Novel power system of gantry crane

Abstract

The invention relates to the technical field of industrial automation and intelligent control, in particular to a novel gantry crane power system which comprises an initialization module, a planning module, a moving module and an optimization module, corrected path planning data is generated in combination with a distributed reinforcement learning algorithm, and load stability is adjusted through sound field and magnetic field technologies. And generating optimization model data by using a multi-objective joint optimization algorithm to update the key parameters. The system also realizes storage and management of task completion state, energy consumption and stress data through a data archiving unit. The method is obviously superior to the traditional technology in the aspects of path planning efficiency, load stability and energy consumption distribution, is suitable for a gantry crane operation scene in a complex dynamic environment, and provides an efficient and reliable solution for industrial automation and intelligent control.

Description

Technical Field

[0001] The present invention relates to the field of industrial automation and intelligent control technology, and in particular to a new power system for a gantry crane. Background Art

[0002] The gantry crane power system is a key technology in modern port logistics and industrial operations. Its performance directly affects the gantry crane's operating efficiency, load stability and operational safety. However, the application of existing power systems in complex operating scenarios has significant deficiencies. The design of traditional gantry crane engine rooms often fails to fully consider issues such as dust and water prevention and sewage discharge. Especially in long-term operation, unreasonable design of the exhaust pipeline may lead to water accumulation and pollution problems. At the same time, the integration and installation layout of the diesel unit and generator lack compactness and flexibility, and cannot meet the requirements for maintenance convenience and operational reliability in efficient operating scenarios.

[0003] In the existing technology, the power system is difficult to adapt to dynamic and complex operational requirements in terms of path planning, load control and energy consumption management. For example, the operation control mechanism of the diesel unit usually does not realize the "one-button" operation mode, and there is a lack of dynamic adjustment mechanism between the load support status and the path execution data, which leads to problems such as uneven energy consumption distribution and unstable load support during operation. Especially in a dynamic environment, the existing system lacks the ability to adjust the path for dynamic obstacle avoidance, which is prone to low operating efficiency and insufficient operational safety.

[0004] The present invention takes the complex operation requirements of the gantry crane as the core, and provides a solution based on corrected path planning data and dynamic obstacle trajectory data, focusing on path planning optimization, dynamic load support and improvement of operation efficiency. Summary of the invention

[0005] In view of the many problems existing in the above-mentioned prior art, the present invention provides a new power system for gantry cranes. The present invention realizes dynamic load control and path optimization of gantry crane systems through real-time monitoring, path planning optimization, sound field control and thrust distribution strategy combined with multi-objective joint optimization algorithm. The system optimizes path nodes using distributed reinforcement learning algorithms, dynamically adjusts load states in combination with sound field and magnetic field technologies, and supports long-term optimization through data archiving and analysis. The present invention significantly improves operating efficiency, load stability and energy consumption balance.

[0006] A new power system for a gantry crane, comprising: An initialization module is used to receive target position information and load parameters, collect the initial stress state data of the gantry crane, the load center of gravity distribution data, the obstacle distribution data of the site environment, the site structure data and the dynamic obstacle information data, and generate load state data and site environment model data; A planning module, used to generate corrected path planning data based on site environment model data, load status data and mission target data, and calculate sound field parameter data and stress prediction data to initialize path planning and load support systems; A movement module, used to control the dynamic movement of the gantry crane based on the corrected path planning data, generate obstacle avoidance path planning data through dynamic obstacle trajectory data, and dynamically adjust the sound field parameter data and load support status data according to the path execution data; The optimization module is used to complete the lifting or placement of loads in the target area, monitor the stress state data of the gantry crane in real time, record the mission energy consumption data, and generate optimization model data through a multi-objective joint optimization algorithm to update the path planning parameter data, sound field parameter data and thrust distribution strategy data.

[0007] Preferably, the initialization module includes: A task receiving unit, used to receive task target position information and load parameters, the task target position information includes the spatial coordinates of the lifting point and the spatial coordinates of the placement point, and the load parameters include the weight, shape characteristics and center of gravity position of the load; A data acquisition unit, used to obtain the initial stress state data of the gantry crane structure and the center of gravity distribution data of the load through a liquid metal sensor network; The environmental perception unit is used to collect obstacle distribution data, site structure data, and real-time position and motion trajectory data of dynamic obstacles in the site environment through a multimodal sensor array; The data fusion unit denoises, completes and fuses the collected initial stress state data, load center of gravity distribution data and site environment data based on the deep convolutional neural network algorithm to generate load state data and site environment model data.

[0008] Preferably, the planning module generates corrected path planning data through a distributed reinforcement learning algorithm. The distributed reinforcement learning algorithm iteratively optimizes the path planning by constructing a graph model including path nodes, distances between path nodes, and feasibility weights between path nodes. The optimization goals include minimizing path length, improving obstacle avoidance efficiency, and reducing propulsion energy consumption.

[0009] Preferably, the planning module further includes a path torque coupling path correction unit, which dynamically adjusts the magnetic field direction and magnetic field strength between path nodes through a path torque mapping model, wherein the calculation formula of the path torque mapping model is:

[0010] in, Represents a path node and path nodes The thrust between It represents the quality parameter of the path node, reflecting the relative strength of the force on the path node; Represents a path node and path nodes The straight-line distance between Represents a path node and path nodes The angle between them reflects the smoothness of the change in path direction; The thrust vector direction and magnitude between path nodes are calculated through the path torque mapping model, thereby optimizing the thrust distribution between path nodes and the smoothness of the path curve.

[0011] Preferably, the planning module generates stress prediction data through a deformation-stress prediction fusion algorithm. The deformation-stress prediction fusion algorithm is based on real-time stress distribution data and load state data collected by the liquid metal sensor network, and calculates the stress change trend and potential deformation area of ​​the gantry crane structure under different working conditions through a finite element analysis model.

[0012] Preferably, the mobile module includes a magnetic torque propulsion unit, which performs dynamic propulsion control between path nodes through the driving mechanism of variable polarity magnetic field material, according to the thrust direction and thrust magnitude generated by the corrected path planning data, and adjusts the magnetic field polarity and magnetic field strength in real time in combination with the dynamic obstacle trajectory data to generate updated obstacle avoidance path planning data.

[0013] Preferably, the mobile module further includes an acoustic levitation load optimization unit, which adjusts the sound field parameter data through a time-domain sound pressure phase control algorithm, wherein the time-domain sound pressure phase control algorithm calculates the frequency of the sound wave, the phase of the sound wave and the driving signal of the sound wave transmitter according to the path execution data and the load vibration characteristics to maintain the stability of the load.

[0014] Preferably, the optimization module generates optimization model data through a multi-objective joint optimization algorithm. The multi-objective joint optimization algorithm constructs an objective function based on real-time monitored stress state data, path planning execution data and task energy consumption data, where the objective function includes improving path planning efficiency, enhancing load stability and balancing energy consumption distribution, and obtains global optimization results through iterative calculation.

[0015] Preferably, the optimization model data generated by the optimization module includes updated path planning parameter data, sound field parameter data and thrust distribution strategy data, wherein the path planning parameter data includes updated path node coordinates and distances between path nodes, the sound field parameter data includes adjusted sound wave frequency and sound wave phase, and the thrust distribution strategy data includes recalculated magnetic field direction and magnetic field strength.

[0016] Preferably, the optimization module further includes a data archiving unit, which is used to store task completion status data, task energy consumption data and structural stress data generated during the operation process. The data archiving unit realizes data management through classification index and timestamp recording mechanism.

