Double-truck-crane cooperative hoisting method, device and equipment based on hoisting path dynamic planning and storage medium

Abstract

The embodiment of the invention discloses a hoisting path dynamic planning-based double-truck-crane cooperative hoisting method, device and equipment and a storage medium, and relates to the technical field of truck-crane cooperative hoisting. According to the method, a hoisting task model and environment information of a hoisting scene are combined, the motion path of double truck-cranes is optimized through a path planning algorithm, and the hoisting path dynamic planning-based double-truck-crane cooperative hoisting efficiency is improved. And stability evaluation and correction are carried out on the path points. And the central control system generates a cooperative control instruction according to the corrected motion path, coordinates the rotation angle, the variable amplitude angle and the lifting height of the truck crane in real time, and ensures stable lifting of the component pose. Therefore, the precision and the safety of collaborative operation of the double truck cranes are effectively improved, the stability problem possibly occurring in the hoisting process can be responded in real time, and the stability and the high efficiency of the operation process are ensured.

Description

Technical Field

[0001] This invention relates to the field of truck crane collaborative lifting technology, and in particular to a method, apparatus, equipment and storage medium for collaborative lifting of two truck cranes based on dynamic planning of lifting path. Background Technology

[0002] With the continuous development of lifting technology and the increasing complexity of crane operating environments, traditional lifting methods can no longer meet the demands for efficient, safe, and precise operations. Especially in scenarios involving two truck cranes working together, ensuring precise coordination between the two cranes and guaranteeing their stability along complex operating paths has become a pressing technical challenge for the industry.

[0003] While existing crane coordination technologies can achieve basic coordinated control of cranes under certain specific conditions, in actual operation, the position, posture, and path deviation of cranes are often affected by many factors, such as terrain, wind, and the swaying of the load. These factors can easily lead to path errors or instability in cranes, thus affecting operational efficiency and safety. Therefore, traditional static path planning and preset paths cannot adapt to dynamically changing operating environments and cannot effectively cope with real-time deviations and instabilities that occur during crane operations. Summary of the Invention

[0004] This invention provides a method, device, equipment, and storage medium for collaborative lifting of two truck cranes based on dynamic planning of the lifting path, in order to solve the technical problems existing in the traditional collaborative lifting process of two cranes, such as insufficient static path planning, insufficient real-time path deviation monitoring, low accuracy of collaborative control of two cranes, and insufficient assessment of operational stability.

[0005] In a first aspect, the present invention provides a method for coordinated lifting of two truck cranes based on dynamic planning of lifting paths, comprising: S1. Construct a hoisting task model based on the feature data of the component to be hoisted and the environmental information of the hoisting scenario; S2. Within the six-dimensional configuration space of the two truck cranes, the central control system optimizes the movement path of the truck cranes using a path planning algorithm based on the target state and constraint set output by the lifting task model. S3. Based on the optimized motion path, perform static stability evaluation on each discrete path point in the optimized motion path. When the stability index of any discrete path point is lower than the preset safety threshold, correct and optimize the discrete path point and its adjacent path segments based on the motion constraints and work boundaries of the truck crane to obtain the corrected motion path. S4. The modified motion path is input to the central control system. The central control system generates a coordinated control command for the two truck cranes based on the modified motion path. The execution of the modified motion path is initiated based on the coordinated control command. During the path execution, the central control system coordinates the slewing angle, amplitude angle and lifting height of each truck crane in real time to achieve controlled lifting of the component's position and posture. S5. During the path execution process, the central control system dynamically monitors the hoisting operation status based on real-time operation data acquired by sensor devices. It also performs rolling predictive analysis on subsequent segments of the path by combining a dynamic stability assessment model of the path segment built based on historical hoisting task data. If the prediction results show that there is a stability risk or abnormal control trend in the subsequent segments of the path, the central control system will trigger a risk warning mechanism and adjust the control strategy through the path correction module of the central control system to ensure the safety and stability of the component hoisting operation.

[0006] Furthermore, based on the feature data of the components to be hoisted and the environmental information of the hoisting scenario, a hoisting task model is constructed, including: The characteristic data of the component to be lifted include the mass m, length l, width w, height h, and center of gravity position of the component. ; in, Let X be the centroid coordinates of the component in the X-axis direction. Let Y be the centroid coordinates of the component in the Y-axis direction. Here are the coordinates of the centroid of the component along the Z-axis. The environmental information of the hoisting scenario includes a set of obstacle locations within the work area. The set of obstacle locations within the work area Represented as a set containing the locations and sizes of multiple obstacles: ; in, Indicates the number of obstacles; The position of each obstacle is represented by three-dimensional coordinates. ; in, The X-axis coordinate of the obstacle; The Y-axis coordinate of the obstacle; The Z-axis coordinate of the obstacle; The size of the obstacle is represented by a size vector. ; in, Indicates the size of the obstacle in the X-axis direction. Indicates the size of the obstacle in the Y-axis direction. Indicates the dimension of the obstacle in the Z-axis direction; The environmental information for the hoisting scenario also includes the ground tilt angle. and the wind speed in the working environment ; The hoisting task model is represented as follows: ; Among them, the hoisting task model Output the target state and constraint set, where the target state includes the final position and attitude requirements of the hoisting component; The set of constraints includes path planning constraints and stability assessment information, which serve as the basis for path planning optimization; This is a task modeling function based on input features, used to generate path planning constraints and stability evaluation results, and output the target state and constraint set for use in the subsequent path planning optimization process.

[0007] Furthermore, the six-dimensional configuration space of the dual truck cranes is composed of the degree-of-freedom vector set of the dual truck cranes. Composition, the This represents the state vector of the two truck cranes at any given time. The set of degrees of freedom vectors of the dual truck cranes Including the set of degrees of freedom vectors of the first truck crane and the degree-of-freedom vector set of the second truck crane ; , The slewing angle of the first truck crane indicates its rotation state. The angle between the boom of the first truck crane and the ground or work platform represents the luffing angle of the first truck crane. The vertical height of the boom of the first truck crane represents the lifting height of the first truck crane. , The rotation angle of the second truck crane indicates its rotation state. The angle between the boom of the second truck crane and the ground or work platform indicates the luffing angle of the second truck crane. The vertical height of the boom of the second truck crane indicates the lifting height of the second truck crane; The set of degrees of freedom vectors of the dual truck cranes , represented as: .

[0008] Furthermore, within the six-dimensional configuration space of the two truck cranes, the path planning algorithm is based on the lifting task model. The output set of target states and constraints is used to optimize the movement path of the truck crane. The optimization process of path planning is expressed as follows: ; in, This represents the weighted scoring function; This represents the optimal movement path of the truck crane; and These represent the initial state and the target state of the truck crane, respectively. Let be the set of constraints.

[0009] Furthermore, the evaluation criteria used in the path point static stability assessment are based on the set of constraints output in the hoisting task model; The method for assessing the static stability of path points includes the following steps: S301. Component attitude assessment: Using an angle sensor installed on the truck crane, the tilt angle of the component at each discrete path point is obtained. The attitude stability of the component is assessed based on the tilt angle to determine whether the component will become unstable due to excessive changes in the hoisting angle. The tilt angle is the offset angle of the component relative to the vertical line of the truck crane; S302. Component load stress assessment: Real-time stress data of lifting points is obtained through load sensors and lifting point sensors of the truck crane. Based on the stress data, the stress distribution of each truck crane on the component is calculated to determine whether there is an imbalance in the stress on the component. S303. Evaluation of the force balance of component lifting points: By analyzing the torque and traction force applied by two truck cranes to the component lifting points, the force balance of the truck cranes on the component in the vertical and horizontal directions is evaluated. S304. Based on the component attitude assessment results, component load force assessment results, and component lifting point force balance assessment results, a stability index is calculated comprehensively. The various assessment results are quantified into a stability score through weighted average or risk assessment algorithm to assess whether the safety threshold of discrete path points meets the safety requirements of lifting operations. S305. When the stability score of any discrete path point is lower than the preset safety threshold, the central control system automatically performs local path correction. The path correction includes adjusting the position, speed and angle of the discrete path point, as well as performing local replanning to ensure that the corrected path can meet the motion constraints and operational boundary requirements of the truck crane.

