695 results about "Flight data" patented technology

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 discloses a CRITIC-based man-machine cooperation system trust degree evaluation method, and belongs to the technical field of man-machine interaction and human factor engineering, and the method comprises the steps: S1, obtaining the physiological data and flight data of a pilot when the pilot executes a manned and unmanned system task; s2, preprocessing and standardizing the physiological data and the flight data respectively; s3, weight calculation is carried out based on a CRITIC method, and standardized multi-dimensional feature data is used as input of a CRITIC model and is used for calculating objective weights of all indexes; and S4, calculating a credibility value by using the generated weight matrix. By adopting the CRITIC-based man-machine cooperation system trust degree evaluation method, objective real-time quantitative evaluation is realized; the weight is adaptively determined according to the data features, the method is more scientific than artificial experience, physiological and behavior data are fused to capture psychological changes, the trust level can be monitored, the cooperation strategy is adjusted accordingly, and the task safety and efficiency are improved.

The invention relates to a multi-task-point cruise path planning method and device for an unmanned aerial vehicle. The method comprises the following steps: acquiring flight data of the unmanned aerial vehicle in multiple flight stages; the flight data comprises corner data when the flight stage is a corner stage; constructing a flight energy consumption model according to the undirected graph, the flight data and the rotation angle data; the energy consumption of each task point in the cruising process of the unmanned aerial vehicle is obtained; performing iterative optimization on the energy consumption of the plurality of task points based on a wild dog optimization algorithm to obtain a path planning result, and controlling the unmanned aerial vehicle to fly according to the path planning result; energy consumption of different flight stages is quantified by constructing a flight energy consumption model, including energy consumption of rotation angle data in a rotation angle stage, so that the complex environment adaptability is improved, iterative optimization is performed on a path in combination with a wild dog optimization algorithm, the problems of inaccurate energy consumption modeling and easy falling into local optimum are solved, and the energy consumption modeling efficiency is improved. The method has the advantages that the energy consumption optimization efficiency and accuracy of path planning are improved, and the local optimum problem is avoided.

The invention relates to the technical field of unmanned aerial vehicle searchlighting control, in particular to an unmanned aerial vehicle searchlighting control system, which comprises a multi-source data acquisition module for acquiring a water surface image and unmanned aerial vehicle flight data; the water surface type analysis module is used for judging a calm or wave water surface according to the image gray variance and the edge density; the searchlighting output sub-control module is used for selecting a lamp control strategy according to a water surface type, identifying a high-confidence target in combination with multiple features and determining a wick power distribution parameter; the searchlighting output execution module drives the lampwick to illuminate according to the parameters; the target real-time tracking module is used for calculating the projection angle adjustment amount of the searchlight to aim at the target; according to the system and the method, the problem that an existing searchlight system cannot dynamically adapt to water surface optical characteristics, suppress reflection interference and accurately illuminate in a complex water environment at night can be solved, and the accuracy and efficiency of target recognition are remarkably improved.

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

The invention discloses an anomaly diagnosis method and system for flight data of an unmanned aerial vehicle. The method comprises the following steps: S1, carrying out dynamic time-frequency sensing preprocessing; s2, multi-scale feature adaptive extraction is carried out; s3, performing causal constraint contrast learning; s4, self-adaptive abnormal decision making is carried out; and S5, performing causal root cause tracing, and outputting a root cause analysis report. The invention provides an anomaly diagnosis scheme for flight data of an unmanned aerial vehicle, which can dynamically perceive data time-frequency characteristics, mine a parameter causal relationship and adapt to a complex flight scene.

