895 results about "Flight safety" patented technology

The invention relates to the field of unmanned aerial vehicle environment perception, in particular to a multi-mode environment perception method and system for a low-altitude medical unmanned aerial vehicle. The method comprises the following steps: collecting a multi-modal data stream, carrying out adaptive data optimization processing, and constructing a multi-modal fusion data set; performing environment multi-level obstacle identification and evaluation on the multi-modal fusion data set to generate a threat mapping environment map; multi-dimensional environment parameters are collected based on the unmanned aerial vehicle, wind field time-varying prediction and safe flight area calculation are performed based on the threat mapping environment map, and a flight area map is constructed; performing multi-position collision risk assessment based on the multi-modal fusion data set to generate collision risk coefficients of different positions; and carrying out safe flight constraint analysis on the flight area map according to the collision risk coefficient, and extracting an optimal flight path. According to the invention, in combination with real-time environment data, comprehensive flight path planning is provided, and the flight safety and task completion efficiency of the unmanned aerial vehicle are improved.

The invention provides a navigation airport low-altitude meteorological intelligent management system based on multi-source meteorological data fusion, and the system comprises a three-dimensional grid monitoring network which is used for achieving the three-dimensional scanning and dynamic perception of an airspace meteorological environment through real-time collection of meteorological detection data; the data fusion system is used for carrying out assimilation processing on the collected real-time detection data and multi-source forecast data to generate a high-resolution three-dimensional gridding meteorological field covering an airport airspace range; the dynamic safety research and judgment module is used for researching and judging the influence of the current and future meteorological conditions of the airspace on the flight safety of each aircraft type in real time; and the management system establishment module is used for visually displaying the influence degree of the current meteorological condition on the flight through a visual chart by establishing a flight management system. According to the invention, a comprehensive meteorological safety protection system is constructed through an innovative system of intelligent perception, data fusion and decision pre-judgment, so that the problem of insufficient low-altitude meteorological service capability of an existing navigation airport is solved.

The invention provides a construction method of an urban low-altitude wind field digital twinning system, which comprises the following steps: constructing an urban basic road network skeleton and performing region division, generating a building block model with real textures by using oblique photography data, and forming an urban three-dimensional space geometric model library; capturing atmospheric information in real time through a radar to generate high-resolution three-dimensional wind field scanning data covering a target area; processing detection data of the laser wind finding radar, performing simulation calculation on a wind field in combination with an urban three-dimensional space geometric model, and generating sub-meter gridding dynamic wind field data of an urban low-altitude area; and performing three-dimensional reduction and vivid representation on the obtained dynamic wind field data by using a visual rendering technology to form an interactive urban low-altitude wind field digital twin system. By integrating multi-scale modeling, laser wind finding radar, wind field simulation and visual rendering technologies, real-time monitoring, dynamic simulation and visual display of an urban low-altitude three-dimensional wind field are achieved, and low-altitude flight safety and operation efficiency are improved.

The invention discloses a shortest-time-based bird flock repelling effect evaluation method for an airport intelligent bird repelling system, and the method comprises the steps: S1, collecting the multi-dimensional monitoring data of an airport bird flock repelling process through a biological feature sensing system, and enabling the multi-dimensional monitoring data to comprise a repelling duration parameter, a region clearing index, and bird flock adaptability response data; s2, based on the multi-dimensional monitoring data, analyzing the spatio-temporal behavior characteristics of the bird flock through an intelligent analysis terminal, and constructing a multi-dimensional bird flock expelling effect characteristic matrix; s3, based on the feature matrix, performing dynamic weight optimization of bird flock expelling effect evaluation indexes with the shortest expelling time as a target, and generating a self-adaptive weight coefficient group; and S4, based on the self-adaptive weight coefficient group, performing panoramic evaluation on the bird flock repelling effect by adopting a time sequence perception memory network, and outputting a repelling efficiency analysis result taking the repelling timeliness as a core. While the flight safety is guaranteed, efficient utilization of bird repelling resources is realized.

