361 results about "Flight operations" patented technology

The invention provides a health state assessment method and system for a rescue helicopter, and relates to the technical field of aviation engineering, and the method comprises the steps: 1, building an integrated data aggregation platform, synchronously collecting flight operation parameters, maintenance files, abnormal event records and surrounding environment monitoring indexes, and carrying out the maintenance of the maintenance files; comprehensively covering key dimensions of a rotor device, a power transmission unit, a propelling device and sea climate conditions to form a comprehensive original data set; and step 2, based on the comprehensive original data set, applying a self-adaptive parameter adjustment mechanism to perform comprehensive calculation on an accumulated operation time length index, a periodic component residual efficiency value, a core health score, an abnormal repeated occurrence probability, typical problem association strength, an emergency severity degree and a marine climate erosion factor, and generating a comprehensive state index. The method accurately evaluates the health state of the rescue helicopter, reduces the flight risk, and improves the rescue decision and execution efficiency.

The invention discloses a pilot manipulation ability evaluation method and system based on multi-modal data and online learning driving. The method comprises the steps of determining an input parameter set, establishing a flight vision time sequence fusion network, and extracting each feature of the parameter set. And completing feature fusion of the multi-modal data through an attention mechanism and global aggregation. And constructing a pilot maneuvering ability evaluation architecture, and generating an offline pilot maneuvering ability evaluation model by designing a loss function based on a comprehensive scoring error and sub-scoring consistency constraint. Flight data and eye movement data streams generated in real time are fragmented and preprocessed by using a sliding window mechanism, a lightweight model based on pruning and parameter quantification technologies is designed for real-time scoring requirements, and feature expression ability in an offline pilot manipulation ability evaluation model is migrated to a lightweight online evaluation model. The method can be used for improving a pilot training scheme, optimizing the man-machine interface design of a cockpit, and improving the efficiency and safety of flight operation.

Examples relate to a fatigue detection system to monitor and assess pilot fatigue levels during flight operations. An example system includes a wearable biometric sensor (WBS) integrated into a wristband that captures biometric data from the pilot. A processor analyzes the captured data to determine the pilot's fatigue level. When this level exceeds a predetermined threshold, the system provides an alert to the pilot through a haptic feedback mechanism in the wristband. Additionally, the system generates personalized fatigue mitigation advice, which is displayed on an electronic flight bag (EFB) application accessible to the pilot. The advice may include recommendations for taking a controlled rest, consuming caffeine, or engaging in physical activity. The system enhances flight safety by providing real-time alerts and actionable advice to combat pilot fatigue.

The invention belongs to the technical field of air traffic management, and relates to a flight time adjustment management method and system in a special scene. According to the method, the flight release priority sequence is constructed, the airport taxiway network topology and the dynamic limitation information are combined, the taxiing path is planned, the space-time conflict is detected, and the dynamic adjustment of flight ground operation and the coordination verification of the runway access time window are realized. The problems of frequent flight ground taxiing path conflicts, low scheduling efficiency and difficulty in accurate take-off time control in special scenes are solved, the safety and efficiency of airport ground operation are effectively improved, the flight taxiing time and the runway waiting time are reduced, the flight release sorting and the runway use plan are optimized, and the flight taxiing efficiency is improved. The flight scheduling cooperation capability in a complex operation environment is enhanced, and the orderliness and stability of flight operation are guaranteed.

The invention, which relates to the technical field of aviation training, discloses a low-altitude pilot AI model training application system comprising a data module, an intelligent decision-making module, a flight simulation module, a communication module and an interface module. The data module comprises an aircraft type database, a flight operation knowledge base, a meteorological data interface and an airspace and airport database; and the aircraft type database is used for storing eVTOL detailed information of various aircrafts. According to the low-altitude pilot AI model training application system provided by the invention, by integrating the aircraft type database, the flight operation knowledge base, the real-time meteorological data and the airspace and airport database, a highly-simulated training environment is provided for a pilot, so that the coping ability of the pilot under the conditions of complex weather and special airspace can be effectively improved, and the coping ability of the pilot under the conditions of complex weather and special airspace can be improved. The training cost can be remarkably reduced, the training period is shortened, the flight state can be monitored and evaluated in real time through the built-in intelligent decision module, and the authenticity of flight simulation is improved.

