Lateral balance control method and system for heavy telescopic boom forklift

Abstract

The invention discloses a lateral balance control method and system for a heavy telescopic boom forklift, and relates to the field of winding, lifting, traction or pushing which is not included in other categories, and the lateral balance control method comprises the steps that three-dimensional data of a ground environment and goods in a container are obtained through scanning of an unmanned aerial vehicle, and an accurate three-dimensional map and a three-dimensional model of the goods are constructed; and path nodes are divided according to the topographic features and the roll angle of the forklift body, dynamic changes of the cargoes are predicted based on the types of the cargoes, and then the navigation path and the running speed of the forklift between the path nodes are optimized in real time according to the dynamic changes of the cargoes, so that lateral balance of the forklift body is kept.

Description

Technical Field

[0001] The present invention relates to the field of hoisting, lifting, traction or pushing that is not included in other categories, and specifically to a lateral balance control method and system for a heavy telescopic arm forklift. Background Art

[0002] A heavy-duty telescopic forklift is a multi-purpose forklift with off-road capabilities and a telescopic arm, commonly used in the construction industry and certain industrial fields.

[0003] Heavy-duty telescopic forklifts face a major technical challenge when transporting goods in complex terrain: how to maintain the balance of the vehicle body and complete the cargo transportation task safely and efficiently in an uneven and changing ground environment. The core of this problem is that traditional forklifts are prone to rollover when dealing with complex terrain, which seriously threatens the safety of operators and the integrity of cargo. Specifically, when the forklift is driving on uneven terrain, the tilt angle of the vehicle body will continue to change, and the cargo in the cargo box will also change dynamically. If this change is not perceived and responded to in a timely and accurate manner, it is easy to cause the vehicle body to lose balance. To make it more complicated, different types of cargo may produce different degrees of displacement or deformation during transportation, further exacerbating the difficulty of maintaining balance. In addition, due to the complexity of the environment, the pre-planned transportation path may not be able to adapt to the actual situation and needs to be adjusted in real time during transportation. These factors combined form a complex technical problem involving many aspects and variables, which places extremely high demands on the intelligent control and path planning of forklifts. Summary of the invention

[0004] The purpose of the present invention is to provide a lateral balance control method and system for a heavy telescopic forklift, which aims to overcome the above-mentioned problems existing in the prior art.

[0005] To achieve the purpose, the present invention provides the following technical solutions: A lateral balance control method for a heavy telescopic forklift, with a drone as a leader and a forklift as a follower, to control the lateral balance of the forklift body, which specifically includes the following steps: Step S1, the drone scans the ground environment; the central control module obtains the drone scanning data and constructs a three-dimensional map data structure; Step S2: the central control module provides a navigation path and a driving speed from the starting point to the end point according to the three-dimensional map data structure; Step S3: The drone scans the goods in the cargo box in real time to create three-dimensional model data of the goods; Step S4, determine the type of goods; Step S5: Taking the three-dimensional model data of the cargo at the current node as the initial value, predicting the dynamic change of the cargo according to the cargo type, the navigation path from the current node to the next node, the driving speed and the three-dimensional map data structure, calculating the three-dimensional model data of the cargo at the next node, and then calculating the cargo difference between the left half and the right half of the cargo box at the next node; Step S6: If the cargo difference of the next node exceeds a second preset threshold, the navigation path and driving speed of the forklift from the current node to the next node are optimized.

[0006] Furthermore, in the above step S1, the central control module obtains the laser point cloud data collected by the drone scanning, extracts the spatial feature points based on the above laser point cloud data, and generates the spatial distribution resolution data of the environment; obtains the spectral data collected by the drone scanning, and fuses the above spectral data with the noise-filtered point cloud data to construct a three-dimensional map data structure.

[0007] Furthermore, in the above step S3, the drone scans the goods in the cargo box in real time and establishes the three-dimensional model data of the goods, specifically: The Hertz imaging device carried by the drone performs an all-round, multi-angle transmission scan of the cargo box to obtain the cargo transmission depth data and material reflection coefficient; the resolution and acquisition frequency of the above transmission depth data are analyzed to determine the cargo boundary clarity threshold, and a terahertz image is generated in combination with the dynamic range of the material reflection coefficient; the central control module uses the collected multi-angle terahertz images as projection data and uses the computed tomography algorithm to construct the three-dimensional model data of the cargo.

