2264 results about "Route planning" patented technology

The invention discloses an unmanned aerial vehicle route automatic planning method for electric power inspection, and relates to the technical field of unmanned aerial vehicle inspection. Comprising the following steps: starting an unmanned aerial vehicle, carrying out environment perception initialization, calculating the total cruise mileage, evaluating the interference risk, carrying out real-time obstacle avoidance, dynamically optimizing an inspection route, carrying out energy monitoring management, generating a return flight strategy, recording an inspection task and carrying out adaptive learning. Through dynamic electromagnetic interference modeling, multi-modal fusion perception, self-adaptive risk decision and cloud collaborative learning, the problem of insufficient adaptability of a traditional electric power inspection unmanned aerial vehicle in a complex electromagnetic environment and a dynamic obstacle scene is solved, the safety and the inspection efficiency are improved, the robustness is enhanced, and the method is suitable for popularization and application. Intelligent upgrading is carried out through continuous learning and multi-machine cooperation, the overall operation and maintenance cost of the system is reduced, a high-reliability and full-automatic inspection solution is provided for intelligent power grid construction, and the industrial application value is remarkable.

The invention discloses a traffic transportation operation monitoring early warning and decision analysis method based on vehicle infrastructure cooperation, and relates to the technical field of intelligent traffic. According to the method, real-time collection of dynamic traffic elements is realized through a multi-dimensional sensing network of a vehicle end, a road side and an environment and V2X communication, time-space reference unification of multi-source data is ensured, a road-vehicle-environment-event semantic network is constructed, multi-dimensional recognition of abnormal events such as accidents, congestion and severe weather is realized in combination with hierarchical feature extraction, and the method has the advantages of being high in practicability and high in practicability. The method is advantaged in that identification accuracy is improved, abnormal event propagation paths can be predicted, global road network situation prediction capability is realized, differential early warning is generated based on a comprehensive risk index, multi-level responses such as traffic signal adjustment and path planning are triggered, response time is greatly shortened, emergency response efficiency is optimized, a decision execution effect real-time feedback mechanism is established, and the method is suitable for popularization and application. The system performance is continuously optimized along with data accumulation, and the defect that a big data platform lacks an intelligent decision closed loop is avoided.

The invention relates to the technical field of patrol path optimization, and particularly discloses an unmanned aerial vehicle patrol path optimization method and system based on a mobile unmanned aerial vehicle nest, and the method comprises the steps: carrying out the dynamic environment perception and multi-source data fusion, generating a global environment situation map, carrying out the task intelligent analysis and dynamic priority sorting, and obtaining a task distribution matrix; the method comprises the steps of performing multi-target hierarchical path planning, outputting a dynamic flight path set with a timestamp, constructing a real-time obstacle avoidance and emergency self-healing system, obtaining a safety correction path set, performing mobile nest collaborative scheduling and energy optimization, outputting a nest mobile instruction set, triggering global environment situation map updating, obtaining an unmanned aerial vehicle execution log, and performing knowledge evolution. And end-to-end technical iteration is completed. According to the method, the problems that the route planning of the traditional patrol path planning unmanned aerial vehicle cannot achieve refinement and is not high in applicability under complex terrain conditions are solved.

The invention relates to the technical field of navigation and positioning, in particular to an intelligent positioning and navigation early warning system and method for ships and boats. The method comprises the following steps: collecting and carrying out sea condition sensing and positioning signal evaluation according to a multi-source sensor synchronous data set to obtain a sensor reliability score table; performing target motion situation analysis according to the sensor reliability score table to obtain a fusion situation vector diagram; performing target motion state judgment according to the fused situation vector diagram to obtain a target motion state judgment result; performing multi-target collision risk assessment according to the target motion state judgment result to obtain a risk degree assessment matrix; performing navigation maneuvering performance constraint according to the risk assessment matrix to obtain a route planning maneuvering constraint rule set; and performing route trajectory search according to the route planning manipulation constraint rule set to obtain a navigation trajectory scheme. According to the invention, through the positioning navigation early warning technology, the positioning precision, the navigation safety and the early warning system practicability of the ship in a complex environment are obviously improved.