The invention discloses a plant protection unmanned aerial vehicle operation method fusing weed recognition and real-time variable spraying, and the method comprises the following steps: employing a plant protection unmanned aerial vehicle to carry a multispectral sensor, collecting multispectral data in real time, and generating a high-resolution multispectral image; converting the radiation intensity value into an actual reflectivity value by using a spectral reflectivity standard plate; according to the generated multispectral image, an NDVI value is calculated in real time through an airborne calculation module; in combination with flight path data, mapping the NDVI index and the geographic position of the flight operation; the classification standard of the NDVI value is adjusted in combination with a machine learning algorithm, and a spraying operation prescription map is generated; the electronic control spraying device adjusts the spraying flow in real time; and establishing a spraying model, and calculating an adjustment coefficient alpha to obtain the average pesticide application amount Y per mu. According to the plant protection unmanned aerial vehicle operation method, the spraying amount can be adjusted according to field weed distribution and crop growth vigor, the precision of plant protection operation is improved, the pesticide usage amount is reduced, and environmental pollution is reduced.

The invention discloses an airport pavement crack identification method and system, electronic equipment and a medium, and relates to the technical field of data processing. The method comprises the following steps: acquiring multi-temporal image data and flight operation data of an airport pavement; extracting the multi-temporal image data to obtain crack evolution characteristics of a plurality of regions, and calculating load response characteristics of each region; taking the crack evolution characteristics as node attributes, determining load transmission paths and transmission intensities between adjacent areas as edge weights, and generating a damage network structure; determining crack evolution rates of a plurality of damage nodes and evolution coefficients of corresponding areas through the damage network structure, and constructing a load-evolution incidence matrix; screening each damage node, determining a target damage node, and determining a propagation path and an influence range of the target damage node; the crack expansion trend in the target period is obtained, and a corresponding maintenance report is generated. By implementing the technical scheme provided by the invention, the accuracy of airfield pavement crack identification can be improved.

The invention discloses a self-walking unmanned aerial vehicle and a control method. The self-walking unmanned aerial vehicle comprises an unmanned aerial vehicle module, a walking module, an electrical connector assembly and a power system. The unmanned aerial vehicle module comprises a first driving mechanism; the walking module is detachably arranged at the bottom of the unmanned aerial vehicle module and comprises a second driving mechanism; the electrical connector assembly comprises a first electrical connector piece arranged on the unmanned aerial vehicle module and a second electrical connector piece arranged on the walking module, the first electrical connector piece and the second electrical connector piece are detachably connected in a matched mode, and the second driving mechanism is electrically connected with the second electrical connector piece; the power system is arranged on the unmanned aerial vehicle module and comprises a flight control unit and a power supply unit, and the first driving mechanism and the first electrical connector are electrically connected with the flight control unit and the power supply unit respectively. The self-walking unmanned aerial vehicle is good in environmental adaptability, small in load during flight operation and low in energy consumption, the unmanned aerial vehicle module and the walking module can share one set of power system, and the manufacturing cost is low.

The invention relates to an unmanned aerial vehicle traffic control method fused with dynamic obstacle avoidance, and relates to the field of unmanned aerial vehicle cooperative control. Static obstacle parameters are obtained, and an unmanned aerial vehicle control twin space is built in combination with the multiple dynamic potential energy field models; and when the multiple unmanned aerial vehicles are in flight operation, the unmanned aerial vehicles are utilized to control twin spaces, a deadlock detection mechanism and a deadlock solving mechanism are combined to execute dynamic regulation and control of an air route, and the positions of the unmanned aerial vehicles are iteratively updated until all the unmanned aerial vehicles reach respective target points. According to the invention, the problems of local minimum value, route conflict and deadlock of a multi-unmanned aerial vehicle system cannot be effectively solved by a traditional unmanned aerial vehicle control method can be solved; efficient sensing of the unmanned aerial vehicle can be realized through dynamic sensing range adjustment; the deadlock problem in flight can be solved through deadlock detection and a multi-dimensional solution mechanism; and the obstacle avoidance efficiency of the multiple unmanned aerial vehicles is optimized through priority dynamic allocation and a vertical separation strategy.

