52 results about "Dynamic planning" patented technology

The application belongs to the field of robots, relates to a data analysis technique, and aims to solve the problem that the existing intelligent control system for robot moving route cannot timely adjust the planned route of the robot according to the obstacle analysis result, in particular to an intelligent control system for robot moving route based on the Internet of Things, which comprises an intelligent control platform, wherein the intelligent control platform is communicatively connected with a dynamic planning module, a decision analysis module, an environment evaluation module, a route optimization module and a storage module; the starting position and the target position of the robot are acquired, all static planning routes between the starting position and the target position are acquired, a plurality of sub-nodes are set on the static planning routes, and the static planning route with the shortest path distance value is marked as a preliminary selected route; the application can plan and analyze the moving route of the robot in a dynamic environment, and obstacle analysis is performed on each sub-node to realize dynamic adjustment of the moving route of the robot.

This invention relates to the field of substation inspection technology, and discloses a method, system, equipment, and medium for automatic planning of substation video inspection points based on multi-source data fusion. The method includes: acquiring substation equipment status data and environmental monitoring data, fusing them to generate a target inspection influencing factor set; generating multiple initial inspection candidate points based on this set, and simultaneously performing multiple rounds of point optimization processing in conjunction with equipment status and environmental data to obtain the target inspection points and their coverage evaluation results; finally, integrating and generating inspection point planning data for configuring camera deployment parameters. This invention achieves dynamic planning of inspection points by setting up a multi-source data-driven and iterative optimization mechanism, enabling video surveillance to fully cover the entire substation area.

The present application relates to a kind of layered reinforcement learning driven car-road cooperation automatic driving longitudinal following control method and system, belong to the field of automatic driving vehicle control.For the problem that the generalization ability of existing following control method is insufficient, strategy update is easy to lose stability, easy to fall into local optimum, adopt layered architecture, upper layer adapts intersection, high-speed straight road and ramp multiple typical traffic scenes, based on real-time car-road cooperation communication information dynamic planning follow target, after normalizing processing to multi-source heterogeneous traffic characteristics, generate environment state vector;Lower layer uses proximal policy optimization algorithm, outputs continuous accelerator or brake command through actor-critic double network, and updates network parameters by combining strategy entropy with pruning loss with Jacobian correction.The method improves the cross-scene generalization ability and perception robustness, effectively guarantees the stability and monotonicity of strategy update, avoids instruction step mutation, breaks the exploration deadlock, and balances driving safety, traffic efficiency and driving comfort.

This invention discloses a robot system for intelligent installation and path planning of busbar trunking, relating to the field of busbar trunking path planning technology. The system includes an environmental perception unit, a path planning unit, an installation execution unit, a collaborative control unit, and a power supply guarantee unit. Each unit interacts with the others via an industrial-grade wireless communication link. The frequency band of this communication link is adapted to the electromagnetic environment characteristics of the busbar trunking installation scenario, and its signal transmission characteristics match the structural distribution characteristics of the busbar trunking installation space. By collecting environmental parameters of the installation space in real time through the environmental perception unit, and combining this with the dynamic planning and obstacle avoidance decisions of the path planning unit, deep collaboration between environmental perception and path planning is achieved, solving the problems of low efficiency and poor adaptability in traditional manual planning.

The application provides a vaccine cold storage AGV operation navigation method and a cold storage AGV, and the operation navigation method comprises the following steps: S1, dividing the cold storage area by temperature collection points as boundaries, and determining the temperature distribution in each area; S2, acquiring AGV position data and temperature data at the current position in real time; S3, determining a starting point and a target point, establishing a multi-objective function with the minimum temperature change, the lowest path length and the lowest energy consumption, and solving the multi-objective function to obtain an optimal dynamic planning path. The application plans the path according to the temperature distribution in each area, avoids the temperature sudden change area, reduces the temperature fluctuation of the vaccine in the transportation process, and reduces the damage rate. Moreover, the path planning of the multi-objective function considers the adaptability of the distance, the temperature and the energy consumption, improves the operation efficiency, the dynamic energy consumption distribution strategy reduces the energy loss in the low-temperature environment, prolongs the endurance time of the AGV, reduces the charging frequency of the AGV in and out of the cold storage, and thus reduces the disturbance to the temperature field in the cold storage.