The invention relates to the technical field of unmanned aerial vehicles, and discloses an unmanned aerial vehicle low-altitude complex obstacle detection and avoidance system based on multi-sensor fusion, and the system comprises a multi-mode sensing module which is used for synchronously collecting heterogeneous sensing data of the surrounding environment of an unmanned aerial vehicle; and the sensing fusion processing module is in communication connection with the multi-mode sensing module and is used for carrying out timestamp alignment, space coordinate system unification and deep fusion processing on the heterogeneous sensing data, and all-weather and high-reliability environment sensing is realized through multi-sensor fusion and hardware synchronization. The system has dynamic obstacle prediction and three-dimensional risk map construction capabilities, and realizes crossing from passive obstacle avoidance to active foresight obstacle avoidance. And a safe and smooth flight path is generated in combination with hierarchical planning and model prediction control, so that the autonomous flight safety, reliability and intelligent level of the unmanned aerial vehicle in a low-altitude complex environment are fundamentally improved.

The invention relates to the technical field of digital data processing, and discloses an aircraft safety data multi-source heterogeneous data acquisition method, which comprises the following steps of: synchronously controlling multiple types of sensors to concurrently execute physical obstacle three-dimensional investigation, micro-meteorological wind field construction and space electromagnetic spectrum surveying and mapping operation through a single unmanned aerial vehicle platform; and integrating the acquired three types of heterogeneous data into a three-dimensional spatio-temporal data base with a unified timestamp in real time. According to the method, endogenous space-time consistency of multi-source safety data at a collection source is ensured, information dislocation during traditional data fusion is avoided, a data base can truly reflect the instant coupling state of multiple risks, meanwhile, the exploration positioning capacity of the dynamic risks is arranged at a collection end in advance, and the data collection efficiency is improved. And an accurate and reliable decision basis is provided for flight safety assessment.

The invention belongs to the technical field of low-altitude navigation safety monitoring, and particularly relates to a low-altitude navigation multi-source heterogeneous data adaptive fusion method for multiple scenes, which is a low-altitude navigation multi-source heterogeneous data adaptive fusion method for multiple scenes such as urban air traffic, low-altitude logistics and emergency rescue. Efficient integration of multiple types of monitoring data and position output of the trusted aircraft can be achieved, and accurate data support is provided for low-altitude navigation anomaly recognition and risk deduction. The method comprises the following specific steps: constructing a low-altitude navigation scene classification system and fusion demand mapping, collecting and preprocessing multi-source heterogeneous data, quantifying data credibility and resolving conflicts, constructing a hierarchical adaptive fusion framework and outputting a fusion result. According to the method, efficient and accurate fusion of multi-source data in different scenes is realized by constructing a layered scene adaptation framework and a credibility fusion model, and finally, a high-credibility aircraft position is output, so that reliable data support is provided for low-altitude navigation anomaly recognition and risk management and control.

The invention discloses a multi-agent cooperative control system based on adaptive learning, and belongs to the technical field of multi-agent system control, and the system specifically comprises the steps: collecting and analyzing the wind field data of the position of each agent; distinguishing two typical working conditions of a global uniform wind field and a local disturbance wind field based on a consistency analysis result; for a global uniform wind field, a model prediction control method is adopted to generate a wind field compensation strategy for keeping formation stability; for a local disturbance wind field, an accurate airflow disturbance state is obtained through environment three-dimensional reconstruction and fluid simulation, then the expected track deviation of each agent is predicted, and a collaborative avoidance instruction set is generated in combination with a relative position relationship. According to the method, adaptive cooperative control of multiple agents under different wind field working conditions is realized, the hidden conflict problem caused by local wind field disturbance is effectively solved, and the flight safety and task reliability of an agent cluster in a complex environment are remarkably improved.