The invention relates to the technical field of aircraft simulation training, and discloses a multi-mode man-machine interaction aircraft simulation training system. A multi-modal feature extraction module of the system generates a multi-modal feature map including a flight environment situation map and an equipment operation health map through a hierarchical feature extraction network; the incremental analysis module divides an incremental data set, updates flight state prediction model parameters through an incremental clustering algorithm and outputs an incremental state prediction result; the parameter optimization layer initializes a tabu search population according to a prediction result, and iteratively optimizes parameters of an operation rule base through a dynamic efficiency evaluation matrix; the decision generation module fuses the optimized rule base parameters and the real-time multi-modal feature map, and generates a flight operation instruction sequence based on dynamic confidence score; and the execution optimization layer analyzes the instruction sequence, satisfies constraint conditions through a constraint projection algorithm, and completes instruction sequence integer programming in combination with the operation portrait deviation. The system improves the interaction accuracy and scene adaptability of simulation training.

The invention provides a carrier-based aircraft intelligent re-flight decision-making method based on a Bagging algorithm, and relates to the technical field of carrier-based aircraft re-flight decision-making. According to the method, a carrier-based aircraft is simulated in different flight states to obtain carrier-based aircraft re-flight track clusters, a re-flight boundary criterion is used for carrying out flag bit definition on the re-flight tracks, and the carrier-based aircraft re-flight track clusters are obtained; therefore, a re-flight decision-making system is converted into a dichotomy problem of re-flight track cluster data, and in a carrier landing process, real-time flight state quantity of a shipboard aircraft is input into a re-flight decision-making model, and the model calculates whether current carrier landing is dangerous or not in real time. In the process, the shipboard aircraft can predict whether re-flight operation needs to be executed or not in real time and whether re-flight has risks or not. Therefore, the safety of carrier landing and re-flying of the shipboard aircraft is ensured. Finally, the method can effectively improve the safety of the shipboard aircraft during carrier landing and re-flight, ensures the carrier landing recovery efficiency, and lays a technical foundation for the carrier landing and re-flight decision of the shipboard aircraft.

The invention discloses a multi-unmanned aerial vehicle cooperative lifting method, device, platform and system. The method comprises the steps that a flight control computer obtains platform state information of a whole subsystem at the current moment and unmanned aerial vehicle state information of all unmanned aerial vehicles at the current moment; the flight control computer calculates the total control input of the overall subsystem at the current moment; the flight control computer calculates the total control input based on a set multi-unmanned aerial vehicle cooperative dynamics model and the state information of the unmanned aerial vehicle at the current moment to obtain sub-control input corresponding to each unmanned aerial vehicle at the current moment; and the flight control computer generates a control instruction based on the sub-control input and sends the control instruction to each unmanned aerial vehicle, so that the unmanned aerial vehicle responds to the control instruction and executes flight operation according to the sub-control input. According to the multi-unmanned-aerial-vehicle cooperative hoisting system, efficient coordination of the multi-unmanned-aerial-vehicle cooperative hoisting system on operation tasks of the multiple unmanned aerial vehicles is achieved, the limitation that the carrying capacity of a single unmanned aerial vehicle is insufficient is overcome, and the carrying capacity requirement of large-mass transportation is met.