The embodiment of the invention provides an airspace fine management method and device based on acceptable risks, and a risk assessment model is constructed by creatively adopting a grid airspace division mode and fusing ground attributes, historical accidents, meteorological environments and aircraft activity multi-source data. The self-adaptive adjustment of the risk tolerance threshold is realized through a deep reinforcement learning method, and a differentiated regional access standard is established. And through combination of task grading evaluation and a risk early warning mechanism, accurate evaluation of the operation safety of the unmanned aerial vehicle is realized. A multi-objective optimization algorithm is adopted to carry out route planning, cooperative management and control are realized in cooperation with an emergency avoidance strategy, and a prevention and control mechanism is continuously optimized through risk traceability analysis. According to the method, the defects of the traditional technology in the aspects of fine management, risk assessment, collaborative management and control and the like are effectively overcome, and the safety and efficiency of urban low-altitude airspace management are remarkably improved.

The invention discloses an intelligent maritime affair security situation awareness system and method, and relates to the technical field of path planning, and the method comprises the steps: obtaining multi-source ship data, carrying out the space-time dimension alignment and feature weight distribution, generating a fusion feature tensor, extracting a spatial topological relation through a graph convolution operation, and capturing a time dependence feature through an LSTM model. Constructing a ship interaction potential energy field model in combination with the spatial topological relation and the time dependence features, calculating a collision risk index in combination with a conflict cone algorithm, generating a dynamically updated sea area risk thermodynamic diagram, and performing situation map construction on the sea area risk thermodynamic diagram and the fusion feature tensor to obtain a dynamic situation map; a ship-environment-event triple relationship is extracted by adopting a hierarchical graph attention network, an interpretable semantic situation map is constructed, a threat matrix is generated through node-level risk assessment in sequence, and an air route planning scheme is obtained through regional-level path optimization; high-risk areas can be avoided, and navigation safety and efficiency are improved.

The invention belongs to the technical field of road deformation monitoring, and discloses a road slope deformation monitoring system and method, and the system comprises a fixed point monitoring mechanism which monitors the displacement, strain, underground water level and inclination parameters of a slope in real time at key points and key positions of the road slope; the route planning module plans the flight route of the unmanned aerial vehicle; the cruise monitoring mechanism is used for monitoring the side slope in all directions; the weather data acquisition module is used for acquiring local weather data; the three-dimensional laser scanner data analysis module is used for identifying abnormal conditions on the side slope; the image analysis module is used for identifying abnormal conditions on the slope; the comprehensive evaluation module is used for comprehensively evaluating the stability of the slope by utilizing a comprehensive evaluation model; and the alarm module comprises an alarm. According to the invention, the fixed-point monitoring mechanism and the cruise monitoring mechanism are combined to monitor the slope in all directions, the comprehensive evaluation module fuses data of various monitoring means, and the comprehensive evaluation model is utilized to comprehensively evaluate the slope stability.

The invention relates to the technical field of electrical equipment defect detection, in particular to an electrical equipment defect detection system and method based on deep learning, which are characterized in that a three-dimensional model of a power grid region is constructed, and based on historical defect data, a neural network model is adopted to mark an importance score of an inspection object in the three-dimensional model; quantitative evaluation of the equipment fault risk level is completed, and the matching degree of inspection resources and defect risk distribution is improved. A power grid three-dimensional model and importance scores are combined, a reinforcement learning model is utilized to construct an unmanned aerial vehicle inspection route planning strategy, the inspection route comprehensively considers power equipment defect risks and space factors in the planning stage, task allocation is optimized, and the problem that a static route cannot adapt to equipment changes is solved. In the inspection execution process, the unmanned aerial vehicle is dispatched according to the planning strategy, and the image flow is synchronously acquired for defect detection, so that the linkage of the inspection action and the detection process is realized, and the response efficiency and the detection quality of potential defects are improved.

