805 results about "Route planning" patented technology

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 invention provides a low-airspace public route construction method, device and equipment, and relates to the technical field of aircraft airspace route planning. The method comprises the following steps: determining a plurality of target candidate nodes corresponding to a city based on ground traffic topological characteristics of the city corresponding to a low airspace and spatial distribution characteristics of low-risk ground features; performing triangulation on the plurality of target candidate nodes to obtain an initial edge set; wherein the initial edge set comprises a plurality of initial edges; performing sparse processing on the plurality of initial edges based on a greedy tensor graph algorithm, and determining a plurality of target edges from the plurality of initial edges; wherein the length of the shortest path between the two nodes of the target edge is greater than the greedy tensor coefficient times of the Euclidean distance between the two nodes; and constructing a low-airspace public route based on the plurality of target edges. By adopting the technical scheme provided by the invention, the utilization efficiency and safety of the low airspace can be effectively improved by constructing the unified low airspace public route.

The invention discloses an urban low-altitude-oriented grid dynamic evaluation and intelligent route planning system, and belongs to the field of urban low-altitude airspace and routes. And the data processing module is used for inputting static data including the task number, the task time, the unmanned aerial vehicle number, the take-off and landing field number and the total airspace, the continuous airspace is divided into grid units by a project, and a quantitative evaluation model with seven factors including the underlying surface, the communication quality, the navigation quality, the meteorological environment, the electromagnetic environment, the noise and the countering as the core is constructed. The airworthiness score of each grid is calculated through normalization processing and weighted summation, a real-time and scientific decision basis is provided for path planning of the unmanned aerial vehicle, crossing from static planning to dynamic evaluation is achieved, and a set of airspace gridding dynamic evaluation and task planning system is provided and implemented. The system aims to cope with flight risks caused by multiple factors such as complex underlying surfaces, dynamic weather and communication navigation interference in urban low-altitude flight, and the safety and reliability of unmanned aerial vehicle logistics are improved.

The invention discloses a low-drift laser scanning control method and system based on dynamic error compensation, relates to the technical field of laser scanning control, and aims to solve the drift problem in the laser scanning process and improve the scanning precision. The system comprises a system initialization and calibration module, a data acquisition and storage module, an error modeling module, a route planning and correction module and a real-time compensation and closed-loop optimization module, wherein the error modeling module and the route planning and correction module are key modules. The error modeling module comprises a key route screening unit and a data preprocessing unit, and is responsible for constructing a space-time joint error correction model; the route planning and correction module comprises an initial route generation unit, an error prediction unit and the like, and dynamic correction of a pre-planned route is achieved. According to the system, real-time compensation and closed-loop optimization are achieved through cooperation of all the modules in combination with a dynamic error compensation algorithm, scanning drift is effectively reduced, low-drift and high-precision laser scanning is guaranteed, and meanwhile follow-up scanning precision is optimized.

The invention discloses a ship route planning method and system in a polar region sea ice environment and a program product. The ship route planning method comprises the steps of preprocessing satellite remote sensing radiation intensity data, performing image reconstruction of inter-ice water channel detection through an improved Vision Transform model, planning and visualizing a ship route and the like. The deep features of the image blocks are extracted through a Transformer encoder which comprises a multi-head self-attention mechanism and is processed by a multi-layer perceptron; and the Transform encoder firstly performs layer normalization on the input, and then performs multi-head attention mechanism and multiple linear transformation and regularization processing. And realizing path planning in an inter-ice water channel environment by fusing a dynamic planning cost function through a fast marching method. Compared with a traditional algorithm, the method has the advantages that the calculation precision is higher in the same scene, the calculation efficiency is greatly improved, and the planning result obtained after multiple costs are comprehensively considered is given instead of pursuing the shortest distance.

The invention provides an unmanned aerial vehicle route planning operation safety monitoring system and method based on virtual-real fusion, belongs to the technical field of unmanned aerial vehicle safety monitoring, and aims to solve the problems that the test training difficulty of an unmanned aerial vehicle is increased and the experiment cost of the unmanned aerial vehicle is high due to interference and terrain transformation in the task execution process of the unmanned aerial vehicle. The safety monitoring system comprises a ground distributed semi-physical virtual-real fusion simulation platform and an air experiment module. The ground distributed semi-physical virtual-real fusion simulation platform is used for constructing a virtual test scene of task execution of the unmanned aerial vehicle, optimizing a waypoint planning decision according to feedback information in a task execution process, and performing safety judgment and real-time obstacle avoidance on the current waypoint planning decision so as to optimize the waypoint planning decision; and the space-time experiment module is used for monitoring the flight state of the unmanned aerial vehicle and executing the optimized waypoint planning decision.