There is disclosed a multirotor UAV in which electric motors are arranged to drive propellors under the control of an on-board flight controller. The UAV has a plurality of sensors and a first processor for providing sensor signals to the flight controller in normal mode of in-flight operation. The UAV has a three-dimensional depth scanner and a further data processor for processing the three-dimensional depth scanner, wherein the further data processor is selectively activated to process the three-dimensional depth scanner data in a second mode of in-flight operation when fling in close proximity to an external structure. The further data processor creates a three-dimensional map of the external structure for control of the flight of the UAV proximate thereto. The UAV can operate in a GPS-denied and / or compass-denied mode. One or more sensor can detect a profile of an external structure within a predetermined vicinity of the UAV. A depth sensor can detect a visible feature in the environment. The control system is operable to log the visible feature as reference point and to determine yaw readings for use by the flight controller relative to said reference point. The control system comprising can receive the outputs of the distance scanning sensor and the location determining system and can define a mapping boundary around a current position of the UAV. The mapping boundary is of a defined size that is smaller than the scanning range of the position scanning sensor. The data processor generates a map of any surfaces within the mapping boundary and the flight controller is arranged to output control instructions for control of the propulsive rotors based upon the map.

Disclosed herein is a method for performing communication link handoffs between ground-based stations and aircraft in an aviation communications network. In the communications network, ground based stations interspersed throughout a coverage area of the network can be utilized to provide one or more aircraft flying within the coverage area with communications links that facilitate communication between an operator and an aircraft. In one or more examples, during the operation of the flight, the spectrum management system or other controller can coordinate the transfer of communications responsibilities between ground-stations of the network. The network can use a priori information from the flight plan to pre-coordinate handovers before the actual handover is to be initiated. In one or more examples, pre-coordination can include exchanging cryptographic keys between a source base station and a probable target base station of a handover.

The invention relates to the technical field of aircraft wake flow risk analysis, and discloses a mixed operation airport small single-shot training aircraft wake flow encountering risk analysis method. Collecting flight operation programs and information of each subject of training flight, extracting key flight features, and obtaining an average movement track of typical flight tasks of the public transport aircraft and the small training aircraft through clustering analysis; secondly, analyzing a running track and a cross point, carrying out numerical simulation analysis on wake flow of the cross point of the track based on CFD, and constructing an aircraft trailing vortex wake flow dissipation database; finally, an improved strip-shaped belt method is used for calculating extra lift force changes, caused by wake flow vortex fields, of all the parts, then the rolling torque coefficient of the small training aircraft during wake flow encounter is calculated, and the wake flow encounter risk is evaluated. According to the method, the risk that the general single-shot coach aircraft encounters the wake flow of the front civil aviation transport aircraft in the take-off, approach and other stages of the mixed-purpose airport can be accurately evaluated, and the operation efficiency of the mixed-purpose airport is further improved.

The invention belongs to the technical field of flight delay prediction, and discloses a flight delay prediction method based on multi-modal data fusion and large model fine tuning. According to the method, firstly, a flight delay prediction state space model is established, and dynamic characteristics and random characteristics of flight delay are effectively captured by dividing flight operation stages and analyzing delay generation and propagation modes of each stage, so that system states and observation data which are crucial to prediction are focused on. Secondly, performing fine adjustment on the Qwen large model by adopting a LoRA method, performing fine adjustment on the large model by utilizing historical flight data, and performing real-time delay prediction in combination with real-time flight data so as to improve the prediction precision and response speed of the large model; a final flight delay prediction result is obtained by performing weighted fusion on a prediction result obtained by reasoning based on the fine-tuned Qwen large model and a state space model estimated value obtained through processing, and the accuracy and reliability of the prediction result are effectively ensured.