The invention discloses an unmanned aerial vehicle route planning method based on an improved grey wolf optimization algorithm. The method comprises the following steps: 1, environment modeling and parameter setting; 2, designing constraint conditions and a target function; 3, initializing an algorithm and generating a population; 4, improving an algorithm and updating a track; and step 5, iteration termination and result output. The flight path of the unmanned aerial vehicle is efficiently coded in a spherical vector coding mode, and the flexibility and the calculation efficiency of flight path representation are remarkably improved; by introducing the Levy flight strategy, the global search capability of the algorithm is enhanced, and falling into a local optimal solution is avoided; meanwhile, a parameter adaptive adjustment strategy is combined, so that the convergence speed and precision of the algorithm are remarkably improved. According to the method, the problem of flight path planning in a complex mountain environment and a threat area is effectively solved, a safer and more efficient flight path is planned for the unmanned aerial vehicle, and the method has higher practicability and reliability.

The invention provides an unmanned aerial vehicle surveying and mapping method and system, electronic equipment and a storage medium, and relates to the technical field of unmanned aerial vehicle surveying and mapping. The method comprises the following steps: carrying out base station position planning based on geographic data and preset factors, and arranging a CORS platform base station; then, acquiring a remote sensing image to construct a three-dimensional model, and performing semantic segmentation to obtain key ground feature information; and generating a multi-modal environment semantic map for surveying and mapping route planning based on registration of the key surface feature information and the geographic data. The unmanned aerial vehicle obtains surveying and mapping data, CORS positioning information and surveying and mapping image data in real time according to the surveying and mapping route, and environment semantic positioning data is obtained based on the surveying and mapping image data and multi-mode environment semantic map registration. And finally, performing registration processing by comprehensively utilizing the CORS positioning information, the environmental semantic positioning data and the surveying and mapping data to obtain target surveying and mapping data. Through the scheme, the surveying and mapping accuracy of the unmanned aerial vehicle is improved.

The invention relates to the field of airline planning, and discloses a digital twin system for low-altitude airline planning and a construction method, and the method comprises the steps: obtaining a spatial perception data set of a preset sensor in a target airspace, carrying out the fusion of the spatial perception data set, obtaining the fusion perception data, obtaining the historical flight hidden danger data of the target airspace, and obtaining the historical flight hidden danger data of the target airspace; performing multi-layer convolution on the fusion perception data by using the historical flight hidden danger data to obtain a risk analysis result, performing fuzzy fusion on preset risk factors, generating a dynamic obstacle risk field according to the risk factors after fuzzy fusion and the risk analysis result, performing flight simulation on the dynamic obstacle risk field, and obtaining a flight simulation result; and collecting flight simulation real-time data in the flight simulation process, generating an air route space cost map according to the flight simulation real-time data, and performing optimal path search on the air route space cost map to obtain a low-altitude air route scheme. In the face of airspace actual risks, the searching efficiency of the optimal path in low-altitude route planning is improved.

The invention belongs to the technical field of topographic mapping, and discloses a topographic mapping system and method based on an unmanned aerial vehicle, and the method comprises the steps: a data collection module collects map geological data; the route planning module plans an optimal driving route; the data acquisition module acquires topographic images and point cloud data; the environment parameter monitoring module monitors flight environment parameters in real time; the image data analysis module is used for preprocessing and analyzing the image data; the radar data analysis module preprocesses and analyzes the radar data; the feature extraction and matching module performs feature extraction and matching on the image and the point cloud data; the terrain model construction module constructs a terrain three-dimensional model; and the terrain analysis module performs quantitative analysis on the terrain. According to the method, key feature extraction is performed on images and point cloud data, and image splicing and point cloud registration are realized; the terrain model construction module constructs a three-dimensional terrain model according to feature extraction and matching results, and the terrain analysis module obtains accurate terrain information.