[0017] Compared with the prior art, the advantages and beneficial effects of the present invention are: The present invention realizes dynamic optimization of path planning through a distributed reinforcement learning algorithm based on modified path planning data, significantly improves obstacle avoidance capability and path execution efficiency in complex environments, and solves the problem of insufficient dynamic obstacle avoidance capability of existing systems; The present invention uses a technical means combining acoustic field control with a dynamic thrust distribution strategy to dynamically adjust the load support state, ensure load stability and balanced stress distribution, and solve the problem of unstable load support in traditional systems. The present invention realizes global optimization of path planning parameters, sound field parameters and thrust distribution strategy through real-time monitoring and feedback mechanism of path execution data combined with multi-objective joint optimization algorithm, thereby reducing operation energy consumption and improving operation efficiency. The present invention realizes real-time adjustment of dynamic obstacle avoidance paths through an obstacle avoidance path planning module based on dynamic obstacle trajectory data, improves operational safety, and meets the needs of highly complex operating scenarios; The modular design of the system of the present invention supports efficient operation control and maintenance optimization. For example, load lifting, movement and placement can be completed through a one-button operation mode, which improves the convenience of operation and reduces manual intervention. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 It is a structural block diagram of the system of the present invention; Figure 2 This is a schematic diagram of path planning and correction in the present invention; Figure 3 It is a flow chart for optimizing the sound field and thrust distribution strategy in the present invention; Figure 4 This is a flow chart of the dynamic obstacle avoidance path adjustment in the present invention. DETAILED DESCRIPTION

[0019] Hereinafter, embodiments of the present disclosure will be described with reference to the accompanying drawings. However, it should be understood that these descriptions are exemplary only and are not intended to limit the scope of the present disclosure. In the following detailed description, for ease of explanation, many specific details are set forth to provide a comprehensive understanding of the embodiments of the present disclosure.

[0020] like Figure 1 As shown, a new power system for a gantry crane includes: An initialization module is used to receive target position information and load parameters, collect the initial stress state data of the gantry crane, the load center of gravity distribution data, the obstacle distribution data of the site environment, the site structure data and the dynamic obstacle information data, and generate load state data and site environment model data; Preferably, the initialization module includes: A task receiving unit, used to receive task target position information and load parameters, the task target position information includes the spatial coordinates of the lifting point and the spatial coordinates of the placement point, and the load parameters include the weight, shape characteristics and center of gravity position of the load; A data acquisition unit, used to obtain the initial stress state data of the gantry crane structure and the center of gravity distribution data of the load through a liquid metal sensor network; The environmental perception unit is used to collect obstacle distribution data, site structure data, and real-time position and motion trajectory data of dynamic obstacles in the site environment through a multimodal sensor array; The data fusion unit denoises, completes and fuses the collected initial stress state data, load center of gravity distribution data and site environment data based on the deep convolutional neural network algorithm to generate load state data and site environment model data.

[0021] The initialization module is responsible for the entire process from task initialization to environmental data modeling. Its functions include receiving task instructions, collecting structural and environmental data, and generating load status data and site environment model data through fusion algorithms. The module design makes full use of liquid metal sensor networks, multimodal sensor arrays and deep learning algorithms to ensure high-precision and high-reliability input for subsequent path planning, dynamic movement and optimization modules.

[0022] The initialization module consists of a task receiving unit, a data acquisition unit, an environment perception unit, and a data fusion unit. The specific principles and implementations of each unit are as follows: The task receiving unit is the entrance to the initialization module and is responsible for receiving the task target position information and load parameters. The task target position information includes the spatial coordinates of the lifting point and the placement point, which is usually input through task instructions and processed by the system calibration. The load parameters include the weight, shape characteristics and center of gravity position of the load to ensure that the necessary data is provided for subsequent load support. Taking the rectangular load as an example, its shape characteristics are used to calculate the initial center of gravity position. The center of gravity height is usually initially set to half the load height, and then dynamically adjusted in actual operation to improve accuracy. Through the task receiving unit, the system can efficiently process complex task inputs and output the initial parameters required for the task.

[0023] The data acquisition unit collects the initial stress state data and load center of gravity distribution data of the gantry crane in real time through the liquid metal sensor network. The liquid metal sensor network is laid on key structures such as the main beam and suspension arm of the gantry crane. Based on the deformation characteristics of the conductive liquid metal material, it can accurately monitor stress changes. The stress state is calculated by the formula Calculate, where represents stress, is the force value at the collection point, is the cross-sectional area. Based on this formula, the stress distribution of key parts of the gantry crane can be displayed graphically, providing a basis for structural safety assessment.

[0024] The center of gravity distribution data of the load is collected in real time by the bottom pressure sensor array. The contact pressure value collected by the sensor is used to calculate the center of gravity coordinates using the following formula:

[0025]

[0026] in, For the The pressure value of each sensor; , For the The location coordinates of the sensors; , is the coordinate of the center of gravity of the load.

[0027] Taking a rectangular load as an example, assuming that the readings of the pressure sensors at the four corners of the bottom are 200 N, 250 N, 220 N and 230 N respectively, combined with their coordinate input formula, the system can accurately calculate the real-time center of gravity coordinates of the load to ensure the load is balanced.

[0028] The environmental perception unit collects obstacle distribution data, site structure data and dynamic obstacle information of the site environment through a multimodal sensor array. The sensor array includes photon radar, ultrasonic sensor and visual sensor, which are used to detect static obstacles, site boundaries and motion trajectories of dynamic targets in the environment. The photon radar captures the three-dimensional distribution of static obstacles and calculates the distance from the obstacle to the gantry crane through the time-of-flight method; the ultrasonic sensor combines the reflection time and the incident angle to detect the boundary range of the site structure in real time; the visual sensor identifies dynamic obstacles and predicts their future trajectory through the object detection algorithm, and uses the Kalman filter algorithm to correct the obstacle position estimation. Through the joint use of multi-sensor information, the environmental perception unit ensures the comprehensiveness and accuracy of the site environment modeling.

[0029] The data fusion unit is the core of the initialization module. It is used to integrate the output data of the task receiving unit, data acquisition unit and environmental perception unit, and generate load state data and site environment model data. Data fusion is implemented based on the deep convolutional neural network algorithm, and its main steps include data denoising, missing data completion and multi-dimensional data fusion. For the initial stress state data and center of gravity distribution data, the algorithm identifies outliers in the data through the self-attention mechanism and automatically removes them; for the site environment data, the algorithm maps the data generated by different sensors to a unified coordinate system through the feature alignment method, and generates a three-dimensional distribution map of obstacles during the fusion process.

[0030] After data fusion, the load status data includes the load center of gravity position and real-time stress distribution; the site environment model data includes the spatial distribution of obstacles, the motion trajectory of dynamic targets and site boundary information. For example, in a highly restricted operation scenario, the system can mark dangerous areas in advance based on the environmental model, providing key input for the subsequent path planning module.

[0031] The initialization module realizes efficient conversion from task instructions to environmental data modeling through the collaborative work of the above units. Through the combination of liquid metal sensor network and multimodal sensor, the module can accurately monitor the structural status and operating environment of the gantry crane to ensure the safety and reliability of the system; through data fusion by deep learning algorithm, it can generate highly accurate load status data and site environment model data, providing strong data support for subsequent path planning and load support. The innovation of this module lies in the combination of liquid metal sensing technology and multimodal fusion algorithm, which significantly improves the accuracy and real-time performance of environmental modeling, thereby providing efficient initialization capabilities for the new power system of the gantry crane.

[0032] like Figure 2 As shown, the planning module is used to generate corrected path planning data based on site environment model data, load status data and mission target data, and calculate sound field parameter data and stress prediction data to initialize path planning and load support system; The planning module is responsible for generating corrected path planning data based on site environment model data, load status data and mission target data, and calculating sound field parameter data and stress prediction data to initialize path planning and load support systems. This module works together through distributed reinforcement learning algorithms, path moment mapping models and deformation-stress prediction fusion algorithms to achieve comprehensive processing of path planning and load support optimization, ensuring the safety, efficiency and stability of the operation process.

[0033] Principle and implementation of corrected path planning Corrected path planning is one of the core functions of the planning module, which is used to generate optimized path planning data based on site environment model data, load status data and task target data. Its implementation relies on distributed reinforcement learning algorithm and path torque mapping model.

[0034] The distributed reinforcement learning algorithm constructs a graph model containing path nodes, distances between nodes, and path feasibility weights, and determines the optimal path planning through iterative optimization. The goal of the algorithm is to minimize the path length while improving obstacle avoidance efficiency and reducing propulsion energy consumption. Specifically, the algorithm's reward function can be expressed as:

[0035] in, is the reward value; is the total length of the path; is the obstacle avoidance success rate of the path; To promote energy consumption; , , It is an adjustable weight coefficient used to balance the importance of different optimization objectives.

[0036] By training in multiple path planning tasks, the algorithm can dynamically adjust the feasibility weights between path nodes to generate efficient and feasible path planning data.

