1779 results about "Flight data" patented technology

The invention relates to the technical field of data transmission, and discloses an intelligent flight data transmission method and system based on satellite communication and a storage medium. The method comprises the following steps: carrying out real-time data acquisition and calculation on signal strength, bandwidth, delay and packet loss probability of an available satellite communication link to obtain a real-time link parameter table and a transmission channel allocation sequence; based on the real-time link parameter table, according to the priority information of the encrypted data packet queue and the transmission channel distribution sequence, performing multi-channel distribution and parallel transmission processing on the data packet to be transmitted to obtain a data packet transmission record; and carrying out statistical analysis and parameter calculation on the transmission success rate, delay time and retransmission times in the data packet transmission record to obtain transmission quality data and a system optimization parameter set. Under the condition of limited satellite communication resources, intelligent hierarchical transmission of flight data is realized, timely and reliable transmission of important data is ensured, and meanwhile, a real-time monitoring and optimization mechanism for data transmission quality is provided.

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 relates to the technical field of data verification, and discloses an integrity verification and correction method and system for flight data and a storage medium. The method comprises the following steps: performing spatial-temporal feature extraction processing on a multi-modal feature data stream, and constructing an incidence matrix according to a multi-level data mapping relationship to obtain a spatial-temporal feature association network; performing hierarchical inspection processing on the spatial-temporal feature association network to obtain abnormal mark data; performing regional recursion positioning processing on the abnormal mark data to obtain an abnormal propagation link diagram; performing correction scheme design processing on the abnormal propagation link diagram, and obtaining a data correction execution sequence based on a data reconstruction and influence evaluation optimization scheme; and performing progressive processing on the data correction execution sequence to obtain corrected flight data. According to the method, a data integrity checking and correcting mechanism is established in a distributed data acquisition environment, so that data exception can be accurately identified and positioned, and the corrected data can be ensured to meet physical constraint and space-time consistency requirements.

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 data encryption, and discloses an encrypted storage method and system for flight data and a storage medium. The method comprises the steps that an encrypted data packet is compressed through a time sequence compression algorithm, a wavelet transform compression algorithm and a DCT compression algorithm, redundancy check processing is carried out, a coded data packet is obtained, and the coded data packet comprises a data frame with a predefined format; carrying out storage space distribution on the data frames in the coded data packet according to the data hierarchy to which the data frames belong, dividing the storage space into a fixed area and a dynamic area, and establishing a storage space mapping table to obtain a stored data packet; and performing cyclic redundancy check and integrity scanning on the stored data packet, performing anomaly detection by comparing the storage mapping table, and performing data repair by encoding redundant information to obtain an encrypted and stored flight data packet. According to the invention, differentiated encrypted storage and dynamic space distribution are realized, and meanwhile, the integrity and restorability of data are ensured.

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 relates to the technical field of unmanned aerial vehicle management, in particular to an unmanned aerial vehicle low-altitude flight record management system. The invention aims to solve the problems that unmanned aerial vehicle low-altitude flight records are mainly used for flight data storage and simple playback in an existing unmanned aerial vehicle management system, and deep analysis and judgment of the unmanned aerial vehicle low-altitude flight records are lacked. According to the invention, the task evaluation module evaluates the feasibility of the low-altitude flight task of the unmanned aerial vehicle and generates an initial flight scheme, the path optimization module optimizes the initial flight scheme to generate a flight path adapted to a low-altitude environment, and the real-time monitoring module evaluates the flight state of the unmanned aerial vehicle in real time. The recording and judging module evaluates the flight state of the unmanned aerial vehicle after the low-altitude flight task of the unmanned aerial vehicle is completed; and the recording and playback module provides whole-course playback of the low-altitude flight process of the unmanned aerial vehicle according to low-altitude flight records of the unmanned aerial vehicle. Through mutual cooperation among the modules, comprehensive data support and application support are provided for low-altitude flight management of the unmanned aerial vehicle.

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 invention relates to the technical field of unmanned aerial vehicles. The invention discloses a task allocation and endurance optimization method for a multi-unmanned aerial vehicle system. The method comprises the steps of S1, task modeling, S2, initial population generation, S3, fitness function design, S4, genetic algorithm task optimization, S5, iterative optimization, S6, path planning and S7, data collection, prediction and adjustment. The multi-unmanned aerial vehicle system task allocation and endurance optimization method can process temporary tasks in real time, flexibly adjust a task allocation scheme, dynamically adjust flight parameters according to real-time flight conditions and task requirements, comprehensively consider various factors such as task priorities, flight paths, energy consumption and the like, and optimize the task allocation and endurance optimization of the multi-unmanned aerial vehicle system on the basis of actual flight data. And an energy consumption prediction model is established, and accurate energy consumption management and path planning are realized. According to the method, temporary tasks can be processed, task allocation and path planning are dynamically adjusted, and task execution efficiency and safety are ensured.

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.