The invention discloses a building fire-fighting automatic verification and evacuation path verification system based on a rule engine, and relates to the technical field of building fire-fighting intellectualization. Comprising the steps that a rule generation module analyzes a fire-fighting standard text and generates an executable rule set; the model analysis module is used for extracting component attributes and a spatial topological relation from the building information model; the real-time sensing module collects environment state data. The rule engine module matches the component attributes with the executable rule set to generate a first verification result; spatial topology and real-time sensing data are fused, the dynamic passing weight of each path segment is calculated, and an evacuation path network model is constructed. The path verification module verifies the safety of a path from a preset point to an exit based on the model, and generates a path verification result. And the result generation module outputs fire-fighting compliance data. According to the invention, through the dynamic optimal evacuation path planning from the static fire-fighting standard examination of the building design to the occurrence of the disaster, the guarantee capability and the emergency response efficiency of the building fire-fighting safety are improved.

The invention discloses an intelligent navigation method and device of an unmanned aerial vehicle, equipment and a storage medium. The method comprises the following steps: acquiring point cloud data and motion data, and calibrating to obtain a relative position and time synchronization parameter; performing map construction on the point cloud data and the motion data to generate a three-dimensional point cloud map; analyzing the boundary coordinates of the flight area, the flight height and the route step length, and generating route planning parameters; the boundary of the target inspection area is defined by combining the three-dimensional point cloud map, and an optimized route and corresponding waypoint sequence data are generated through route planning; acquiring real-time pose data of the unmanned aerial vehicle, comparing the current position with the waypoint sequence, and triggering image acquisition when a preset waypoint reaches a tolerance range; inspection data is obtained; and integrating the real-time pose data, the three-dimensional point cloud map, the flight state and the inspection data to form an intelligent navigation route. According to the invention, high-precision positioning and linkage of waypoints and data acquisition are realized, and the navigation precision, the inspection efficiency and the operation safety are improved.

The invention discloses a photovoltaic station unmanned aerial vehicle three-dimensional autonomous route planning method and system based on deep reinforcement learning, and belongs to the technical field of unmanned aerial vehicle autonomous navigation. The method comprises the following steps: constructing a grid map based on three-dimensional point cloud data of a photovoltaic station, and generating an initial path population by adopting a segmented intermediate transition point strategy; the core innovation lies in that deep reinforcement learning and a genetic algorithm are deeply fused, crossover variation strategy selection of a deep Q network dynamic optimization genetic algorithm is constructed, and a multi-target reward function is designed; experience playback and a target network mechanism are combined in training to improve stability; and finally, deploying the lightweight model to an unmanned aerial vehicle system to realize real-time autonomous flight integrating online re-planning and multiple safety barriers. According to the method, the problems of slow convergence, poor quality and insufficient real-time performance of path planning in a complex environment are solved, and the inspection efficiency and safety are remarkably improved.

The invention relates to the technical field of ship performance prediction, and discloses a marine bulk carrier navigational speed prediction method and system, and the method comprises the steps: obtaining route planning data of a target bulk carrier, dividing the route planning data into different route segments, obtaining route segment sea area feature data and route segment navigation time sequence data for each route segment, and building a sea condition space-time sequence data set, performing multi-dimensional time sequence feature extraction to obtain a sea condition feature vector of each leg; determining a current working condition according to ship design parameters and cargo loading information of the target bulk cargo ship, and calculating a ship body wet surface area and a drainage volume under the current working condition; analyzing the total resistance value and the effective propulsion power of each leg in combination with the sea condition feature vector of each leg; and based on the total resistance value and the effective propulsive power of each leg, solving the predicted navigational speed value of the leg, and combining with the voyage distance of each leg to generate a segmented navigational speed prediction curve and full voyage number voyage time along the route. According to the method, the reliability of forecasting the long-voyage shipping speed is improved.