The invention discloses an unmanned aerial vehicle low-altitude route intelligent planning and cooperation system and method based on multi-source perception and edge calculation, and relates to the technical field of unmanned aerial vehicle route planning. Comprising a multi-source data sensing module, a data processing module, a planning execution module and a feedback monitoring module, and the multi-source data sensing module is used for acquiring meteorological data and airborne data and generating an initial data set; the data processing module is used for generating an environment model and inputting the initial data set into the environment model to obtain physical prediction data and obstacle information data; the planning execution module is used for generating a planning path according to the physical prediction data and the obstacle information data and controlling the unmanned aerial vehicle group to fly; and the feedback monitoring module is used for monitoring the flight state of the unmanned aerial vehicle group in real time and feeding data back to the ground control unit. According to the invention, the flight safety, efficiency and cooperative capability of the unmanned aerial vehicle group are improved.

The invention discloses a ship path planning method based on deep reinforcement learning. The ship path planning method is suitable for a complex sea area environment. Through environment modeling and dynamic obstacle identification, a detailed state space is constructed, and perception of the environment is enhanced by using an artificial potential field. An ID3QN-PER algorithm is adopted to train a model, an adaptive exploration strategy and a network parameter updating mechanism are designed, and navigation safety and efficiency in a dynamic environment are ensured. The method further comprises the steps of discretizing an action space, designing an obstacle avoidance strategy in combination with a COLREGS specification, smoothing a route through a B-spline technology, adjusting a navigation strategy according to real-time environment data, and improving the real-time performance and the global optimization performance of route planning.

The invention discloses a reinforcement learning unmanned aerial vehicle route planning method and system based on a safe flight corridor, and relates to the technical field of route planning. A safe flight corridor is generated through a grid map of a target scene and starting point information; calculating a plurality of initial B spline control points through observation information of the unmanned aerial vehicle; iteratively generating a new B-spline control point based on the plurality of initial B-spline control points, the key features extracted based on the safe flight corridor and the time variable through a reinforcement learning algorithm; and generating a B spline path of the unmanned aerial vehicle according to all B spline control points. According to the invention, a feasible flight area in a complex environment is abstracted as a safe flight corridor. Environment characteristics are hidden in the safe flight corridor, and common application in different complex environments can be achieved through training in one environment. A B-spline control point is iteratively generated by using a reinforcement learning algorithm, and environment change can be iteratively adapted in real time, so that the generated B-spline path better meets the actual flight demand of the unmanned aerial vehicle.

The invention relates to the technical field of route planning, in particular to an underground cavern inspection method and system. The method comprises the following steps: acquiring geological survey data, micro-seismic monitoring data and historical deformation monitoring data; according to the geological survey data and the micro-seismic monitoring data, rock mass viscoelasticity local modeling is carried out, and a rock mass viscoelasticity model is obtained; performing deformation trend prediction according to the rock mass viscoelastic model and the historical deformation monitoring data to obtain deformation trend data; performing deformation disturbance resonance analysis according to the deformation trend data and the micro-seismic monitoring data to obtain deformation disturbance resonance data; performing risk area grading on the deformation disturbance resonance data to obtain risk area grading data; and generating a routing inspection graph according to the risk area grading data to obtain routing inspection graph data so as to carry out underground cavern routing inspection auxiliary operation. The method accurately identifies the potential risk area, dynamically plans the inspection task, and improves the inspection efficiency and decision timeliness.

In various examples, a technique for generating a path between a current location and a target waypoint is disclosed that includes receiving a route plan that is associated with a plurality of waypoints representing locations in a physical environment. The technique also includes identifying a search space that includes the route plan, and identifying a target waypoint of the plurality of waypoints—the target waypoint being in a portion of the search space between a current location of a mobile robot and an end waypoint of the route plan. A path between the current location of the mobile robot and the target waypoint may then be generated.

