2428results about "Aircraft traffic control" patented technology

The invention discloses a low-altitude unmanned aerial vehicle trajectory tracking and monitoring method based on a 5G-A communication sensing integrated base station, and relates to the technical field of low-altitude traffic management and communication sensing fusion, and the method comprises the steps: firstly collecting multi-source data such as a communication sensing fusion signal, environment interference and unmanned aerial vehicle attributes, and carrying out the alignment and packaging of a unified timestamp and a coordinate system into a synchronous data frame; then, deep fusion and anti-interference processing are carried out on the data frames, noise is filtered out, and pure fusion data is generated; and furthermore, real-time track calculation and motion trend prediction are carried out on pure data by utilizing multi-base-station cooperative calculation and prediction. Based on this, through a multi-target feature recognition and clustering separation mechanism, independent individual trajectories are accurately stripped from a complex mixed data stream, and compliance verification and anomaly judgment are performed on the trajectories in combination with an airspace rule base. In this way, the problems of signal interference and multi-target aliasing in a complex environment can be effectively solved, and therefore high-precision global tracking of the low-altitude unmanned aerial vehicle and real-time monitoring of abnormal behaviors are achieved.

The invention relates to the technical field of risk prediction, in particular to a collision risk prediction method of a low-altitude aircraft in high-density flight in a complex environment, which comprises the following steps: acquiring a point cloud through a three-dimensional laser radar, clustering to generate an obstacle trajectory, matching the trajectory to identify a disturbance characteristic segment, calculating a deviation angle by combining a path vector to generate a disturbance frequency graph, and calculating the collision risk of the low-altitude aircraft. And extracting a parameter modeling dynamic safety interval, and fusing multiple factors to evaluate a collision risk level. According to the method, obstacle trajectory topology is constructed through combination of three-dimensional laser radar point cloud time window division and density clustering, sudden change features are identified through trajectory similarity matching, a Gaussian kernel dynamic safety envelope is generated through combination of course offset statistics and included angle operation, and a self-matching threshold value is established through normalization parameters and radial basis weighting. The method improves the high-density flight collision prediction precision, enhances the weather and obstacle heterogeneity matching capability, reduces the misjudgment early warning delay, and achieves the multi-variable flight situation collaborative analysis.

The invention discloses an unmanned aerial vehicle intrusion real-time alarm and countering guidance system based on RID information. The system comprises a multi-source information acquisition and fusion module, an intrusion detection and threat evaluation module, a real-time alarm and countering strategy generation module, a feedback evaluation and strategy iteration module, a signal loss prediction and compensation module, a comprehensive processing terminal module and the like. The system establishes a three-dimensional geofence model by analyzing remote identification information of the unmanned aerial vehicle and fusing radar, photoelectric, electromagnetic and other multi-source data, and performs dynamic judgment and multi-factor threat grading on a target intrusion state. Multi-mode alarm is automatically triggered according to the threat level, and a hierarchical countering scheme from broadcast alarm and electromagnetic interference to physical capture is generated; and when the RID signal is lost, the system realizes continuous tracking and state recovery of the target by using a motion prediction and probability association algorithm.

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 discloses a low-altitude traffic flow collaborative awareness and conflict prediction method and system based on a multi-modal large model. The method comprises the following steps: collecting multi-source heterogeneous data of an aircraft, fusing and mapping the multi-source heterogeneous data to a space-time grid with dynamically adjustable granularity based on a real-time traffic situation, and realizing adaptive characterization; the method comprises the following steps of: constructing a robust special prediction model by introducing aircraft dynamics constraint and airspace rule knowledge as priori and performing fine adjustment by adopting a loss function containing an adversarial regularization item based on a pre-trained large model of the body-equipped agent; inputting spatial-temporal characteristics into the model, outputting behavior intentions and probability trajectories in an end-to-end manner, constructing an incomplete information game deduction model based on predicted trajectories, and calculating a comprehensive collision risk index fusing trajectory uncertainty, a motion state and a game deduction result; and generating a hierarchical early warning and collaborative avoidance strategy, and performing network distribution. According to the method, the accuracy, the robustness and the decision intelligence of conflict prediction under the low-altitude dense traffic flow are remarkably improved.