The invention discloses a multi-rotor unmanned aerial vehicle capable of automatically adjusting the position and the deflection angle of a vehicle arm, and belongs to the technical field of multi-rotor unmanned aerial vehicles. The multi-rotor unmanned aerial vehicle comprises the vehicle arm of the multi-rotor unmanned aerial vehicle and rotors arranged on the vehicle arm, and the vehicle arm and the rotors are symmetrically distributed on the two sides of a vehicle body of the unmanned aerial vehicle in a high-low mode; a folding mechanism is arranged between a vehicle arm and a vehicle body of the multi-rotor unmanned aerial vehicle, so that the vehicle arm of the multi-rotor unmanned aerial vehicle can be driven to perform position adjustment and angle deflection through the folding mechanism; the multi-rotor unmanned aerial vehicle further comprises damping structures arranged on two adjacent side plates of the multi-rotor unmanned aerial vehicle. According to the multi-rotor unmanned aerial vehicle capable of automatically adjusting the positions and the deflection angles of the vehicle arms, position adjustment and angle deflection of the vehicle arms are achieved through the folding mechanisms, so that the multi-rotor unmanned aerial vehicle can adapt to flight operation of different space sizes or variable spaces, and meanwhile due to the fact that the vehicle arms can achieve adjustment of the axle distance through the folding mechanisms, the vehicle arms can be conveniently folded. The occupied space of the multi-rotor unmanned aerial vehicle can be reduced, and carrying and transportation are convenient.

The invention relates to a flight delay time prediction method and system based on dynamic gating MoE, and the method comprises the following steps: collecting flight historical operation data, carrying out the data preprocessing, and constructing a prediction data set; performing feature vectorization coding on the prediction data set in the form of a time sequence matrix, and extracting time sequence features of the prediction data set by using a multi-head attention mechanism; inputting the time sequence characteristics of the prediction data set into a dynamic gating MoE, adaptively selecting and combining the outputs of the routing expert sub-model and the shared expert sub-model through a dynamic gating mechanism, and obtaining fusion characteristics; and generating flight delay time prediction results according to the fusion features, wherein the flight delay time prediction results comprise departure delay time prediction results and arrival delay time prediction results. Compared with the prior art, the method has the advantages that the accuracy and stability of departure and arrival delay time prediction are remarkably improved, and the adaptability and expandability of the model to the complex flight operation situation are enhanced.

The invention relates to a material delivery-oriented flying operation robot visual servo control method. Comprising the following steps: S1, constructing a four-rotor unmanned aerial vehicle system model carrying a mechanical arm and a two-degree-of-freedom manipulator model; s2, constructing pixel points of the dynamic target in the imaging plane of the flight platform, and constructing a virtual image moment according to the pixel points so as to realize tracking control of the flight operation robot on the dynamic target; s3, designing a position controller under the conditions that the target is visible and the target is invisible, and achieving the position control of the flight platform; s4, on the basis of attitude angle information acquired by an inertial measurement unit and a constructed virtual yaw image moment, designing a global fast nonsingular terminal sliding mode attitude controller to carry out attitude control on the flying operation robot; and S5, converting the image moment acquired by the airborne camera, designing an image-based flight operation robot mechanical arm control method, and realizing stable tracking of a mechanical arm end effector on a dynamic target.

Methods, systems and apparatus, including computer programs encoded on computer storage media for unmanned aerial vehicle flight operations near physical structures or objects. In particular, a point cloud of the physical structure is generated using aerial images of the structure. The point cloud is then referenced to determine a flight path for the UAV to follow around the physical structure, determine whether a planned flight path to desired locations around the structure is possible, determine the fastest route to return home and land from a given position around the physical structure, determine possibility of inflight collision to surface represented in point cloud, or determine an orientation of a fixed or gimballed camera given a position of the UAV relative the point cloud.

A processing device in a memory sub-system identifies a power loss event associated with the memory sub-system including a memory device and an ultra-high endurance storage class memory device. In response to the power loss event, the processing device further identifies a set of data corresponding to one or more in-flight operations associated with the memory device. The processing device further causes the set of data corresponding to the one or more in-flight operations to be stored in the ultra-high endurance storage class memory device. The processing device further causes execution of a data recovery operation using the set of data corresponding to the one or more in-flight operations to be stored in the ultra-high endurance storage class memory device.