[0010] Furthermore, when the central control system generates coordinated control commands for the two truck cranes based on the modified motion path, it distributes the force on the two truck cranes based on the center of gravity position of the components. By synchronously scheduling the lifting force and traction force of each truck crane, the two truck cranes maintain coordinated force during lifting and operation, avoiding component posture deviation or abnormal force on the truck cranes due to uneven force distribution.

[0011] Furthermore, when the central control system executes the corrected motion path, it acquires the real-time operation data of the truck crane through the sensor device, calculates the real-time path deviation between the current position of the truck crane and the corrected motion path based on the real-time operation data, and compares the real-time path deviation with a preset tolerance threshold. When the real-time path deviation exceeds the preset tolerance threshold, the central control system triggers a risk warning mechanism and dynamically adjusts the coordinated control commands of the two truck cranes through the path correction module according to the magnitude and direction of the real-time path deviation. The coordinated control commands include adjusting the slewing angle, amplitude angle and lifting height of the truck cranes to reduce the real-time path deviation and ensure that the truck cranes operate stably according to the corrected motion path.

[0012] Secondly, the present invention provides a dual-truck crane collaborative lifting device based on dynamic planning of lifting paths, comprising: The construction module is used to build a hoisting task model based on the feature data of the component to be hoisted and the environmental information of the hoisting scenario; The optimization module is used to optimize the motion path of the truck cranes within the six-dimensional configuration space of the two truck cranes, based on the target state and constraint condition set output by the lifting task model, using a path planning algorithm. The evaluation module is used to evaluate the static stability of each discrete path point in the optimized motion path based on the optimized motion path. When the stability index of any discrete path point is lower than the preset safety threshold, the discrete path point and its adjacent path segments are corrected and optimized based on the motion constraints and operation boundaries of the truck crane. The control module is used to input the modified motion path to the central control system. The central control system generates a coordinated control command for the two truck cranes based on the modified motion path and initiates the execution of the modified motion path based on the coordinated control command. During the path execution, the central control system coordinates the slewing angle, amplitude angle and lifting height of each truck crane in real time to achieve controlled lifting of the component's position and posture. The prediction module is used by the central control system to dynamically monitor the hoisting operation status based on real-time operation data acquired by sensor devices during path execution. It also combines a path segment dynamic stability assessment model built based on historical hoisting task data to perform rolling prediction analysis on subsequent segments of the path. If the prediction results show that there is a stability risk or abnormal control trend in subsequent segments of the path, the central control system will trigger a risk warning mechanism and adjust the control strategy through the path correction module of the central control system to ensure the safety and stability of component hoisting operations.

[0013] Thirdly, the present invention provides an electronic device, comprising: a processor, and a memory communicatively connected to the processor; The memory stores computer-executed instructions; When the processor executes the computer execution instructions stored in the memory, it is used to implement the dual truck crane collaborative lifting method based on dynamic planning of the lifting path, which is the subject of the first aspect of the invention.

[0014] Fourthly, the present invention provides a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, are used to implement the dual-truck crane collaborative lifting method based on dynamic planning of lifting path as described in the first aspect of the invention.

[0015] This invention provides a method, device, electronic equipment, and storage medium for collaborative lifting of two truck cranes based on dynamic path planning. This method achieves efficient, safe, and precise control during the lifting operation by introducing dynamic path planning, real-time stability assessment, and collaborative control mechanisms. First, based on the characteristic data of the components to be lifted and the environmental information of the lifting scenario, a lifting task model is constructed to provide a scientific basis for subsequent path optimization and control decisions. Next, within the six-dimensional configuration space of the two truck cranes, the central control system, according to the task model and the set of constraints, applies a path planning algorithm to optimize the most suitable motion path, thereby ensuring the adaptability and accuracy of the dual-crane operation in complex environments. Simultaneously, to further ensure path stability, the system performs static stability assessments on each discrete path point in the optimized path. When the stability index of a discrete path point is found to be below the safety threshold, the path is promptly corrected based on the crane's motion constraints and operational boundaries to avoid unsafe operations. Subsequently, the corrected motion path is input into the central control system to generate precise collaborative control commands, ensuring that the two cranes can execute the path in a coordinated manner, accurately controlling the crane's slewing angle, amplitude angle, and lifting height to achieve controlled lifting of the components. Meanwhile, the central control system dynamically monitors the crane's operating status based on real-time sensor data and, combined with a path segment dynamic stability assessment model constructed from historical lifting task data, performs rolling predictive analysis on subsequent path segments to identify potential stability risks or control abnormal trends in advance. If the system predicts a risk, it immediately triggers a risk warning mechanism and dynamically adjusts the path through the path correction module to ensure that the lifting operation remains in a safe and stable state. Through this series of interconnected steps, this invention not only improves the accuracy and real-time performance of dual-crane collaborative control but also significantly enhances the safety, automation level, and construction efficiency of lifting operations through intelligent feedback and dynamic adjustment mechanisms. It solves the problems of static path planning, inaccurate collaborative control, and insufficient stability assessment in existing technologies, achieving efficient, safe, and precise dual-crane collaborative lifting operations. Attached Figure Description

[0016] The accompanying drawings, which form part of this invention, are used to provide a further understanding of the invention. The illustrative embodiments of the invention and their descriptions are used to explain the invention and do not constitute an undue limitation of the invention. In the drawings: Figure 1 This is a flowchart illustrating the collaborative lifting method for two truck cranes based on dynamic planning of the lifting path provided in Embodiment 1 of the present invention. Figure 2 This is a schematic diagram of the structure of the dual truck crane collaborative lifting device based on dynamic planning of lifting path provided in Embodiment 2 of the present invention; Figure 3 This is a schematic diagram of the hardware structure of the electronic device provided in Embodiment 3 of the present invention. Detailed Implementation

[0017] The present invention will now be described in further detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and not intended to limit it. Furthermore, it should be noted that, for ease of description, the accompanying drawings show only the parts relevant to the present invention, and not all of the structures.

[0018] To facilitate a clear description of the technical solutions in the embodiments of the present invention, some terms and technologies involved in the embodiments of the present invention will be briefly introduced below: Lifting Task Model: This is a model that models the characteristics of the components to be lifted and the environmental information of the lifting scenario, and outputs a set of target states and constraints. This model provides a basis for decision-making in subsequent path planning, collaborative control and stability assessment.

[0019] Lifting component characteristic data: Mass (m) refers to the total mass of the component to be lifted, usually expressed in kilograms (kg) or tons (t). Mass affects the lifting force required during lifting; the length l, width w, and height h of the component to be lifted represent its three-dimensional dimensions, usually expressed in meters (m). These data are crucial for path planning and stability assessment; the center of gravity position of the component to be lifted... The center of gravity of a component is a three-dimensional coordinate, which represents the location of the component's center of mass and is very important for path planning and stability analysis.

[0020] obstacle location set This is a set describing the locations of obstacles within the work area. The locations of obstacles can be represented using three-dimensional coordinates. The location of each obstacle is represented by three-dimensional coordinates as follows: , The X-axis coordinate of the obstacle; The Y-axis coordinate of the obstacle; The Z-axis coordinate of the obstacle.

[0021] Obstacle size vector The size of each obstacle is represented by a vector, typically in three dimensions. , Indicates the size of the obstacle in the X-axis direction. Indicates the size of the obstacle in the Y-axis direction. This indicates the size of the obstacle in the Z-axis direction.

[0022] Ground tilt angle: refers to the tilt angle of the ground in the hoisting operation area relative to the horizontal plane.

[0023] Wind speed: Wind speed in the working environment affects the stability of hoisting tasks.

[0024] Six-dimensional configuration space: The motion state of the dual truck cranes is described by six degrees of freedom, specifically including, , The slewing angle of the first truck crane indicates its rotation state. The angle between the boom of the first truck crane and the ground or work platform represents the luffing angle of the first truck crane. The vertical height of the boom of the first truck crane represents the lifting height of the first truck crane. , The rotation angle of the second truck crane indicates its rotation state. The angle between the boom of the second truck crane and the ground or work platform indicates the luffing angle of the second truck crane. The vertical height of the boom of the second truck crane represents the lifting height of the second truck crane. The combination of the six degrees of freedom constitutes the motion state of the two cranes, affecting the coordination and stability during the lifting process.