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 a dynamic airspace gridding management method and system for low-altitude economy, and belongs to the technical field of unmanned aerial vehicle traffic management, and the method comprises the steps: collecting airspace state data in real time through a multi-source sensing device, and constructing a four-dimensional space-time grid model; generating a four-dimensional space-time grid with a block chain hash code by fusing meteorological data, an airspace control rule and a real-time flight demand; receiving a space-time grid use request submitted by the aircraft through the smart contract, and calculating an optimal grid allocation scheme based on a deep reinforcement learning model; and the edge computing node executes local track prediction, issues a navigation instruction to the aircraft through the distributed account book, monitors a grid occupation state in real time, and triggers a dynamic grid recombination mechanism when sudden conflicts are detected. According to the method, the rigid constraint of static airspace division can be broken through, the cooperative conflict of multiple aircrafts is eliminated, and the marketization configuration of airspace resources is realized.

The invention provides a flight path prediction method and system based on predictive coding. The method comprises the following steps: collecting historical flight path data, and performing flight path prediction on the historical flight path data by using a reference prediction module to obtain a reference prediction point; calculating a high-order motion track residual error between the reference prediction point and the real track point by using a residual error encoder, and carrying out one-hot coding on the high-order motion track residual error to establish a residual error coding index; a high-order probability distribution mapping module is used for learning a deep mapping relation between historical flight path data and residual error coding indexes so as to carry out training; inputting a to-be-predicted historical flight track into the reference prediction module and the trained high-order probability distribution mapping module in parallel to respectively obtain a predicted track point and a predicted residual error coding index; decoding the prediction residual error coding index through a residual error decoder to obtain a residual error at a prediction moment; and adjusting the predicted track point based on the residual error at the prediction moment to obtain a final prediction result.

The invention relates to three-dimensional traffic navigation, in particular to a three-dimensional traffic navigation system based on space-time grid coding, which comprises a low-altitude path planning module for planning an optimal flight path for an aircraft in a low-altitude airspace based on a four-dimensional space-time grid coding model and adjusting the path in real time in combination with dynamic environmental factors; the airspace conflict detection module is used for carrying out conflict detection on aircrafts in a low-altitude airspace, judging whether conflicts exist or not and finding potential conflicts in time; the path collaborative planning module is used for carrying out collaborative path planning on the multiple aircrafts with conflicts in the low-altitude airspace and solving the problem of conflicts among the multiple aircrafts; the cross-modal traffic coordination module is used for integrating ground and low-altitude traffic systems, realizing seamless connection and efficient coordination of an optimal ground path and an optimal flight path, and providing an integrated three-dimensional traffic navigation scheme; the method can overcome the defects that in the prior art, path planning efficiency is low, airspace conflicts are difficult to accurately detect, and a cross-modal traffic cooperation mechanism is lacked.

The invention relates to the technical field of low-altitude operation and maintenance management, and discloses a digital twinning-based low-altitude takeoff and landing field infrastructure full-process operation and maintenance system, which comprises a real-time digital twinning construction module, a digital twinning construction module, a low-altitude takeoff and landing field infrastructure full-process operation and maintenance module and a digital twinning construction module, an airspace conflict detection module; an intelligent route planning module; a control instruction generation and execution module; and a data driving optimization module. By integrating multi-source data, updating the digital twin model in real time, carrying out airspace conflict detection and optimizing flight path planning, the path and scheduling time slot of the aircraft can be dynamically adjusted, the aircraft efficiency and the resource utilization rate are maximized, meanwhile, real-time feedback and continuous optimization are provided, and the flight path planning efficiency is improved. And the operation and maintenance intelligence and automation level of the low-altitude take-off and landing field is remarkably improved.

The invention relates to the technical field of airspace control, and discloses an AI-based low-altitude airspace information center collaborative control method, which comprises six core steps of multi-source sensing fusion, abnormal behavior discrimination, dynamic risk assessment, environmental constraint re-planning, communication collaborative optimization and collaborative execution decision-making to construct a closed-loop intelligent decision-making mechanism. Microsecond-level time synchronization is realized through a Beidou or GPS satellite time service system, and the target identification precision is improved in combination with a weighted evidence fusion theory; an R-tree and space-time bounding box dual rule engine is combined to accurately detect abnormal flight; the collision risk between the aircrafts is quantified by using a three-layer stacked multi-head map attention network, and trajectory re-planning under environmental constraints is realized through a multi-dimensional environment voxel model; a ground and satellite hybrid networking architecture is adopted for communication collaborative optimization, so that terrain shielding and electromagnetic interference are avoided in management and control communication, global coverage and accurate adjustment are realized, and the accuracy, real-time performance and safety of management and control are ensured.