The application provides a man-machine cooperative cognitive decision space construction and on-the-spot decision method, that is, in the action planning process, on the basis of the main line action plan designed based on the static task purpose, a person estimates possible variables in action execution in advance, refines situation cognitive characteristics of key decision points, and proposes countermeasures, the system explores diversified possible action processes, analyzes and excavates key variables which have greater influence on action results, explores the best action options under the guidance of countermeasures and trains a dynamic planning intelligent agent, finally, the cognitive characteristics and countermeasure options, the planning intelligent agent are added to the main line action plan, and a cognitive-decision space is constructed; in the action execution process, the cognitive-decision space is used to guide the real-time monitoring of the change of situation characteristics and the dynamic triggering of key decision points, the online optimization of the best countermeasure and the dynamic generation of subsequent action planning.

The application belongs to the technical field of robot control, and relates to a robot collision-free trajectory dynamic planning method and system for a machining unit, which comprises the following steps: obtaining kinematic parameters of each rigid arm segment and operating temperatures of each joint motor; constructing a space-dependent chain for determining minimum geometric gaps between each rigid arm segment and an environmental obstacle based on the kinematic parameters; determining an effective approaching speed according to a gap change rate of the minimum geometric gaps, combining equivalent structural stiffness mapped by the operating temperatures and preset dynamic parameters, and deducing a composite dynamic safety bottom line composed of a physical deceleration distance, an inertial sliding distance and an inertial flexure intrusion distance; and performing anti-collision control intervention on the robot based on a trajectory convergence index determined by the minimum geometric gaps and the composite dynamic safety bottom line. The application can realize dynamic compensation of multi-dimensional limit displacement, perceive over-travel collision risks in advance, and improve the precision of anti-collision intervention under complex limit working conditions.

The application provides a kind of adaptive spraying trajectory planning method and system based on visual identification, it is related to computer vision technical field, in spraying operation, by real-time video stream of the surface of the workpiece being sprayed is carried out surface solvent deposition state analysis, build the workpiece surface non-deposited area connected graph, in combination with the spatial direction distribution of deposition thickness gradient in workpiece surface cumulative deposition thickness distribution field and the geometric barycenter coordinate sequence of each connected region in non-deposited area connected graph, construct spraying trajectory dynamic planning space under the workpiece surface three-dimensional model coordinate system, in the space, the movement path of end effector of spraying robot is planned in real time by using dynamic search algorithm, adaptive spraying trajectory is generated by grid node sequence etc., and is converted into the motion instruction code sequence recognizable by spraying robot controller, in the form of real-time data stream is sent to servo driver, realize efficient, uniform spraying operation.

This invention relates to the field of eSIM number management, and more particularly to an AI-based eSIM number lifecycle management system. The system includes a data acquisition module, a region calculation module, a demand analysis module, a planning module, and a strategy adjustment module. The data acquisition module is used to: collect all behavioral data of eSIM numbers, perform data preprocessing, and output a number behavior data stream. The region calculation module is used to: calculate effective activation in different regions based on the number behavior data stream, and construct activation curves for different regions. The demand analysis module is used to: calculate the multi-point activation growth rate of the activation curves in different regions, estimate the number activation demand, and output activation demand values ​​for different regions. The planning module is used to: perform dynamic planning and intelligent allocation based on the activation demand values, and output a number deployment strategy. This invention achieves precise deployment of eSIM number resources, thereby improving the resource utilization efficiency of numbers.

This invention relates to the field of mobile robot control and path planning technology, and particularly to a dynamic planning method for multi-robot cooperative patrol paths considering energy constraints. The method includes: first, fusing information from multiple sensor sources to acquire patrol area, key monitoring points, and robot energy status data; dynamically dividing the area into sub-regions using remaining battery life as a hard constraint, combined with task load and path constraints, and generating an initial patrol path through weighted coverage and energy consumption prediction; updating battery power in real time during execution and comparing local replanning results; initiating cooperative requests including incomplete paths when battery power is insufficient, and achieving cross-robot cooperative optimization based on path length, energy consumption, and patrol priority; finally, generating a review report based on actual energy consumption and path adjustment records, updating the energy consumption prediction model and allocation strategy, and using it for the next round of partitioning. This invention significantly improves the patrol reliability and energy utilization efficiency of multiple robots in dynamic environments.