[0008] Furthermore, a database of material reflectance coefficients and three-dimensional model data of known cargo types is pre-built; a convolutional neural network (CNN) is pre-built, and the data in the database is used as prior knowledge to train and optimize the convolutional neural network (CNN); In the above step S4, the cargo type is determined by: taking the material reflectance coefficient and three-dimensional model data obtained by the drone scan as input parameters, and inputting them into the trained convolutional neural network, which then identifies and outputs the cargo type. Furthermore, in the above step S6, if the cargo difference exceeds the second preset threshold, the navigation path and driving speed of the forklift from the current node to the next node are optimized, specifically: A comprehensive optimization model is established by combining navigation path optimization and driving speed adjustment. The goal of this comprehensive optimization model is to minimize the risk of forklift body roll and maximize the driving efficiency of the forklift.

[0009] Furthermore, a multi-objective optimization method is used to optimize the roll risk and driving efficiency as two independent objective functions: Minimize the risk of forklift body rollover: min f 1( x)=ω1×θ+ω2×F 轮胎 +ω3×Δm; Among them, x is the decision variable, including navigation path parameters and driving speed parameters; θ is the vehicle body roll angle; F 轮胎 is the tire force; Δm is the cargo difference; ω1, ω2, ω3 are the weight coefficients of each factor, and ω1+ω2+ω3=1; Maximize the driving efficiency of the forklift: max f2(x)=(L 优化 / L 初始 )×(T 初始 / T 优化 ); Among them, L 初始 is the path length before optimization; L 优化 is the optimized path length; T 初始 is the travel time before optimization; T 优化 is the optimized travel time.

[0010] Furthermore, the two objective functions are transformed into a comprehensive objective function through the weighted summation method: F(x)=α×min f1(x)+β×max f2(x); Among them, α and β are weight coefficients, and α+β=1, which are used to balance the impact of roll risk and driving efficiency.

[0011] The present invention also discloses a lateral balance control system for a heavy-duty telescopic forklift, which is used to execute the above-mentioned control method, and includes a drone, a central control module and a forklift, wherein the central control module is communicatively connected to the drone and the forklift; the forklift is equipped with a lateral acceleration sensor, a speed sensor and a dual-axis inclination sensor; the drone is equipped with a laser radar, a Hertz imaging device and a hyperspectral camera.

[0012] Furthermore, the central control module is mounted on a forklift.

[0013] Compared with the prior art, the present invention has the following beneficial effects: First, the present invention discloses a lateral balance control method for a heavy-duty telescopic arm forklift, which obtains three-dimensional data of the ground environment and the cargo in the cargo box through drone scanning, constructs an accurate three-dimensional map and a three-dimensional model of the cargo, divides the path nodes according to the terrain characteristics and the roll angle of the vehicle body, and predicts the dynamic changes of the cargo based on the cargo type, and then optimizes the navigation path and driving speed of the forklift between the path nodes in real time according to the dynamic changes of the cargo to maintain the lateral balance of the vehicle body.

[0014] Second, the present invention optimizes the path by continuously obtaining feedback data, thus achieving stable and efficient transportation of forklifts in complex terrains. This method effectively solves the problem that traditional forklifts are prone to rollover when transporting on uneven terrains, and significantly improves the safety and efficiency of cargo transportation. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 This is a flow chart of the lateral balance control method in the present invention.

[0016] Figure 2 This is a flow chart of the lateral balance control system in the present invention. DETAILED DESCRIPTION

[0017] The specific implementation of the present invention is described below with reference to the accompanying drawings. In order to fully understand the present invention, many details are described below, but for those skilled in the art, the present invention can be implemented without these details.

[0018] like Figure 1 and Figure 2 As shown, a lateral balance control method for a heavy telescopic forklift, which uses a drone as a leader and a forklift as a follower to control the lateral balance of the forklift body, specifically includes the following steps: Step S1: The drone scans the ground environment; the central control module obtains the drone scanning data and constructs a three-dimensional map data structure.

[0019] like Figure 1 and Figure 2 As shown, in a specific embodiment, step S1 specifically includes the following contents: Step S101: The drone is equipped with a laser radar and a hyperspectral camera. The drone scans the ground environment while flying, collects laser point cloud data through the laser radar, and collects spectral data through the hyperspectral camera.

[0020] Step S102: The central control module obtains the laser point cloud data collected by the drone scanning, extracts spatial feature points based on the laser point cloud data, and generates spatial distribution resolution data of the environment; and obtains the spectral data collected by the drone scanning.