The invention discloses a low-altitude economic flight data management method based on a block chain technology, and relates to the technical field of low-altitude traffic management, and the method comprises the steps: verifying an encrypted positioning proof, generating a verification state set, decrypting flight coordinates according to the verification state set, and generating a credible coordinate data stream; performing sensitive airspace intrusion detection according to the credible coordinate data stream and the high-security protection node, performing collision risk analysis by the traffic management node to obtain an encrypted aggregated threat vector, and identifying a threat level according to the encrypted aggregated threat vector; and dynamically adjusting the working parameters of the airborne equipment according to the threat level, generating an airborne equipment regulation and control instruction set, scanning the signal-to-noise ratio and the threat intensity in real time, dynamically allocating a communication frequency band, and generating a new positioning strategy parameter. According to the method, the real-time signal-to-noise ratio and the threat intensity scanning result are combined, communication frequency band dynamic allocation and encryption strategy dynamic optimization are driven, the airspace situation awareness capacity is formed, the resource utilization rate is increased, and the anti-interference capacity is enhanced.

The invention discloses an unmanned aerial vehicle routing inspection path planning method and system for a power grid line, and particularly relates to the technical field of power grid routing inspection, and the method comprises the steps: 1, dynamically fusing a multi-source sensor and prior data, and constructing and updating a comprehensive threat field with a time attenuation mechanism and a passability map in real time; 2, decomposing an inspection line into subtask sections, and generating a reference route of each section by combining static safety cost; 3, on the basis of a real-time map in the flight of the unmanned aerial vehicle, a local optimal track is solved online through rolling window optimization of adaptive weight adjustment; and step 4, realizing multi-machine collaboration and automatic handover of blocked tasks through a block chain type distributed task state account book and an intelligent contract rule. According to the invention, the real-time avoidance capability, the environmental adaptability and the task continuity of the unmanned aerial vehicle to dynamic obstacles are remarkably enhanced, and the safety and the efficiency of power grid inspection operation are effectively guaranteed.

The invention discloses a distributed RID receiving system based on edge multi-source fusion, which relates to the technical field of low-altitude supervision, and is characterized in that a distributed RID receiving node cluster adopts a fixed node and maneuvering node collaborative grid deployment scheme to capture and preliminarily analyze low-altitude aircraft RID signals in real time, and node data is output after the low-altitude aircraft RID signals are processed by a primary fusion algorithm; the edge multi-source fusion processing unit is deployed in each management and control partition, converges node data in the area, completes calibration, de-duplication and noise reduction through a secondary fusion algorithm, and outputs structured RID data; the cloud management and control platform realizes global data overall planning, situation visualization, intelligent decision making and instruction issuing through a three-level fusion algorithm, and is linked with the urban low-altitude scheduling system to form closed-loop management and control; the clock synchronization module provides nanosecond time reference for the whole system. According to the method, the problems of incomplete coverage and insufficient data processing precision of a traditional RID receiving scheme are solved, and global sensing, accurate data processing and closed-loop management and control of the low-altitude aircraft are realized.

The invention discloses a highway accident scene sensing system based on AI identification and unmanned aerial vehicle cooperation, and relates to the technical field of intelligent traffic. Comprising a central planning node for fusing geographic information, meteorological information and road obstacle information of a target site with the real-time state of an unmanned aerial vehicle, constructing a digital twin scene model, generating an initial four-dimensional collaborative awareness route plan, and dividing sub-airspaces; the distributed airborne intelligent agent obtains peripheral multi-source data of the unmanned aerial vehicle, constructs a surrounding environment real-time situation map through an AI recognition algorithm, and determines a flight instruction of the unmanned aerial vehicle according to the surrounding environment real-time situation map and the divided sub-airspace; and the reconfigurable intelligent metasurface is used for adjusting surface electromagnetic characteristics according to the divided sub-airspaces and sending directional beams to the unmanned aerial vehicle. Therefore, the problems of cooperative scheduling, obstacle avoidance and communication guarantee of multiple unmanned aerial vehicles in a complex accident scene are effectively solved, and the efficiency and safety of emergency disposal are remarkably improved.