[0037] In the process of optimizing the thrust between path nodes, the path moment mapping model is used to dynamically adjust the thrust direction and magnitude between path nodes to ensure path smoothness and load stability. The path moment mapping model generates thrust vectors to optimize the force distribution between path nodes, making the path curve smoother and reducing operating energy consumption.

[0038] The calculation of sound field parameters is another important function of the planning module. Its purpose is to initialize the sound field configuration parameters for the acoustic suspension load optimization module. Based on the load status data and path planning data, the sound field parameter calculation adopts the time domain sound pressure phase control algorithm, which is specifically implemented as follows: First, calculate the pressure requirements of the acoustic field based on the mass, shape and center of gravity distribution of the load. :

[0039] in, is the required sound field pressure; is the weight of the load; is the sound field action area; It is the inclination angle of the sound field and the load.

[0040] Then, the frequency of the sound wave is calculated by the distribution of the path nodes and Phase :

[0041]

[0042] in, is the sound wave frequency; is the speed of sound; is the wavelength of the sound wave; is the phase of the sound wave; is the distance from the sound source to the load.

[0043] Through the above calculations, the system can generate sound field parameter data that meets the load stability requirements, providing basic support for acoustic suspension load optimization.

[0044] Stress prediction is completed through the deformation-stress prediction fusion algorithm, which aims to predict the stress change trend and potential deformation area of ​​the gantry crane structure during operation based on real-time stress state data and path planning data. The algorithm combines the finite element analysis model and real-time monitoring data. The specific steps are as follows: Firstly, the finite element analysis model of the gantry crane is established, the structure is discretized into several units, and the corresponding stiffness matrix is ​​assigned to each unit according to the mechanical properties of the material.

[0045] Then, the thrust direction and magnitude in the path planning data are used as external load input, and the stress change is calculated using the following formula:

[0046] in, is the stress change; is the structural stiffness matrix; is the external load vector.

[0047] By calculating stress changes, the system can identify high stress areas and potential deformation locations in advance, and generate stress prediction data for use by the optimization module.

[0048] The planning module uses a distributed reinforcement learning algorithm to achieve dynamic optimization of path planning, generate efficient and reliable corrected path planning data, and significantly reduce the obstacle avoidance failure rate and energy consumption during path execution. The introduction of the path torque mapping model further optimizes the path smoothness and provides a guarantee for the stability of the load. The sound field parameter calculation module provides an optimization basis for the load support system through precise pressure and sound wave regulation, making the suspension and movement of the load more stable. The stress prediction module uses a deformation-stress prediction fusion algorithm to monitor and predict the structural stress change trend of the gantry crane in real time, enhancing the operational safety and reliability of the system.

[0049] For example, in a complex operation scenario (with dense obstacles and heavy loads), the corrected path planning data generated by the planning module effectively avoids the interference of dynamic obstacles, while optimizing the force distribution between path nodes, enabling the gantry crane to complete the task with lower energy consumption. The calculation of the acoustic field parameter data ensures the stability of the load in a highly dynamic environment, while the stress prediction data provides important support for the safety of key structural components. This combined effect significantly improves the system's task completion efficiency and reliability.

[0050] Preferably, the planning module generates corrected path planning data through a distributed reinforcement learning algorithm. The distributed reinforcement learning algorithm iteratively optimizes the path planning by constructing a graph model including path nodes, distances between path nodes, and feasibility weights between path nodes. The optimization goals include minimizing path length, improving obstacle avoidance efficiency, and reducing propulsion energy consumption.

[0051] The planning module is used to generate corrected path planning data, and its main implementation method is the distributed reinforcement learning algorithm. The distributed reinforcement learning algorithm iteratively optimizes the path planning by constructing a graph model containing path nodes, distances between path nodes, and feasibility weights between path nodes to minimize the path length, improve obstacle avoidance efficiency, and reduce propulsion energy consumption. The following is a detailed description from the perspective of principle and application: The core of the distributed reinforcement learning algorithm is to build a graph model based on the path planning task, which includes path nodes, distances between nodes, and feasibility weights.

[0052] Path nodes represent key locations in the site environment, such as lifting points, placement points, and key obstacle avoidance locations. These nodes are automatically annotated by the site environment model data and dynamic obstacle information, and have clear spatial coordinates.

[0053] The inter-node distance represents the straight-line distance between two nodes and is calculated using the Euclidean distance formula:

[0054] in, Represents a path node and path nodes The distance between and For Node and nodes The spatial coordinates of .

[0055] The feasibility weight indicates the feasibility of the path between nodes, which is calculated by the obstacle distribution data and dynamic obstacle trajectory data. The higher the weight value, the more feasible the path; conversely, the greater the degree of interference of obstacles on the path.

[0056] The graph model is represented as a directed weighted graph, where the path nodes are the vertices of the graph, the distance between nodes is the weight of the edge, and the feasibility weight is an additional attribute used to describe the passability conditions of the edge.

[0057] After building the graph model, the distributed reinforcement learning algorithm implements iterative optimization of path planning through a multi-agent system. Each agent represents a path node, and its goal is to find the global optimal path based on the local optimal path.

[0058] The state of each agent consists of the current position and the path nodes to be visited, expressed as ,in is the current node, is the set of nodes to be visited.

[0059] The agent's action is to choose the next path node ,in .

[0060] The reward function is used to evaluate the quality of path planning and is defined as:

[0061] in, is the reward value; is the total length of the path in meters; is the path obstacle avoidance success rate, with a value range of [0,1]; is the path propulsion energy consumption, in joules; , , is the weight coefficient used to balance the importance of path length, obstacle avoidance efficiency, and propulsion energy consumption.

[0062] The algorithm calculates the value function of the state-action pair through the Bellman equation :

[0063] in, For the current state Select Action the value of is the immediate reward for the current action; is a discount factor used to weigh immediate rewards against future rewards; is the new state after executing the action; is the value of the optimal action in the next state.

[0064] The agent iteratively calculates the Q value and gradually updates the path selection strategy until the global path converges to the optimal solution. The optimization goals of the distributed reinforcement learning algorithm include minimizing the path length, improving the obstacle avoidance efficiency, and reducing the propulsion energy consumption. The optimal path in the graph model is initialized by the shortest path algorithm (such as the Dijkstra algorithm) to provide an initial solution for reinforcement learning. The path weight is adjusted in real time using the dynamic obstacle trajectory data to avoid conflicts with dynamic obstacles and improve the obstacle avoidance success rate. The thrust size and direction are dynamically adjusted through the path torque mapping model to reduce the energy consumption between path nodes and further optimize the path execution efficiency.

[0065] The planning module realizes dynamic optimization of path planning through distributed reinforcement learning algorithm, and can quickly generate efficient and stable corrected path planning data. Taking complex site operations as an example, when there are multiple dynamic obstacles in the site and the path length is long, the algorithm significantly improves obstacle avoidance efficiency and reduces propulsion energy consumption by adjusting the path weight and optimizing the thrust distribution in real time. The introduction of reward function and value function enables path planning to flexibly switch between different task objectives to meet diverse needs.

[0066] For example, in a port hoisting scenario where dynamic obstacles frequently shuttle back and forth, the corrected path planning data generated by the planning module can effectively avoid dynamic obstacles and ensure path smoothness through the path torque mapping model, allowing the gantry crane to complete high-precision operations with low energy consumption. This combined effect improves the system's task execution efficiency and reduces operational risks, providing high-quality input data for subsequent dynamic movement and optimization modules.

[0067] Preferably, the planning module further includes a path torque coupling path correction unit, which dynamically adjusts the magnetic field direction and magnetic field strength between path nodes through a path torque mapping model, wherein the calculation formula of the path torque mapping model is:

[0068] in, Represents a path node and path nodes The thrust between (unit: Newton, N); Indicates the mass parameter of the path node (unit: kilogram, kg), reflecting the relative strength of the force acting on the path node; Represents a path node and path nodes The straight-line distance between them (unit: meter, m); Represents a path node and path nodes The angle between them (unit: radian, rad) reflects the smoothness of the change of path direction; The thrust vector direction and magnitude between path nodes are calculated through the path torque mapping model, thereby optimizing the thrust distribution between path nodes and the smoothness of the path curve.