A method and system for resolving a multi-operator distributed collaborative conflict of an unmanned air vehicle is disclosed, including: resolving a conflict in a strategic stage: generating multidimensional data based on time and spatial elements, rasterizing the multidimensional data, performing preliminary conflict resolution according to a preset airspace and an airspace requirement after rasterizing, and generating strategy approval information; and completing strategy conflict resolution by checking the strategy information; resolving a conflict in a pre-tactical stage: acquiring flight plan approval information, designating checking parties, performing multiple checks on the flight plan information, and performing judgment in combination with checking results of a plurality of checking parties to generate a conflict checking result; and resolving a conflict in a tactical stage: identifying a trajectory deviation according to real-time flight data, and sending out a warning according to a deviation category; and optimizing a path according to the deviated trajectory.

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 aviation information, in particular to a flight data change publishing system and method, and the system comprises a flight state monitoring module, a network modeling and analysis module, a flight influence prediction module, an optimization scheduling decision module, a scheduling execution and state updating module, and an information publishing and updating module. According to the method, the state change of the flights and the airport is monitored in real time, passengers and airport operators are ensured to obtain real-time flight information, key nodes and influence chains in an aviation network are identified by analyzing the mutual influence relationship among the flights and constructing an interaction relationship graph among the flights, and flight faults and changes are predicted and coped with. According to the method, system analysis is carried out on the influence of flight change, the flexibility and efficiency of flight scheduling are optimized, inconvenience to passengers and operation chaos caused by inaccurate information and untimely updating are reduced, and the experience and satisfaction degree of the passengers are enhanced by updating an airport information system and navigation facilities and pushing real-time updating to the passengers.

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.

A low-altitude aircraft intelligent identification and emergency countering system relates to the technical field of low-altitude aircraft safety management and comprises a detection unit, an identification unit, a command decision platform, a scheduling unit and a countering unit. The detection unit is used for detecting all low-altitude aircrafts in a prevention and control area and obtaining flight data of all the low-altitude aircrafts; the identification unit is used for receiving the flight data and analyzing and judging the threat level of the low-altitude aircraft based on a fusion algorithm; the command decision platform is used for receiving the threat level of each low-altitude aircraft and sending a disposal instruction to the countering unit; the scheduling unit is used for giving a specific countering equipment scheduling instruction through an intelligent optimization scheduling algorithm according to the disposal instruction; the countering unit controls different countering devices to accurately strike low-altitude aircrafts with corresponding threat levels according to the scheduling instruction; according to the method, the malicious unmanned aerial vehicle can be accurately identified and can be countered in time.

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 invention discloses a monocular depth estimation method based on hybrid CNN-Transformer, belongs to the aviation field and the computer vision field, and is applied to the field of environment depth perception in low-altitude flight of an unmanned aerial vehicle. Aiming at the problem that local-global information cannot be effectively extracted by a current depth estimation method, the design content mainly comprises the following steps: improving a visual Transform architecture based on an additive attention mechanism, optimizing feature interaction operation between image blocks, and improving the capability of capturing global semantic information; the method comprises the following steps: designing a deep network encoder mixed with a CNN-Transform module, and effectively extracting local-global feature information; a channel attention mechanism is introduced, and the feature fusion capability of the decoder is enhanced. The method is simple in process, an excellent test result is obtained through training on the flight data set Mid-Air of the low-altitude unmanned aerial vehicle, the extraction capability of local-global feature information is effectively enhanced, and the depth perception accuracy of the monocular depth estimation method in the flight scene of the low-altitude unmanned aerial vehicle is improved.

A computer implemented method of identifying anomalous flight data is provided. The method comprises: receiving a plurality of flight data units in a time series from each of a plurality of different flights, wherein each flight data unit comprises a value for each of a plurality of flight parameters at the same time point; mapping the flight data units as respective data points to a multi-dimensional space, wherein the dimensions of the multi-dimensional space comprise a dimension for each of the plurality of flight parameters; and identifying one or more anomalous flight data units in the received plurality of flight data units by applying a local outlier factor algorithm to the mapped flight data units. A method of maintaining an aircraft, a flight data analyzer, a computer program and a computer-readable storage medium is also provided.

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 patrol route optimization method fusing weather conditions and power consumption prediction, and the method comprises the steps: (1), according to the historical flight data of a patrol unmanned aerial vehicle, combining a random forest and a linear regression method; establishing an unmanned aerial vehicle power consumption prediction model considering the coupling influence of environmental factors and task factors, and drawing a time-power-power curved surface; (2) inputting a patrol object digital map, a required patrol point set, a selectable patrol point set, unmanned aerial vehicle performance data, a route starting point and a route ending point, and obtaining an initial flight route without considering real-time weather conditions; and (3) optimizing the initial flight route in combination with the real-time weather condition and the time-power-electric quantity curve, and when the predicted consumed electric quantity is greater than the acceptable consumed electric quantity, sequentially giving up the selectable patrol points according to the importance sequence and re-drawing the route until the predicted consumed electric quantity is less than or equal to the acceptable consumed electric quantity. The method has a remarkable optimization effect on precise patrol task execution of the unmanned aerial vehicle.