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

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

The invention discloses an unmanned aerial vehicle dynamic projection map construction method and system based on spatio-temporal data fusion, and belongs to the technical field of unmanned aerial vehicle video monitoring and geographic scene fusion, and the method comprises the steps: obtaining multi-modal flight data in real time based on an unmanned aerial vehicle, and the multi-modal flight data comprise video image data and spatio-temporal reference data; and performing data preprocessing on the multi-modal flight data to obtain route data. And constructing an unmanned aerial vehicle dynamic projection model, and performing space-time synchronous projection rendering on the route data to obtain an unmanned aerial vehicle dynamic projection map. And constructing a dynamic calibration model, and correcting the dynamic projection map of the unmanned aerial vehicle. According to the method, an integrated dynamic geographic information sensing system is constructed through deep cooperation of a spatio-temporal data fusion framework and an intelligent calculation engine, accurate mapping of the geographic space driven by spatio-temporal reference fusion is realized, millimeter-level space registration capability is constructed through multi-modal data intelligent solution and terrain adaptive rendering, and the real-time dynamic geographic information sensing system is constructed. And a technical breakthrough is formed in the dimensions of accurate positioning, data fusion, real-time response and the like.

The invention discloses an unmanned aerial vehicle control method. According to the method, identity recognition information and flight data information of an unmanned aerial vehicle can be obtained in real time; and processing the flight data information by using the dynamic queue processing model to obtain a control instruction for controlling the unmanned aerial vehicle to execute operation. The scheme can dynamically allocate the priority of the unmanned aerial vehicle, so that the unmanned aerial vehicle can automatically complete landing, the labor cost is reduced, and the landing safety risk is reduced. The scheme can be integrated and matched with an unmanned aerial vehicle take-off and landing platform and a low-altitude flight scheduling control platform, a platform system does not need to be additionally designed, the research and development cost can be reduced, and integrated automatic scheduling of route planning, task execution and queuing landing of the unmanned aerial vehicle can be met.

The invention relates to the technical field of flight safety of civil aircrafts, in particular to a flight trajectory prediction method under the condition of air high-speed target hood perforation, which comprises the following steps: acquiring calculation coefficient parameter data of a target flight dynamics model, establishing a trajectory prediction framework based on a three-degree-of-freedom target flight dynamics model and target real-time flight data; the method comprises the following steps: performing numerical simulation on a flow field of a hood perforation area through fluid mechanics analysis software to obtain speed field and pressure field data, establishing a hood perforation flow field pneumatic analysis model by adopting a high-order polynomial fitting method, and calculating Mach number and pressure distribution after hood perforation; dynamically updating aerodynamic coefficient data and an aerodynamic parameter database; the speed and pressure distribution data are introduced into the three-degree-of-freedom target flight dynamics model for registration updating, a prediction track is obtained, the prediction precision is improved by 30%-50%, and especially under the condition of a large angle of attack, the prediction error is controlled to be within 5% from 20% or above.

The invention provides a dual-unmanned aerial vehicle cooperation method and a cooperation control system, and relates to the technical field of unmanned aerial vehicles. The method comprises the steps that a flight task issued by a ground control part is acquired from an unmanned aerial vehicle, first flight data and second flight data are acquired, the first flight data comprise main image data, and the second flight data comprise auxiliary image data; the slave unmanned aerial vehicle determines a space vector between the master unmanned aerial vehicle and the slave unmanned aerial vehicle as a baseline; the slave unmanned aerial vehicle constructs an accompanying flight control model based on the obstacle influence data, the baseline length corresponding to the baseline and the observation sight line included angle, generates an accompanying flight control instruction enabling the accompanying flight control model to reach the flight condition, controls flight according to the accompanying flight control instruction, and the obstacle influence data is used for describing the obstacle avoidance risk of the flight route to the obstacle; the ground control unit fuses the main image data and the auxiliary image data to obtain an observation result of the object to be observed. The flight stability of the multiple unmanned aerial vehicles is improved, and the efficiency of surveying and mapping tasks is improved.