[0069] The path torque coupling path correction unit is an important part of the planning module. It is responsible for dynamically adjusting the magnetic field direction and magnetic field strength between path nodes through the path torque mapping model, thereby optimizing the thrust distribution between path nodes, making the path smoother and improving the system operation efficiency. The following is a detailed explanation of the principle, calculation formula and its application in the new power system of the gantry crane.

[0070] The design of the path torque mapping model is based on the mechanical relationship between the path nodes. The purpose is to optimize the path smoothness and execution efficiency by dynamically adjusting the thrust vector between the path nodes. In the gantry crane power system, each path node is regarded as a point with mass attributes, and the movement of the gantry crane is guided by virtual thrust coupling between these nodes. The thrust size and direction in the model are jointly determined by the distance, angle and mass parameters between the path nodes.

[0071] Thrust size Calculated by the following formula:

[0072] Through this formula, the system can dynamically adjust the thrust according to the relative position and mass parameters between the path nodes, making the path smoother and reducing the vibration amplitude of the gantry crane during movement.

[0073] The thrust direction is determined by the direction cosines of the vector, which is calculated as follows:

[0074] in, Represents a path node To path node The thrust vector (unit: Newton, N); , Represents a path node The coordinates of , Represents a path node The coordinates of Represents the straight-line distance between path nodes.

[0075] Thrust vector calculation can ensure that the forces between path nodes always act in the reasonable direction, thereby reducing energy consumption and unnecessary path deviation.

[0076] In actual applications, the path torque coupling path correction unit generates corrected path planning data by dynamically adjusting the magnetic field direction and strength between path nodes. The specific implementation steps are as follows: Based on the path planning data, mark the location of the path nodes and assign initial quality parameters to each node The initial value of the mass parameter can be set according to the importance of the node or the load situation. For example, a node close to the lifting point may be assigned a larger mass parameter to ensure thrust concentration.

[0077] Use the path torque mapping model to calculate the thrust between each path node in turn and thrust vector By applying the thrust vector to the node position, the motion trend of the node is updated.

[0078] According to the direction and magnitude of the thrust vector, the direction and strength of the magnetic field between the path nodes are dynamically adjusted. The direction of the magnetic field is consistent with the direction of the thrust vector, and the strength of the magnetic field is proportional to the thrust magnitude. For example, when the distance between the path nodes is As it increases, the magnetic field strength automatically increases to compensate for the reduction in thrust.

[0079] The system recalculates the thrust distribution after each path correction until the path curve reaches the optimal smooth state. The smoothness of the path is measured by the uniformity of the angles of the path nodes. If the angle fluctuation is lower than the preset threshold, the optimization is stopped.

[0080] Through the path torque mapping model, the path torque coupling path correction unit can significantly improve the smoothness and execution efficiency of path planning. In complex operation scenarios (such as when there are dense dynamic obstacles or the load center of gravity is greatly offset), the unit can dynamically adjust the thrust distribution and magnetic field parameters to ensure the stability and reliability of path planning.

[0081] For example, in an actual lifting task, there are multiple dynamic obstacles between the lifting point and the placement point. The initial results of path planning show that the path curve is relatively steep, which may lead to a decrease in load stability. The path torque coupling path correction unit smoothes the steep path by optimizing the thrust size and direction, while keeping the load moving smoothly and avoiding interference from dynamic obstacles. In addition, due to the precise distribution of the thrust vector, the gantry crane's operating energy consumption is reduced, significantly improving the task efficiency and system economy.

[0082] Preferably, the planning module generates stress prediction data through a deformation-stress prediction fusion algorithm. The deformation-stress prediction fusion algorithm is based on real-time stress distribution data and load state data collected by the liquid metal sensor network, and calculates the stress change trend and potential deformation area of ​​the gantry crane structure under different working conditions through a finite element analysis model.

[0083] The main function of the deformation-stress prediction fusion algorithm in the planning module is to use the stress distribution data and load status data collected in real time by the liquid metal sensor network, and use the finite element analysis model to predict the stress change trend and potential deformation area of ​​the gantry crane structure under different working conditions. The algorithm integrates real-time data and theoretical models during operation to provide key safety and stability guarantees for path planning and load support.

[0084] The liquid metal sensor network covers the key structural parts of the gantry crane (such as the main beam, suspension arm and support legs), and monitors the stress distribution in real time through its high sensitivity to tiny deformations. The collected stress data includes the force value of each sensing point, and the stress magnitude is calculated using the following formula:

[0085] in, Stress value (unit: Pascal, Pa); The force magnitude at the collection point (unit: Newton, N); The cross-sectional area of ​​the collection point (unit: square meters, m²).

[0086] This data provides the real-time stress state of the gantry crane under the current load conditions, laying the foundation for the prediction of stress change trends.

[0087] The load status data includes the load weight, center of gravity position and its dynamic changes, which are obtained through the bottom pressure sensor and the dynamic center of gravity calculation module. The real-time position of the load center of gravity is calculated by the following formula:

[0088]

[0089] in, Indicates The pressure value of each sensor; , Indicates The location coordinates of the sensors; , Indicates the center of gravity of the load.

[0090] Real-time load state data is combined with stress distribution data to form a complete input data set, providing multi-dimensional input parameters for finite element analysis.

[0091] In the deformation-stress prediction fusion algorithm, the finite element analysis model is a key tool for numerically calculating the stress distribution and deformation trend of the gantry crane structure under different load conditions. The specific process is as follows: The structure of the gantry crane is discretized into multiple units (such as beam units and shell units), each of which has independent material properties and geometric parameters.

[0092] Generate a stiffness matrix for each element , this matrix represents the anti-deformation ability of the unit, and its specific form is:

[0093] in, represents the element stiffness matrix; Represents the deformation matrix, reflecting the geometric deformation of the unit; represents the material property matrix, including Young's modulus and Poisson's ratio of the material; Unit volume.

[0094] The real-time stress distribution data and load state data are used as boundary conditions and external load inputs to solve the displacement vector of the overall structure. :

[0095] in, represents the global stiffness matrix, which is assembled from the element stiffness matrices; represents the displacement vector of the structure; represents the external load vector.

[0096] According to the displacement vector, the stress change of the element is further calculated:

[0097] Through the above calculations, the algorithm can generate stress distribution data under the current load conditions and predict high stress areas and potential deformation areas.

[0098] The deformation-stress prediction fusion algorithm provides important support for the safety and stability of the new power system of the gantry crane. Its effects are reflected in the following aspects: The system can capture the stress distribution of the gantry crane structure in real time through the liquid metal sensor network, and dynamically monitor the stress change trend in combination with the load status data. For example, when the stress value at a certain point of the main beam approaches the material yield limit, the algorithm will issue a warning signal and suggest adjusting the path planning or reducing the load.

[0099] Through the finite element analysis model, the algorithm can predict the deformation area of ​​the gantry crane under different load conditions. For example, when lifting heavy objects, the main beam may have a concentrated deformation area. The algorithm optimizes the layout of the support points in advance by predicting the deformation trend to reduce the stress concentration effect.

[0100] The stress prediction data generated by the algorithm provides important input for the path planning module. For example, in complex operation scenarios, the system can adjust the position of path nodes and thrust distribution to avoid repeated stress in high stress areas and extend the service life of the gantry crane.

[0101] In actual application, the deformation-stress prediction fusion algorithm showed significant advantages in an operation with a lifting weight of 10 tons. There were multiple obstacles between the lifting point and the placement point, which caused the initial path planning results to show that the stress in the middle section of the main beam was too high. The system identified the high-stress area through the stress prediction fusion algorithm, and dynamically adjusted the path node position and redistributed the thrust direction to reduce the stress in the middle section of the main beam. At the same time, the algorithm predicted that the support legs near the placement point might have concentrated deformation, and the system automatically optimized the load support layout, successfully avoiding structural damage.

[0102] A movement module, used to control the dynamic movement of the gantry crane based on the corrected path planning data, generate obstacle avoidance path planning data through dynamic obstacle trajectory data, and dynamically adjust the sound field parameter data and load support status data according to the path execution data; The mobile module is responsible for controlling the dynamic movement of the gantry crane based on the corrected path planning data. The module generates obstacle avoidance path planning data through dynamic obstacle trajectory data, and adjusts the sound field parameter data and load support status data in real time according to the path execution data, thereby optimizing the dynamic movement path and enhancing the load stability.

