1344 results about "Flight data" patented technology

The invention relates to an AI real-time intelligent guidance and self-adaptive obstacle avoidance method for multi-source sensing fusion of an unmanned aerial vehicle, and the method comprises the steps: collecting flight data through a visual sensor and an infrared sensor, obtaining a timestamp and space coordinate information, calculating a sampling frequency difference, and generating a fusion data set of time-space alignment through a self-adaptive interpolation algorithm; image and heat source features in the fusion data set are extracted, and a multi-dimensional feature set is generated, so that whether an obstacle exists or not is judged, and related information is extracted; and analyzing environment data, calculating an environment complexity index, generating a risk level in combination with obstacle information, predicting a dynamic weight of the sensor, and adjusting a working mode of the sensor. And then based on the obstacle condition, calculating a comprehensive risk value and predicting a motion trend through a path risk model, and finally generating an optimal obstacle avoidance path and a flight instruction, thereby realizing high-efficiency processing of sensor data, accurate risk assessment and real-time obstacle avoidance planning, and ensuring stable and safe flight of the unmanned aerial vehicle in a complex environment.

The invention discloses a low-altitude integrated management system based on a grid digital twin model and an intelligent algorithm, and relates to the technical field of low-altitude aircrafts. The method comprises the following steps: multi-source sensing data fusion and grid coding are carried out, and an airspace environment space-time database is constructed; constructing an airspace environment space-time database to carry out three-dimensional subdivision on the airspace; generating a multi-scale grid twinborn body, and associating a corresponding attribute for each level of grid according to a management requirement; generating an airspace risk thermodynamic diagram in the future 5-30 minutes; establishing a grid state change triggering rule; and generating a multi-target optimal path set by taking the grid navigation cost as a weight. According to the invention, by combining the real-time operation data of the low-altitude aircraft, the meteorological environment and other data with the artificial intelligence algorithm and the navigation rule base, the airspace traffic flow is analyzed, the flight plan is optimized, the flight route is intelligently distributed, the automatic route setting of any two points is realized, and the response speed of the low-altitude flight service system and the processing capability of various flight data are improved.

The invention discloses a bionic group intelligent low-altitude logistics unmanned aerial vehicle cluster anti-wind interference cooperation method, and the method comprises the steps: collecting the historical flight data and three-dimensional wind field data of an unmanned aerial vehicle cluster, and generating a bionic formation feature set with a wind field label; inputting the bionic formation feature set into a swarm intelligence model fused with fluid mechanics, and generating a dynamic formation topology instruction; according to the dynamic formation topology instruction, adjusting the relative position and attitude angle of each unmanned aerial vehicle through a distributed cooperative control algorithm, and generating an anti-wind disturbance cooperative flight state; and continuously monitoring the deviation between the three-dimensional wind field change and the cooperative flight state, dynamically correcting the weight of the formation density-anti-wind disturbance intensity mapping relation through a reinforcement learning algorithm, updating a dynamic formation topology instruction, and realizing adaptive control of bionic group anti-wind disturbance cooperation. According to the embodiment of the invention, high-disturbance-rejection cooperative flight of the unmanned aerial vehicle cluster in the dynamic wind field can be realized, the formation energy consumption is reduced, and the obstacle avoidance capability under the sudden wind condition is improved.

The invention discloses a multi-unmanned aerial vehicle combined flight control system coping with wind power change, and relates to unmanned aerial vehicle flight control, and the system comprises a flight data collection module which is used for collecting unmanned aerial vehicle flight data in real time, and the unmanned aerial vehicle flight data comprises wind power data, attitude angle data and height data of each unmanned aerial vehicle; the data receiving and processing module is electrically connected with the flight data acquisition module, and the data receiving and processing module is used for receiving unmanned aerial vehicle flight data acquired in real time and performing noise removal on the acquired data. According to the multi-unmanned aerial vehicle combined flight control system provided by the invention, the wind field prediction model is constructed by combining the LSTM neural network with the time attention mechanism, wind power periodic change characteristics are accurately captured, real-time correction is performed in cooperation with the Kalman filter, the prediction precision and timeliness are both optimized, and the system is suitable for large-scale popularization and application. A multi-unmanned aerial vehicle cooperative kinetic model is constructed based on a prediction result, so that the formation trajectory tracking error is reduced.