The invention provides a low-altitude airspace management system and method, and the system comprises a channel planning module which is used for determining a current flight channel of a target aircraft according to the flight data of the target aircraft and the target airspace data of a target low-altitude airspace through which the target aircraft passes; the flight adjustment module is used for adjusting the current flight state of the dangerous aircraft with the flight risk; the airspace coordination module is used for establishing real-time information sharing and controlling the multiple aircrafts to perform coordination work according to the shared information; and the fault response module is used for determining a target processing strategy of the fault aircraft for the fault aircraft with the fault condition, and processing the fault aircraft according to the target processing strategy. Therefore, an intelligent, efficient and safe low-altitude aircraft management scheme is provided, the flight safety can be guaranteed, and the airspace resource utilization rate can be improved.

The invention discloses a low-altitude economic intelligent scheduling method based on multi-dimensional data fusion and dynamic topological optimization, and belongs to the technical field of aircraft air traffic control, and the method comprises the following steps: S1, collecting and fusing multi-dimensional data, including meteorological data, equipment data, airspace data, task data and historical data, S2, constructing a dynamic airspace topological network based on the fused data, the topology network comprises a vertex set and an edge set, vertexes represent unmanned aerial vehicles, and edges represent cooperative paths between the unmanned aerial vehicles, S3, generating a global optimal scheduling strategy through a multi-objective optimization algorithm according to the dynamic airspace topology network, S4, executing the global optimal scheduling strategy, updating historical data and model parameters after a task is completed, and S5, executing the global optimal scheduling strategy according to the updated historical data and model parameters. And closed-loop feedback is formed. Multi-target dynamic balance of unmanned aerial vehicle dispatching safety, efficiency and energy consumption is achieved through multi-dimensional data fusion and dynamic topological optimization, the system supports full-process closed-loop automation, and the accident rate and resource consumption are remarkably reduced.

The invention aims to provide an unmanned aerial vehicle operation airspace information processing method, a flight plan optimization method and a flight information management system, in the scheme, when an operation airspace topological graph is constructed, the calculation complexity is obviously reduced, the calculation efficiency is effectively improved, meanwhile, the connectivity of a key airspace is effectively reserved, and the structural interpretability is improved; when the flight plan is optimized, a double-layer optimization decision framework is adopted and comprises a strategy self-adaptive selection layer and a mixed integer nonlinear programming scheduling layer, so that optimization scheduling is more global, and local optimum is avoided; besides, the flight information management system effectively utilizes calculation results of related methods, airspace operation limitation information including an operation airspace topological graph can be obtained more efficiently and accurately, a flight plan is better optimized on the basis, the cooperative operation capability of the unmanned aerial vehicle and the manned aerial vehicle in the airspace is improved, and the flight efficiency is improved. And comprehensive operation situation information is provided for the controller to assist the controller in making an accurate decision.

Systems and methods for automated unmanned aerial vehicle recognition. A multiplicity of receivers captures RF data and transmits the RF data to at least one node device. The at least one node device comprises a signal processing engine, a detection engine, a classification engine, and a direction finding engine. The at least one node device is configured with an artificial intelligence algorithm. The detection engine and classification engine are trained to detect and classify signals from unmanned vehicles and their controllers based on processed data from the signal processing engine. The direction finding engine is operable to provide lines of bearing for detected unmanned vehicles.

An autonomous drone swarm system and method utilizing artificial intelligence for coordinated operations comprises command drones equipped with large language model processors and subordinate drones coordinated through a hierarchical Queen-Worker architecture. The system processes natural language commands, generates autonomous mission plans, and coordinates multi-drone operations through an encrypted self-healing mesh communication network utilizing laser, radio frequency, and visual communication channels. Multi-modal sensor integration including electro-optical, infrared, LiDAR and photogrammetry, radio frequency, and chemical detection provides comprehensive environmental awareness while federated learning algorithms enable distributed coordination in signal-denied environments. The system implements configurable operational modes spanning tactical (15 minutes-2 hours over 2 to 5 square kilometers), operational (6-24 hours over 20 to 50 square kilometers), and strategic (7-30+days over 200 to 500 square kilometers) mission profiles for military and commercial applications. Fault-tolerant protocols ensure continued operation despite individual failures through automatic task redistribution and leader election procedures.