[0025] Path planning algorithm: This algorithm optimizes the movement path of the two truck cranes based on the target state and constraint set output by the lifting task model. The optimization objective is to ensure the shortest, safest, or most energy-efficient path, and heuristic or optimization algorithms (such as Dijkstra's algorithm) are typically used to find the optimal path.

[0026] Static stability assessment of path points: The stability of each discrete path point in the path is assessed, mainly considering the attitude of the component, the load force, and the force balance of the lifting point. The stability assessment is mainly completed through the following aspects: (1) Component attitude assessment: the tilt angle of the hoisted component at each discrete path point is measured by angle sensor to assess whether the component will become unstable; (2) Component load force assessment: the load data is obtained by the load sensor and lifting point sensor of the crane to analyze whether the load applied by the crane is uniform; (3) Component lifting point force balance assessment: the balance of the torque and traction force applied by the two truck cranes is analyzed to ensure the stability of the component.

[0027] Risk warning mechanism: Based on real-time operational data, the system analyzes potential stability risks or abnormal trends during path execution. If warning conditions are triggered, the system will notify the operator and make automatic adjustments.

[0028] Path correction module: When there is a risk in the path, the correction module performs local path correction by adjusting the position information, speed and angle of discrete path points to ensure that the path meets safety requirements and avoids danger.

[0029] Coordinated control instructions: The central control system generates instructions based on the optimized and corrected paths to coordinate the movement of the two truck cranes; these instructions involve the following: (1) coordinated control of the slewing angle to ensure that the two cranes rotate synchronously; (2) coordinated control of the luffing angle to ensure that the boom is adjusted synchronously; (3) coordinated control of the lifting height to ensure that the components remain stable during the lifting process.

[0030] Real-time path deviation: refers to the deviation between the crane's current position and the corrected movement path obtained in real time. When the deviation exceeds the tolerance threshold, the central control system will trigger a risk warning mechanism and adjust control commands to ensure stable operation.

[0031] In modern engineering construction, material handling, and large equipment hoisting operations, the safety, efficiency, and precision of lifting operations are crucial standards for evaluating the technical level of a hoisting system. Especially when hoisting heavy or large components, the load-bearing capacity of a single crane is often insufficient, and the complex and variable operating environment presents challenges such as confined spaces, numerous obstacles, and strong winds, all of which negatively impact safety and accuracy. Therefore, the demand for dual or multiple cranes working in coordination is increasing to improve the stability, accuracy, and efficiency of hoisting tasks.

[0032] Traditional dual-crane operations rely primarily on manual experience and static path planning methods. These methods often suffer from the following problems during implementation: Static Path Planning: Traditional path planning methods typically rely on static calculations based on a known environment, lacking dynamic feedback and real-time adjustment capabilities. Factors such as obstacle locations and wind speed changes in the work environment may alter during operations, causing the pre-planned path to deviate from the actual environment, thus affecting operational safety and efficiency.

[0033] Insufficient lifting stability: In dual-lift operations, the two cranes need to work together to ensure the stability of the lifted components. However, traditional methods often fail to adequately consider the stress conditions at each lifting point, leading to uneven stress distribution during the lifting process. This can easily cause problems such as changes in component posture and crane instability. Especially under complex working conditions, the lack of a precise real-time stability assessment mechanism increases operational risks.

[0034] Lack of real-time monitoring and feedback mechanisms: In traditional operations, hoisting operations rely heavily on experienced operators for real-time monitoring and adjustments. However, personnel have limited reaction speed and judgment, especially in complex hoisting tasks. Human intervention may lead to misoperation or delays, thereby increasing operational risks and costs.

[0035] In conclusion, achieving dynamic path planning, real-time stability assessment, and feedback adjustments in dual-lift collaborative operations has become a key issue in improving the efficiency and safety of lifting operations. Especially when facing complex offshore operating environments, traditional methods often fail to adapt to changing site conditions and accurately handle various dynamic factors, leading to low operational efficiency, frequent accidents, and significant losses.

[0036] Based on this, embodiments of the present invention provide a method, device, electronic device, and storage medium for collaborative lifting of two truck cranes based on dynamic planning of lifting path, in order to solve the above-mentioned technical problems.

[0037] Example 1 Figure 1 This is a flowchart illustrating the collaborative lifting method for two truck cranes based on dynamic planning of the lifting path provided in Embodiment 1 of the present invention. Figure 1 As shown, the method includes: S1. Construct a hoisting task model based on the feature data of the component to be hoisted and the environmental information of the hoisting scenario; Specifically, the process begins by collecting various characteristic data of the components to be hoisted, including their mass, dimensions (such as length, width, and height), and the position of their center of gravity. Simultaneously, environmental information from the hoisting site is collected, such as the location of obstacles, spatial limitations, wind speed, and ground inclination. The characteristic data of the components to be hoisted and the environmental information of the hoisting scenario will help build a hoisting task model, forming a comprehensive task description based on multiple input variables, providing accurate basic data for subsequent path planning and motion control.

[0038] The characteristic data of the component to be hoisted include its mass m, length l, width w, height h, and center of gravity position. These data can be obtained directly using conventional measuring tools such as electronic scales, laser rangefinders, and 3D scanners.

[0039] Environmental information for the hoisting scenario includes a set of obstacle locations within the work area. ground tilt angle and the wind speed in the working environment This information can be collected in real time by sensor devices (such as lidar, inclinometers and anemometers), which is a standard technical means in hoisting operations.

[0040] S2. Within the six-dimensional configuration space of the two truck cranes, the central control system optimizes the movement path of the truck cranes using a path planning algorithm based on the target state and constraint set output by the lifting task model. Specifically, within the six-dimensional configuration space, the slewing angle, amplitude angle, and lifting height of each truck crane are considered as degrees of freedom. The central control system calculates the optimal path for dual-crane operation based on the target state and constraints output in the task model. Path planning algorithms such as A* or genetic algorithms are used to find the most suitable motion path to ensure that obstacles are avoided and safe and stable working conditions are met during the movement of the dual cranes.

[0041] S3. Based on the optimized motion path, perform static stability evaluation on each discrete path point in the optimized motion path. When the stability index of any discrete path point is lower than the preset safety threshold, perform correction and optimization on the discrete path point and its adjacent path segments based on the motion constraints and operation boundaries of the truck crane to obtain the corrected motion path.

[0042] Specifically, the central control system performs static stability analysis on each discrete path point in the optimized motion path to check whether the discrete path points meet the stability requirements. The evaluation indicators include the tilt angle of the component, the force distribution of the lifting point, and the torque balance. When the stability index of a discrete path point is found to be lower than the safety threshold, the central control system will correct the discrete path point and its surrounding path segments according to the motion constraints of the truck crane (such as minimum radius, maximum lifting height, etc.) and the on-site operation boundaries (such as obstacle positions, space limitations, etc.), thereby avoiding potential instability or dangerous conditions and ensuring the smooth completion of the lifting task.

[0043] Specifically, the discrete path point and its surrounding path segments are corrected. This correction is performed by the path correction module of the central control system, and the techniques include dynamically adjusting the path point parameters and performing local replanning. 1. Position Adjustment: Based on the motion constraints of the truck crane (such as minimum slewing radius and maximum lifting height) and the working boundary (such as obstacle location), the central control system fine-tunes the three-dimensional coordinates (X, Y, Z) of discrete path points through optimization algorithms (such as gradient descent or genetic algorithms) to deviate them from the danger zone. For example, if a path point is too close to an obstacle and lacks stability, the system will translate its coordinates to within the safety margin.

[0044] 2. Speed ​​and Angle Adjustment: Corrections include adjusting the crane's speed and slewing angle at path points. Amplitude and lifting height For example, reducing the speed of movement or optimizing the boom angle can decrease the risk of component sway. These adjustments are based on calculations using real-time sensor data, such as load and angle sensors, to ensure that the corrected path meets stability thresholds.