The invention relates to the technical field of aviation communication, and discloses a true north calibration system and method for an aviation airborne laser communication equipment alignment system, and the method comprises the steps: synchronously collecting flight data through an IMU sensor and a double-antenna GPS at a preset sampling frequency, and outputting the real-time attitude angle information of an aircraft through an IMU; filtering and de-noising the synchronously acquired IMU and double-antenna GPS data, and carrying out noise suppression and deviation compensation on accelerometer and gyroscope data output by the IMU by adopting a Kalman filtering algorithm; based on the preprocessed sensor data, calculating an initial course angle by measuring relative position change and phase difference between antennas by using a double-antenna GPS (Global Positioning System); the calibration controller transmits the calculated true north direction information to an alignment system of the laser communication equipment in real time through a communication interface; and the attitude change and external environment disturbance of the aircraft are continuously monitored in the whole flight process. The method has the advantage that the alignment performance and the communication stability of the laser communication equipment are improved.

The invention provides a remote identity recognition method and system based on unmanned aerial vehicle management and control requirements. Wherein the preparation state of the unmanned aerial vehicle is determined by generating an inquiry code, sending the inquiry code to all online unmanned aerial vehicles in a target airspace, and receiving an identity response signal comprising a unique identifier, a task brief description and a flight path. A multi-dimensional data fusion algorithm is combined with historical flight data, meteorological conditions and geographic information to predict an unmanned aerial vehicle behavior mode, and a behavior prediction report is formed. The block chain technology is used for cross verification, and the real identity and authorization state of the unmanned aerial vehicle are verified through the dual key encryption technology and the consensus algorithm. And according to a verification result, using machine learning to construct a temporary virtual fence and plan an optimized route, and automatically adjusting an air corridor to form a traffic management scheme. And finally, analyzing actual flight parameters by applying a self-adaptive control algorithm, and further optimizing route setting. According to the technical scheme provided by the embodiment of the invention, the safety and efficiency of air traffic management of the unmanned aerial vehicle are improved.

The invention relates to the technical field of data management, in particular to a low-altitude economic flight data management method and system based on edge computing, and the system comprises edge computing nodes deployed on an aircraft and a ground base station, a sensor for collecting multi-source data, a module for preprocessing the data, and an annular data queue caching mechanism. The system comprises a composite biological feature code generation module, a distributed storage edge node, an abnormal fluctuation feature extraction module, an equipment health state and trend analysis and calculation module, a virtual power supply manager, a main power supply, a standby power supply, an abnormal behavior monitoring module and a defense strategy library. After preprocessing and caching, feature codes are generated and encrypted distributed storage is carried out, dynamic resource adjustment is realized through state evaluation and a digital twinning and optimization algorithm, a neural network is utilized to manage a power supply, data security is guaranteed in combination with abnormal monitoring, and a whole-process management system is formed.

The invention discloses an unmanned aerial vehicle system testing method and system based on a model, and relates to the technical field of unmanned aerial vehicle system testing, and the method comprises the steps: collecting the operation data of an unmanned aerial vehicle, preprocessing the data, and carrying out the labeling classification of the preprocessed data; inputting the labeled data into a generative adversarial network model, training a generator, and generating a simulated flight scene data set; carrying out transfer learning model pre-training based on the simulated flight scene data set; deploying the unmanned aerial vehicle in an actual test, collecting incremental data from a real environment, and performing fine tuning on the pre-trained transfer learning model; a flight mission plan is generated, and simulation evaluation is completed in combination with the simulated flight scene data set. By generating the high-quality flight data and continuously updating the model, the test and task planning capability of the unmanned aerial vehicle system in a complex environment is effectively improved, the test cost is reduced, and the coping capability of the model in different flight scenes and the stability of the model in actual operation are enhanced.

The invention belongs to the field of civil aviation, provides a civil aircraft field service operation scheduling control system, and aims to solve the problem of low cooperative efficiency of multiple links of ground service scheduling. File management is based on an Ali cloud OSS development interface, supports multi-format files, and realizes functions of permission access, encryption and the like; communication adopts an MQ / MQTT architecture to construct an IM system, and supports multi-type message transmission and http network communication; data processing packages a multi-language database interface, and internal and external network secure intercommunication is realized through a gatekeeper agent. Flight functions cover and guarantee node management and control, dynamic updating, cross-department communication, real-time flight data butt joint, task distribution of unified operation according to work types, and support of multi-terminal such as a web terminal and a handheld terminal and nailing access. Ground service multi-scene requirements can be met, and the operation efficiency and the management level are improved.

