402: It can be known that the energy consumption deviation value ΔW = Wz-Wp.
[0018] Step 5: Combine the energy consumption deviation value and the load influence coefficient, establish an energy consumption analysis model according to the deep learning algorithm, and optimize the ideal driving route and ideal operation path of the loader based on the energy consumption analysis model to obtain the optimized loader driving route and optimized operation path, and send them to the overall control system.
[0019] The specific process of obtaining the optimized loader driving route and optimized operation path is as follows: S501, obtaining historical operation data of the loader, the historical operation data including the historical driving route and historical operation path of the loader and the historical energy consumption data of the loader, and constructing an initial energy consumption analysis model for analyzing the direction of energy consumption deviation based on the historical operation data and in combination with coefficients for adjusting various types of historical operation data; S502, using historical operation data as training samples and the load influence coefficient corresponding to the historical operation data as labels, training the initial energy consumption analysis model to obtain an energy consumption analysis model; S503, performing energy consumption analysis on the loading process of the loader through the energy consumption prediction model, obtaining multiple path optimization schemes, and determining the energy consumption deviation value corresponding to each path optimization scheme; S504. Obtain the actual energy consumption value of each path optimization scheme, and determine the optimal path optimization scheme in combination with the energy consumption deviation value of each path optimization scheme. Based on the optimal path optimization scheme, optimize the loader's ideal driving route and ideal operating path to obtain an optimized loader driving route and optimized operating path.
[0020] The overall control system includes a control unit and an execution unit, wherein the execution unit is used to obtain an optimized loader travel route and an optimized operation path to generate a multi-level execution instruction, and send the multi-level execution instruction to the control unit; The control unit is used to control the power system and hydraulic system of the loader and perform loading tasks according to multi-level execution instructions.
[0021] The present invention calculates the load influence coefficient based on the material weight data in the bucket of the loader and the loading parameters, and formulates the ideal driving route and the ideal operating path of the loader according to the starting point position and the target point position of the material to be loaded, and then calculates the theoretical energy consumption value, compares the actual energy consumption value with the theoretical energy consumption value to obtain the energy consumption deviation value, establishes an energy consumption analysis model, and optimizes the ideal driving route and the ideal operating path of the loader based on the energy consumption analysis model to obtain the optimized loader driving route and the optimized operating path, so that the actual energy consumption value of the loader is closer to the theoretical energy consumption value to reduce energy consumption loss.
[0022] The above formulas are all dimensionless and numerical calculations. The formula is a formula obtained by collecting a large amount of data and performing software simulation to obtain the most recent real situation. The preset parameters in the formula are set by technicians in this field according to actual conditions. The above description is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can make equivalent replacements or changes according to the technical scheme and inventive concept of the present invention within the technical scope disclosed by the present invention, which should be covered by the protection scope of the present invention.
Claims
1. An intelligent control method for a loader, characterized in that: The following steps are involved: Step 1: During the loading process of the loader, the weight data of the material in the bucket of the loader is obtained through a weighing sensor, and the loading parameters of the loader are obtained, and the load influence coefficient is calculated according to the material weight data and the loading parameters; Step 2: Obtain the starting point position and the target point position of the material to be loaded, and formulate the ideal driving route and the ideal operation path of the loader according to the starting point position and the target point position; Step 3: Obtain the rated parameters of the power system and hydraulic system of the loader, and calculate the theoretical energy consumption value in combination with the ideal driving route and ideal operation path of the loader; Step 4: Obtain the actual parameters of the power system and hydraulic system of the loader, calculate the actual energy consumption value according to the actual parameters, and compare the actual energy consumption value with the theoretical energy consumption value to obtain the energy consumption deviation value; Step 5: Combine the energy consumption deviation value and the load influence coefficient, establish an energy consumption analysis model according to the deep learning algorithm, and optimize the ideal driving route and ideal operation path of the loader based on the energy consumption analysis model to obtain the optimized loader driving route and optimized operation path, and send them to the overall control system.
2. The intelligent control method of a loader according to claim 1, characterized in that: The overall control system includes a control unit and an execution unit, wherein the execution unit is used to obtain an optimized loader travel route and an optimized operation path to generate a multi-level execution instruction, and send the multi-level execution instruction to the control unit; The control unit is used to control the power system and hydraulic system of the loader and execute the loading task according to the multi-level execution instructions.