[0103] The core task of the mobile module is to convert the corrected path planning data generated by the planning module into actual movement instructions for controlling the dynamic movement of the gantry crane. The corrected path planning data includes the spatial coordinates, thrust direction and magnitude of the path nodes. The mobile module calculates the thrust vector based on this data and executes the movement between the path nodes through the magnetic torque propulsion unit.

[0104] After the thrust vector is determined, the system adjusts the direction and strength of the magnetic field in real time through the magnetic torque propulsion unit to make the thrust vector consistent with the path direction, thereby pushing the gantry crane to complete the movement between path nodes.

[0105] The dynamic obstacle trajectory data is collected in real time by a multimodal sensor array, and the data includes the current position, movement speed, and predicted trajectory of the obstacle. The mobile module uses the Kalman filter algorithm to predict the obstacle trajectory. The specific formula is as follows:

[0106] in, Indicates the obstacle at time The predicted position at the moment; Indicates the obstacle at time The location at the moment; represents the velocity vector of the obstacle; Represents the prediction time interval.

[0107] According to the predicted obstacle trajectory, the mobile module dynamically adjusts the feasibility weights of the path nodes and re-optimizes the path through the distributed reinforcement learning algorithm to generate obstacle avoidance path planning data. This process can effectively avoid the interference of dynamic obstacles and ensure the safe movement of the gantry crane.

[0108] In order to improve the stability of the load, the mobile module adjusts the sound field parameter data in real time according to the path execution data and the vibration characteristics of the load. The sound field parameters include the frequency, phase and pressure of the sound wave, which are calculated by the time domain sound pressure phase control algorithm.

[0109] By adjusting the acoustic field parameters in real time, the system can compensate for disturbances in path execution and keep the load suspended and moving stably. For example, when the center of gravity of the load shifts, the acoustic field pressure will automatically increase the acoustic force in the direction of the shift to maintain the load's balance.

[0110] The load support status data is acquired in real time through the liquid metal sensor network. The data includes the center of gravity of the load, the force distribution of the support points, and the dynamic change trend. The mobile module combines the path execution data and the real-time support data to dynamically adjust the support point layout through the following optimization formula:

[0111] in, Indicates the distributed force at each support point; Indicates the total weight of the load; Indicates the number of support points.

[0112] When the load center of gravity shifts or the support points are unevenly stressed during path execution, the system will automatically adjust the force distribution value of each support point. and optimize the load support state by controlling the physical position of the support points.

[0113] The mobile module significantly improves the dynamic operation capability and load stability of the gantry crane by correcting the dynamic mobile control of the path planning data, generating the obstacle avoidance path planning, adjusting the sound field parameters in real time, and optimizing the load support status. The system can cope with the interference of dynamic obstacles in complex working environments, while ensuring the stability and safety of the load during movement.

[0114] For example, in a complex port operation scenario, the gantry crane needs to travel between multiple transport equipment and complete the load lifting task. The mobile module avoids the risk of collision with the transport equipment through real-time obstacle avoidance path planning, and maintains the stable suspension state of the load through dynamic adjustment of the sound field parameters. With the support of support state optimization, the force fluctuation of the main beam during load lifting is reduced, and the safety and efficiency of system operation are significantly improved.

[0115] Preferably, Figure 4 As shown, the mobile module includes a magnetic torque propulsion unit, which performs dynamic propulsion control between path nodes through the driving mechanism of variable polarity magnetic field material, according to the thrust direction and thrust magnitude generated by the corrected path planning data, and adjusts the magnetic field polarity and magnetic field strength in real time in combination with the dynamic obstacle trajectory data to generate updated obstacle avoidance path planning data.

[0116] The magnetic torque propulsion unit is the core component of the mobile module. It is responsible for the driving mechanism of the variable polarity magnetic field material, and performs dynamic propulsion control between path nodes according to the thrust direction and thrust magnitude generated by the corrected path planning data. It also adjusts the polarity and intensity of the magnetic field in real time in combination with the dynamic obstacle trajectory data to generate updated obstacle avoidance path planning data. The design of this unit achieves efficient and flexible path propulsion capabilities through the combination of magnetic materials and dynamic magnetic fields.

[0117] The working principle of the magnetic torque propulsion unit is based on the magnetic induction characteristics of variable polarity magnetic field materials. When the material is in an external magnetic field, the direction and magnitude of the magnetism generated inside it changes with the polarity and strength of the external magnetic field. By dynamically regulating the magnetic field, precise control of the thrust direction and thrust magnitude can be achieved.

[0118] The system uses coils to generate a control magnetic field. The direction and strength of the magnetic field are determined by the following formula:

[0119] in, Indicates the magnetic field strength (unit: Tesla, T); represents the vacuum magnetic permeability (unit: T· m / A); Indicates the number of coil turns; Indicates the coil current (unit: ampere, A); Indicates the length of the coil (unit: meter, m).

[0120] The thrust magnitude is related to the thrust vector between the magnetic field and the path nodes, and the specific calculation formula is:

[0121] in, Indicates the thrust (unit: Newton, N); Represents the magnetic charge of the moving load; represents the motion velocity vector of the load; Indicates the external magnetic field strength.

[0122] By controlling The system can accurately adjust the direction and magnitude of the thrust, thereby achieving dynamic propulsion between path nodes.

[0123] During the propulsion process, the magnetic torque propulsion unit generates the corresponding magnetic field direction and strength according to the thrust vector provided by the corrected path planning data. The direction of the thrust vector is determined by the coordinate difference between the path nodes:

[0124] in, represents the thrust vector; , Indicates the coordinates of the current path node; , Represents the coordinates of the target path node; The straight-line distance between nodes.

[0125] The system achieves dynamic propulsion control between path nodes by calculating the thrust vector and adjusting the magnetic field in real time, ensuring that the load moves smoothly along the optimal path.

[0126] The magnetic torque propulsion unit combines dynamic obstacle trajectory data during the propulsion process and predicts the future position of the obstacle through the Kalman filter algorithm:

[0127] in, Obstacles in time The predicted position at the moment; represents the velocity vector of the obstacle; Indicates a time interval.

[0128] According to the predicted trajectory of the obstacle, the system dynamically adjusts the polarity and strength of the magnetic field between the path nodes, and updates the path planning data at the same time to generate obstacle avoidance path planning to ensure the safe movement of the gantry crane in complex environments.

[0129] The magnetic torque propulsion unit achieves high-precision control and dynamic adjustment of thrust between path nodes through its variable polarity magnetic field material driving mechanism. The system can generate obstacle avoidance path planning data based on the corrected path planning combined with real-time dynamic obstacle data to ensure safe and efficient operation in complex environments.

[0130] For example, in a port operation scenario with multiple dynamic obstacles (such as transport vehicles and other lifting equipment), the system uses a magnetic torque propulsion unit to adjust the direction and strength of the magnetic field in real time to accurately control the path propulsion of the load. At the same time, obstacle avoidance path planning ensures that the gantry crane can bypass dynamic obstacles and optimizes the path execution efficiency. In this task, the introduction of the magnetic torque propulsion unit reduces system energy consumption, shortens operating time, and significantly improves the stability of the load and the safety of operation.

[0131] In actual applications, a large gantry crane performs load lifting tasks in a narrow yard. The corrected path planning data shows that there are multiple dynamic obstacles interfering with the initial path, which may cause the path execution to fail. The magnetic torque propulsion unit dynamically adjusts the direction and intensity of the magnetic field according to the thrust direction generated by the corrected path planning data, and accurately realizes the path propulsion. At the same time, by predicting the obstacle trajectory in real time, the system automatically generates obstacle avoidance path planning to ensure that the gantry crane smoothly bypasses the obstacle and reaches the target point according to the optimized path. Through this solution, the system completes complex tasks while maintaining high load stability and low energy consumption.

[0132] Preferably, the mobile module further includes an acoustic levitation load optimization unit, which adjusts the sound field parameter data through a time-domain sound pressure phase control algorithm, wherein the time-domain sound pressure phase control algorithm calculates the frequency of the sound wave, the phase of the sound wave and the driving signal of the sound wave transmitter according to the path execution data and the load vibration characteristics to maintain the stability of the load.