[0021] Step S103: The central control module fuses the spectral data with the noise-filtered point cloud data to construct a three-dimensional map data structure.

[0022] Step S2: The central control module provides a navigation path and a driving speed from a starting point (i.e., the position where the forklift loads the cargo box) to an end point (i.e., the position where the forklift unloads the cargo box) according to the three-dimensional map data structure; and divides the navigation path into several segments according to the three-dimensional map data structure, taking the position where the vehicle body roll angle exceeds the first preset threshold as a node. It should be noted that the three-dimensional map data structure includes a starting point, an end point, and a navigation path.

[0023] like Figure 1 and Figure 2 As shown, in a specific embodiment, step S2 specifically includes the following contents: Step S201: The central control module provides a navigation path and a driving speed from the starting point to the end point according to the three-dimensional map data structure. The navigation path can at least avoid obstacles, and the driving speed must not exceed the maximum speed limit. In addition, providing a navigation path and a driving speed according to the starting point and the end point belongs to the prior art and will not be described in detail here.

[0024] Step S202: The central control module divides the navigation path into several segments, taking the position where the vehicle body roll angle exceeds the first preset threshold as a node. It should be noted that when the forklift is unloaded and driving in a straight line at a preset speed (e.g., 3 km / h), if the road surface tilts left and right to a certain critical value, the forklift will roll. This critical value is the first preset threshold.

[0025] Step S3: The drone scans the goods in the cargo box in real time to create three-dimensional model data of the goods; like Figure 1 and Figure 2 As shown, in a specific embodiment, step S3 includes the following contents: Step S301: The Hertz imaging device carried by the drone performs a full-scale, multi-angle transmission scan of the cargo box to obtain the cargo transmission depth data and material reflection coefficient; the resolution and acquisition frequency of the transmission depth data are analyzed to determine the cargo boundary clarity threshold, and a terahertz image is generated in combination with the dynamic range of the material reflection coefficient; Step S302: The central control module uses the collected multi-angle terahertz images as projection data and constructs three-dimensional model data of the goods using a computer tomography algorithm.

[0026] Step S4, determine the type of goods; like Figure 1 and Figure 2 As shown, in a specific embodiment, in step S4, the type of goods is determined by prior knowledge of the goods, which specifically includes the following contents: Step S401: pre-build a database of material reflectance coefficients, three-dimensional model data, etc. of known cargo types. The database is established and stored in the central control module.

[0027] Step S402: Construct a convolutional neural network (CNN). The convolutional neural network (CNN) is established and stored in the central control module; Step S403: Use the data in the database as prior knowledge to train and optimize the convolutional neural network (CNN).

[0028] Step S404: The material reflection coefficient and three-dimensional model data obtained in step S301 are used as input parameters and input into a trained convolutional neural network (CNN), and the convolutional neural network (CNN) recognizes and outputs the type of goods.

[0029] For example, cargo types can be classified according to one or more of the following methods: (1) Classification by material. By analyzing the reflection coefficient, the material of the goods can be identified, so they can be classified by material.

[0030] For example, metal products: have a higher reflection coefficient, and have higher density and hardness; wood products: have a lower reflection coefficient and low density; plastic products: have a low reflection coefficient and density; glass products: have a higher reflection coefficient and are fragile, etc.

[0031] (2) Classification by shape and size. By analyzing the 3D model data, the shape and size of the goods can be identified, so they can be classified by shape and size.

[0032] For example, regular-shaped goods: such as cubes, rectangular prisms, cylinders, etc., the 3D model data of these goods are relatively simple and easy to identify; irregular-shaped goods: such as furniture, mechanical parts, etc., the 3D model data of these goods are relatively complex and require more detailed scanning and analysis; large goods: such as mechanical equipment, etc., the weight and center of gravity position of these goods have a greater impact on the stability of the vehicle; small goods: such as electronic products, packaging boxes, etc., the weight and center of gravity position of these goods have less impact on the stability of the vehicle.

[0033] (3) Classification by fixing method. By analyzing the 3D model data, the fixing method of the goods can be identified, so they can be classified by fixing method.

[0034] For example, goods fixed with straps: such as goods fixed with straps, these goods require special attention to the tightness of the straps and the fixing points during transportation; goods fixed with brackets: such as goods fixed with brackets, these goods usually have a higher center of gravity, and special attention needs to be paid to the stability of the vehicle; free-placed goods: such as bulk goods or freely placed packaged goods, these goods are easy to move during transportation, and special attention needs to be paid to fixation and distribution.