The invention provides a dynamic path planning method and system based on a generative adversarial network model, and the method comprises the steps: firstly receiving a flight task instruction of an air traffic control system and an airspace real-time operation data set, inputting the flight task instruction and the airspace real-time operation data set into a generator unit of a generative adversarial network, and generating a plurality of candidate dynamic flight paths; and calling a discriminator unit to evaluate the candidate dynamic flight paths and output comprehensive evaluation values by taking the airspace control constraint rule and the path operation efficiency index as references, feeding back the candidate dynamic flight path with the highest comprehensive evaluation value to the generator unit to adjust parameters, and repeating evaluation and adjustment operations until a preset optimization threshold value is reached. And the final path is sent to an air traffic control command system, so that the airspace utilization rate and the flight operation efficiency can be improved, and the flight safety is guaranteed.

The present invention relates to atmospheric sampling technology and discloses an unmanned aerial vehicle (UAV)-mounted atmospheric sampler for green and low-carbon monitoring. The sampler comprises a UAV body and a removably mounted air tank on its exterior. The sampler also includes an air guide channel extending through the UAV body, an air intake assembly fixed to the center of a concave cavity at the bottom of the UAV body, an elastic air intake pipe for pressure-intake air at the top of the air intake assembly, a positioner provided at the center of the exterior of the air intake assembly, a turbine disc threadedly mounted on the exterior of the positioner, and an airflow-driven reciprocating threaded connection to achieve reciprocating lifting and lowering control under wind energy to control pressure intake through the elastic air intake pipe. The UAV-mounted atmospheric sampler has a simple structure and utilizes the atmospheric airflow during conventional UAV flight operations to perform efficient atmospheric sampling without affecting UAV control. The sampler also avoids problems such as sampling inaccuracy caused by air pressure, thereby improving the efficiency and accuracy of atmospheric sampling.

The invention belongs to the field of flight evaluation, and provides a flight operation guarantee service quality evaluation analysis method and system, and the method comprises the steps: collecting multi-dimensional guarantee service index data in a flight operation guarantee process; collecting airport operation situation data; inputting the airport situation vector into a trained neural network model, and outputting a corresponding index weight correction factor to obtain a correction weight; performing weighted calculation on the standardized index matrix based on the correction weight to obtain a comprehensive service quality evaluation value; calculating a variance of the comprehensive service quality evaluation value in a preset time period, and performing optimization iteration on the neural network model by adopting a back propagation algorithm by taking the variance as a loss function until the loss function is converged; and outputting a comprehensive service quality evaluation result obtained based on the final correction weight.

The invention relates to the technical field of aviation data, in particular to an airport transit level measurement method and system.The built transit service index system covers the flight time transit connection level, passenger transit waiting and transit air flight; influences of airport operation, passenger service and flight operation on the transfer service are comprehensively considered, and multi-source description of the airport transfer service level is effectively realized. According to the airport transfer level fusion method fusing the multi-flight transfer service, measurement of flight transfer service priorities is achieved based on flight operation characteristics such as airline company attributes, peak periods and the number of transfer passengers, and the comprehensive service level of a plurality of transfer connectable flight pairs is effectively fused.

A system and method for implementing one or more real-time flight operations quality assurance (RTS-FOQA) systems and protocols is disclosed. RTS-FOQA systems assist pilots and other personnel in managing hazards and risks to protect the safety of passengers, pilots, flight attendants, crew, as well as people on the ground. The system is configured to capture voice data from the cockpit, recognize words spoken by the pilots, air traffic controllers (ATC), and other personnel; convert the recognized words into text; compare the text derived from the words spoken by the pilots, ATC, and other personnel to a standard operational procedure (SOP) sequence, wherein the sequence comprises a plurality of SOP operational steps; identify at least one of a plurality of flight operations executed by the pilots; compare the plurality of flight operations executed by the pilots with the required SOP operational procedures; identify at least one of the plurality of SOP operational steps not executed or incorrectly executed by the pilots; and alert the pilots if and when the pilots fails to execute or incorrectly execute any of the plurality of SOP operational steps. In addition to alerting the pilots of the deviation(s) from the SOP sequence(s), the system may automatically generate a SOP exceedance report for recording the SOP operational step(s) not executed or incorrectly executed by the pilot(s), as well as automatically execute an emergency response, automatically execute an evasive maneuver, and / or automatically implement an autopilot—E.G., initiate execution of remotely-piloted, semi-autonomous, or autonomous, arrival approach, and landing procedures.