The invention relates to the technical field of aircrafts, and discloses a noise-considered urban low-altitude unmanned aerial vehicle path optimization method, which comprises the steps of urban environment rasterization and environment parameter setting; constructing an unmanned aerial vehicle noise attenuation model; calculating a noise interference critical distance and building sensitivity weight assignment; solving a noise interference grid matrix; a low-altitude unmanned aerial vehicle path planning model; and improving A * algorithm design. According to the method, the urban environment is rasterized, environmental parameters are set, an outdoor noise attenuation model is established by integrating geometric divergence, atmospheric absorption, ground effect and other factors, the propagation law of unmanned aerial vehicle noise in a complex environment is accurately described, the overall noise exposure and the sensitive area noise exposure are remarkably reduced while the flight safety is guaranteed, and the unmanned aerial vehicle noise attenuation performance is improved. The calculation efficiency is improved through the critical distance, path redundancy is avoided, collaborative optimization of noise control and operation efficiency is achieved, and technical support is provided for standardized operation of urban ultra-low-altitude unmanned aerial vehicles.

The invention provides a pilot manipulation quality real-time analysis method and system in a wind shear environment, and relates to the technical field of aviation flight, and the method comprises the steps: firstly capturing flight manipulation and environment interaction data including flight state feedback data and pilot manipulation execution data; secondly, establishing a response association link set of the manipulation action and the environment change, taking the wind shear environment change as a trigger node, taking pilot manipulation execution as a response node, and taking flight state feedback as an association node; tracing the matching relation between the time sequence track of the control action and the environment change, and outputting a time sequence matching relation set; analyzing the adaptability dynamic change of the manipulation action based on the time sequence fit relationship set evolution, and outputting an adaptability dynamic change result; and finally, generating a manipulation adjustment guide based on the dynamic change result of the adaptability, assisting a pilot in optimizing manipulation actions in real time, and improving the flight safety and manipulation quality.

The invention provides an airspace simulation deduction method and system based on digital twinning, and belongs to the technical field of airspace simulation, and the method comprises the steps: integrating geographic bottom plate data and airspace element data, carrying out the meshing based on an airspace grid engine, and constructing an airspace digital twinning model; receiving at least one flight plan, and mapping the flight plan to the airspace digital twin model to generate a corresponding simulation aircraft entity; based on a preset simulation engine, driving the simulation aircraft entity to perform flight simulation in the airspace digital twin model, and calculating a simulation flight situation; in the simulation process, airspace conflict detection and early warning are carried out based on the simulation flight situation and the airspace digital twin model; and outputting a simulation deduction result, performing three-dimensional visual output on the simulation flight situation data and the early warning information, and generating an analysis report including conflict statistics and airspace evaluation. The airspace resource allocation is optimized, the flight safety is improved, and intelligent and efficient management is realized.

Disclosed in the present invention is an unmanned aerial vehicle flight trajectory anomaly tracing method based on a fusion clustering algorithm. A hierarchical clustering algorithm is applied to classify and organize complex and sensitive spatio-temporal data features to identify a potential anomaly point; then, weights of various data features obtained by using a Lasso regression algorithm are used to calculate the sum of products of the potential anomaly point and the weights of the various data features; a system detects an anomaly point in a flight trajectory of an unmanned aerial vehicle to ensure flight safety and stability; the anomaly point is confirmed; and the system instantly calls an improved KD tree algorithm to quickly notify nearest on-duty personnel to go to the site, thereby comprehensively inspecting and thoroughly verifying factors interfering with the normal flight of the unmanned aerial vehicle, quickly and accurately processing anomaly conditions of the flight of the unmanned aerial vehicle, ensuring the flight safety and data integrity of the unmanned aerial vehicle, and providing reliable guarantee and support for a flight system of the unmanned aerial vehicle.