[0035] Accordingly, the database can further include the following data: (1) Cargo weight and center of gravity position: This helps predict the stability of the cargo during vehicle travel. (2) Cargo size and shape: This helps accurately represent the physical characteristics of the cargo in prior knowledge or models. (3) Cargo fixing method: This determines how the cargo is fixed to the vehicle, such as using straps, brackets, or other fixing devices. This data helps identify the type of cargo, helps assess the stability of the cargo during travel, and predicts the dynamic changes of the cargo.

[0036] Step S5: Taking the three-dimensional model data of the cargo at the current node as the initial value, predict the dynamic change of the cargo according to the cargo type, the navigation path from the current node to the next node, the driving speed and the three-dimensional map data structure, and calculate the three-dimensional model data of the cargo at the next node, and then calculate the cargo difference between the left and right halves of the cargo box at the next node.

[0037] like Figure 1 and Figure 2 As shown, in a specific embodiment, step S5 specifically includes the following contents: Step S501: The central control module uses the three-dimensional model data of the cargo at the current node as the initial value, predicts the dynamic change of the cargo according to the cargo type, the navigation path from the current node to the next node, the driving speed and the three-dimensional map data structure, and calculates the three-dimensional model data of the cargo at the next node.

[0038] Step S502: Using a first vertical plane as a dividing plane, the cargo is divided into a left half area and a right half area, wherein the first vertical plane is a plane parallel to the front-to-back direction of the forklift body and passes through the symmetry axis of the cargo fork.

[0039] Step S503: Calculate the cargo difference between the left and right halves of the cargo box at the next node based on the three-dimensional model data of the cargo at the next node. Preferably, the cargo difference is the cargo weight difference between the left and right halves.

[0040] Step S6: If the cargo difference of the next node exceeds a second preset threshold, the navigation path and driving speed of the forklift from the current node to the next node are optimized.

[0041] like Figure 1 and Figure 2 As shown, in a specific embodiment, step S6 specifically includes the following contents: Step S601: pre-combining navigation path optimization and driving speed adjustment, establishing a comprehensive optimization model, the goal of which is to minimize the risk of the forklift's body rollover and maximize the forklift's driving efficiency. The comprehensive optimization model is established and stored in the central control module.

[0042] More specifically, step S601 includes but is not limited to the following: Step S6011: adopt a multi-objective optimization method to optimize the roll risk and driving efficiency as two independent objective functions: Minimize the risk of forklift body rollover: min f1(x)=ω1×θ+ω2×F 轮胎 +ω3×Δm; Among them, x is the decision variable, including navigation path parameters and driving speed parameters; θ is the vehicle body roll angle (unit: radian); F 轮胎 is the tire force (unit: Newton); Δm is the cargo difference (unit: kilogram); ω1, ω2, ω3 are the weight coefficients of each factor, and ω1+ω2+ω3=1; Maximize the driving efficiency of the forklift: max f2(x)=(L 优化 / L 初始 )×(T 初始 / T 优化 ); Among them, L 初始 is the path length from the current node to the next node before optimization; L 优化 is the length of the path from the current node to the next node after optimization; T 初始 is the travel time from the current node to the next node before optimization; T 优化 is the travel time from the current node to the next node after optimization. Both travel times can be calculated by dividing the path length by the travel speed.

[0043] Step S6012: transform the two objective functions into a comprehensive objective function by weighted summation method: F(x)=α×min f1(x)+β×max f2(x); Among them, α and β are weight coefficients, and α+β=1, which are used to balance the impact of roll risk and driving efficiency.

[0044] The above comprehensive objective function is the core of the comprehensive optimization model, which is used to balance the impact of roll risk and driving efficiency. It can directly guide the method of the comprehensive optimization model from the perspective of global optimization. By adjusting the weight coefficients α and β, the priority of roll risk and driving efficiency can be flexibly controlled.

[0045] Step S602, the central control module determines whether the difference in goods exceeds a second preset threshold; if it does not exceed the second preset threshold, execute step S603; if it exceeds the second preset threshold, execute step S604.

[0046] Step S603: The forklift moves from the current node to the next node according to the navigation path and driving speed given in step S201; after the forklift reaches the next node, it jumps to step S5 until it reaches the end point.

[0047] Step S604, optimize the navigation path and driving speed of the forklift from the current node to the next node; the forklift moves from the current node to the next node according to the optimized navigation path and driving speed; after the forklift reaches the next node, jump to step S5 until it reaches the end point.