3. The intelligent control method of a loader according to claim 1, characterized in that: The specific process of calculating the load influence coefficient is as follows: S101, obtaining material weight data Mk in a bucket on a loader through a weighing sensor; S102, obtaining loading parameters of the loader from a database, wherein the loading parameters include a rated working load Mi, a maximum bucket unloading height Hi, and a maximum heap load capacity Vi; S103. Calculate the load influence coefficient Yk according to the following formula: , where e1, e2 and e3 are preset proportional coefficients, and the load influence coefficient Yk is used to reflect the influence of the current material weight in the bucket on the loading performance of the loader during the loading task.
4. The intelligent control method of a loader according to claim 1, characterized in that: The specific process of obtaining the ideal driving route and ideal operation path of the loader is as follows: S201, acquiring point cloud data of the loader operating area environment, preprocessing the point cloud data of the loader operating area environment to obtain preprocessed point cloud data, and constructing a three-dimensional environment model of the loader operation according to the preprocessed point cloud data; S202, obtaining the starting point position and the target point position of the material to be loaded, and obtaining the starting point coordinates and the target point coordinates in the three-dimensional environment model; S203, using an ant colony algorithm to calculate the shortest path between the starting point coordinates and the target point coordinates as the ideal driving route for the loader; S204, using the three-dimensional environment model to generate multiple candidate shoveling points at the starting point, calculating and recording the shoveling parameters of the candidate shoveling points, and evaluating the candidate shoveling points according to the shoveling parameters of the candidate shoveling points to obtain the optimal shoveling point; S205, obtaining the initial point position of the bucket with the center position of the bucket teeth as a reference point, obtaining the initial point coordinates and the optimal digging point coordinates, so as to generate an ideal operation path.
5. The intelligent control method of a loader according to claim 1, characterized in that: The specific process of calculating the theoretical energy consumption value is as follows: S301, obtaining the rated driving speed va of the loader and the ideal driving route of the loader, obtaining the path length Ls, and then calculating the theoretical driving time ts of the loader according to the speed formula: ts=Ls / va; S302, obtaining the rated power Ps of the motor of the loader, and calculating the power energy consumption value Ws of the loader according to the following formula: ; S303, obtaining the rated output power Wt of the loader hydraulic system, and obtaining the ideal operation path, disassembling the ideal operation path to obtain the total path length Lt, and calculating the hydraulic energy consumption value Wt of the loader according to the following formula: , where η is the rated working efficiency of the hydraulic system; S304. It can be known that the theoretical energy consumption value Wz=Ws+Wt.
6. The intelligent control method of a loader according to claim 1, characterized in that: The specific process of obtaining the energy consumption deviation value is as follows: S401, obtain the actual parameters of the power system and hydraulic system of the loader, the actual parameters including the actual fuel consumption rate u and the real-time engine speed vi, and calculate the actual energy consumption value Wp according to the following formula: , where ti is the actual working time of the loader; S402: It can be known that the energy consumption deviation value ΔW = Wz-Wp.
7. The intelligent control method of a loader according to claim 1, characterized in that: The specific process of obtaining the optimized loader driving route and optimized operation path is as follows: S501, acquiring historical operation data of the loader, the historical operation data including historical driving routes and historical operation paths of the loader and historical energy consumption data of the loader, and constructing an initial energy consumption analysis model for analyzing the direction of energy consumption deviation based on the historical operation data and in combination with coefficients for adjusting various types of the historical operation data; S502, using the historical operation data as training samples and the load influence coefficient corresponding to the historical operation data as labels, training the initial energy consumption analysis model to obtain an energy consumption analysis model; S503, performing energy consumption analysis on the loading process of the loader through the energy consumption prediction model, obtaining multiple path optimization schemes, and determining an energy consumption deviation value corresponding to each of the path optimization schemes; S504. Obtain the actual energy consumption value of each of the path optimization schemes, and determine the optimal path optimization scheme in combination with the energy consumption deviation value of each of the path optimization schemes. Based on the optimal path optimization scheme, optimize the ideal driving route and the ideal operating path of the loader to obtain the optimized loader driving route and the optimized operating path.