The invention provides a multi-source flight data analysis method and device, and the method comprises the steps: carrying out the evaluation based on the historical flight data of each data source, and obtaining the reliability level of each data source; based on the reliability level, fusing the current flight data of the plurality of data sources under the same attribute to obtain fused flight data; and performing data analysis based on the fused flight data to obtain a data analysis result. According to the method provided by the invention, the reliability level of each data source is evaluated based on the historical flight data of each data source, and the current flight data of the plurality of data sources under the same attribute are fused according to the reliability level, so that the problem of uneven data quality is solved from the source, and the accuracy and credibility of the fused flight data are remarkably improved; and analysis is carried out based on the fused flight data, so that the accuracy and reliability of a data analysis result are improved, and powerful technical support is provided for improving the flight safety monitoring capability.

The invention discloses an aircraft flight data recording system health degree evaluation method, device and equipment, a storage medium and a product, firstly, aircraft data recorded by an aircraft flight data recording system is acquired, and the aircraft data comprises an engine rotation speed initial record value; then, recording timeliness is determined according to the engine rotation speed initial recording value, and the engine rotation speed initial recording value and the recording timeliness are in a negative correlation relation; and finally, evaluating the health degree of the aircraft flight data recording system according to the recording timeliness. Therefore, according to the embodiment of the invention, the timeliness of data recording of the aircraft flight data recording system is verified by using the operation characteristics of the aircraft engine, the system health degree is reflected to a certain extent, and the data quality condition is reflected.

The invention relates to the technical field of aviation safety and data processing, in particular to a flight data anomaly detection method based on DEWMA-IForest, and the method comprises the following steps: carrying out the preprocessing of original flight data; initializing a smoothing coefficient and a sliding window size parameter; calculating an arithmetic mean value and a standard deviation by taking data in the sliding window as a statistical sample, and respectively taking the arithmetic mean value and the standard deviation as a DEWMA statistic initial value and a control limit calculation reference value; setting an initial control limit range; calculating the DEWMA statistical magnitude and the standard deviation of the data to be measured; dynamically updating the control limit range; and DEWMA fitting reconstruction data and a control limit are calculated. According to the method, the dynamic control limit is adapted to the time-varying property of the QAR data, so that the sensitivity of small-amplitude trend anomaly is improved; high-dimensional data dimension disasters are avoided through multi-algorithm fusion; and the abnormal criterion can be explained, so that a scientific basis is provided for aircraft operation monitoring and intelligent maintenance decision making.

The invention discloses a power inspection-oriented low-altitude electromagnetic abnormal trend early warning method and system, and the method comprises the steps: synchronously collecting electromagnetic spectrum data and flight state data of an unmanned plane when the unmanned plane executes a low-altitude power inspection task, and generating a space-time aligned electromagnetic-flight data sequence; performing feature extraction and matching on the electromagnetic-flight data sequence based on an electromagnetic interference feature library of a typical power interference source, and constructing a dynamic electromagnetic environment situation map by fusing spatial position information of the unmanned aerial vehicle; performing electromagnetic abnormal risk analysis on the dynamic electromagnetic environment situation map by adopting an analysis model trained by a historical power electromagnetic interference event to obtain a dynamic risk assessment coefficient; according to the dynamic risk assessment coefficient, predicting an electromagnetic anomaly trend index in a future preset time period; and comparing the pre-warning threshold value with the multi-level pre-warning threshold value conforming to the electric power safety regulation, and when the pre-warning threshold value exceeds the multi-level pre-warning threshold value, triggering a corresponding pre-warning level and generating a risk avoidance strategy adaptive to the low-altitude electric power inspection scene.