The invention relates to the technical field of low-altitude safety emergency, and discloses a low-altitude safety emergency system based on large model application. The system comprises a low-altitude situation awareness module which constructs a dynamic situation prediction model based on historical flight data, collects a low-altitude target track coordinate sequence, radar echoes and communication signal characteristics in real time, and outputs a theoretical security situation value. The multi-source data fusion analysis module performs three-dimensional difference analysis on theoretical and actually measured situation values, which relates to time domain trend deviation, space domain coverage offset and event sequence matching degree, and generates a target-level difference feature matrix; and the risk propagation path deduction module inputs the matrix into a geographic topology analysis network, generates a probability thermal distribution diagram of an abnormal risk propagation path in combination with airspace node communication parameters and a target position, and locates a risk influence area. And the intelligent disposal strategy generation module configures parameters according to the graph, starts high-frequency monitoring for a high-probability area, and performs communication disturbance test on an adjacent target.

The invention provides a low-altitude airspace collaborative management method and system based on digital twinning, and the method comprises the steps: analyzing the maneuvering characteristics and flight envelope characteristic parameters of an aircraft based on the obtained real-time state data of the aircraft, and calculating the maneuvering characteristics and flight envelope characteristic parameters of the aircraft by using kinematics and dynamics models, describing motion laws of different aircrafts; real-time flight data of the aircraft are combined, the motion track and attitude change of the aircraft are synchronized in real time in a virtual environment through a digital twin technology, the dynamic behavior of the aircraft is accurately reproduced, and a high-fidelity digital model is provided for flight conflict detection and flight path prediction; a multi-level and multi-granularity digital twin model is constructed according to the difference of the motion characteristics of the aircrafts, and the digital twin model comprises an airspace structure model of a macroscopic level, an aircraft group behavior model of a mesoscopic level and a single aircraft dynamic model of a microscopic level.

The embodiment of the invention provides an aircraft attitude control model generation method and device, equipment and a medium, and is applied to the technical field of aircraft control. The method comprises the following steps: acquiring a flight task and a flight environment; according to a pre-constructed simulation model, carrying out analogue simulation on the flight task and the flight environment, and outputting simulated flight data; training a pre-constructed reinforcement learning model by taking a flight strategy as an output and the simulation flight data as an input; and when a preset stop condition is met, stopping training, and taking the trained reinforcement learning model as an aircraft attitude control model. In this way, the training period and training cost of the controller can be reduced, and the aircraft attitude control model with a better control effect can be trained.

The invention provides a gate position distribution method and device, and the method comprises the steps: obtaining flight data, resource data and gate position data, the resource data comprises dragging resource data, and the gate position data comprises the state data of each gate position; based on a pre-configured dragging rule, determining a dragging constraint and an optimization target corresponding to the flight data; based on a pre-configured parking space distribution rule, determining a parking space distribution constraint and an optimization target corresponding to the flight data; and based on the flight data, the resource data, the gate position data, the dragging constraint and optimization target corresponding to the flight data and the gate position allocation constraint and optimization target corresponding to the flight data, generating a flight dragging scheme and a gate position allocation scheme. According to the method and the device, the flight dragging and the aircraft position distribution are considered at the same time, the flight dragging scheme is automatically generated while the aircraft position distribution scheme is generated, a collaborative plan of the dragging scheme and the aircraft position distribution is realized, and various indexes of the aircraft position distribution in an airport are effectively improved.

The invention relates to the technical field of unmanned aerial vehicles, and discloses a gas-electric hybrid composite wing unmanned aerial vehicle system, which comprises an intelligent energy management module, a gas-electric hybrid composite wing unmanned aerial vehicle control module and a gas-electric hybrid composite wing unmanned aerial vehicle control module, the self-adaptive flight control module is used for optimizing the flight attitude, dynamically adjusting the flight route and improving the flight safety; the task adaptive scheduling module is used for adaptively adjusting a task execution strategy according to the task category, the load condition and the environmental parameters; the data monitoring and remote control module is used for collecting the operation state of the unmanned aerial vehicle in real time and storing and analyzing flight data; the intelligent safety management module is used for improving the autonomous safety of the unmanned aerial vehicle; and the environment perception optimization module is used for perceiving external environment factors in real time. According to the invention, the intelligent energy management module realizes intelligent scheduling of fuel and electricity power, and dynamically optimizes the power distribution of the battery and fuel oil, so that the flight time of the unmanned aerial vehicle is greatly prolonged.