The application discloses a mountain and hilly biomass collection, storage and transportation collaborative scheduling method and system based on multi-agent game, relates to the technical field of wisdom agriculture and supply chain collaboration, and aims to solve the problems of unbalanced benefit distribution, ignored terrain constraints and rigid collaborative mechanism in the prior art. The application first acquires static attribute data, dynamic operation data of the collection and storage nodes, and geographical distribution and resource quantity of all resource points of the biomass to be collected and transported; determines the dispersion degree of the geographical distribution of all resource points, the balance degree of the resource quantity, and the difference degree of the appeals of the benefit related parties; takes the dispersion degree, the balance degree, the static attribute data of each collection and storage node and the difference degree of the appeals as judgment indexes, determines the collaborative mode of the benefit related parties according to a preset judgment mechanism, executes collaborative planning, and completes dynamic planning of the collection path and the corresponding resources; and finally realizes dynamic allocation of resources.

The application discloses a kind of automobile thermal management system energy distribution methods, it includes the following steps: according to navigation data or driving condition, driving process is dispersed into at least two driving stages according to time interval;According to the dynamic planning mathematical model of temperature in vehicle, the optimal objective function of each driving stage and the decision variable corresponding to optimal objective function are solved reversely;According to initial state variable, optimal objective function and the decision variable corresponding to optimal objective function, the optimal state variable sequence is solved forward, and the optimal curve of passenger cabin temperature is output;According to the optimal curve of passenger cabin temperature, thermal management system components are controlled, so that actual temperature in vehicle tracks the optimal curve of passenger cabin temperature.The application solves the optimal objective function of each driving stage and corresponding decision variable reversely, then the optimal curve of passenger cabin temperature is solved forward, actual temperature in vehicle tracks the optimal curve of passenger cabin temperature by controlling thermal management system components, closed loop is formed in energy, so that actual temperature in vehicle reaches the temperature required by user while thermal management system components maintain low energy consumption state, thereby reducing the energy consumption of automobile thermal management system.The application also discloses a kind of device, equipment and medium.

This invention discloses a method for mission relay and dynamic route planning for stationary UAVs based on energy relay, belonging to the field of UAV control technology. It includes a smart relay station receiving macro-level mission commands and dynamically dividing them into multiple route segments, generating a mission beacon broadcast containing route and priority information; stationary UAVs listening to the beacons and autonomously deciding whether to relay based on local bidding rules and their own status; the UAV deciding to relay becomes a mission UAV and executes the corresponding flight segment. The mission UAV monitors its battery level in real time, and when it triggers a low battery threshold and meets safety conditions, it generates and broadcasts an incomplete mission beacon, simultaneously selecting a target station to return to based on real-time charging queuing information broadcast by each smart relay station; this incomplete beacon triggers other stationary UAVs to initiate a new round of autonomous relay decision-making. This invention employs a dynamic mission decomposition and release mechanism, combined with autonomous bidding decision-making, enabling dynamic relay and dynamic route planning for long-endurance UAV missions.

The application discloses a high-rise building construction path dynamic planning method based on artificial intelligence, and comprises the following steps: S1, data acquisition, construction task sequence and initial resource supply relationship graph generation; S2, current resource supply state, construction rhythm change state and work interference factor generation, current actual construction state formation; S3, construction-resource confrontation graph model construction; S4, path search tree structure initialization; S5, path sampling and evaluation are carried out through an improved heuristic Monte Carlo search algorithm, and a plurality of feasible path branches and evaluation scores thereof are output; S6, path branch comparison and dynamic optimization are executed, and a current optimal construction path is obtained; S7, construction step adjustment suggestion and resource allocation adjustment instruction generation; S8, optimal construction path and evaluation score recording, construction path optimization result formation. The application improves the intelligentization, self-adaptation and real-time optimization capability of the high-rise building construction path planning.