The invention discloses a multi-constraint emergency material vehicle path optimization method and system, and belongs to the technical field of vehicle path planning. Comprising the following steps: acquiring input data from a data source, and constructing a multi-objective optimization model containing an objective function and constraint conditions based on the input data; based on the input data and the constructed multi-objective optimization model, generating an initial path scheme meeting all constraint conditions by adopting a heuristic algorithm combining nearest distance distribution and an earliest time window priority rule; and constructing a hierarchical collaborative optimization architecture consisting of an ALNS execution layer and a PPO decision layer, carrying out iterative optimization by taking the initial path scheme as a starting point, terminating the iterative optimization when a preset termination condition is met, and finally outputting an optimal path scheme. Compared with the prior art, the method has the beneficial effects that the key indexes are collaboratively optimized through the multi-objective optimization function, and the global solution quality and the scheduling precision are remarkably improved.

The invention relates to the technical field of unmanned aerial vehicle navigation, and relates to an unmanned aerial vehicle inspection path planning method, system and device and a storage medium. The unmanned aerial vehicle inspection path planning method comprises the following steps: acquiring a digital elevation model and a digital surface model of a plateau forest region and laser point cloud data of the plateau forest region; determining a three-dimensional voxel space of the plateau forest region based on the digital elevation model, the digital surface model and the laser point cloud data, performing reachability judgment on the three-dimensional voxel space based on the vertical safety margin, and determining a reachable region of the unmanned aerial vehicle; carrying out three-dimensional path search from a starting point to a terminal point by using an improved algorithm in an unmanned aerial vehicle reachable area; and smoothing the searched three-dimensional path, and applying a maximum turning radius constraint and a maximum climbing rate constraint to generate a final three-dimensional track. According to the invention, the safety and the performability of route planning of the unmanned aerial vehicle can be improved.

The invention discloses an unmanned aerial vehicle power transmission line patrol method, which comprises the steps of S1, intelligent route planning, constructing a power transmission corridor digital twin model based on three-dimensional laser point cloud data, S2, autonomous flight control, adopting an STM32F4 series MCU as a bottom layer controller, and running a real-time operation system, S3, multi-modal data acquisition, adopting a multi-modal sensor matrix and a cooperative acquisition mechanism, and carrying out multi-modal data acquisition. S4, real-time transmission processing is carried out, and adaptive code rate control is adopted; and S5, intelligent analysis and decision making are carried out, and a deep learning module is adopted. According to the unmanned aerial vehicle power transmission line patrol method, key defects such as insulator damage and wire strand breakage in a power transmission line are recognized and analyzed in real time, image processing and target detection are directly completed at an equipment end through edge computing capability, and only high-definition images with suspected defects are screened and returned to a ground station or a data center; the transmission data volume and the communication load are greatly reduced, the mechanism effectively realizes the real-time preliminary screening of the line defects, and the inspection efficiency and the response speed are improved.

The invention provides a dynamic planning method and system for a low-altitude route of an unmanned aerial vehicle, and relates to the technical field of intelligent planning, and the method comprises the steps: receiving a new delivery point instruction, obtaining a three-dimensional coordinate, carrying out the task integration of a new delivery point and an existing delivery point, defining a geographic range through three objects, namely a communication base station, a wireless tower and a permanent landmark building, and carrying out the task integration. Performing space structure division on the geographic range, and disassembling the geographic range into uniformly distributed three-dimensional voxel grids; and generating an adjustment value according to the coordinate interval of the three-dimensional voxel grid, including the delivery point density, the linear distance with the landmark building and other structural attributes, and adjusting the task integration result by using the adjustment value to obtain the adjusted task integration result of all delivery points. According to the invention, safe, efficient and accurate route planning of a multi-delivery-point dynamic newly-added scene in a complex low-altitude environment is realized.

This invention provides a workflow of processes and procedures, system, platform and method for cross-border delivery using a peer-to-peer (P2P) transshipment network. It connects order placers who wish to transfer packages from any country with travelers who can deliver these items during their trips, establishing a global delivery network. This network leverages unutilized baggage space with travelers and secure relay points to efficiently handle deliveries without relying on traditional distribution centers. The system optimizes route planning and transshipment points, ensuring timely and cost-effective delivery services with the lowest carbon print possible. This invention integrates a robust online user interface, secure payment processing, comprehensive user support, dynamic matching algorithms, and sophisticated logistics management to facilitate efficient, reliable, and cost-effective deliveries. International and global trade is promoted by leveraging travelers' existing routes, reducing the need for dedicated logistics services. It stimulates local economies by providing travelers with new income opportunities.