The invention discloses an unmanned aerial vehicle optimal path self-adaptive planning method adopting particle swarm optimization, and relates to the field of unmanned aerial vehicle path planning, and the method comprises the steps: constructing a space constraint model of an unmanned aerial vehicle flight task; initializing a particle swarm based on the spatial constraint model; combining the path smoothness, the path threat probability and the voyage efficiency to establish a multi-target fitness function, and calculating the fitness value of each particle based on a particle swarm; hierarchical particle swarm optimization iteration is executed, in each iteration, control parameters are adaptively adjusted based on the current particle swarm distribution characteristics, and the path node positions of particles are updated; and when a convergence condition is satisfied, outputting a global optimal path as a final flight path of the unmanned aerial vehicle, and controlling the unmanned aerial vehicle to execute. According to the unmanned aerial vehicle optimal path self-adaptive planning method based on particle swarm optimization, the problems that the unmanned aerial vehicle route path optimization target is single and difficult to cooperate, and the route planning effect is poor are solved.

The invention discloses a line planning method and system, and relates to the technical field of electric power line inspection. According to the line planning method, a plurality of feasible inspection paths are generated by obtaining a list of tasks to be inspected and three-dimensional coordinates of an electric tower and an inspection part of the electric tower, combining regional environment data, constructing a semantic knowledge graph and a spatial topological structure chart, determining an optimal path through evaluation, screening and analysis, and transmitting the optimal path to an unmanned aerial vehicle to execute an inspection task. According to the method, the flight state is monitored in real time, the abnormal condition is found in time, and the residual path is dynamically replanned in the inspection process, so that the smooth execution of the inspection task is ensured. The flight state and the environment interference of the unmanned aerial vehicle are monitored in real time, and the dynamic path is replanned according to the actual condition; according to the method, the flexibility and emergency response capacity of the inspection task are remarkably improved, the efficiency and safety of the inspection task are effectively improved by dynamically adjusting the path, and the adaptive capacity of the unmanned aerial vehicle in a complex environment is enhanced.

The invention discloses an expressway and airspace cooperative low-altitude aircraft route planning method, and relates to the field of path planning, and the method comprises the steps: optimizing the classification precision of each airspace hierarchy based on a decision tree algorithm and a Bayesian network; repulsive force field parameters are updated through a dynamic situation field updating mechanism; in each airspace level, calculating a gravitational field, a static repulsive force field and a dynamic repulsive force field of the expressway, and fusing to obtain a comprehensive potential field value of each airspace level; acquiring an initial path of an aircraft route by utilizing a greedy strategy and a priority rule and combining the comprehensive potential field value of the airspace level; and according to an artificial potential field local optimization algorithm and a dynamic conflict detection mechanism, adjusting the initial path of the aircraft route to obtain an aircraft route path. According to the invention, an artificial potential field method is combined, a layered collaborative planning algorithm is provided based on airspace ground collaborative potential field modeling, and efficient and safe navigation of the low-altitude aircraft is realized.

The invention relates to the technical field of unmanned aerial vehicle intelligent surveying and mapping, in particular to an unmanned aerial vehicle surveying and mapping method and system based on deep learning, and the method comprises the following steps: S1, collecting the obstacle information in the surrounding environment in real time, including the position, shape and motion state, and generating an environment data set; s2, classifying obstacles, and distinguishing static obstacles from dynamic obstacles; for a dynamic obstacle, using a recurrent neural network to predict a moving track of the dynamic obstacle; s3, updating the route plan of the unmanned aerial vehicle in real time; s4, adjusting the speed of the unmanned aerial vehicle according to the obstacle density and the dynamic obstacle frequency in the current area; and S5, when the route conflicts with the predicted path of the dynamic obstacle, adjusting the flight height or temporarily staying. According to the invention, accurate recognition and dynamic prediction of the obstacle are realized through deep learning, and the route planning, speed and data acquisition frequency of the unmanned aerial vehicle are optimized in real time, so that the safety and efficiency of surveying and mapping are significantly improved in a complex environment.