A flexible approach trajectory planning method for aircraft in extended terminal areas is proposed. It includes the following steps: designing the airspace structure of the extended terminal area, consisting of a pre-operation area and a dynamic planning area; constructing an aircraft pre-sorting model for the pre-operation area; solving the pre-operation area aircraft pre-sorting model; rasterizing the dynamic planning area airspace into multiple square grids and constructing a network graph; constructing a flexible approach trajectory planning model for the dynamic planning area; and solving the flexible approach trajectory planning model for the dynamic planning area using mathematical programming methods to generate a flexible approach trajectory for each aircraft from the entrance to the exit of the dynamic planning area. This invention, through a collaborative optimization framework of pre-sorting and dynamic trajectory planning in two stages, and innovatively incorporating airspace complexity into the optimization objective, can effectively reduce flight delays and controller workload while improving trajectory flexibility, thereby enhancing the overall safety and efficiency of terminal area operations.

This invention belongs to the field of PCB board and intelligent manufacturing technology, and discloses an AI-driven intelligent alignment control method and system for clamping and drilling high-multilayer PCBs. The method includes: high-multilayer PCB loading and initial positioning, multi-view visual layer offset precision identification, AI dynamic clamping point planning, distributed force control clamping execution, full-process closed-loop detection and deviation correction, and processing data feedback AI self-learning optimization. The system of this invention includes: an automatic alignment unit, a force control servo clamping unit, a high-precision drilling unit, a machine vision acquisition unit, an AI linkage control unit, and a cloud control unit. This invention adopts hardware and software collaboration, using an eccentric clamping column structure for progressive clamping force adjustment, a distributed force control network to achieve collaborative control of K clamping points, and AI dynamic planning to generate an asymmetric clamping layout for active correction of inter-layer offset. It is applicable to the precision drilling preparation of 12-layer and above high-multilayer PCBs.

An autonomous driving behavior decision-making method, a driving decision-making equipment, and a computer-readable storage medium are provided. By using a driving behavior policy network and a driving cost evaluation network trained based on an adaptive dynamic planning technology to perform a Monte Carlo tree search algorithm according to a real-time driving environment state of a target vehicle, the adaptive dynamic planning technology is integrated into each decision-making stage of the Monte Carlo tree search algorithm to guide the Monte Carlo tree search algorithm to focus on the effective searches associated with the high-value decisions for reducing the workload of invalid searches, the loss burden of calculation resources, and the time loss so as to improve the efficiency and the accuracy of the decision-making of the Monte Carlo tree search algorithm.

The application discloses a power system planning method considering extreme scenarios and seasonal energy storage coordination, relates to the technical field of power system planning and optimization, and comprises the following steps: extracting operation scenarios covering source- load bilateral extremes from historical operation data to construct a mixed scenario set; a hierarchical coordination model containing battery energy storage, pumped storage and hydrogen energy storage is established, and three-level energy state coupling constraints of intra-day, inter-month and inter-year are set; based on the hierarchical coordination model, a dynamic planning framework considering asset construction period, operation life and cross-year state connection is constructed to realize investment decision optimization under a full life cycle perspective; based on the mixed scenario set and the dynamic planning framework, investment decision and operation decision are respectively made with the full life cycle cost minimization as the target, and after refining the cost items and the constraint condition system, the planning of the power system is completed, and the full life cycle cost is significantly reduced under the premise of ensuring the safety of the system.

The application discloses a cross-center collaborative task dynamic planning method based on a pipeline mechanism, and steps of the method comprise the following steps: 1) according to a collaborative requirement, arranging a data source and a collaborative model algorithm, and configuring a cross-center collaborative computing task; 2) dividing the cross-center collaborative computing task into subtasks according to whether data migration behavior will be generated, and forming a DAG (Directed Acyclic Graph); 3) according to the DAG, dividing different data sources into different subtasks; 4) when a subtask T is allocated with a data source D, the subtask T is sent to a data center where the data source D is located to be executed; for a subtask T' which is not allocated with a data source, according to execution conditions of an upstream subtask of the subtask T' and resource conditions of each data center, a data center for executing the subtask T' is determined, and then the subtask T' is sent to the corresponding data center to be executed. The application realizes efficient arrangement and execution of cross-data-center collaborative tasks for complex analysis scenarios.