The invention discloses an aircraft wake flow real-time monitoring method based on laser radars, and the method comprises the steps: collecting the real-time wake flow data in a five-side approaching stage of a front aircraft through a plurality of laser radar devices, and determining the space coordinates and three-dimensional contour of the edge of a wake flow region through an edge detection algorithm; according to the coordinates of the left and right edges and the upper and lower edges of the wake flow area, the front and tail flow areas are displayed in real time; the method comprises the following steps: intercepting a two-dimensional cross section of a front aircraft tail flow area at a rear aircraft real-time flight height, calculating a rolling instability index generated by the two-dimensional cross section area to a rear aircraft wing, determining a wake flow minimum safety interval according to a preset rolling instability index threshold value, and displaying the wake flow minimum safety interval on an air traffic control system radar interface in real time, thereby realizing the purpose that in a five-side approach stage of an aircraft, the flight speed of the aircraft is increased. The front aircraft wake flow area and the rear aircraft wake flow area are displayed in real time, and the minimum safety interval of the front aircraft and the rear aircraft considering the real-time flight height is considered, so that an air traffic controller can clearly know the real-time state and the potential risk of the aircraft wake flow, and the wake flow monitoring accuracy and the flight safety are improved.

The invention discloses an unmanned aerial vehicle channel management method, platform and device under low-altitude economy, and relates to the technical field of unmanned aerial vehicle navigation and path planning, and the method comprises the steps: determining a preset airspace range, dividing a height layer and longitude and latitude grid units, and constructing a three-dimensional airspace resource map; distributing state label generation mapping; receiving a flight task; performing conflict detection and mediation to generate a target decision in combination with the atlas and the mapping; and executing channel management according to the decision to realize low-altitude unmanned aerial vehicle channel management. The technical problem that a traditional unmanned aerial vehicle channel management mode lacks a low-altitude airspace three-dimensional refined modeling, dynamic conflict detection and priority scheduling mechanism in a low-altitude economic scene is solved, and refined management, real-time conflict avoidance and differentiated resource scheduling of a low-altitude unmanned aerial vehicle channel are achieved. And thus, the airspace utilization efficiency and the flight safety are improved.

The invention discloses a multi-source data fusion-based low-altitude aircraft safety assessment method and system, and the method comprises the steps: analyzing the real-time flight data of a low-altitude aircraft, determining key flight parameters, determining the data characteristics of the key flight parameters, and assessing the safety state of the low-altitude aircraft based on the real-time flight data and the data characteristics, thereby obtaining a flight safety state assessment value; constructing a prediction model for prediction based on the historical flight environment data and a preset model to obtain flight environment prediction data; analyzing the flight environment prediction data, determining an environment state coefficient, determining an influence coefficient, and determining an environment influence coefficient based on the two coefficients; and correcting the flight safety state evaluation value based on the environmental influence coefficient to obtain a comprehensive safety evaluation value, and determining the safety level of the low-altitude aircraft based on the comprehensive safety evaluation value. According to the method, the capability of comprehensively monitoring and evaluating the safety state of the low-altitude aircraft is improved, the safety state of the aircraft can be accurately determined, the flight safety level of the aircraft is improved, and the flight risk is reduced.

The invention relates to an urban low-altitude unmanned aerial vehicle flight path optimization method and system, and relates to the technical field of unmanned aerial vehicle path optimization, and the method comprises the steps: obtaining a three-dimensional building map of a target flight region, marking the positions of a flight starting point and a flight ending point, and determining an initial flight path; constructing a fast airflow simulator, and combining with the macroscopic airflow data to obtain microscopic airflow data; calculating the path cost for executing the initial flight path based on the microscopic airflow data at the current moment in combination with the performance parameters of the target model unmanned aerial vehicle; and performing iterative optimization on the initial flight path by taking the three-dimensional building map and the target model unmanned aerial vehicle as constraints and taking the lowest path cost as a target to obtain an optimal flight path. The problems that in traditional unmanned aerial vehicle path planning, the wind direction and the wind speed on the air route are difficult to pre-judge due to the urban low-altitude complex building environment, the path is planned only from the single distance dimension, the urban low-altitude airflow difference cannot be adapted, and the flight safety of the unmanned aerial vehicle is affected are solved.