The invention discloses an intelligent warehouse management method and system for hazardous wastes, and the method comprises the steps: carrying out the real-time monitoring of the state of an object in a hazardous waste warehouse through a deployment sensor network, obtaining the basic data of the position of the object and the environment parameter, and transmitting the basic data to a central processing platform, thereby obtaining a preliminary data set; performing integration processing on multi-source information by adopting a data fusion technology according to the preliminary data set, eliminating redundancy and noise, and determining a standardized data record; aiming at the standardized data record, dynamically analyzing the distribution of goods locations in the warehouse by utilizing a path optimization algorithm, and if the change of the occupation state of the goods locations is detected, adjusting a goods location allocation scheme in real time, and outputting an optimal storage location combination; according to the optimal storage position combination, a dynamic route planning scheme is generated in combination with warehouse layout information, and the optimal moving track of the operation equipment is obtained; according to the invention, intelligent management of the hazardous waste warehouse is realized, and the storage efficiency and safety are improved.

The invention relates to the technical field of unmanned aerial vehicle route planning, and discloses a low-altitude route risk map construction method and system based on artificial intelligence, and the system comprises a low-altitude environment perception constraint module, a low-altitude route feature extraction module, a dynamic risk constraint judgment module, a low-altitude route map construction module, and a low-altitude route risk analysis module. And the optimal path calibration output module is used for determining a low-altitude air route flight area by acquiring a first constraint condition of low-altitude air route environment perception, extracting key feature parameters from low-altitude air route data, performing second constraint condition analysis on the low-altitude air route, intelligently planning a low-altitude air route route according to an analysis result, and constructing a low-altitude air route map. According to the method, the low-altitude air route is acquired, the constant direction line risk analysis is performed on the low-altitude air route to calibrate the optimal path, the optimal path is output to the user terminal, the path three-dimensional display is performed in the low-altitude air route map, and the execution efficiency and the safety of the flight task are improved.

The invention discloses an electric power communication multi-route intelligent planning method and device based on scene classification. The multi-route intelligent planning method in the electric power field comprises the steps that S1, multi-source heterogeneous data fusion processing is carried out; s2, classifying three-dimensional scene features; s3, dynamic routing modeling is carried out; s4, a hybrid intelligent optimization algorithm; and S5, real-time verification and feedback optimization are carried out. According to the multi-route intelligent planning method in the electric power field, the problems of static route planning, poor adaptability and insufficient multi-source data fusion capability in a traditional electric power communication network are solved, intelligent perception and dynamic adaptation to a complex communication environment are realized, and the multi-source data fusion capability is improved. And the network resource scheduling efficiency, the flexibility of the routing strategy and the overall service quality are improved.

The invention relates to an inverse synthesis route planning method and system based on a multi-modal large model, and belongs to the technical field of chemical synthesis and artificial intelligence crossing. The method comprises the following specific steps: firstly, extracting three-dimensional molecular structure characteristics of a target product through a molecular encoder, and aligning the three-dimensional molecular structure characteristics with text modals of molecules; secondly, reasoning a possible reaction mechanism and a reaction type, and reducing an inverse synthesis search space; secondly, splitting a target product into reasonable synthesis subunits through structured marking and vocabulary expansion, data-driven atomic assignment learning and reaction center recognition and activity prediction; finally, SMILES representations and reaction paths of the reactants are generated. According to the method, the accuracy and interpretability of inverse synthesis path planning can be improved, and a new technical means is provided for automation and intellectualization in the fields of new drug development, material design and the like.