The invention belongs to the technical field of air transportation management, and discloses a multi-source data fused sorting method and system for airport flight coordinated launch. The method comprises the following steps: collecting a multi-source data source of a flight; performing multi-source data fusion, generating a five-dimensional dynamic situation matrix, and reflecting the comprehensive state of the flight in real time; judging whether manual priority ranking processing is carried out or not; by constructing an intelligent decision-making system for aircraft departure sorting, optimization target decision-making and intelligent sorting are carried out; the visual sorting interface displays the alternative push-out sorting schemes; obtaining a sorting result of flight coordination deduction; establishing a dynamic reentry mechanism for flights with updated resources or changed states, and reinserting the flights into the global sorting queue; and balancing the adaptation degree of the original priority and the new state of the flight through a dynamic weight distribution algorithm. The airport flight scheduling efficiency is improved, so that flight delay is reduced, the operation efficiency is improved, and the passenger satisfaction degree is enhanced.

The invention discloses an unmanned aerial vehicle multi-source data fusion unmanned aerial vehicle management method and system, and the method comprises the steps: obtaining original monitoring data from a plurality of unmanned aerial vehicle detection devices, carrying out the time-space standardization processing of the heterogeneous data, building a unified time-space reference, building an unmanned aerial vehicle mapping relation through target correlation matching, and carrying out the target correlation matching. And performing multi-source data fusion on the standardized monitoring data after association mapping, finally performing airspace supervision analysis based on the generated unmanned aerial vehicle comprehensive state data, and outputting an airspace supervision decision report including monitoring information, an authentication state and an early warning prompt. According to the invention, through cooperative sensing and fusion of multi-source data, the problems of sensing blind areas, insufficient data reliability and the like existing in single detection equipment are effectively solved, the all-time accurate supervision capability of cooperative and non-cooperative unmanned aerial vehicles is remarkably improved, and reliable technical support is provided for airspace safety management.

The invention discloses an aircraft taxiing path automatic generation method and system, and the method comprises the steps: obtaining airport GIS map data, and constructing a taxiway hierarchy graph based on an H3 space grid; after an instruction containing a sliding path name sequence is received, the instruction is automatically integrated with machine position and target point information and mapped into starting and ending nodes in a graph structure; and furthermore, a search algorithm combining path name matching and turning angle punishment is executed in the graph, and a continuous and reasonable optimal geometric sliding path is automatically generated. According to the method, full-automatic mapping from a text instruction to a standardized geometric path is realized, the defects of dependence on manpower, low efficiency and high error rate in the prior art are overcome, and the intelligent level and operation safety of airport ground sliding guidance are remarkably improved.

The invention relates to the technical field of artificial intelligence and unmanned aerial vehicle control, discloses a scenic spot unmanned aerial vehicle live broadcast control system and method based on AI interaction, and aims to solve the problems that existing scenic spot live broadcast depends on manual operation, is poor in interactivity, cannot normally run and is weak in environmental adaptability. The method comprises the following steps: starting an unmanned aerial vehicle through an automatic parking apron and completing self-inspection; a flight request or an interaction instruction is received via a user platform. According to the scheme, 7 * 24-hour normalized live broadcast is realized, the user interaction rate is improved by more than 80%, natural language-driven route adjustment and personalized virtual anchor explanation are supported, the autonomous obstacle avoidance capability in a complex environment is achieved, privacy compliance is guaranteed through AI face fuzziness, a'live broadcast + e-commerce + reward 'multi-element income model is constructed, and the user experience is improved. The monthly income of a single scenic area is increased by 0.4 million yuan, the system can be quickly deployed within 3 days, and the copying cost is reduced by 60%.

The invention discloses a low-altitude aircraft intelligent identification system based on an edge network, and belongs to the technical field of intelligent identification. Comprising a data acquisition module used for acquiring time sequence sensor data; the knowledge base construction module is used for constructing a physical rule knowledge base; the penalty value generation module is used for generating a physical violation penalty value; the model generation module is used for obtaining a physical rule embedded model; and the result output module is used for outputting an aircraft intelligent identification result conforming to the physical motion law. According to the method, the physical rule knowledge base is introduced into the edge network, and the lightweight model is trained by using the dual-target collaborative loss function, so that the effect of still outputting a physically consistent aircraft recognition result under the condition of limited computing power is achieved; the problem that in the prior art, a marginal small model generates jumping, over-limit or abnormal tracks due to lack of motion law constraints is solved.