The invention discloses an extreme climate self-adaptive flight control system for an unmanned aerial vehicle in a complex terrain, and relates to the technical field of unmanned aerial vehicle self-adaptive flight control. The extreme climate adaptive flight control system for the unmanned aerial vehicle in the complex terrain comprises a flight data acquisition and preprocessing module, a climate prediction and risk assessment module, an optimal path and task sequence adjustment module, a real-time flight control and path optimization module and a safety emergency response and recording module. Wherein the flight data acquisition and preprocessing module is used for acquiring flight data; the climate prediction and risk assessment module is used for constructing a neural network model to predict climate; the optimal path and task sequence adjusting module is used for performing path and task security evaluation; the real-time flight control and path optimization module is used for converting a path and a task sequence into an aircraft instruction; and the safety emergency response and recording module is used for executing and recording the emergency strategy. The problem that the unmanned aerial vehicle is difficult to adjust flight in real time due to continuous change of climate states in the prior art is solved.

The invention relates to the technical field of flight control quality evaluation, and discloses a flight control quality evaluation method and system based on artificial intelligence, and the method comprises the following steps: S101, obtaining multi-source flight data in real time; s102, extracting flight state characteristics, cognitive load characteristics of a pilot and environment state characteristics according to the multi-source flight data; s103, determining key control parameters and key flight state parameters, and constructing a space-time diagram; s104, recognizing the cognitive load state through a clustering algorithm, and dynamically adjusting the weight of a corresponding edge in the space-time diagram to generate a dynamic space-time diagram; s105, space-time sensitive features of the nodes are extracted, a comprehensive feature vector is obtained through calculation, and a flight control quality score is output; and S106, grading, and taking corresponding operation measures. According to the invention, real-time, accurate and dynamic flight control quality evaluation is realized, and flight safety and control quality evaluation scientificity are effectively improved.

The invention provides a construction method, a control method and a system of a flight control network, and the method comprises the steps: employing a depth deterministic strategy gradient algorithm, building an attitude control network of an unmanned aerial vehicle in a flight process based on the state information and a target flight value of the unmanned aerial vehicle in the flight process, employing a depth reinforcement learning algorithm, and carrying out the attitude control of the unmanned aerial vehicle in the flight process. Based on the state information and the information of the environment where the unmanned aerial vehicle is located, a path planning network and an intelligent avoidance network of the unmanned aerial vehicle in the flight process are built respectively; based on the attitude control network, the path planning network and the intelligent avoidance network, constructing a hierarchical deep reinforcement learning network of the unmanned aerial vehicle in the flight process; and training the hierarchical deep reinforcement learning network by adopting historical flight data of the unmanned aerial vehicle in different flight environments and a preset flight reward mechanism to obtain a flight control network of the unmanned aerial vehicle. According to the invention, the adaptability and flexibility of the unmanned aerial vehicle in the flight process can be improved.

A method for fueling an aircraft comprises (a) predicting a sequence of fuel-burn quantities over a planned flight based on at least one sequence of multivariate flight data recorded during a prior flight and on a hidden state of a trained machine. For each of a pre-selected series of prior flights, a corresponding sequence of multivariate flight data recorded during the prior flight is processed in a trainable machine to develop the hidden state, which minimizes an overall residual for replicating the fuel-burn quantities in each corresponding sequence. The method further comprises (b) summing the fuel-burn quantities over the planned flight to obtain a fueling estimate; and (c) fueling the aircraft based in part on the fueling estimate.

The disclosed invention provides a system and method for processing air traffic control (ATC) communications to ensure real-time validation of aircraft operations and improve pilot logbook accuracy. The system receives ATC voice communications through a digital radio system, converts them to text using a natural language processing model trained on aviation terminology, and extracts key operational data, including aircraft identifiers, instructions, and corrections. This data is correlated with Automatic Dependent Surveillance-Broadcast (ADS-B) and GPS data to verify compliance with ATC instructions. The system creates a continuous transcript of ATC-pilot communications, stitching together conversations across multiple frequencies. Deviations from ATC instructions are detected, generating alerts and logging the events. The method further provides automated reporting, archiving of communications, and enhanced safety metrics in immutable pilot logbooks, using blockchain technology for secure storage. This invention improves operational safety, transparency, and flight data integrity.