[0133] The acoustic suspension load optimization unit is responsible for adjusting the acoustic field parameter data through the time domain sound pressure phase control algorithm to ensure that the load maintains stability during the path execution process. The unit combines the path execution data and the load vibration characteristics to dynamically adjust the frequency and phase of the sound wave and the driving signal of the sound wave transmitter, thereby forming a stable acoustic suspension environment, effectively improving the safety of the load and the reliability of path execution.

[0134] Acoustic levitation technology uses high-intensity sound waves to form standing wave nodes and anti-nodes in the air, thereby achieving stable suspension of objects at the nodes. The key parameters of the sound field include the sound wave frequency , Acoustic Phase and sound pressure The precise control of these parameters determines the stability of acoustic levitation.

[0135] In the acoustic levitation load optimization unit, the acoustic wave pressure The calculation formula is:

[0136] in, Indicates the sound wave pressure (unit: Pascal, Pa); Indicates the intensity of sound waves (unit: Watt / square meter, W / m²); Indicates the speed of sound (unit: meter / second, m / s).

[0137] The system controls the The value of the sound wave pressure is changed in real time .

[0138] The time-domain sound pressure phase control algorithm is the core of the acoustic suspension load optimization unit and is used to dynamically calculate the frequency of the sound wave. , Phase and driving signal. The key steps of the algorithm include: Determine the standing wave frequency of the acoustic wave based on the load center of gravity vibration characteristics in the path execution data :

[0139] in, Indicates the frequency of sound waves (unit: Hertz, Hz); Indicates the speed of sound (unit: meter / second, m / s); Indicates the wavelength of sound waves (unit: meter, m).

[0140] When the load vibration frequency is high, the system automatically increases the sound wave frequency , to enhance suspension stability.

[0141] Acoustic Phase It is used to synchronize the outputs of multiple acoustic wave transmitters to form a stable standing wave node. The phase calculation formula is:

[0142] in, Indicates the phase of the sound wave (unit: radian, rad); Indicates the distance between the acoustic wave transmitter and the load (unit: meter, m).

[0143] The system monitors the load position in real time and dynamically adjusts , ensuring that the acoustic node is always aligned with the load center of gravity.

[0144] According to frequency and Phase The calculation results generate the driving signal of the acoustic wave transmitter:

[0145] in, Real-time value of the drive signal; Indicates the amplitude of the driving signal; Indicates time; Indicates the phase of the sound wave.

[0146] The acoustic suspension load optimization unit collects path execution data in real time through the liquid metal sensor network, including the load center of gravity position, vibration amplitude and frequency. These data are input into the time-domain sound pressure phase control algorithm to dynamically adjust the sound field parameters. For example, when the load center of gravity deviates from the standing wave node, the algorithm will automatically adjust the sound wave phase to return the load to the equilibrium position; when the load vibration frequency increases during the path execution process, the algorithm will increase the sound wave frequency in real time to enhance the suspension stability.

[0147] The acoustic suspension load optimization unit achieves precise suspension and stable control of the load by dynamically adjusting the sound field parameters. The system can maintain the stability and suspension height of the load in a complex path execution environment, significantly reducing the risk of load overturning due to path disturbance or center of gravity shift.

[0148] For example, in a complex lifting task, the gantry crane needs to complete the lifting and transportation of heavy objects in a site with strong winds. The acoustic suspension load optimization unit uses the time-domain sound pressure phase control algorithm to adjust the sound wave pressure and phase in real time to ensure that the load remains stably suspended throughout the entire moving path. When the load vibration frequency increases due to wind interference, the system automatically increases the sound wave frequency to reduce the load vibration amplitude, significantly improving the safety and efficiency of the operation.

[0149] Example, in actual application, a gantry crane completed the cargo lifting task in a highly dynamic obstacle environment. The path execution data showed that the center of gravity of the load shifted frequently due to environmental interference. The acoustic suspension load optimization unit repositioned the load to the standing wave node by dynamically adjusting the phase of the sound wave. At the same time, the system detected that the vibration amplitude of the load exceeded the warning threshold, and immediately increased the sound wave frequency and enhanced the sound wave pressure, ultimately reducing the load vibration amplitude to a safe range, ensuring the stability and safety of the entire transportation process.

[0150] The optimization module is used to complete the lifting or placement of loads in the target area, monitor the stress state data of the gantry crane in real time, record the mission energy consumption data, and generate optimization model data through a multi-objective joint optimization algorithm to update the path planning parameter data, sound field parameter data and thrust distribution strategy data.

[0151] The optimization module is responsible for completing the load lifting or placement tasks within the target area. At the same time, it monitors the stress state data of the gantry crane in real time and records the task energy consumption data. It uses a multi-objective joint optimization algorithm to generate optimization model data, and dynamically updates the path planning parameter data, sound field parameter data and thrust distribution strategy data, thereby improving the system operation efficiency and operation safety.

[0152] The optimization module accurately controls the lifting and placement of the load based on the terminal path data provided by the path planning module, combined with real-time stress monitoring and energy consumption recording. During operation, the load support mechanism of the gantry crane distributes the load weight through the following optimization formula:

[0153] in, represents the force distributed at each support point; Indicates the total weight of the load (unit: Newton, N); Indicates the number of support points.

[0154] To avoid stress concentration at the support points due to uneven load, the module uses a liquid metal sensor network to adjust in real time. , and combined with sound field control to further optimize the balance of the load.

[0155] The optimization module covers the key parts of the gantry crane structure through a liquid metal sensor network, collecting and analyzing stress state data in real time. These data reflect the force distribution of the gantry crane structure during dynamic operation and help identify high stress areas. The stress calculation formula is:

[0156] in, Indicates stress value (unit: Pascal, Pa); Indicates the magnitude of the force (unit: Newton, N); Indicates the cross-sectional area of ​​the collection point (unit: square meters, m²).

[0157] Real-time stress monitoring is combined with the finite element analysis model to generate stress change trend prediction data, and the optimization module adjusts the path planning parameters accordingly to reduce stress concentration.

[0158] During the lifting and placement process, the optimization module records the energy consumption data in real time. The energy consumption calculation formula is:

[0159] in, represents energy consumption (unit: joule, J); Indicates operating power (unit: Watt, W); Indicates the operation time (unit: seconds, s).

[0160] The energy consumption data is combined with the path planning parameters to provide an evaluation basis for the optimization algorithm to achieve continuous optimization of energy consumption.

[0161] The core of the optimization module is the multi-objective joint optimization algorithm, which generates optimization model data by comprehensively considering path planning, sound field control and thrust distribution strategy. The objective functions include: Path planning optimization goal: minimize path length and obstacle avoidance cost ; Sound field control optimization goal: minimize sound field pressure fluctuations ; Thrust distribution optimization goal: balanced thrust distribution .

[0162] The optimization objective function expression is:

[0163] in, represents the optimization target value; , , , represents the weight coefficient; Indicates the fluctuation range of sound field pressure; Indicates the imbalance of thrust distribution.

[0164] The algorithm uses genetic algorithm to perform multi-objective optimization and dynamically adjust the weight coefficients , in order to balance the priorities among various objectives and generate optimization model data for updating system parameters.

[0165] The generated optimization model data is used to update the following key parameters: Path planning parameter data: readjust path nodes to reduce path length and obstacle avoidance cost; Acoustic field parameter data: optimize the acoustic field frequency and phase, and enhance the stability of the load; Thrust distribution strategy data: Dynamically optimize thrust distribution to reduce stress concentration in the gantry crane structure.

[0166] The optimization module provides precise operation capability and efficient parameter optimization capability for the gantry crane system through the combination of real-time monitoring, energy consumption recording and multi-objective optimization. The system can complete load lifting and placement operations in complex environments while significantly reducing operation risks and energy consumption costs.

[0167] Example: In a port hoisting scenario with many dynamic obstacles, the optimization module identified the high stress area of ​​the main beam through real-time stress monitoring, and deviated the path from the high stress area through path planning parameter update. At the same time, the optimization of the sound field parameters reduced the load vibration amplitude and the task energy consumption. Through the optimization model data generated by the multi-objective joint optimization algorithm, the system further optimized the thrust distribution strategy in subsequent tasks and extended the service life of key components of the gantry crane.