[0048] like Figure 1 and Figure 2 As shown, in a specific implementation, in step S604, the comprehensive optimization model takes the path length, the number of turning points and the lateral acceleration as weight factors to give the optimal navigation path; takes the vehicle body roll angle, tire force and the cargo difference at the current node as weight factors to dynamically adjust the driving speed to ensure the lateral balance of the forklift during driving. Specifically, it includes the following contents: Step S6041: The weight calculation formula for navigation path optimization is: Path optimization score = a1 × path length + a2 × number of turning points + a3 × lateral acceleration; Among them, path length refers to the path length from the current node to the next node after optimization; the number of inflection points refers to the number of points where the direction of the forklift changes significantly in the path from the current node to the next node after optimization; lateral acceleration refers to the maximum acceleration generated by the forklift when turning at each inflection point after optimization; a1, a2 and a3 are the corresponding weight coefficients of path length, number of inflection points and lateral acceleration, respectively, and a1+a2+a3=1.

[0049] The comprehensive optimization model improves the driving efficiency of the forklift by optimizing the path length, reducing the number of turning points and reducing the lateral acceleration. Therefore, the path optimization score directly affects the driving efficiency max f2(x), thereby indirectly affecting the comprehensive objective function F(x).

[0050] Step S6042: The weight calculation formula for adjusting the driving speed is: Speed ​​adjustment score = ω1×θ+ω2×F 轮胎 +ω3×Δm; Among them, θ is the body roll angle of the forklift when it reaches the next node after optimization; F 轮胎 is the maximum tire force of all wheels when the forklift reaches the next node after optimization; Δm is the cargo difference when the forklift reaches the next node after optimization; ω1, ω2 and ω3 are the corresponding weight coefficients of roll angle, tire force and cargo difference, respectively, and ω1+ω2+ω3=1.

[0051] The comprehensive optimization model reduces the risk of vehicle rollover by dynamically adjusting the driving speed. Therefore, the speed adjustment score directly affects the vehicle rollover risk min f1(x), thereby indirectly affecting the comprehensive objective function F(x).

[0052] Step S6043: Calculate the comprehensive score using the following formula: Comprehensive score = γ × path optimization score + δ × speed adjustment score; Among them, γ and δ are the weight coefficients of path optimization and speed adjustment, and γ+δ=1.

[0053] The comprehensive score is a specific quantitative indicator in the actual optimization process of the comprehensive optimization model, which is used to guide the final decision of navigation path optimization and driving speed adjustment. The comprehensive score is the specific implementation form of the comprehensive objective function F(x). By optimizing the comprehensive score, the optimization of the comprehensive objective function can be indirectly achieved.

[0054] Step S6044: if the comprehensive score reaches the third preset threshold, the optimized navigation path and driving speed are output; if the comprehensive score does not reach the third preset threshold, steps S6041 to S6044 are repeated; when the number of repetitions exceeds N times, the forklift is paused, and a warning signal is issued that the forklift is at risk of rollover, waiting for manual intervention.

[0055] like Figure 2 As shown, the present invention also discloses a lateral balance control system for a heavy-duty telescopic arm forklift, which is used to execute the above-mentioned control method, and includes a drone, a central control module and a forklift. The central control module is communicatively connected to the drone and the forklift; the forklift is equipped with a lateral acceleration sensor, a speed sensor and a dual-axis tilt sensor. Among them, the dual-axis tilt sensor is used to measure the roll angle of the forklift body; the drone is equipped with a laser radar, a Hertz imaging device and a hyperspectral camera. Preferably, the central control module is mounted on the central control system of the forklift. Of course, the central control module can also be set in a computer in the control room, connected to the forklift and the drone by wireless communication, to achieve remote communication and control.

[0056] The above is only a specific implementation of the present invention, but the design concept of the present invention is not limited thereto. Any non-substantial changes to the present invention using this concept shall be deemed as an infringement of the protection scope of the present invention.