The invention discloses an unmanned aerial vehicle fault diagnosis method and system fusing random convolution data enhancement and deep learning, and effectively alleviates the problem of poor model generalization and robustness caused by the class imbalance of an unmanned aerial vehicle flight data set. According to the method, a random convolution data enhancement technology is provided, convolution calculation is performed on an original fault sample, diversified enhanced time sequence samples which maintain original fault features but introduce diversified noise and deformation into local details are generated, and weighted fusion is performed on the enhanced time sequence samples and the original samples, so that a fault sample library is effectively expanded, and the balance of a data set is improved; in order to efficiently capture the time sequence characteristics of the unmanned aerial vehicle fault signals, a fault diagnosis model based on a time convolution network is adopted, and a decision basis is provided for the accuracy of the fault diagnosis model. According to the method, the problem caused by data set category imbalance is effectively solved, the fault diagnosis performance can be improved, and a new thought and method are provided for the unmanned aerial vehicle fault detection method based on data driving.

The invention belongs to the technical field of low-altitude flight safety early warning, and provides a multi-modal data fusion method for low-altitude flight risk early warning. Comprising the steps of flight state data and meteorological data alignment processing, missing data filling, spatial feature extraction, spatial feature and flight state data fusion and space-time convolutional neural network and space-time diagram convolutional network analysis. According to the invention, the flight state and the meteorological data are fused, so that the prediction accuracy and reliability are improved; through the space-time convolutional neural network and the space-time diagram convolutional network, the space and time dependency relationship in the flight data and the meteorological data can be captured at the same time; through spatio-temporal data fusion, missing data can be accurately aligned and complemented, and the data quality and the real-time performance of the system are improved.

A method for determining improved flight trajectory. The method includes receiving one or more weather parameters to determine contrail forecast data; receiving one or more flight parameters associated with at least one aircraft 204 to determine flight data thereof; receiving flight schedule including at least one flight plan of at least one aircraft; analyzing at least one flight plan to determine at least one navigational avoidance between at least two aircraft; determining contrail likelihood associated with at least one aircraft; altering one or more flight parameters to determine improved flight trajectory for at least one flight plan; sending at least one flight plan including improved flight trajectory to at least one aircraft; and validating improved flight trajectory using imagery data, when at least one aircraft flies according to at least one flight plan including improved flight trajectory. A system for determining improved flight trajectory.

The embodiment of the invention provides a low-altitude flight target conflict identification method and device, and the method comprises the steps: obtaining a basic scale of a space grid according to the features of a target airspace and the performance features of an aircraft, carrying out the multi-level grid division of the target airspace according to the difference logic and the basic scale of the space grid, and constructing a distributed grid reference space; performing height reference conversion and time reference synchronization on the flight data in the reference space, and associating the flight data with the reference space to obtain a target airspace index space; performing high layering on target airspace index space, performing secondary fine-grained flight conflict detection on each index space layer according to a preset time period, judging whether a potential flight conflict risk exists or not, if yes, acquiring real-time state data of the flight targets, predicting the minimum distance between the flight targets, judging whether an actual flight conflict risk exists or not, and if not, determining whether the actual flight conflict risk exists or not; the method can support the real-time conflict recognition of large-scale aircrafts, and improves the flight efficiency and safety of low-altitude aircrafts.

A method for selecting a cruise phase route for an aircraft is presented. The method comprises receiving sequences of multivariate flight data from at least one prior flight and receiving a set of candidate cruise phase routes. Upcoming atmospheric conditions including at least upcoming tail winds are received for each of the set of candidate cruise phase routes. For each candidate cruise phase route, a sequence of fuel burn quantities is predicted based on the sequences of multivariate flight data and the upcoming atmospheric data. The fuel burn quantities are summed over the candidate cruise phase route to obtain an estimated fuel burn. A preferred candidate cruise phase route having a lowest estimated fuel burn is indicated.

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.