The invention discloses a parachute control system based on an unmanned aerial vehicle and an unmanned aerial vehicle system. The parachute control system based on the unmanned aerial vehicle comprises an airborne module used for receiving a starting instruction of a parachute control module to activate a flight control module; the flight control module is used for controlling the flight operation of the unmanned aerial vehicle, monitoring the flight state of the unmanned aerial vehicle and sending the data of the flight state to the parachute control module; the parachute control module is used for receiving data of the flight control module to judge whether the unmanned aerial vehicle needs to open a parachute or not and sending a parachute control signal to the parachute driving circuit module when the parachute needs to be opened; and the parachute driving circuit module is used for receiving the parachute control signal and controlling the parachute to unfold according to the parachute control signal. Whether the parachute needs to be opened or not is evaluated through the parachute control module. And when the requirement is confirmed, a control signal is sent to the parachute driving circuit module, so that the parachute is unfolded, and the safe landing of the unmanned aerial vehicle is ensured in an emergency.

A flight operation system, comprising: an aircraft (2) and a flight operation device (1) arranged in a load area of the aircraft (2). The flight operation device (1) comprises a drive control unit (10), an active / passive adjustment unit (20) and an operation unit (30). The drive control unit (10) is located at the base of the flight operation device (1), and the weight of the device body of the flight operation device (1) is concentrated at the drive control unit (10). The active / passive adjustment unit (20) is connected to the drive control unit (10), and the weight of the active / passive adjustment unit (20) is far lower than that of the drive control unit (10), so that in the process of the drive control unit (10) driving the active / passive adjustment unit (20) to move, the dynamic center of gravity of the device body of the flight operation device (1) can change in the vicinity of the static center of gravity of the device body, thereby reducing the impact on the stable operation of the system due to the dynamic center of gravity being far away from the static center of gravity. The operation unit (30) is connected to the tail end of the active / passive adjustment unit (20), and by means of cooperation between the active / passive adjustment unit (20) and the operation unit (30), disturbance in the operation environment is resisted, thereby achieving precise operation of the flight operation device (1).

The invention relates to the technical field of operation state monitoring and risk assessment, provides a spatial-temporal feature mining and risk assessment method, system and device for flight operation data, and a storage medium, and solves the problem of low early recognition accuracy of a composite fault of a mobile device. The method comprises the following steps: acquiring flight operation data and optical signal data of the mobile equipment, wherein the flight operation data comprises component state data and environmental parameter data; according to the optical signal data, a structural deformation field of the mobile equipment is combined to form a strain thermodynamic diagram of the structural surface; performing space-time alignment fusion on the strain thermodynamic diagram, the component state data and the environmental parameter data, and extracting space-time features from a fusion result through an attention enhancement mechanism; and performing kernel fuzzy C-means clustering operation on the spatial-temporal characteristics to identify a potential fault mode combination, and determining a flight risk assessment result according to the potential fault mode combination. According to the invention, the early recognition accuracy of the composite fault of the mobile equipment is improved.

A flight operations scheduling method and apparatus, and a device. The method comprises: obtaining an estimated time of arrival and an estimated taxi‑in time of each inbound flight, and obtaining an earliest cleared pushback time and an estimated taxi‑out time of each outbound flight (step 1003); and on the basis of the estimated time of arrival of each inbound flight, the estimated taxi‑in time of each inbound flight, the earliest cleared pushback time of each outbound flight and the estimated taxi‑out time of each outbound flight, determining a suggested take-off time and / or a suggested pushback time of each outbound flight (step 1004). The method can perform relatively more rational scheduling on operation moments of airport flights.