This invention discloses a dynamic path planning method for electric aircraft for low-altitude patrol, belonging to the field of low-altitude aircraft planning technology. The method includes constructing a composite environmental situation map containing a terrain obstacle grid map and an airspace access cost map; extracting potential feasible route segments from the current position to the target patrol point and performing energy consumption prediction modeling to obtain the estimated energy consumption value for each segment; comparing this value with the remaining battery capacity percentage; eliminating route segments exceeding the energy budget; and retaining a set of candidate routes that meet the constraints. The candidate routes are then smoothed to eliminate curvature abrupt changes, generating geometrically continuous executable route geometry. A power consumption rate curve is superimposed on the path to calculate the theoretical endurance and assign a time attribute label. A comprehensive score is then combined with the patrol task priority weight to select the optimal dynamic planning path. This method ensures that the path matches the aircraft's energy state, improving the flight adaptability and planning rationality of the route.

The application relates to the technical field of unmanned aerial vehicle path planning, and discloses a low-altitude unmanned aerial vehicle route dynamic planning method and system based on disturbance fluid, wherein the method comprises the following steps: in the process of unmanned aerial vehicle flight, continuously sensing the environment through a multi-source sensor to obtain multi-modal sensing information, and obtaining different threats through fusion clustering and segmentation; envelope modeling is carried out according to the position and shape information of the threats, the static terrain and dynamic obstacles in the real world are abstracted into multi-class basic geometric bodies and combinations thereof, a boundary equation after simplifying the obstacles is obtained; the position of a target point and the boundary equation are combined, a weight coefficient and a disturbance matrix are solved through a disturbance fluid dynamic system to obtain a disturbance flow rate, and the unmanned aerial vehicle route dynamic planning result is obtained after the disturbance flow rate is integrated; and the execution device of the unmanned aerial vehicle is controlled according to the unmanned aerial vehicle route dynamic planning result, so that the unmanned aerial vehicle moves along a predetermined track. The application can realize the evasion performance of the unmanned aerial vehicle to multiple dynamic obstacles.

This invention relates to the field of logistics route planning technology, and more particularly to a logistics route dynamic planning system based on reinforcement learning, including a logistics planning and management center, a data acquisition module, a processing and acquisition module, a delivery access module, a verification and optimization module, a dynamic planning module, and a feedback module. This invention achieves dynamic perception and real-time adaptation of logistics scenarios through multi-dimensional data acquisition and fusion processing. It also constructs an initial route framework based on road network topology, reducing subsequent adjustment costs, and builds a region-weight-timeliness coordinated delivery access sequence to avoid time waste caused by cross-regional round trips. Furthermore, through time chain verification, it can quickly resolve conflicts and inefficient route problems without disrupting the overall sequence. In addition, for the optimal selection of multiple candidate planned routes, a weighted scoring method is used to achieve a dynamic balance of time, cost, and reliability, significantly improving delivery efficiency, reducing operating costs, and ensuring delivery reliability.

The application relates to the technical field of power distribution equipment maintenance management, and discloses a multi-target optimization-based dynamic planning method and system for the maintenance path of a branch box, which has the technical scheme as follows: an enhanced task list, a composite travel time matrix and a collaborative maintenance reward calculation function are obtained through temperature sensing equivalent service time modeling, communication blind area task closed loop construction and multi-loop collaborative maintenance reward; a non-dominated path scheme set is obtained through temperature communication sensing weighted clustering, cluster level dynamic planning and cluster internal dynamic planning screening through a Pareto frontier; an updated complete maintenance path scheme is obtained through reverse path re-inspection value evaluation and residual task re-planning; and an updated model parameter configuration file and system performance evaluation report are obtained through statistical fitting and parameter rolling update. The application unifies temperature, communication and collaborative maintenance constraints into a multi-target dynamic planning framework, realizes high-precision adaptation of the maintenance path to actual working conditions and improves operation and maintenance efficiency.

The application belongs to the technical field of rehabilitation medicine, and particularly relates to a personalized rehabilitation path dynamic planning system based on machine learning. The application discloses a personalized rehabilitation path dynamic planning system based on machine learning. The system comprises a clinical medical knowledge base, a multi-modal data acquisition terminal, a central cloud processing server, a knowledge-enhanced constraint generator, a dynamic adversarial discriminator and a nonlinear dynamic stability controller. The application converts a medical atlas into a neural network hard constraint, guides path evolution by using an adversarial game mechanism, and realizes real-time fine-tuning or reconstruction of a rehabilitation path based on a phase space model. The application realizes deep integration of medical prior knowledge and a data-driven model, and improves the personalization degree, response sensitivity and global stability of rehabilitation path planning under the premise of ensuring clinical safety.