[0045] 3. Local Replanning: When a single-point correction is insufficient, the system will trigger a local path replanning algorithm (such as Dijkstra's algorithm) to recalculate the local path segment centered on that point. During replanning, the system prioritizes maintaining path smoothness, avoids abrupt turns, and ensures that the corrected path is consistent with the global target state (such as the final position of the component).

[0046] The surrounding path segment is not a fixed value, but a range of variables determined based on the dynamic characteristics of the path, and its definition is based on the following factors: 1. Path point spacing and path curvature: The range typically includes several path points adjacent to the current discrete path point. The specific number is determined by the step size parameter in the path planning algorithm. For example, in a six-dimensional configuration space.

[0047] 2. Stability impact radius: The range can also be dynamically calculated using a stability assessment model.

[0048] 3. Operating environment constraints: The scope is limited by the operating boundary (such as the distribution of obstacles). The system uses environmental information (such as the set of obstacles) to determine the scope. Real-time path segment trimming ensures that corrections only affect the operable areas.

[0049] S4. The modified motion path is input to the central control system. The central control system generates a coordinated control command for the two truck cranes based on the modified motion path. The execution of the modified motion path is initiated based on the coordinated control command. During the path execution, the central control system coordinates the slewing angle, amplitude angle and lifting height of each truck crane in real time to achieve controlled lifting of the component position.

[0050] Specifically, the corrected motion path will be transmitted to the central control system. The central control system will generate real-time control commands based on the corrected motion path. These control commands include the slewing angle, amplitude angle, and lifting height of each crane to ensure coordinated movement between the two cranes. The central control system will also dynamically adjust the motion state of the two cranes to ensure that the components maintain a stable posture during the hoisting process and avoid violent shaking or posture deviation.

[0051] S5. During the path execution process, the central control system dynamically monitors the hoisting operation status based on real-time operation data acquired by sensor devices. It also performs rolling predictive analysis on subsequent segments of the path by combining a dynamic stability assessment model of the path segment built based on historical hoisting task data. If the prediction results show that there is a stability risk or abnormal control trend in the subsequent segments of the path, the central control system will trigger a risk warning mechanism and adjust the control strategy through the path correction module of the central control system to ensure the safety and stability of the component hoisting operation.

[0052] Specifically, the central control system continuously monitors real-time operational data during the hoisting process using sensor devices. Simultaneously, it combines this data with historical hoisting task data and employs a dynamic stability assessment model to perform rolling predictive analysis of future path segments. Based on the prediction results, the central control system evaluates the stability of subsequent path segments in real time and determines whether any control anomalies or risks exist. Once a potential problem is detected, the system triggers a risk warning mechanism and adjusts the control strategy through the path correction module to promptly correct the path and prevent unsafe situations during hoisting operations.

[0053] This embodiment provides a method, device, electronic equipment, and storage medium for collaborative lifting of two truck cranes based on dynamic planning of the lifting path. This method effectively solves the problems in existing technologies regarding static path planning, insufficient stability assessment, low precision of manual control, and imperfect risk warning and correction mechanisms in dual-crane lifting operations. Specifically, it has the following significant beneficial effects: 1. By integrating component characteristics with hoisting environment elements to construct a hoisting task model, the system gains a global understanding of operational objectives and on-site constraints. This model not only supports path planning input but also lays the foundation for subsequent stability assessment and control strategies, enabling a shift from "experience-driven" to "data-driven" hoisting operations.

[0054] 2. By performing static stability assessments on each discrete path point, potential instability risks can be identified before the path is executed. When the stability index falls below the safety threshold, the system will automatically correct the path to ensure that the component's attitude is controllable, its center of gravity is balanced, and its stress is uniform during the hoisting process, thus avoiding accidents such as swaying, tilting, or breakage of the hoisting point.

[0055] 3. In the face of complex and ever-changing on-site operating environments (such as sudden wind disturbances, uneven ground, and changes in obstacles), this invention has the ability to perceive and adaptively correct abnormal states during the path execution process in real time, thereby significantly enhancing the environmental adaptability of the hoisting system.

[0056] In some implementations, by combining the characteristic data of the component to be lifted with the environmental information of the lifting scenario to construct a lifting task model, the central control system can optimize the lifting path, improve coordination accuracy, and ensure the stable and safe completion of the lifting task in a changing operating environment. The characteristic data of the component to be lifted includes its mass m, length l, width w, height h, and center of gravity position. ; in, Let X be the centroid coordinates of the component in the X-axis direction. Let Y be the centroid coordinates of the component in the Y-axis direction. Here are the coordinates of the centroid of the component along the Z-axis. Environmental information for the hoisting scenario includes a set of obstacle locations within the work area. The set of obstacle locations within the work area Represented as a set containing the locations and sizes of multiple obstacles: ; in, Indicates the number of obstacles; The position of each obstacle is represented by three-dimensional coordinates. ; in, The X-axis coordinate of the obstacle; The Y-axis coordinate of the obstacle; The Z-axis coordinate of the obstacle; The size of the obstacle is represented by a size vector. ,in, Indicates the size of the obstacle in the X-axis direction. Indicates the size of the obstacle in the Y-axis direction. Indicates the dimension of the obstacle in the Z-axis direction; Environmental information for the hoisting scenario also includes the ground tilt angle. and the wind speed in the working environment ; By incorporating the location and size of obstacles, ground tilt angle, and wind speed into the lifting task model, dynamic path optimization can be performed based on real-time environmental changes. Specifically, the location of obstacles is represented by three-dimensional coordinates, and the size of obstacles is represented by a size vector. This information provides a more comprehensive and accurate environmental description for the path planning algorithm, thereby enabling dynamic correction and optimization of the path during lifting operations. This ensures that path planning adapts to complex and changing lifting scenarios and reduces the risk of obstacle collisions or path instability.

[0057] The hoisting task model is represented as follows: ; Among them, the hoisting task model Output the target state and the set of constraints. The target state includes the final position and attitude requirements of the hoisting component. The set of constraints includes path planning constraints and stability assessment information, which serve as the basis for path planning optimization.

[0058] For example, task modeling function The implementation relies on the construction module 110, which executes a predefined algorithm through a processor to map input data into operable outputs. For example, in a scenario of hoisting a steel box girder for a cross-sea bridge, the model dynamically outputs the target position and stability threshold based on inputs such as the girder segment mass of 1200t, length of 60m, and wind speed of 13m / s.

[0059] Specifically, in this invention, the output of the hoisting task model includes a set of target states and constraint conditions. The set of target states and constraint conditions is the basis for path planning and stability assessment, providing clear targets and constraints for path optimization.

[0060] The target state refers to the "ideal state" that the component needs to reach in the lifting task. Specifically, the target state includes: (1) the final position of the lifting component, which is the precise position that the lifting component needs to reach when the task is completed. The position is usually represented by three-dimensional coordinates (X, Y, Z). Since lifting operations often involve precise positioning (e.g., bridge components, large equipment, etc.), this position usually requires precise control within the centimeter or even millimeter range. For example, suppose a bridge lifting task is underway. The target position of the lifting component may be a support position of the bridge, requiring the component to be accurately aligned with the predetermined pier or fixed point. In this case, the "final position" of the target state is the coordinates of the lifting component precisely reaching the support position. (2) the attitude requirements of the lifting component. Attitude usually refers to the angle or direction of the component in space, usually involving roll angle, pitch angle and yaw angle. These three angles describe the rotation of the component around the three coordinate axes. During the lifting process, the attitude control of the component is crucial, especially in large lifting tasks. Attitude deviation can lead to uneven stress on the component, tilting or other safety hazards. For example, if a hoisting task requires lifting a piece of heavy machinery onto a platform, the orientation requirements of the component may include precise rotation angles. For instance, the equipment may need to be mounted on the platform at a specific angle (such as 90 degrees rotation) to ensure a stable center of gravity and prevent tilting.

[0061] The constraint set is the "restriction" in the path planning process. It defines some physical and environmental limitations that must be followed during the execution of the hoisting task. The constraint set contains two types of content: path planning constraints and stability assessment information.