The invention discloses a method and a system for processing route planning data for electric power line inspection. The method comprises the following steps: acquiring line distribution and topographic feature data; gridding the power network region, determining the patrol priority, and generating a region priority sequence; the overall patrol task is decomposed, a plurality of task subsets are generated, one task is allocated to each unmanned aerial vehicle, and each task subset comprises an initial position and a target line section; generating an initial route path based on the topographic features; comprehensively analyzing all initial paths, identifying potential airspace intersections and performing conflict prediction, and generating a conflict-free route path set by performing height hierarchical adjustment on related paths; and calculating predicted flight time and energy consumption of each unmanned aerial vehicle, redistributing boundary tasks according to a load balancing principle, and finally generating a balanced multi-unmanned aerial vehicle cooperative route scheme. According to the method, the safe and efficient multi-machine collaborative inspection route can be automatically generated through systematic task decomposition, conflict prediction and load balancing optimization.

The invention provides a vehicle path planning method based on an adaptive optimization algorithm, and relates to the technical field of adaptive algorithm application, and the method comprises the steps: 1, based on a current order demand, a vehicle resource and a preset constraint condition, obtaining a vehicle path planning result; an initial path scheme set meeting the requirements of a time window, the number of splitting times, vehicle type matching and a temperature zone is generated through a step-by-step progressive decision-making process; 2, starting a plurality of independent optimization links according to the initial path scheme set, and dynamically adjusting local path nodes by each link through a greedy strategy to generate an optimization candidate scheme set; and step 3, performing real-time evaluation on the optimized candidate scheme set, determining a final candidate scheme based on the cost saving rate, the demand satisfaction rate and the soft service quality index, and updating the final candidate scheme as the current reference scheme. According to the method, dynamic events are sensed in real time through an adaptive optimization algorithm, influences are evaluated, the path is intelligently adjusted, and the timeliness and accuracy of path planning are improved.

The invention provides an accompanying robot scenic spot route intelligent planning management method driven by reinforcement learning, and relates to the technical field of artificial intelligence, and the method comprises the steps: generating an initial touring path through a reinforcement learning strategy based on user physiological data, scenic spot opening time constraint and real-time people flow information, and dynamically adjusting the path according to the real-time physiological monitoring signal, optimizing the residence time, and outputting a physiological adaptive path. According to the invention, the service intelligence level and the user experience of the accompanying robot are improved.

The invention discloses a low-altitude airway flow field sensitive area dynamic identification optimization method based on set simulation, and the method comprises the steps: building a low-altitude flow field preprocessing data base with consistent time and space based on Beidou subdivision grids and multi-source heterogeneous data fusion; constructing a low-altitude airspace digital twinning environment based on the data; based on the low-altitude airspace digital twin environment and the cellular automaton-fluid coupling model, generating a diversified flow field evolution scene covering extreme weather and equipment faults; based on a set simulation result, extracting a high-conflict probability region through a spatio-temporal clustering algorithm and quantifying region risk features; generating an air route planning scheme meeting security constraints through a multi-objective evolutionary algorithm based on the quantitative regional risk features; on the basis of a low-altitude airspace digital twin environment and an air route planning scheme, verifying the feasibility of the air route planning scheme through historical data playback and virtual-real fusion test; and according to a verification feedback result, carrying out dynamic feedback optimization on the low-altitude air route flow field sensitive area identification and air route planning scheme.

The present application discloses an optimization method and apparatus for a transportation strategy under a feeder logistics network, an electronic device and a storage medium. The method comprises: acquiring logistics information under a feeder logistics network, wherein the logistics information comprises information of each logistics node; inputting the logistics information into a digital twin model, by means of the digital twin model and by taking transportation rules of the feeder logistics network as a constraint condition and transportation cost minimization as an objective, performing route planning to generate a transportation strategy to be optimized, and simulating a transportation process of goods between the logistics nodes on the basis of the transportation strategy to be optimized, so as to obtain a simulation result; and on the basis of the simulation result, optimizing the transportation strategy to be optimized, so as to obtain an optimized transportation strategy. In the present application, on the basis of the computing power of the electronic device, the transportation strategy during logistics transportation can be quickly obtained; and by simulating the logistics transportation process, the reliability of the transportation strategy can be improved.