The invention relates to the technical field of intelligent traffic and smart city waste management, and discloses a path planning method and system for a garbage transport vehicle based on multi-source data, and the method comprises the steps: generating a reference path based on historical collection and transportation data, the method comprises the following steps: performing data cleaning on pre-acquired garbage quantity data, vehicle state data, real-time traffic data, hydroelectric data and fuel consumption data, performing multi-source time sequence feature fusion anomaly detection on a cleaned data set, performing trajectory inversion on the cleaned data set, performing target constraint through a mixed integer programming model, and performing multi-source time sequence feature fusion anomaly detection on the cleaned data set. Then gradient descent parameter optimization is carried out on the initial optimization model, elastic resource scheduling is carried out on an optimized path scheme based on a large event plan library, a path instruction set is generated, dynamic re-optimization evaluation is carried out on the path instruction set based on garbage amount change and traffic condition data fed back in real time, and an updated path is generated; according to the invention, the path planning efficiency of the garbage transport vehicle based on the multi-source data can be improved.

The invention relates to a route planning method and device for unmanned aerial vehicle cluster collaborative inspection, and the method comprises the steps: obtaining the geographic information of a rail transit control and protection region, and constructing an environment model based on the geographic information; combining the environment model and the telemetry data of each unmanned aerial vehicle to construct an unmanned aerial vehicle individual twin with a motion state so as to obtain the pose of each unmanned aerial vehicle; constructing a corresponding cluster relation graph in combination with the pose and preset cooperation logic; and in combination with the environment model, the pose and the cluster relation graph, constructing a twin scene meeting a preset interaction condition, so as to output a route planning path of each unmanned aerial vehicle based on the twin scene. Therefore, the technical problems that in the related technology, the inspection coverage capacity of a single unmanned aerial vehicle is insufficient, multiple unmanned aerial vehicles lack effective cooperation, and balance between application requirements and economical efficiency is difficult to achieve are solved.

The present disclosure relates to a method for route planning for automated driving vehicles, which includes determining a plurality of candidate driving routes from a starting point to a destination; determining an availability of a driving assistance function for automated driving for each candidate driving route of the plurality of candidate driving routes; and selecting a candidate driving route from the plurality of candidate driving routes, having a maximum availability of the driving assistance function for automated driving, as final driving route.

The invention discloses an unmanned aerial vehicle low-altitude channel planning method based on two-stage manifold optimization, and the method comprises the steps: obtaining a low-altitude operation task of an unmanned aerial vehicle, and determining obstacles in a three-dimensional map of the task, a virtual obstacle region where a complex building group is located, and intersection points which do not exist around the virtual obstacle region; carrying out initial path planning on the unmanned aerial vehicle to avoid obstacles and a virtual obstacle area, and carrying out double-stage popular optimization on the initial path; the two-stage popular optimization comprises first-stage optimization for establishing a first cost function according to obstacle avoidance, energy consumption, smoothness and constraints of approaching an intersection point of a path of the unmanned aerial vehicle and second-stage optimization based on the path of the first-stage optimization; and adopting the optimal path trajectory or the suboptimal path trajectory according to the task requirements of the unmanned aerial vehicle. The method has the advantages that airspace density control strategies can be conveniently formulated, hotspot airspace congestion and conflicts can be avoided, task priority scheduling schemes can be deployed and the like, and key technical support can be provided for safe, smooth and efficient flight under complicated constraints by the aid of two-stage path planning schemes on the basis of manifold optimization.

The invention relates to an intelligent scheduling decision support method and system for a community bus special line, and solves the problems of unreasonable transport capacity distribution and insufficient key travel demand guarantee caused by incapability of distinguishing travel purposes in an existing scheduling method, and the method comprises the steps: firstly, integrating card swiping, reservation and static data to construct a travel demand data set; secondly, through a clustering algorithm, identifying travel destination modes and extracting multi-modal features, generating a prediction report containing a passenger flow thermodynamic diagram and a space-time probability matrix through prediction of an adaptive multi-head attention model, and further calculating service priorities of the modes; then, a decision framework coordinated by route planning and station jump decision intelligent agents is constructed, a global reward function is endowed with differentiated weights according to priorities, and a dynamic scheduling scheme is jointly generated; the method has the following effects: community bus accurate scheduling based on travel purpose identification and priority guarantee is realized, and transport capacity sharing is upgraded to on-demand intelligent distribution.