This invention discloses a dynamic planning method and system for emergency evacuation routes in smart buildings. The method includes the following steps: S1: Multi-source dynamic data acquisition and preprocessing. This invention overcomes the limitations of ideal static modeling by constructing three dynamic models, employing an improved dynamic weighted algorithm and a real-time optimization mechanism. It accurately adapts to dynamic disasters and personnel factors, improving the robustness, real-time performance, and adaptability of route planning, ensuring evacuation safety. Furthermore, by building a standardized data platform, it achieves data interoperability and format unification across multiple systems, establishing a linkage channel between planning results and execution, and connecting relevant departments such as property management and fire protection. This breaks down data silos and cross-system collaboration barriers, resolving the disconnect between "planning changes and guidance not being updated," achieving seamless collaboration between planning, execution, and rescue, significantly improving emergency evacuation efficiency, and meeting the core needs of smart building emergency management.

The application provides a UAV low-altitude air route dynamic planning method and system, and relates to the technical field of intelligent planning. The method comprises the following steps: receiving an added delivery point instruction and obtaining a three-dimensional coordinate, integrating the added delivery point with an existing delivery point, defining a geographical range through a communication base station, a radio tower and a permanent landmark building, dividing the geographical range in a spatial structure, and decomposing the geographical range into a uniformly distributed three-dimensional voxel grid; generating an adjustment value according to the coordinate interval, the delivery point density and the straight-line distance from the landmark building of the three-dimensional voxel grid, adjusting the task integration result using the adjustment value, and obtaining the adjusted task integration result of all delivery points. The application realizes safe, efficient and accurate air route planning for the dynamic addition of multiple delivery points in a complex low-altitude environment.

The application discloses an airport taxiway path dynamic planning method based on an airport mapping database (AMDB) and a civil aviation navigation notice (NOTAM), integrates AMDB static topological data and NOTAM dynamic information, adopts a taxiway path cost calculation model design and a taxiway path planning algorithm based on initialization data, covers steps such as model parameter determination, dynamic correction mechanism establishment and taxiway path total cost calculation, and generates an optimal path considering physical distance, taxiway time and taxiway priority; meanwhile, by means of real-time receiving and processing of NOTAM dynamic information, the weight of the taxiway network is dynamically adjusted, the actual operation scene of the airport is fitted, and real-time response to dynamic changes of the airport scene and dynamic planning of the airport taxiway path are realized.

This application relates to the field of intelligent navigation and path planning technology for maritime vessels, and discloses an autonomous collaborative perception and dynamic planning system and method for patrol boats. The system includes a base station and a patrol boat end: the base station consists of main modules for marine environment modeling, group federated learning aggregation, task management, and communication, responsible for generating and distributing lightweight knowledge packages; the patrol boat end comprises four main modules: multi-sensor perception, local lightweight intelligence, online planning, and propulsion and steering control, and is equipped with a dual-mode communication module supporting satellite links and inter-boat micro-communication. This method, through an architecture of "knowledge package infusion - local perception - intelligent prediction - rolling planning - dual-mode collaboration," realizes the transformation of patrol boat groups from centralized command to distributed edge intelligence in communication-constrained open-sea environments, significantly improving the system's autonomy, adaptability, and mission reliability in dynamic marine environments.

The application discloses an immersive virtual fitting method and system based on a multi-modal large model, and the method comprises the following steps: S1, receiving and analyzing a multi-modal intention instruction input by a user, a user reference video and a target clothing image; S2, inputting the instruction, the video and the image into a multi-modal intention understanding and dynamic planning module; S3, inputting an intention-driven posture sequence, the target clothing image and a semantic label into a semantic condition dynamic diffusion rendering engine; S4, inputting an initial fitting image sequence into a physical and inter-frame consistency enhancement module; and S5, outputting and storing an immersive fitting video sequence; through the cooperation of multi-modal intention understanding and condition diffusion rendering, the user can intuitively control the semantic level of dynamic and physical properties of clothing, and the degree of freedom of interaction is significantly improved; through a time sequence diffusion model and a physical enhancement module, a high-quality fitting video is generated, which conforms to the user's intention and maintains physical rationality and inter-frame consistency.