The invention provides a civil aviation pilot on-duty physiological early warning method based on LSTM and GBDT. The civil aviation pilot on-duty physiological early warning method comprises the following steps that S1, multi-source physiological data and associated information are collected; s2, data preprocessing and quality verification; s3, carrying out engineering construction on the physiological features and the associated features; s4, training and optimizing an anomaly recognition model; s5, carrying out real-time physiological anomaly recognition before attendance; s6, multi-level early warning triggering and information pushing are carried out; s7, performing early warning feedback and model iterative optimization; and S8, performing abnormal event tracing and data closed-loop updating. The limitation of a single model is overcome through multi-algorithm fusion, and the recognition accuracy in a complex scene is improved; a hierarchical early warning and closed loop optimization mechanism is established, safety guarantee and operation efficiency are considered, a standardized and landing technical scheme can be provided for civil aviation pilot health management system construction, and reduction of flight safety risks caused by physiological abnormalities is assisted.

The invention discloses an automatic control method of a four-power-source tiltable aircraft, relates to the technical field of flight control of tiltable aircrafts, is realized based on a PX4 flight control system, and is suitable for a tiltable aircraft provided with four independent tiltable power sources and corresponding tilting execution mechanisms. According to the method, through configuration of a tilting angle control module, a power distribution module and a full-stage flight logic scheduling module, full-stage automatic control of vertical take-off and landing, suspension, horizontal take-off, fixed-point horizontal flight, automatic cruise, one-key return flight, fixed-angle flight, power source fault handling and the like is achieved. Smooth transition of all flight modes is achieved, the stability of the attitude, the height and the position of the tiltable aircraft in different flight stages is maintained, meanwhile, the functions of automatic obstacle avoidance, fault emergency and the like are integrated, flight safety is guaranteed, flight stability and applicability are improved, and the method can be widely applied to various tiltable aircraft operation scenes.

The invention discloses a fleet formation flight implementation method and device, equipment, a medium and a product, and belongs to the technical field of aeronautical communication. The method comprises the following steps: establishing an air-ground interconnection communication link to realize real-time data interaction between a ground control unit and a plurality of flying civil aircrafts and between the plurality of civil aircrafts; based on an air-ground interconnection communication link, an airborne ad hoc network with a leading aircraft as a core is constructed, and an information interaction network in the formation is formed. Acquiring formation flight environment data; determining a safe area of formation flight based on the formation flight environment data, and calculating the maximum number of aircrafts in the formation and the relative positions of the aircrafts in the formation; and issuing the maximum number, the relative position and the flight decision information to each airplane in the formation through the airborne ad hoc network so as to execute formation flight. According to the embodiment of the invention, the real-time performance and accuracy of communication between the fleet can be effectively improved, and the airspace operation efficiency and flight safety are improved through the formation flight of the fleet.

The invention relates to the technical field of unmanned aerial vehicle flight path planning, in particular to a public route network generation method for a small unmanned aerial vehicle, which comprises the following steps: reading urban road network data, removing expressways and branches, and generating a two-dimensional route network; determining the lifting height according to the height of the tree, and generating a three-dimensional air route network; constructing a space matrix by using the surface feature passenger flow data and rasterizing a road network; rasterizing a coherent feasible region of a road network through morphological opening operation, and identifying an intersection and generating a topological structure by openCV; establishing a layered traffic line for the intersection to realize two-way traffic; the airspace utilization rate and the flight safety can be improved, and the problem of large-scale unmanned aerial vehicle path conflict is solved.