[0168] In a complex task, a gantry crane system needs to lift a 15-ton load and place it precisely at the designated location. During the lifting process, the optimization module adjusts the thrust distribution through real-time stress monitoring to reduce the maximum stress of the main beam. During the placement process, energy consumption data records show that through path optimization and sound field control, the total energy consumption of the task is reduced compared with the traditional operation method. The optimization model data generated by the multi-objective joint optimization algorithm enables the system to automatically update the path planning parameters and sound field control strategy after the task, providing efficient support for subsequent tasks.

[0169] Preferably, the optimization module generates optimization model data through a multi-objective joint optimization algorithm. The multi-objective joint optimization algorithm constructs an objective function based on real-time monitored stress state data, path planning execution data and task energy consumption data, where the objective function includes improving path planning efficiency, enhancing load stability and balancing energy consumption distribution, and obtains global optimization results through iterative calculation.

[0170] The optimization module integrates real-time monitoring of stress state data, path planning execution data and task energy consumption data to construct an objective function that includes path planning efficiency improvement, load stability enhancement and energy consumption distribution balance, and obtains global optimization results through iterative calculation. This algorithm plays a core role in the optimization module, providing efficient and stable operation capabilities and parameter update support for the new power system of the gantry crane.

[0171] The multi-objective joint optimization algorithm is centered on three main objectives: Improved path planning efficiency: by minimizing path length and obstacle avoidance cost , improve the efficiency and execution accuracy of path planning; Increased load stability: by optimizing the load support point for uniform force distribution and reduce vibration amplitude , to ensure the smooth operation of the load; Balanced energy consumption distribution: by minimizing the fluctuation of energy consumption per unit time , reducing energy waste caused by power changes during operation.

[0172] The expression of the optimization objective function is:

[0173] in, Indicates the fluctuation of energy consumption per unit time.

[0174] The system dynamically adjusts the weight coefficient according to the task requirements to balance the priorities between different optimization objectives. For example, in a highly dynamic obstacle environment, priority is given to improving obstacle avoidance efficiency, while in the case of significant load center of gravity offset, priority is given to enhancing load stability.

[0175] The input data of the optimization algorithm include: Stress state data: collected through the liquid metal sensor network, reflecting the real-time stress conditions of the key components of the gantry crane to form a stress distribution matrix; path planning execution data: execution deviations and dynamic adjustment records of path nodes, used to evaluate path efficiency; mission energy consumption data: energy consumption curves recorded by sensors, providing the trend of power and time changes.

[0176] The optimization algorithm normalizes these data and constructs the input vector of the multi-objective function to ensure that the weights of data in each dimension are balanced.

[0177] The optimization module uses genetic algorithm as the core method to solve multi-objective functions. The genetic algorithm iterates through steps such as population initialization, fitness evaluation, crossover, mutation and selection, and finally obtains the global optimization result. The specific process is as follows: Population initialization: Generate solutions for initial path planning, acoustic field parameters, and thrust distribution strategies; Fitness evaluation: Calculate the fitness of each solution according to the objective function value, and give priority to solutions with high fitness; Crossover and mutation: Generate new path planning solutions through crossover operations, and adjust sound field parameters and thrust distribution through mutation operations; Selection: Select the next generation of population based on fitness value and retain the high-quality solutions.

[0178] The iterative process of the genetic algorithm continuously approaches the global optimal solution through the fitness function, and its core formula is:

[0179] in, represents the fitness function; Indicates The function value of the target; Represents the weight coefficient.

[0180] After multiple rounds of iterations, the algorithm finally outputs the globally optimal path planning, sound field parameters and thrust distribution strategy.

[0181] The multi-objective joint optimization algorithm significantly improves the system's operating efficiency and load stability, while reducing operating energy consumption. In practical applications, the algorithm dynamically adjusts the weights of the optimization objectives to adapt to the needs of different operating scenarios. For example, in an environment with complex paths and dense obstacles, the algorithm prioritizes path planning to ensure obstacle avoidance efficiency; in the case of unbalanced loads, the algorithm prioritizes thrust distribution to reduce stress concentration.

[0182] Example: In a complex lifting task with a load weight of 30 tons, the optimization module identified high stress areas through real-time stress monitoring and adjusted the path planning parameters to avoid the stress concentration points of key components. The task energy consumption data showed that the total energy consumption of the task was reduced through the dynamic adjustment of the optimization algorithm. The optimization of the sound field parameters significantly reduced the load vibration and improved the stability. The entire task took less time and significantly improved the work efficiency.

[0183] Example: During the lifting and placement process of a gantry crane system, a high-dynamic task was completed using the multi-objective joint optimization algorithm of the optimization module: Real-time stress monitoring shows that the stress value in the middle section of the main beam is close to the material yield limit. The optimization module reduces the stress concentration of the main beam by adjusting the path planning nodes, and dynamically optimizes the thrust distribution strategy to reduce the force fluctuation amplitude of the support point. Energy consumption records show that by optimizing the path length and the sound field pressure fluctuation, the mission energy consumption is reduced compared with the traditional solution. The final optimization results show that the stability of load lifting is improved, the maximum stress value of the main beam is reduced, and the overall operating life of the gantry crane is extended.

[0184] Preferably, the optimization model data generated by the optimization module includes updated path planning parameter data, sound field parameter data and thrust distribution strategy data, wherein the path planning parameter data includes updated path node coordinates and distances between path nodes, the sound field parameter data includes adjusted sound wave frequency and sound wave phase, and the thrust distribution strategy data includes recalculated magnetic field direction and magnetic field strength.

[0185] The optimization model data generated by the optimization module includes updated path planning parameter data, sound field parameter data, and thrust distribution strategy data. By real-time monitoring of the dynamic state of the operation, the optimization module combines the multi-objective joint optimization algorithm to update and adjust these core data, such as Figure 3 As shown in the figure, the gantry crane has the best running path, the highest load stability and the most balanced energy consumption distribution. These updated data cover the adjustment of path node coordinates and distances, the optimization of sound wave frequency and phase, and the recalculation of magnetic field direction and strength, which is an important basis for the system to achieve efficient operation.

[0186] The optimization module readjusts the path node coordinates and the optimal distance between path nodes based on the real-time path execution data and dynamic obstacle trajectory data. The update of path node coordinates is achieved through the shortest path algorithm combined with obstacle avoidance weight calculation, and the calculation formula is:

[0187] in, Represents a path node To Node Total cost; Represents the Euclidean distance between path nodes; Represents the obstacle avoidance weight introduced by dynamic obstacles.

[0188] The system gives priority to the path node coordinate combination with the lowest total cost and generates updated path planning parameter data.

[0189] The updated path planning parameter data ensures that the gantry crane can improve its obstacle avoidance efficiency while shortening the total path length as much as possible. For example, in a complex port operation scenario, the updated path node coordinates reduce the total length of the task path and effectively avoid the interference of dynamic obstacles.

[0190] The update of the sound field parameter data is based on the real-time monitoring of the load vibration characteristics and path execution data. The optimization module recalculates the frequency of the sound wave through the time domain sound pressure phase control algorithm. and Phase , to enhance the suspension stability of the load.

[0191] The updating formula of the sound wave frequency is:

[0192] The optimization formula for the acoustic wave phase is:

[0193] The updated sound field parameter data significantly reduced the vibration amplitude of the load. For example, in a hoisting task, the updated sound wave frequency and phase reduced the load vibration amplitude by about 30%, ensuring the safety and stability of the equipment.

[0194] The thrust distribution strategy data is updated based on the dynamic force changes during path execution. The optimization module optimizes the thrust distribution strategy by adjusting the direction and strength of the magnetic field, thereby reducing the stress concentration of the gantry crane structure.

[0195] The calculation formula of magnetic field strength is:

[0196] The relationship between the magnetic field direction and the thrust vector between the path nodes is:

[0197] The updated thrust distribution strategy data significantly reduces stress in high stress areas by balancing the forces at the load support points. For example, in a task of lifting a 20-ton load, the optimization module reduced stress in the stress concentration area of ​​the main beam, extending the service life of key components.

[0198] The optimization model data generated by the optimization module significantly improves the operating efficiency, load stability and operation safety of the gantry crane through comprehensive updates of path planning parameters, sound field parameters and thrust distribution strategies.

[0199] In a complex lifting task, the application of optimized model data reduced the task path length and improved the efficiency of avoiding dynamic obstacles. The update of acoustic field parameters significantly reduced the load vibration amplitude and improved the suspension stability of the load. The optimization of thrust distribution strategy reduced the stress in high stress areas, made the energy consumption distribution more balanced, and reduced the total task energy consumption.