Systems and methods for automated unmanned aerial vehicle recognition. A multiplicity of receivers captures RF data and transmits the RF data to at least one node device. The at least one node device comprises a signal processing engine, a detection engine, a classification engine, and a direction finding engine. The at least one node device is configured with an artificial intelligence algorithm. The detection engine and classification engine are trained to detect and classify signals from unmanned vehicles and their controllers based on processed data from the signal processing engine. The direction finding engine is operable to provide lines of bearing for detected unmanned vehicles.

The invention relates to the technical field of unmanned aerial vehicle obstacle avoidance, and particularly discloses an unmanned aerial vehicle side end power transmission channel dynamic obstacle sensing fusion early warning method and system, and the method comprises the steps: collecting the multi-modal data of a power transmission channel in real time through a plurality of sensors carried by an unmanned aerial vehicle; processing the multi-modal data to determine a dynamic obstacle in the power transmission channel and the type of the dynamic obstacle, and predicting the future trajectory of the dynamic obstacle; and calculating a corrected dynamic risk level based on the type of the dynamic obstacle, the current distance between the dynamic obstacle and the conductor in the power transmission channel and the future trajectory. According to the invention, adaptive switching is carried out between different multimode-state data fusion architectures according to dynamically evaluated risk levels, and airborne computing resources are managed in a feedforward manner, so that the system can concentrate computing power in a high-risk scene to realize accurate perception, resources are saved in a low-risk scene, and the reliability of the system is improved. Therefore, the contradiction between the sensing precision and the airborne computing power overhead is intelligently balanced.

The invention discloses a smooth processing method and system for flight path data of an aircraft, and the method comprises the steps: obtaining an original flight path data set, each flight path point in the original flight path data set comprising a timestamp, a flight number, latitude and longitude coordinates, height, speed and course angle attributes; grouping according to flight numbers and sorting according to timestamps to obtain a plurality of flight track sequences; the method comprises the following steps of: calculating the time interval of adjacent points for each flight track sequence, dividing the flight track sequence into a plurality of continuous track sections according to an interruption threshold, dynamically calculating an interpolation interval according to a timestamp difference value for each pair of adjacent starting point and ending point in each section, and ensuring that the time interval of any adjacent point in the continuous track sections does not exceed the interruption threshold. According to the method and the system, the problems of wrong interpolation, course angle sudden change, low interpolation interval fixing efficiency and unsmooth track caused by data interruption in the prior art are solved, and the continuity, the accuracy and the visual reality sense of the track data are improved.

The invention relates to the technical field of urban low-altitude safety monitoring, and discloses a low-altitude economic airspace passive safety early warning method and system. The method comprises the following steps: collecting multi-modal observation data streams of a target at different moments; generating multi-modal frame data with a unified timestamp; establishing a spatial confidence distribution map; extracting fusion discrimination features; executing multi-channel small target detection; continuously tracking the preliminary target; and introducing a constant false alarm rate framework to set a dynamic confidence threshold to execute alarm judgment. Compared with the prior art, the technical problem that high-confidence all-weather passive monitoring cannot be realized in urban core areas with dense buildings, serious signal shielding and easy GPS failure due to dependence on single vision or radar information in the prior art is solved. According to the method, the space confidence distribution diagram and the dynamic constant false alarm mechanism based on the shielding diagram are constructed by fusing the multi-modal sensing data, so that the safety early warning of the low-altitude small aircraft is realized, and the passive safety guarantee capability of the low-altitude economic airspace is improved.

The invention relates to the technical field of low-altitude traffic, in particular to a low-altitude traffic flow prediction and dispersion method based on a space-time diagram network, and the method comprises the steps: obtaining and fusing multi-source data; the takeoff and landing field monitors demand abnormity; monitoring the real-time state of the air route node; node traffic health degree combined diagnosis is carried out; quantizing spatial domain resource constraint conditions; performing multi-dimensional assessment on congestion risk levels; dynamically evaluating conflict risks; generating a dynamic grooming strategy; and potential risk evolution prediction and plan preparation. According to the invention, multi-source heterogeneous real-time and historical data are deeply fused through a time-space diagram network, multi-dimensional and progressive intelligent perception and prediction are carried out on the low-altitude corridor traffic situation, different levels of congestion risks from microscopic node overload to macroscopic region paralysis are diagnosed, a dredging strategy is automatically generated, and the traffic guidance efficiency is improved. The problems of decision lag, extensive study and judgment, low efficiency in dredging and incapability of coping with dynamic congestion due to adoption of an isolated static threshold and single-dimensional analysis are effectively solved.