The invention relates to a damage assessment method and device for a composite material structure and electronic equipment, and relates to the technical field of aircraft maintenance, and the method comprises the steps: obtaining an aircraft typical structure operation and maintenance database and an actual service environment database; extracting structure damage characteristics from the aircraft typical structure operation and maintenance database, and extracting service environment factor data of the aircraft from the actual service environment database; performing clustering analysis on the extracted service environment factor data to obtain a clustering analysis result, and performing correlation analysis on the extracted structure damage features and the clustering analysis result to obtain a correlation analysis result; and based on the correlation analysis result, constructing an evaluation index system, and performing damage evaluation on the composite material structure of the aircraft by using the evaluation index system. According to the scheme, various environmental factors and structural damage characteristics can be comprehensively considered, the service reliability and safety of the structure are evaluated more accurately, a more reliable basis is provided for structure maintenance decision, and potential risks for flight safety are reduced.

The invention relates to the technical field of unmanned aerial vehicle navigation and control, in particular to an improved sparrow searching method for three-dimensional unmanned aerial vehicle path planning. According to the method, a three-dimensional mountain terrain model is constructed through a Gaussian function superposition method, obstacles and no-fly zones are defined by adopting a cylindrical geometric model, and obstacle avoidance constraints, take-off and landing safety constraints and flight height constraints are set; a comprehensive cost function with path length, threat penalty, flight safety height and path smoothness as sub-targets is constructed, an improved sparrow search algorithm is introduced, and a sine and cosine strategy is adopted in discoverer position updating to dynamically balance horizontal exploration and vertical obstacle avoidance. A Levy flight mechanism is introduced in follower position updating so as to enhance the capability of local development and jumping out of local optimum; the method comprises the steps of population initialization, role division, iterative optimization, collision detection and an elitist retention strategy. According to the method, a safe and smooth three-dimensional flight path with the optimal comprehensive cost is output, and the method is suitable for an actual unmanned aerial vehicle navigation task.

The invention relates to the technical field of navigation control, in particular to an obstacle light-based unmanned aerial vehicle navigation auxiliary system, which comprises the following steps of: combining an image sequence with inertia time information, segmenting a light source area and extracting a frequency label, so that the stable identification capability on an optical signal is enhanced; the signal state and the time index can be accurately judged especially in a complex shielding environment, the robustness of obstacle light recognition in multiple angles and multiple scales is improved through direction vector extraction and a comparison mechanism of a graph neural network, an edge weight matrix is established in combination with light intensity calculation and shielding state elimination, and the robustness of obstacle light recognition in multiple angles and multiple scales is improved. The method effectively enhances the understanding of the spatial layout of the obstruction lights and the navigability of the graph model, extracts the vector group through the graph model, eliminates the shielding, and compares the path instruction selection logic of the action score, so that the path planning process has stronger dynamic adaptability and error tolerance, and enhances the navigation accuracy and flight safety in the complex airspace.

The invention discloses an end-to-end obstacle avoidance method considering flight dynamics of a fixed-wing unmanned aerial vehicle, and the method comprises the steps: constructing a multi-source data synchronous collection module, and obtaining a monocular depth image and inertial navigation state data which are aligned in time sequence; an enhanced visual perception coding module is designed, and spatial position information of a tiny obstacle is reserved at the tail end of feature extraction through an asymmetric convolution strategy and a parallel spatial attention mechanism; constructing a state fusion and time sequence reasoning module, performing long and short time sequence modeling on the multi-modal features by using an encoder with a causal mask mechanism, and outputting potential decision variables; and constructing a kinematics constraint trajectory generation module, and mapping the neural network output into a three-dimensional control trajectory conforming to flight mechanics limitation. The problems that spatial information of a traditional convolutional neural network is seriously lost, an end-to-end model easily generates an illusion trajectory exceeding a physical limit and the like are solved, and the flight safety and robustness of the fixed-wing unmanned aerial vehicle in a complex low-altitude environment 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 application discloses an automatic auxiliary driving method based on multi-modal information fusion, which comprises the following steps: collecting pilot operation data, pilot voice data and pilot image data, preprocessing the collected pilot operation data, pilot voice data and pilot image data, extracting features of each mode and generating a sample set for training a multi-modal Transformer model, adopting the trained multi-modal Transformer model in an online stage to evaluate the behavior state of the pilot in real time, and taking the evaluation result as input to select and adjust the mode of the automatic auxiliary driving system. The application can provide more comprehensive and accurate pilot state information by fusing the data of different sensors of the pilot operation, voice and image, can make up for the deficiency of a single data source in a specific scene, can improve the robustness and accuracy of pilot behavior state recognition, and can adjust the level mode of the automatic auxiliary driving system in real time according to the pilot state and operation, can provide reliable automatic auxiliary driving support for the pilot, and can ensure flight safety.