[0062] Among them, path planning constraints refer to some external restrictions that the hoisting path must meet when planning the path. Common path planning constraints include: (1) Obstacle avoidance constraints: the hoisting path must avoid obstacles (such as buildings, equipment, ground pits, etc.) in the work area. These obstacles are usually described by three-dimensional coordinates and size vectors in the task model. During the path planning process, the system will calculate which places in the path need to be avoided and which areas can be passed based on the location and size of the obstacles. For example, assuming that the hoisting operation needs to pass through a narrow passage, and there are some buildings or other equipment in the middle of the passage, the path planning needs to ensure that the hoisting path bypasses these obstacles and avoids collisions. (2) Work area boundary constraints: the boundary of the work area limits the spatial range of the hoisting operation. Path planning must ensure that the path of the hoisting component always stays within these boundaries. (3) Crane motion constraints: the crane's motion capability (such as maximum lifting height, maximum boom length, minimum turning radius, etc.) is also a key constraint factor in path planning. These constraints need to be taken into account to ensure that the crane does not exceed its working range during the movement.

[0063] Stability assessment information refers to the stability check of each discrete point in the lifting path. This information can be obtained by performing dynamic stability analysis on the lifting path to assess whether there is a potential risk of instability at each discrete point in the path. For example, at some discrete points in the path, if the tilt angle or force distribution of the component exceeds the safe range, it may lead to instability of the lifted component or instability of the crane.

[0064] Specifically, stress and center of gravity analysis: The position of the center of gravity and the stress conditions of the hoisted component are important factors in assessing stability. At each stage of the hoisting path, the system needs to perform real-time analysis of the component's center of gravity and the force distribution on the crane to ensure that the component does not tilt or slip during the hoisting process.

[0065] Dynamic stability during hoisting: Hoisting operations may be affected by environmental factors such as wind speed and ground tilt. Therefore, stability assessment information must consider not only static stability but also dynamic stability. This can be achieved through real-time monitoring and predictive analysis by combining sensor data and historical hoisting task data.

[0066] For example, when hoisting large components, the system needs to calculate and evaluate the stability of each discrete path point. For instance, at a certain discrete path point, the crane may need to significantly rotate its boom or increase its lifting height. If the component's center of gravity is not adjusted in time, it may become unstable along the path. Therefore, path planning needs to ensure that the component maintains a stable posture throughout the hoisting process.

[0067] This is a task modeling function based on input features, used to generate path planning constraints and stability evaluation results, and output the target state and constraint set for use in the subsequent path planning optimization process.

[0068] For example, task modeling function The construction relies on optimization algorithms and physical rule modeling, and its process includes three key stages: data preprocessing, constraint generation, and target state calculation. Specific technical details are as follows: (1) Data preprocessing stage: Task modeling function First, the input variables are normalized to eliminate dimensional differences and enhance algorithm stability. For example, the component mass *m* is converted to a relative load factor (e.g., ...). ,in, (Maximum lifting capacity of the truck crane), set of obstacle locations By mapping the coordinates to the global coordinate system of the hoisting operation, the wind speed... Then convert to wind pressure coefficient (e.g.) ,in, (for air density), this step ensures that the input data meets the algorithm requirements, which is a common technique in this field.

[0069] (2) Constraint generation stage: Task modeling function Based on the preprocessed data, a set of constraints required for path planning is generated using the principles of mechanical equilibrium and geometric constraint models. For example, this involves calculating the center of gravity of a component. By balancing the torque at the lifting points of the two truck cranes, the maximum allowable tilt angle threshold and force distribution tolerance are derived; in the specific calculations, the task modeling function... Representing the force relationship at the lifting points using matrix operations: Let the lifting forces of the two truck cranes be... and Then the stability constraint can be expressed as an inequality ; Meanwhile, task modeling function Based on the set of obstacle locations And the task boundary, generating the mathematical boundary of the avoidance region. For example, using 3D geometric algorithms (such as convex hull detection or spatial segmentation) to determine the obstacle size. Converted into safety distance constraints, forming a set of inequality constraints in path planning (such as...) ),in, For path points, To set a safe distance.

[0070] (3) Target state calculation stage: Task modeling function Based on the requirements of the lifting task (such as the final position and attitude of the component), the target state is output through a geometric transformation algorithm. For example, homogeneous coordinate transformation is used to convert the target attitude of the component (such as roll angle and pitch angle) into the set values ​​of the slewing angle and amplitude angle of the truck crane. This process can be represented as: ,in The transformation matrix is... This is the initial state.

[0071] in, This is a task modeling function based on input features. It generates the constraints and stability assessment results required for path planning by taking the input features (i.e., the physical characteristics of the hoisting components and the data of the working environment). Finally, it outputs the target state and the set of constraints for use in the subsequent path planning optimization process. In other words, the task modeling function transforms the complex hoisting task into clear path planning constraints and stability assessments, ensuring that path optimization can meet both physical and environmental constraints and guarantee the stability and safety of the operation in actual hoisting operations.

[0072] Specifically, the input features of the task modeling function include the feature data of the component to be hoisted and the environmental information of the hoisting scenario. The relevant input features not only provide basic information for subsequent path planning, but also determine the constraints and the focus of stability assessment that should be considered in the path planning process.

[0073] One of the tasks of the task modeling function is to generate path planning constraints. Path planning constraints determine the boundary conditions and limiting factors of path planning. Path planning constraints are the physical limitations and safety requirements that must be met during the hoisting process. For example, path planning constraints include: (1) obstacle avoidance constraints. Through the task modeling function, the system can convert the position and size information of obstacles into avoidance constraints to ensure that the hoisting path will not collide with obstacles. For example, if there is a wall in the work area, the system will calculate and avoid the path passing through the area, thereby ensuring safety during the hoisting process. (2) work area boundary constraints. The hoisting operation area has clear boundary limitations. The task modeling function needs to convert these boundaries into path planning constraints to ensure that the hoisting operation is always carried out within the allowable space. (3) crane working range constraints. The crane's working range (such as maximum lifting height, maximum boom length, minimum turning radius, etc.) is used as a path planning constraint to ensure that the crane's movement trajectory does not exceed its working limits.

[0074] Another task of the task modeling function is to generate stability assessment results, which is a necessary step to ensure the safety and feasibility of the lifting process. Specifically, the stability assessment includes the following aspects: (1) Static stability analysis: The position of the center of gravity and the force balance of the hoisting component determine the stability during the hoisting process. The task modeling function will calculate and analyze the static stability of the hoisting component at different discrete path points to ensure that the component will not tilt or be subjected to unbalanced forces during the hoisting process, thus avoiding accidents.

[0075] (2) Dynamic stability analysis: The hoisting process may be affected by dynamic factors such as wind speed changes and crane operation. The task modeling function will perform dynamic stability assessment on each point of the hoisting path, combine real-time data (such as wind speed, crane speed, etc.) to predict the stability of the path, and adjust the path when necessary.

[0076] In some implementations, the six-dimensional configuration space of the two truck cranes is represented by a set of degree-of-freedom vectors for the two truck cranes. constitute, This represents the state vector of the two truck cranes at any given time. Degrees of freedom vector set of two truck cranes Including the set of degrees of freedom vectors of the first truck crane and the degree-of-freedom vector set of the second truck crane ; , The slewing angle of the first truck crane indicates its rotation state. The angle between the boom of the first truck crane and the ground or work platform represents the luffing angle of the first truck crane. The vertical height of the boom of the first truck crane represents the lifting height of the first truck crane. , The rotation angle of the second truck crane indicates its rotation state. The angle between the boom of the second truck crane and the ground or work platform indicates the luffing angle of the second truck crane. The vertical height of the boom of the second truck crane indicates the lifting height of the second truck crane; Degrees of freedom vector set of two truck cranes , represented as: .

[0077] In some implementations, within the six-dimensional configuration space of the two truck cranes, the path planning algorithm is based on the lifting task model. The output set of target states and constraints is used to optimize the movement path of the truck crane. The optimization process of path planning is expressed as follows: ; in, This represents the weighted scoring function; in, This represents the optimal movement path of the truck crane; and These represent the initial state and the target state of the truck crane, respectively. For the set of constraints; Specifically, ; in, Each factor is assigned a weight, representing its importance in the crane's movement path.