The invention discloses an unmanned aerial vehicle intelligent scheduling and management method based on task planning, and relates to the technical field of dynamic path planning, and the method comprises the steps: collecting flight data, and carrying out the preprocessing; based on the preprocessed data, constructing an intelligent patrol dynamic convolutional network model to analyze the health state of the unmanned aerial vehicle, and generating a health state result; performing dynamic path planning through a recurrent neural network model by using the generated health state result and combining environment perception data; making a task and executing a plan according to dynamic path planning and an intelligent scheduling algorithm; according to the method, hierarchical feature extraction and fusion are performed on the preprocessed flight data through the intelligent patrol dynamic convolutional network model, the health state feature vector is generated, and accurate health state evaluation and anomaly detection are realized in combination with the health state value and the One-Class SVM.

The invention discloses an aerodynamic force prediction method and device for large-scale transportation equipment, and relates to the field of aerodynamic force prediction.The method comprises the steps that firstly, structural parameters and historical flight data of the large-scale transportation equipment are obtained, an aerodynamic force prediction knowledge graph is constructed based on a cloud server, and a self-adaptive CFD numerical simulation model is constructed; and carrying out adaptive optimization on the adaptive CFD numerical simulation model by adopting an adaptive local alternating genetic algorithm, and carrying out iterative updating on the aerodynamic prediction knowledge graph. Then, aerodynamic force prediction is carried out by adopting a deep reinforcement learning model, and knowledge reasoning is carried out in combination with an aerodynamic force prediction knowledge graph; the method solves the problem that the accuracy of a CFD (computational fluid dynamics) method is limited by a physical model and a computational grid in the aerodynamic force prediction of the large transportation equipment, so that the calculation failure under the conditions of complex aerodynamic force phenomena and flow characteristics is easily caused, and can comprehensively consider various physical phenomena and flow characteristics of the large transportation equipment during high-speed operation. And the prediction accuracy and reliability are improved.

The invention relates to a flight data anomaly detection method and system based on dual-view enhanced Transform, and belongs to the technical field of aviation safety. According to the invention, the task of flight safety anomaly detection is expanded beyond a traditional overrun event, aiming at the subtle anomaly which is often not detected in the flight process; according to the fact that QAR data is a typical form of time series data, a flight safety problem is re-expressed as an anomaly detection task in time series analysis; in addition, an unsupervised anomaly detection model specially designed for QAR data is designed in order to solve the challenge of excessively depending on expert marking data in flight safety research. And finally, designing a time sequence anomaly detection model architecture based on reconstruction according to the feature sparse characteristic of the QAR data. According to the method, the condition of deviating from the normal flight mode can be identified in time, early warning information is provided for airlines, experts are effectively assisted in analyzing abnormal flight behaviors, and the method has a wide application prospect.

The invention discloses an aircraft braking system evaluation and prediction method based on fault simulation and geometric features, and belongs to the technical field of electric data processing. The method comprises the following steps: constructing a simulation model of an aircraft brake system, and injecting faults into the simulation model according to different degrees of fault injection control parameters to generate a degradation monitoring data set of the whole life cycle of the system; dividing the degradation monitoring data set into a training data set and a test data set; training the GoogLeNet model by using the training data set to obtain a health assessment model; evaluating the health degree of the aircraft brake system by using the test data set; constructing a topological graph according to the multi-rack operation data and the health degree of the aircraft braking system; nodes in the topological graph are clustered and divided into K clusters; establishing a residual life prediction model for each cluster; and performing similarity matching on the sample and the K clusters, and selecting the best matched residual life prediction model to perform residual life prediction on the system. The method can accurately predict the remaining life of the aircraft brake system.

The invention relates to the technical field of unmanned aerial vehicle low-altitude flight safety monitoring, in particular to a low-altitude safe flight data monitoring system and method based on multi-source perception. The specific implementation process comprises the following steps: constructing a feature vector analysis module, fusing multi-source heterogeneous data through a space-time correlation model, and analyzing a flight feature vector; constructing a safe flight route planning module, and planning a safe flight route on the drawn flight safety map according to the flight feature vector; a risk factor correction module is constructed, when it is monitored that the deviation of the flight route exceeds a threshold value, a risk factor causing the deviation is deduced and quantified into a correction parameter, and dynamic re-planning of the flight route is triggered. According to the method, the flight feature vector and the flight safety map are combined, so that the low-altitude flight data of the unmanned aerial vehicle are monitored, and the continuous safety and the risk avoidance capability of the unmanned aerial vehicle in task execution in a complex, dynamic and unknown environment are effectively improved.