The present invention relates to equipment that can realistically reproduce various types of airflow occurring in environments such as an urban area with tall buildings or regions with mountains and valleys, thereby enabling verification of the flight safety of a multicopter-type drone. The present invention comprises: a housing that provides a tunnel-shaped test space in which a plurality of blowers, together with controllers, are installed along the longitudinal direction of both sidewalls of the interior, the ceiling, and the floor, to generate airflows such as crosswinds, updrafts, downdrafts, and vortices; and an integrated control unit that selectively or simultaneously controls some or all of the plurality of controllers installed in the housing so as to form airflows in pattern corresponding to user input or settings.

The invention relates to the technical field of unmanned aerial vehicle cluster control and intelligent scheduling, and discloses an industrial unmanned aerial vehicle cluster intelligent scheduling management method based on multi-data analysis, and the method comprises the steps: extracting a quantitative risk feature vector based on multi-source operation data; constructing and updating a four-dimensional dynamic risk field; constructing a rolling time domain optimization model to generate a cooperative scheduling scheme including adaptive formation adjustment; decomposing and issuing a scheduling instruction; and carrying out dynamic risk field updating based on the real-time returned data, and triggering the compensatory adjustment. According to the method, real-time sensing and quantitative evaluation of multi-source and dynamic risks in a complex industrial environment can be realized, flight safety and task efficiency are dynamically balanced through rolling optimization and an elastic compensation mechanism, and robustness and intelligence of overall operation of a cluster are improved.

The invention provides a method for systematically monitoring aircraft pilot and air traffic control (ATC) radio communications to detect predefined aviation-specific keywords indicative of operational conditions affecting flight safety or efficiency, such as turbulence, icing, or visibility constraints. Detected keywords and semantic equivalents from voice transmissions are transformed into structured data formats. These structured data are correlated with Automatic Dependent Surveillance-Broadcast (ADS-B) data, including precise aircraft position, altitude, heading, and speed. Leveraging this integrated data, the system generates automated, quantitative visualizations, such as condition-specific heatmaps or other graphical representations, illustrating spatial distributions of reported in-flight phenomena like turbulence, icing, or hazardous conditions. These visualizations enable air traffic controllers, pilots, and aviation stakeholders to rapidly assess and disseminate real-time observations and historical patterns of flight hazards, thereby significantly enhancing situational awareness, operational safety, and decision-making effectiveness without manual input or subjective interpretation, improving overall aviation operational efficiency and safety.

The invention relates to the technical field of signal processing, in particular to a high-speed unmanned aerial vehicle structure servo elastic coupling vibration suppression control method, system and device, a medium and a program product. Obtaining a target control loop causing resonance; spectral analysis is carried out, and double-center frequencies causing resonance are obtained; a cascade wave trap with double center frequencies is designed; and the servo elastic coupling vibration of the unmanned aerial vehicle structure is suppressed. According to the method, the problems of difficulty in structure modification, narrow notch frequency range and large low-pass filtering delay of an existing structure servo elastic coupling resonance solving method are solved, the performance of a control system cannot be reduced, and the flight safety is ensured.