The invention relates to a vehicle-mounted unmanned aerial vehicle take-off and landing platform in a grassland scene, the vehicle-mounted unmanned aerial vehicle take-off and landing platform is composed of an intelligent box body and an integrated connection platform, the intelligent box body integrates various sensors, monitors environment information inside and outside the box, provides conditional decisions for take-off and landing and charging operation of an unmanned aerial vehicle as a basis, and is provided with a high-speed data interface and a single-drive opening and closing mechanism; flight data of the unmanned aerial vehicle can be downloaded quickly, and energy loss of opening and closing actions is reduced; an integrated connection platform is nested in the box body, meanwhile, reset charging and autonomous feeding functions are integrated, and the endurance and material supply capacity of the unmanned aerial vehicle is improved; the platform can be carried on the upper portion of a movable chassis, near-low-altitude remote sensing image collection of the unmanned aerial vehicle is carried out on the rugged grassland terrain, and good scientific basis and technical support are provided for producers to monitor grassland degradation conditions and carry out ecological protection operation such as grass seed reseeding. And powerful technical support is provided for sustainable development of the grassland and promotion of intellectualization of related industries.

The invention discloses a flight data safety management method applied to low-altitude economy, and relates to the technical field of data management, and the method comprises the steps: obtaining heterogeneous monitoring data of an infrastructure and IT equipment in real time; calculating the health degree score and the fault risk probability of each logic area in the low-altitude economic flight data center in real time; based on the health degree score and the fault risk probability, root cause analysis is carried out in combination with a pre-constructed operation and maintenance knowledge graph, and an optimal operation and maintenance disposal plan is generated through matching; and converting the generated operation and maintenance disposal plan into a specific automatic instruction. The method has the advantages that through deep fusion and conjoint analysis of multi-modal data, accurate and panoramic perception of the health state of the low-altitude economic flight data center and prospective prediction of the fault risk are realized, and through automatic execution and a closed-loop feedback mechanism, the safety of the low-altitude economic flight data center is improved. A traditional operation and maintenance mode which is passive in response and highly depends on manpower is converted into a closed-loop autonomous mode which is active in early warning, intelligent in decision making and autonomous in execution.

The invention belongs to the technical field of unmanned aerial vehicles and intelligent lighting, and particularly relates to an unmanned aerial vehicle intelligent dimming and lighting system self-adaptive to ambient light, which comprises an unmanned aerial vehicle platform, a lighting module, an environment sensing module, a flight state sensing module and an intelligent control and decision module. The intelligent control and decision module fuses environment illumination and unmanned aerial vehicle flight data, and outputs optimal illumination parameters based on a deep reinforcement learning algorithm; and the illumination driving and regulating module accurately controls the brightness and color temperature of the LED light source array of the illumination module and the beam angle of the optical lens group. The environment illumination, the flight state and the task requirement are sensed in real time, and the output brightness, the color temperature and the beam angle of the LED light source array are intelligently adjusted based on the deep reinforcement learning algorithm, so that energy waste caused by fixed-intensity illumination is avoided.

The invention relates to the technical field of unmanned aerial vehicle simulation, and discloses an unmanned aerial vehicle flight state simulation method based on digital twin. The method comprises the steps that a digital twin system comprising a physical unmanned aerial vehicle entity and a virtual simulation model is constructed, and multi-source flight data streams such as position information, motion parameters and environment perception data of the physical unmanned aerial vehicle are collected; and performing multi-dimensional feature fusion processing on the multi-source data to generate a fusion feature representation, and determining a flight state estimation value and related confidence information by adopting a probabilistic state modeling algorithm based on the fusion feature. And updating the virtual simulation model according to the estimated value and the confidence coefficient, so that the virtual simulation model simulates the real-time flight state. Flight state clustering analysis is performed in the virtual model to detect anomalous patterns, and an adaptive simulation threshold set is generated based on historical flight data. And comparing the abnormal mode with the adaptive threshold set, and outputting a simulation control instruction for operation of the unmanned aerial vehicle. The method can effectively support accurate simulation and abnormal control of the flight state of the unmanned aerial vehicle.