[0078] In some implementations, the evaluation criteria used in the path point static stability assessment are based on the set of constraints output in the hoisting task model. The method for path point static stability assessment includes the following steps. For ease of understanding, the following example illustrates the hoisting of a steel box girder for a cross-sea bridge (e.g., a 60m segment weighing 1200t) under severe conditions of level 6 wind (13m / s) and wave height of 1.5m: S301, Component attitude assessment: The tilt angle of the component at each discrete path point is obtained by an angle sensor installed on the truck crane. The attitude stability of the component is assessed based on the tilt angle to determine whether the component will become unstable due to excessive changes in the lifting angle. The tilt angle is the offset angle of the component relative to the vertical line of the truck crane. Specifically, a dual-axis inclinometer is installed at the hook of the dual truck crane to monitor the lateral deflection angle of the component relative to the vertical line of the truck crane in real time. and longitudinal deflection angle By comparing the lateral deflection angle and longitudinal deflection angle Compared with the preset threshold (normal operating conditions) =2°, tightened to 1.5° when wave height ≥1m), to determine whether the component will become unstable due to excessive angle.

[0079] S302. Component load stress assessment: Real-time stress data of lifting points is obtained through load sensors and lifting point sensors of the truck crane. Based on the stress data, the stress distribution of each truck crane on the component is calculated to determine whether there is an imbalance in the stress on the component. Specifically, the theoretical output force of the hydraulic cylinders installed on the two truck cranes was measured. Installed at the anchor end of the component's sling, the actual tension is measured. Real-time synchronous acquisition of load sensor data and suspension point sensor data Among them, the dual-path load distribution ratio is calculated, with the main calculation (load sensor): Auxiliary verification (suspension point sensor): ;based on and Perform difference verification and final judgment. ,in, For example, a preset threshold. ;like Take the overall load distribution ratio: ;like This triggers a sensor fault alarm and switches to single-channel control mode (relying only on...). );when When the forces are balanced, when... At that time, the forces are uneven; in, The load distribution ratio obtained from the main calculation. The load distribution ratio was used for secondary verification. The load data, calculated directly from the load sensors installed on the hoisting system, reflects the actual force ratio applied to the two truck cranes and is the main data source used by the system for making main control logic judgments.

[0080] The calculation is based on the tension / force data measured by the suspension point sensors installed on the suspension points, and is used to verify the control accuracy and force coordination.

[0081] S303. Evaluation of the force balance of component lifting points: By analyzing the torque and traction force applied by two truck cranes to the component lifting points, the force balance of the truck cranes on the component in the vertical and horizontal directions is evaluated. Specifically, the vertical lifting force of the two truck cranes is obtained using the load sensor already installed in S302. (i.e., the theoretical output force of the hydraulic cylinder), the horizontal traction force of the two truck cranes is obtained through a horizontal traction force sensor. The horizontal offset of the component is obtained through a displacement sensor. The load distribution calculation includes both vertical and horizontal directions, with the vertical direction using the load distribution ratio calculated in S302. The difference in horizontal force in the horizontal direction is: The criterion is vertical equilibrium: ; Horizontal balance: and Component length; overall balance requires simultaneous satisfaction of both vertical and horizontal conditions.

[0082] S304. Based on the component attitude assessment results, component load force assessment results, and component lifting point force balance assessment results, a stability index is calculated comprehensively. The various assessment results are quantified into a stability score through weighted average or risk assessment algorithm to assess whether the safety threshold of discrete path points meets the safety requirements of lifting operations. Specifically, the component attitude evaluation result is the lateral deflection angle. and longitudinal deflection angle The component load stress assessment result is the comprehensive load distribution ratio. The result of the component lifting point stress balance assessment is the horizontal force difference ratio. Weighted scoring model: ; Among them, the weighted scoring model It is an index used to comprehensively evaluate the stability of a discrete point or segment of the lifting path. , as well as These are the weighting coefficients. Select according to S301 operating conditions; ; .

[0083] when Indicates safety; when This indicates that an early warning is needed; when This indicates that an emergency stop is required.

[0084] S305. When the stability score of any discrete path point is lower than the preset safety threshold, the central control system automatically performs local path correction. Path correction includes adjusting the position, speed and angle of the discrete path point, as well as performing local replanning to ensure that the corrected path can meet the motion constraints and operational boundary requirements of the truck crane.

[0085] For example, suppose the current path point of a hoisting operation is (10, 15, 20), and the stability score S is 1.2, which is lower than the safety threshold of 1.0. The system initiates path correction: (1) Position correction: the path point is adjusted to (12, 14, 18) to make it closer to the center of the operation area. (2) Speed ​​correction: the speed of the hoisting equipment is reduced from 2.5 m / s to 2.0 m / s to increase safety. (3) Angle correction: the angle is adjusted from 3° to 2° to reduce the offset angle and improve stability. (4) Local path replanning: the central control system replans the hoisting path based on the new discrete path point and the corrected speed and angle.

[0086] In some implementations, the central control system acquires the corrected motion path information in real time and generates coordinated control commands for the two truck cranes based on the component's center of gravity position. During the lifting process, the component's center of gravity position directly affects the force distribution between the two truck cranes. If the component's center of gravity shifts, the central control system dynamically adjusts the lifting and traction forces of the two cranes based on this shift to ensure force balance. Specifically, the central control system calculates the force ratio that each crane should bear and adjusts the distribution of lifting and traction forces according to changes in the center of gravity position. For example, when the component's center of gravity is biased to one side, the system increases the traction force of the crane on that side and appropriately adjusts its lifting force to compensate for the imbalance caused by the center of gravity shift. It synchronously dispatches the forces of the two cranes to ensure that they remain coordinated during lifting, moving, and stopping, avoiding component posture shifts caused by uneven force distribution, or even component tilting or rotation during lifting operations.

[0087] Furthermore, by precisely controlling the force applied to the two cranes, abnormal situations such as hydraulic system overload or lifting loss of control due to excessive force on a single crane are avoided. This system not only optimizes force distribution, reducing wear and tear and malfunctions of the crane equipment, but also improves the safety and stability of the lifting process and ensures operational precision. In practical applications, coordinated force ensures efficient and smooth lifting operations, especially in complex working environments, avoiding unforeseen risks caused by environmental factors (such as wind force and lifting angle), thereby significantly improving the overall safety and reliability of lifting operations.

[0088] In some implementations, the central control system executes the corrected motion path by acquiring real-time operational data from the truck crane. When the truck crane is performing lifting operations, the central control system uses sensor devices to monitor and acquire real-time operational status information of the truck crane, including parameters such as current position, speed, and attitude. This real-time data is used to calculate the real-time path deviation between the truck crane's current position and the corrected motion path. By comparing this deviation with a preset tolerance threshold, the central control system can determine whether the current deviation is within an acceptable range.

[0089] When the real-time path deviation exceeds a preset tolerance threshold, the central control system activates a risk warning mechanism. This mechanism aims to promptly identify potential safety risks and prevent accidents by dynamically adjusting the path. Specifically, the system generates path correction instructions based on the magnitude and direction of the real-time path deviation, adjusting the coordinated operation of the two mobile cranes. These correction instructions include adjusting key operating parameters such as slewing angle, amplitude angle, and lifting height to ensure that both mobile cranes return to the predetermined corrected path, thereby reducing the impact of the path deviation.

[0090] By monitoring and correcting path deviations in real time, the central control system ensures that both truck cranes operate stably along the corrected path, avoiding operational instability caused by path deviations. Especially in complex or unstable environments, the system can quickly adjust according to real-time conditions to prevent unsafe conditions such as component displacement or tilting.

[0091] When the path deviation exceeds a preset tolerance threshold, the system not only triggers an early warning but also quickly corrects the path, thus taking countermeasures before potential risks occur. This mechanism effectively reduces uncertainty during operations and ensures that lifting operations are carried out within safe limits.

[0092] Meanwhile, the central control system can calculate path deviations in real time and adjust control commands according to actual conditions, reducing errors caused by human error or external factors (such as wind force, uneven ground), thus ensuring efficient operation. Through precise adjustments to the slewing angle, amplitude angle, and lifting height, various operations during hoisting are optimized, reducing operational difficulty and error risks, and improving overall operational efficiency.