[0200] Example: When a gantry crane system is performing complex operations, the optimization module's multi-objective joint optimization algorithm generates optimization model data in real time. The update of path planning parameter data reduces the total path length, the optimization of sound field parameter data improves load stability, and the adjustment of thrust distribution strategy data significantly reduces the stress concentration of the main beam. Ultimately, the task was completed efficiently while significantly reducing operation energy consumption and equipment wear, providing a strong guarantee for the long-term operation of the system.

[0201] Preferably, the optimization module further includes a data archiving unit, which is used to store task completion status data, task energy consumption data and structural stress data generated during the operation process. The data archiving unit realizes data management through classification index and timestamp recording mechanism.

[0202] The data archiving unit is used to store the task completion status data, task energy consumption data and structural stress data generated during operation, and realizes efficient data management through classification index and timestamp recording mechanism. The design of this unit enables the new power system of the gantry crane to preserve and effectively utilize historical data for a long time, providing important support for subsequent operation optimization, fault diagnosis and system upgrade.

[0203] The data archiving unit uses a classification index and timestamp recording mechanism to manage the collected data. The classification index divides the data into three main categories: task completion status data, task energy consumption data, and structural stress data. Each category of data is stored using an independent index structure.

[0204] Task completion status data: including task start time, task completion time, task path execution status and dynamic obstacle avoidance records; Task energy consumption data: record the real-time power change curve and total energy consumption value during the entire task execution process; Structural stress data: collected from the liquid metal sensor network, reflecting the stress distribution and change trend of key structural components.

[0205] The combination of classification index and timestamp enables the system to quickly locate required data, greatly improving data retrieval efficiency.

[0206] The data archiving unit uses distributed storage technology to ensure efficient data management and access speed: Distributed storage architecture: By storing data in pieces on different nodes, the system's storage capacity and disaster recovery capabilities are improved; Metadata management: Each piece of stored data contains a set of metadata, including data type, timestamp, index path, and data checksum. The metadata management table ensures data consistency and integrity; Compression and deduplication mechanism: For repeated stress monitoring data or similar energy consumption data, the data archiving unit uses hash functions for rapid comparison and reduces storage space consumption through compression algorithms.

[0207] Example: For daily repeated stress distribution data, use hash values Detect duplicates and store only unique data.

[0208] The data archiving unit supports fast query and call of data, and provides the basis for real-time analysis and long-term data mining: through timestamp and classification index, it supports multi-dimensional data query based on time range, task category and keyword; calls real-time task energy consumption data to compare with historical energy consumption curves, and generates energy consumption deviation alarms; analyzes the long-term change trend of structural stress data, evaluates equipment aging, and supports preventive maintenance and optimized design.

[0209] The introduction of the data archiving unit has significantly improved the intelligence and data utilization capabilities of the gantry crane's new power system, especially in long-term optimization and fault diagnosis.

[0210] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems or computer program products. Therefore, the present application may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware.

[0211] The above are only embodiments of the present application and are not intended to limit the present application. For those skilled in the art, the present application may have various changes and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the scope of the claims of the present application.

Claims

1. A new power system for gantry crane, characterized in that: include: An initialization module is used to receive target position information and load parameters, collect the initial stress state data of the gantry crane, the load center of gravity distribution data, the obstacle distribution data of the site environment, the site structure data and the dynamic obstacle information data, and generate load state data and site environment model data; A planning module, used to generate corrected path planning data based on site environment model data, load status data and mission target data, and calculate sound field parameter data and stress prediction data to initialize path planning and load support systems; A movement module, used to control the dynamic movement of the gantry crane based on the corrected path planning data, generate obstacle avoidance path planning data through dynamic obstacle trajectory data, and dynamically adjust the sound field parameter data and load support status data according to the path execution data; The optimization module is used to complete the lifting or placement of loads in the target area, monitor the stress state data of the gantry crane in real time, record the mission energy consumption data, and generate optimization model data through a multi-objective joint optimization algorithm to update the path planning parameter data, sound field parameter data and thrust distribution strategy data.

2. The new power system for gantry crane according to claim 1 is characterized in that: The initialization module includes: A task receiving unit, used to receive task target position information and load parameters, the task target position information includes the spatial coordinates of the lifting point and the spatial coordinates of the placement point, and the load parameters include the weight, shape characteristics and center of gravity position of the load; A data acquisition unit, used to obtain the initial stress state data of the gantry crane structure and the center of gravity distribution data of the load through a liquid metal sensor network; The environmental perception unit is used to collect obstacle distribution data, site structure data, and real-time position and motion trajectory data of dynamic obstacles in the site environment through a multimodal sensor array; The data fusion unit denoises, completes and fuses the collected initial stress state data, load center of gravity distribution data and site environment data based on the deep convolutional neural network algorithm to generate load state data and site environment model data.

3. The new power system for gantry crane according to claim 1 is characterized in that: The planning module generates corrected path planning data through a distributed reinforcement learning algorithm. The distributed reinforcement learning algorithm iteratively optimizes the path planning by constructing a graph model that includes path nodes, distances between path nodes, and feasibility weights between path nodes. The optimization goals include minimizing path length, improving obstacle avoidance efficiency, and reducing propulsion energy consumption.

4. The new power system for gantry crane according to claim 3 is characterized in that: The planning module further includes a path torque coupling path correction unit, which dynamically adjusts the magnetic field direction and magnetic field strength between path nodes through a path torque mapping model, wherein the calculation formula of the path torque mapping model is: in, Represents a path node and path nodes The thrust between It represents the quality parameter of the path node, reflecting the relative strength of the force on the path node; Represents a path node and path nodes The straight-line distance between Represents a path node and path nodes The angle between them reflects the smoothness of the change in path direction; The thrust vector direction and magnitude between path nodes are calculated through the path torque mapping model, thereby optimizing the thrust distribution between path nodes and the smoothness of the path curve.

5. The new power system for gantry crane according to claim 1 is characterized in that: The planning module generates stress prediction data through the deformation-stress prediction fusion algorithm. The deformation-stress prediction fusion algorithm is based on the real-time stress distribution data and load status data collected by the liquid metal sensor network. The finite element analysis model is used to calculate the stress change trend and potential deformation area of ​​the gantry crane structure under different working conditions.

6. The new power system for gantry crane according to claim 1 is characterized in that: The mobile module includes a magnetic torque propulsion unit, which uses the driving mechanism of variable polarity magnetic field materials to perform dynamic propulsion control between path nodes according to the thrust direction and thrust size generated by the corrected path planning data, and adjusts the magnetic field polarity and magnetic field strength in real time in combination with the dynamic obstacle trajectory data to generate updated obstacle avoidance path planning data.

7. The new power system for gantry crane according to claim 6 is characterized in that: The mobile module further includes an acoustic levitation load optimization unit, which adjusts the sound field parameter data through a time-domain sound pressure phase control algorithm, wherein the time-domain sound pressure phase control algorithm calculates the frequency of the sound wave, the phase of the sound wave and the driving signal of the sound wave transmitter according to the path execution data and the load vibration characteristics to maintain the stability of the load.

8. The new power system for gantry crane according to claim 1 is characterized in that: The optimization module generates optimization model data through a multi-objective joint optimization algorithm. The multi-objective joint optimization algorithm constructs an objective function based on real-time monitored stress state data, path planning execution data and task energy consumption data. The objective function includes improving path planning efficiency, enhancing load stability and balancing energy consumption distribution, and obtains global optimization results through iterative calculation.

9. The new power system for gantry crane according to claim 8 is characterized in that: The optimization model data generated by the optimization module includes updated path planning parameter data, sound field parameter data and thrust distribution strategy data, wherein the path planning parameter data includes updated path node coordinates and distances between path nodes, the sound field parameter data includes adjusted sound wave frequency and sound wave phase, and the thrust distribution strategy data includes recalculated magnetic field direction and magnetic field strength.

10. The new power system for gantry crane according to claim 1 is characterized in that: The optimization module further includes a data archiving unit, which is used to store task completion status data, task energy consumption data and structural stress data generated during the operation process. The data archiving unit realizes data management through classification index and timestamp recording mechanism.

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