Claims

1. A lateral balance control method for a heavy telescopic forklift, characterized in that: With the drone as the leader and the forklift as the follower, controlling the lateral balance of the forklift body includes the following steps: Step S1, the drone scans the ground environment; the central control module obtains the drone scanning data and constructs a three-dimensional map data structure; Step S2, the central control module provides a navigation path and a driving speed from a starting point to an end point according to the three-dimensional map data structure; and divides the navigation path into several sections according to the three-dimensional map data structure, taking the position where the vehicle body roll angle exceeds a first preset threshold as a node; Step S3: The drone scans the goods in the cargo box in real time to create three-dimensional model data of the goods; Step S4, determine the type of goods; Step S5: Taking the three-dimensional model data of the cargo at the current node as the initial value, predicting the dynamic change of the cargo according to the cargo type, the navigation path from the current node to the next node, the driving speed and the three-dimensional map data structure, calculating the three-dimensional model data of the cargo at the next node, and then calculating the cargo difference between the left half and the right half of the cargo box at the next node; Step S6: If the cargo difference of the next node exceeds a second preset threshold, the navigation path and driving speed of the forklift from the current node to the next node are optimized.

2. A lateral balance control method for a heavy telescopic forklift according to claim 1, characterized in that: In step S1, the central control module obtains the laser point cloud data collected by the drone scanning, extracts the spatial feature points according to the laser point cloud data, and generates the spatial distribution resolution data of the environment; obtains the spectral data collected by the drone scanning, fuses the spectral data with the noise-filtered point cloud data, and constructs a three-dimensional map data structure.

3. A lateral balance control method for a heavy telescopic forklift according to claim 1, characterized in that: In step S3, the drone scans the goods in the cargo box in real time and establishes three-dimensional model data of the goods, specifically: The Hertz imaging device carried by the drone performs an all-round, multi-angle transmission scan of the cargo box to obtain the cargo transmission depth data and material reflection coefficient; the resolution and acquisition frequency of the transmission depth data are analyzed to determine the cargo boundary clarity threshold, and a terahertz image is generated in combination with the dynamic range of the material reflection coefficient; the central control module uses the collected multi-angle terahertz images as projection data and uses the computer tomography algorithm to construct the three-dimensional model data of the cargo.

4. A lateral balance control method for a heavy telescopic forklift according to claim 3, characterized in that: Pre-build a database of material reflectance coefficients and 3D model data of known cargo types; pre-build a convolutional neural network (CNN), and use the data in the database as prior knowledge to train and optimize the convolutional neural network (CNN); In step S4, the cargo type is determined by taking the material reflectance coefficient and three-dimensional model data obtained by the drone scanning as input parameters and inputting them into a trained convolutional neural network, which then identifies and outputs the cargo type.

5. The lateral balance control method of a heavy telescopic forklift according to claim 1, characterized in that: In step S6, if the cargo difference exceeds the second preset threshold, the navigation path and driving speed of the forklift from the current node to the next node are optimized, specifically: A comprehensive optimization model is established by combining navigation path optimization and driving speed adjustment. The goal of this comprehensive optimization model is to minimize the risk of forklift body roll and maximize the driving efficiency of the forklift.

6. A lateral balance control method for a heavy telescopic forklift according to claim 5, characterized in that: Using a multi-objective optimization method, roll risk and driving efficiency are optimized as two independent objective functions: Minimize the risk of forklift body rollover: min f1(x)=ω1×θ+ω2×F 轮胎 +ω3×Δm; Among them, x is the decision variable, including navigation path parameters and driving speed parameters; θ is the vehicle body roll angle; F 轮胎 is the tire force; Δm is the cargo difference; ω1, ω2, ω3 are the weight coefficients of each factor, and ω1+ω2+ω3=1; Maximize the driving efficiency of the forklift: max f2(x)=(L 优化 / L 初始 )×(T 初始 / T 优化 ); Among them, L 初始 is the path length before optimization; L 优化 is the optimized path length; T 初始 is the travel time before optimization; T 优化 is the optimized travel time.

7. A lateral balance control method for a heavy telescopic forklift according to claim 6, characterized in that: Through the weighted summation method, the two objective functions are transformed into a comprehensive objective function: F(x)=α×min f1(x)+β×max f2(x); Among them, α and β are weight coefficients, and α+β=1, which are used to balance the impact of roll risk and driving efficiency.

8. A lateral balance control system for a heavy-duty telescopic forklift, characterized in that: The system is used to execute a lateral balance control method for a heavy-duty telescopic arm forklift as described in any one of claims 1 to 7, comprising a drone, a central control module and a forklift, and the central control module is communicatively connected to the drone and the forklift; the forklift is equipped with a lateral acceleration sensor, a speed sensor and a dual-axis inclination sensor; the drone is equipped with a laser radar, a Hertz imaging device and a hyperspectral camera.

9. A lateral balance control system for a heavy telescopic forklift according to claim 8, characterized in that: The central control module is mounted on a forklift.

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