[0093] While adjusting the path, the system also ensures coordinated force distribution between the two cranes, preventing equipment malfunctions or structural damage caused by uneven force distribution during path correction. By precisely controlling lifting, slewing, and other actions, the system ensures balanced force distribution on the equipment in all directions, guaranteeing the stability of the lifting equipment.

[0094] Example 2 Figure 2 This is a structural schematic diagram of the dual-truck crane collaborative lifting device based on dynamic planning of the lifting path provided in Embodiment 2 of the present invention. Figure 2 As shown, the dual truck crane collaborative lifting device 100 based on dynamic planning of lifting path provided in Embodiment 2 of the present invention includes a construction module 110, an optimization module 120, an evaluation module 130, a control module 140, and a prediction module 150. Among them, the construction module 110 is used to construct a hoisting task model based on the feature data of the component to be hoisted and the environmental information of the hoisting scenario; The optimization module 120 is used to optimize the motion path of the truck cranes within the six-dimensional configuration space of the two truck cranes, based on the target state and constraint condition set output by the central control system according to the lifting task model. The evaluation module 130 is used to perform static stability evaluation of each discrete path point in the optimized motion path based on the optimized motion path. When the stability index of any discrete path point is lower than the preset safety threshold, the discrete path point and its adjacent path segments are corrected and optimized based on the motion constraints and operation boundaries of the truck crane.

[0095] The control module 140 is used to input the modified motion path to the central control system. The central control system generates a coordinated control command for the two truck cranes based on the modified motion path, and starts the execution of the modified motion path based on the coordinated control command. During the path execution, the central control system coordinates the slewing angle, amplitude angle and lifting height of each truck crane in real time to achieve controlled lifting of the component position.

[0096] The prediction module 150 is used to dynamically monitor the hoisting operation status based on real-time operation data acquired by sensor devices during the path execution process. It also combines the path segment dynamic stability assessment model built based on historical hoisting task data to perform rolling prediction analysis on subsequent segments of the path. If the prediction results show that there is a stability risk or abnormal control trend in the subsequent segments of the path, the central control system will trigger a risk warning mechanism and adjust the control strategy through the path correction module of the central control system to ensure the safety and stability of the component hoisting operation.

[0097] Example 3 Figure 3 This is a structural diagram of an electronic device according to Embodiment 3 of the present invention. Figure 3 A block diagram is shown of an exemplary electronic device 12 suitable for implementing embodiments of the present invention. Figure 3 The electronic device 12 shown is merely an example and should not impose any limitation on the functionality and scope of use of the embodiments of the present invention.

[0098] like Figure 3 As shown, the electronic device 12 is represented in the form of a general-purpose computing device. The components of the electronic device 12 may include, but are not limited to: one or more processors or processing units 16, system memory 28, and bus 18 connecting different system components (including system memory 28 and processing unit 16).

[0099] Bus 18 represents one or more of several bus architectures, including a memory bus or memory controller, a peripheral bus, a graphics acceleration port, a processor, or a local bus using any of the various bus architectures. For example, these architectures include, but are not limited to, the Industry Standard Architecture (ISA) bus, the Micro Channel Architecture (MAC) bus, the Enhanced ISA bus, the Video Electronics Standards Association (VESA) local bus, and the Peripheral Component Interconnect (PCI) bus.

[0100] Electronic device 12 typically includes a variety of computer system readable media. These media can be any available media that can be accessed by electronic device 12, including volatile and non-volatile media, removable and non-removable media.

[0101] System memory 28 may include computer system readable media in the form of volatile memory, such as random access memory (RAM) 30 and / or cache memory 32. Electronic device 12 may further include other removable / non-removable, volatile / non-volatile computer system storage media. By way of example only, storage system 34 may be used to read and write non-removable, non-volatile magnetic media (commonly referred to as "hard disk drives"). Disk drives for reading and writing to removable non-volatile disks (e.g., "floppy disks") and optical disk drives for reading and writing to removable non-volatile optical disks (e.g., CD-ROMs, DVD-ROMs, or other optical media) may be provided. In these cases, each drive may be connected to bus 18 via one or more data media interfaces. System memory 28 may include at least one program product having a set (e.g., at least one) of program modules configured to perform the functions of the embodiments of the present invention.

[0102] A program / utility 40 having a set (at least one) of program modules 42 may be stored, for example, in system memory 28. Such program modules 42 include, but are not limited to, an operating system, one or more application programs, other program modules, and program data. Each or some combination of these examples may include an implementation of a network environment. Program modules 42 typically perform the functions and / or methods described in the embodiments of the present invention.

[0103] Electronic device 12 can also communicate with one or more external devices 14 (e.g., keyboard, pointing device, display 24, etc.), and with one or more devices that enable a user to interact with the electronic device 12 / server / computer, and / or with any device that enables the electronic device 12 to communicate with one or more other computing devices (e.g., network card, modem, etc.). This communication can be performed through input / output (I / O) interface 22. Furthermore, electronic device 12 can also communicate with one or more networks (e.g., local area network (LAN), wide area network (WAN), and / or public networks, such as the Internet) via network adapter 20. Figure 3 As shown, network adapter 20 communicates with other modules of electronic device 12 via bus 18. It should be understood that, although... Figure 3 As not shown, other hardware and / or software modules may be used in conjunction with electronic device 12, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems.

[0104] The processing unit 16 executes various functional applications and data processing by running programs stored in the system memory 28, such as implementing the dual truck crane collaborative lifting method based on dynamic planning of lifting path provided in the embodiments of the present invention.

[0105] Example 4 Embodiment 4 of the present invention also provides a storage medium containing computer-executable instructions, which, when executed by a computer processor, are used to execute the dual-truck crane collaborative lifting method based on dynamic planning of lifting path provided in the above embodiments.

[0106] The computer storage medium of this invention can be any combination of one or more computer-readable media. A computer-readable medium can be a computer-readable signal medium or a computer-readable storage medium. A computer-readable storage medium can be, for example,—but not limited to—an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of computer-readable storage media (a non-exhaustive list) include: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In this document, a computer-readable storage medium can be any tangible medium that contains or stores a program that can be used by or in conjunction with an instruction execution system, apparatus, or device.

[0107] Computer-readable signal media may include data signals propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such propagated data signals may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. Computer-readable signal media may also be any computer-readable medium other than computer-readable storage media, capable of sending, propagating, or transmitting programs for use by or in connection with an instruction execution system, apparatus, or device.

[0108] Program code contained on a computer-readable medium may be transmitted using any suitable medium, including—but not limited to—wireless, wire, optical fiber, RF, etc., or any suitable combination thereof.

[0109] Computer program code for performing the operations of this invention can be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, and C++, as well as conventional procedural programming languages ​​such as "C" or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0110] Note that the above description is merely a preferred embodiment of the present invention and the technical principles employed. Those skilled in the art will understand that the present invention is not limited to the specific embodiments described herein, and various obvious changes, readjustments, and substitutions can be made without departing from the scope of protection of the present invention. Therefore, although the present invention has been described in detail through the above embodiments, the present invention is not limited to the above embodiments, and may include many other equivalent embodiments without departing from the concept of the present invention, the scope of which is determined by the scope of the appended claims.

Claims

1. A method for coordinated lifting of two truck cranes based on dynamic planning of lifting paths, characterized in that, include: S1. Construct a hoisting task model based on the feature data of the component to be hoisted and the environmental information of the hoisting scenario; S2. Within the six-dimensional configuration space of the two truck cranes, the central control system optimizes the movement path of the truck cranes using a path planning algorithm based on the target state and constraint set output by the lifting task model. S3. Based on the optimized motion path, perform static stability evaluation on each discrete path point in the optimized motion path. When the stability index of any discrete path point is lower than the preset safety threshold, correct and optimize the discrete path point and its adjacent path segments based on the motion constraints and work boundaries of the truck crane to obtain the corrected motion path. S4. The modified motion path is input to the central control system. The central control system generates a coordinated control command for the two truck cranes based on the modified motion path. The execution of the modified motion path is initiated based on the coordinated control command. During the path execution, the central control system coordinates the slewing angle, amplitude angle and lifting height of each truck crane in real time to achieve controlled lifting of the component's position and posture. S5. During the path execution process, the central control system dynamically monitors the hoisting operation status based on real-time operation data acquired by sensor devices. It also performs rolling predictive analysis on subsequent segments of the path by combining a dynamic stability assessment model of the path segment built based on historical hoisting task data. If the prediction results show that there is a stability risk or abnormal control trend in the subsequent segments of the path, the central control system will trigger a risk warning mechanism and adjust the control strategy through the path correction module of the central control system to ensure the safety and stability of the component hoisting operation.