The invention provides a high-altitude phased array communication blind area electric power emergency unmanned aerial vehicle route planning method and system, and relates to the technical field of electric power emergency communication, and the method comprises the steps: 1, collecting environment data through a phased array communication device and an airborne environment perception sensor of an unmanned aerial vehicle, and constructing an environment information library; step 2, based on the environment information base, filling and boundary completion are carried out on irregular invalid areas in the polygon of the communication blind area, a continuous effective coverage boundary is formed, the dynamic communication blind area is identified and positioned, and communication characteristic parameters are extracted; and step 3, combining the communication characteristic parameters with preset phased array beamforming capability, unmanned aerial vehicle performance parameters and communication coverage requirements, and constructing a route planning model. According to the invention, the unmanned aerial vehicle can adaptively sense the change of a communication blind area in real time in a dynamic and complex power emergency rescue terrain environment, and the emergency response speed and the power emergency rescue efficiency are improved.

The invention provides a central warehouse selection and logistics route joint optimization method, belongs to the technical field of supply chain management and optimization, and can at least partially solve the problem that global optimization cannot be realized due to mutual independent optimization of central warehouse site selection and logistics route planning in the prior art. The method comprises the following steps: acquiring position coordinates of a production base and each warehouse node in a logistics network and distance parameters between the nodes; constructing a decision variable set based on the position information; establishing a constraint condition set including center bin number constraint, route connectivity constraint, jurisdiction uniqueness constraint and subring elimination constraint; constructing a target function by taking the minimization of the total transportation distance as a target; and integrating the decision variable, the constraint condition and the objective function to construct an integer programming model, solving the integer programming model, and outputting a central bin selection scheme and a logistics route planning scheme. According to the invention, joint global optimization of central warehouse site selection and logistics routes is realized, the logistics cost is effectively reduced, and the overall operation efficiency of a supply chain is improved.

The invention relates to the technical field of ship route planning, in particular to a ship navigation real-time optimal route planning method based on an improved genetic algorithm, and the method comprises the steps: obtaining an input data set containing ship static parameters, multi-period environment prediction data and navigation constraints; an improved path search algorithm based on a comprehensive cost function is adopted to process input data, an initial navigation path is generated, and the cost function synchronously considers leg length, navigation risk and violation penalty. And carrying out speed scheme optimization on the initial path by adopting an improved genetic algorithm containing specific chromosome coding and a multi-target fitness function to generate an optimal speed distribution scheme. And fusing the initial path and the speed scheme, and outputting a route planning result containing a route point sequence and the recommended navigational speed of each route segment. According to the invention, integrated collaborative optimization of the path and the speed is realized.

The invention discloses a dam upstream navigation management prediction method based on multi-feature factor research, and relates to the technical field of dam upstream navigation management. According to the method, multi-dimensional data of ship foundation, traffic flow, scheduling management and environment are collected, and key feature factors are extracted after preprocessing; constructing a navigation efficiency evaluation model fusing the cargo load, the ex-warehouse flow and the waiting time, and determining a weight coefficient by adopting a maximum variance method; a navigation state prediction model is constructed based on a random forest algorithm, and prediction of traffic flow density, collision risk and navigation efficiency is realized; and finally, generating an air route planning, scheduling adjustment and emergency management optimization scheme. According to the method, the limitation of single factor consideration of a traditional method is overcome, the comprehensiveness of navigation efficiency evaluation and the accuracy of navigation state prediction are improved, scientific decision support can be provided for navigation management of the upstream water area of the dam, the collision risk is effectively reduced, and the navigation efficiency is improved.

The invention discloses an unmanned aerial vehicle autonomous route planning method and system capable of adapting to environmental changes. The method comprises the steps of obtaining a dynamic three-dimensional situation map of a flight area of an unmanned aerial vehicle based on multi-source data of the flight area of the unmanned aerial vehicle; acquiring a current no-fly zone and weather forecast information of the flight area of the unmanned aerial vehicle, and analyzing and acquiring a flight risk distribution condition of the flight area of the unmanned aerial vehicle based on the dynamic three-dimensional situation map; considering the flight risk distribution condition of the unmanned aerial vehicle flight area, the unmanned aerial vehicle flight task and the unmanned aerial vehicle self-state information to generate an unmanned aerial vehicle reference route; and automatically adjusting the flight speed, the flight height and the flight course of the unmanned aerial vehicle according to a preset flight parameter adjustment model based on the acquired multi-source data and the state information of the unmanned aerial vehicle in the process of flying along the reference route. According to the invention, the reliability and safety of flight task execution of the unmanned aerial vehicle in a complex dynamic environment can be improved.