2. The method for coordinated lifting of two truck cranes based on dynamic planning of lifting path according to claim 1, characterized in that, Based on the feature data of the components to be hoisted and the environmental information of the hoisting scenario, a hoisting task model is constructed, including: The characteristic data of the component to be lifted include the mass m, length l, width w, height h, and center of gravity position of the component. ; in, Let X be the centroid coordinates of the component in the X-axis direction. Let Y be the centroid coordinates of the component in the Y-axis direction. Here are the coordinates of the centroid of the component along the Z-axis. The environmental information of the hoisting scenario includes a set of obstacle locations within the work area. The set of obstacle locations within the work area Represented as a set containing the locations and sizes of multiple obstacles: ; in, Indicates the number of obstacles; The position of each obstacle is represented by three-dimensional coordinates. ; in, The X-axis coordinate of the obstacle; The Y-axis coordinate of the obstacle; The Z-axis coordinate of the obstacle; The size of the obstacle is represented by a size vector. ; in, Indicates the size of the obstacle in the X-axis direction. Indicates the size of the obstacle in the Y-axis direction. Indicates the dimension of the obstacle in the Z-axis direction; The environmental information for the hoisting scenario also includes the ground tilt angle. and the wind speed in the working environment ; The hoisting task model is represented as follows: ； Among them, the hoisting task model Output the target state and constraint set, where the target state includes the final position and attitude requirements of the hoisting component; The set of constraints includes path planning constraints and stability assessment information, which serve as the basis for path planning optimization; This is a task modeling function based on input features, used to generate path planning constraints and stability evaluation results, and output the target state and constraint set for use in the subsequent path planning optimization process.

3. The method for coordinated lifting of two truck cranes based on dynamic planning of lifting path according to claim 1, characterized in that, The six-dimensional configuration space of two truck cranes is represented by the set of degree-of-freedom vectors of the two truck cranes. Composition, the This represents the state vector of the two truck cranes at any given time. The set of degrees of freedom vectors of the dual truck cranes Including the set of degrees of freedom vectors of the first truck crane and the degree-of-freedom vector set of the second truck crane ; , The slewing angle of the first truck crane indicates its rotation state. The angle between the boom of the first truck crane and the ground or work platform represents the luffing angle of the first truck crane. The vertical height of the boom of the first truck crane represents the lifting height of the first truck crane. , The rotation angle of the second truck crane indicates its rotation state. The angle between the boom of the second truck crane and the ground or work platform indicates the luffing angle of the second truck crane. The vertical height of the boom of the second truck crane indicates the lifting height of the second truck crane; The set of degrees of freedom vectors of the dual truck cranes , is represented as: 。 4. The method for coordinated lifting of two truck cranes based on dynamic planning of lifting path according to claim 3, characterized in that, Within the six-dimensional configuration space of the two truck cranes, the path planning algorithm is based on the lifting task model. The output set of target states and constraints is used to optimize the movement path of the truck crane. The optimization process of path planning is expressed as follows: ； in, This represents the weighted scoring function; This represents the optimal movement path of the truck crane; and These represent the initial state and the target state of the truck crane, respectively. Let be the set of constraints.

5. The method for coordinated lifting of two truck cranes based on dynamic planning of lifting path according to claim 1, characterized in that, The evaluation criteria used in the static stability assessment of the path points are based on the set of constraints output in the hoisting task model. The method for assessing the static stability of path points includes the following steps: S301. Component attitude assessment: Using an angle sensor installed on the truck crane, the tilt angle of the component at each discrete path point is obtained. The attitude stability of the component is assessed based on the tilt angle to determine whether the component will become unstable due to excessive changes in the hoisting angle. The tilt angle is the offset angle of the component relative to the vertical line of the truck crane; S302. Component load stress assessment: Real-time stress data of lifting points is obtained through load sensors and lifting point sensors of the truck crane. Based on the stress data, the stress distribution of each truck crane on the component is calculated to determine whether there is an imbalance in the stress on the component. S303. Evaluation of the force balance of component lifting points: By analyzing the torque and traction force applied by two truck cranes to the component lifting points, the force balance of the truck cranes on the component in the vertical and horizontal directions is evaluated. S304. Based on the component attitude assessment results, component load force assessment results, and component lifting point force balance assessment results, a stability index is calculated comprehensively. The various assessment results are quantified into a stability score through weighted average or risk assessment algorithm to assess whether the safety threshold of discrete path points meets the safety requirements of lifting operations. S305. When the stability score of any discrete path point is lower than the preset safety threshold, the central control system automatically performs local path correction. The path correction includes adjusting the position, speed and angle of the discrete path point, as well as performing local replanning to ensure that the corrected path can meet the motion constraints and operational boundary requirements of the truck crane.

6. The method for coordinated lifting of two truck cranes based on dynamic planning of lifting path according to claim 1, characterized in that, When the central control system generates coordinated control commands for the two truck cranes based on the modified motion path, it distributes the force on the two truck cranes based on the center of gravity position of the components. By synchronously scheduling the lifting force and traction force of each truck crane, the two truck cranes maintain coordinated force during lifting and operation, avoiding component posture deviation or abnormal force on the truck cranes due to uneven force distribution.

7. The method for coordinated lifting of two truck cranes based on dynamic planning of lifting path according to claim 1, characterized in that: When the central control system executes the corrected motion path, it acquires real-time operation data of the truck crane through the sensor device, calculates the real-time path deviation between the current position of the truck crane and the corrected motion path based on the real-time operation data, and compares the real-time path deviation with a preset tolerance threshold. When the real-time path deviation exceeds the preset tolerance threshold, the central control system triggers a risk warning mechanism and dynamically adjusts the coordinated control commands of the two truck cranes through the path correction module according to the magnitude and direction of the real-time path deviation. The coordinated control commands include adjusting the slewing angle, amplitude angle and lifting height of the truck cranes to reduce the real-time path deviation and ensure that the truck cranes operate stably according to the corrected motion path.

8. A dual-truck crane collaborative lifting device based on dynamic planning of lifting path, characterized in that, include: The construction module is used to build a hoisting task model based on the feature data of the component to be hoisted and the environmental information of the hoisting scenario; The optimization module is used to optimize the motion path of the truck cranes within the six-dimensional configuration space of the two truck cranes, based on the target state and constraint condition set output by the lifting task model, using a path planning algorithm. The evaluation module is used to evaluate the static stability of each discrete path point in the optimized motion path based on the optimized motion path. When the stability index of any discrete path point is lower than the preset safety threshold, the discrete path point and its adjacent path segments are corrected and optimized based on the motion constraints and operation boundaries of the truck crane. The control module is used to input the modified motion path to the central control system. The central control system generates a coordinated control command for the two truck cranes based on the modified motion path and initiates the execution of the modified motion path based on the coordinated control command. During the path execution, the central control system coordinates the slewing angle, amplitude angle and lifting height of each truck crane in real time to achieve controlled lifting of the component's position and posture. The prediction module is used by the central control system to dynamically monitor the hoisting operation status based on real-time operation data acquired by sensor devices during path execution. It also combines a path segment dynamic stability assessment model built based on historical hoisting task data to perform rolling prediction analysis on subsequent segments of the path. If the prediction results show that there is a stability risk or abnormal control trend in subsequent segments of the path, the central control system will trigger a risk warning mechanism and adjust the control strategy through the path correction module of the central control system to ensure the safety and stability of component hoisting operations.

9. An electronic device, characterized in that, include: A processor, and a memory communicatively connected to the processor; The memory stores computer-executed instructions; When the processor executes the computer execution instructions stored in the memory, it is used to implement the dual truck crane collaborative lifting method based on dynamic planning of lifting path as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer-executable instructions, which, when executed by a processor, are used to implement the dual-truck crane collaborative lifting method based on dynamic planning of lifting path as described in any one of claims 1 to 7.

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