The invention discloses an urban multi-unmanned aerial vehicle cooperative route planning method based on deep reinforcement learning, and the method comprises the steps: building a flight environment model based on urban three-dimensional point cloud data, and achieving the efficient obstacle query through an R-Tree index; for multiple unmanned aerial vehicles, acquiring distance information of surrounding obstacles by adopting a ray-based environment sensing mode, and constructing a state space containing a current unmanned aerial vehicle state, a target relative position, relative positions of other unmanned aerial vehicles and the distance information of the surrounding obstacles; outputting continuous flight control actions through a deep reinforcement learning strategy of parameter sharing; a multi-target reward function is designed, and the multiple unmanned aerial vehicles are guided to achieve safe and efficient collaborative route planning; in the execution stage, each unmanned aerial vehicle makes an independent decision only by depending on the shared strategy network and local observation information of the unmanned aerial vehicle, and distributed cooperative flight is realized. According to the method, the complexity of a traditional method for environment perception and decision calculation is effectively reduced, and the method is suitable for a multi-unmanned aerial vehicle planning task in a complex urban airspace.

The invention relates to the technical field of unmanned aerial vehicle flight control and intelligent navigation, in particular to an unmanned aerial vehicle low-altitude obstacle avoidance route planning method and system, and the method comprises the steps: firstly fusing three-dimensional space data and airspace rules, constructing a signed distance field, a capacity field and a state covariance, generating a channel graph, and calculating a door time lattice capacity upper limit; establishing joint trajectory optimization in the channel graph, combining geometric feasibility, dynamics reachable and covariance-based opportunity constraint separation, outputting a nominal trajectory, and controlling input and door crossing time; aligning the door penetrating time with the door time grid flow by a unification method to obtain the door time slot flow and the dual price; airborne control is executed according to a nominal trajectory and a time sequence constraint closed loop, and capacity change and price change are re-optimized and fed back in a time window under a triggering condition. According to the invention, the safety interval, the punctuality rate and the traffic efficiency are improved.

The invention relates to a rapid rendering method for an urban Beidou cubic grid. The method comprises the following steps: firstly, acquiring urban low-altitude geographic space data; constructing a multi-level grid system according to conflict detection precision and response requirements by using a Beidou cubic grid subdivision technology, and associating codes and airspace attributes with grid units; unloading the geometric generation task to a GPU for parallel calculation, and generating rendering adaptive grid geometric data; then developing a three-dimensional rapid rendering algorithm to realize real-time rendering of any region and precision grid; subsequently establishing a rendering result and airspace management cooperation mechanism to assist route planning, management and control and zoning; and finally, an algorithm optimization module is linked. The invention aims to solve the problems that the existing ground object 3D rendering adaptive city low-altitude field is insufficient, the conflict detection precision is not matched, the real-time performance is poor and the existing ground object 3D rendering adaptive city low-altitude field is disjointed with a low-altitude management service.

According to the AIS data thinning and typical route extraction method and device based on motion continuity, on the premise of not depending on single-voyage origin-destination identification, redundant trajectories are greatly compressed, and the training efficiency and prediction precision of a subsequent trajectory prediction and anomaly detection model are remarkably improved; meanwhile, the typical route has clear navigation semantics and can directly serve multi-scene services such as route planning, maritime affair supervision and carbon emission evaluation, and high-value data support is provided for real-time maritime affair supervision, intelligent navigation and green shipping. The method comprises the following steps: (1) data preprocessing and cleaning; (2) data thinning and compression; (3) trajectory optimization and combination; and (4) outputting and controlling a typical route.

The invention relates to an unmanned aerial vehicle track decision-making method based on turbulence prediction, and belongs to the technical field of unmanned aerial vehicle navigation and weather prediction. Aiming at the problems of difficulty in monitoring and predicting low-altitude turbulence and high air route planning risk, the defects of data heterogeneity, insufficient numerical weather forecast resolution and the like exist in the prior art; according to the method, a three-dimensional turbulence intensity field is generated through multi-source meteorological observation data fusion to serve as an observation benchmark, a diagnosis model is constructed in combination with numerical weather forecast data, linear calibration is carried out, or short-term prediction is generated by adopting an observation extrapolation model when forecast data is lacked; further, the turbulence field is mapped into a weighted graph, the height, the climbing rate and the airspace constraint are combined, the optimal track is solved by using a path search algorithm, and the accumulated turbulence cost is minimized; the method is clear in structure, full-process optimization from data fusion to decision making is achieved through multi-model complementation, and the method is suitable for the fields of low-altitude logistics, urban air travel and the like.