The application discloses a large model-based QAR data decoding method and device, equipment and medium, relates to the field of aviation flight data processing, and comprises the following steps: preprocessing initial QAR data to generate processed QAR data, determining a target protocol version and parameter number by analyzing structural features by using a preset QAR analysis large model; generating initial flight data by analyzing the processed QAR data based on the target protocol version by using the preset QAR analysis large model, and determining a target sensor channel type and a target encoding mode; converting the initial flight data into converted flight data according to the target sensor channel type and the target encoding mode by using a target mapping function; generating standardized flight data by standardizing and naming the fields, and obtaining decoded flight data by unifying parameter units and parameter value ranges; and generating a visual flight report by sorting and aligning the decoded flight data according to a preset time sequence and a timestamp. The automatic analysis capability of QAR data is improved.

The invention discloses an aircraft performance monitoring method, device, terminal and system and a storage medium, and the method comprises the steps: obtaining a preprocessed flight data stream from an airborne flight data preprocessing device in the flight process of a target leg, analyzing and processing the flight data meeting the effective leg screening condition in the preprocessed flight data stream to obtain the flight data of the effective leg; based on the flight data of the effective leg, calling an embedded performance factor analysis logic to carry out performance factor analysis so as to obtain a performance factor of the effective leg; and in response to a flight end instruction of the target leg, displaying a performance monitoring result on a display based on the performance factor of each effective leg. According to the invention, while the timeliness of airplane performance monitoring is improved, the interference on airborne system data is avoided, the safety is ensured, and an airplane crew can master the performance condition of an airplane in time.

The invention relates to a video stream and OSD information fused unmanned aerial vehicle image positioning method and system. The method comprises the following steps: accessing a rear-end platform through unmanned aerial vehicle equipment, and pushing a real-time video stream and fixed-frequency OSD information to the platform; executing a target detection algorithm based on the video stream, immediately intercepting a key frame image when an event is detected, and recording an image interception timestamp; searching two closest OSD points according to the current image interception time, and recording information such as longitude and latitude, speed and course of the OSD; calculating the distance between the front OSD point and the rear OSD point by adopting a Haversine formula; calculating the distance from a screenshot point to a previous adjacent OSD point by using positioning interpolation, and calculating current screenshot position information according to course angle projection; and marking and storing the position of the intercepted image to finish accurate positioning of the image position. According to the invention, an unmanned aerial vehicle hardware system does not need to be modified, automatic and accurate image positioning is realized only by depending on existing video streams and flight data, and the problem of position missing or insufficient precision caused by asynchronization of video frames and position data streams is overcome.

The invention discloses a multi-source data fusion processing method for air ticket scheme generation and comparison, which relates to the technical field of computers, and comprises the following steps: collecting flight information from at least two flight data sources in real time, recording the collection process item by item according to a price fluctuation range and a seat state updating condition, and calculating a data collection result; multi-source data acquisition errors are reduced through an acquisition precision control mechanism, fusion of multi-source flight data is completed in a preset time window, and an initial flight data set is obtained; according to the multi-source data fusion processing method for air ticket scheme generation and comparison, the accuracy and reference of an air ticket scheme comparison result are improved, and then the user decision-making experience and the overall credibility and practical value of an air ticket information service system are improved.

The present disclosure provides techniques for deep reinforcement learning to achieve full-cycle automation in airplane component failure prognostics. Flight data is preprocessed to identify parameters representing operational characteristics of an airplane component. A reinforcement learning framework is formulated based on the preprocessed flight, comprising defining a state representation as an input to a policy model, determining an action of sending an alert or not based on the state representation, modeling one or more system behaviors in response to the action using the preprocessed flight data, calculating a reward of the action under the state representation using a predefined reward structure, collecting training data by simulating an airplane component prognostic procedure. The policy model is trained using a learning and optimization algorithm with the training data to increase an expected discounted cumulative reward by choosing an action under the state representation.

The invention discloses an unmanned aerial vehicle attitude control method for complex scene cruise, and belongs to the technical field of unmanned aerial vehicle attitude control. The invention aims to solve the problems that an existing unmanned aerial vehicle attitude control method is insufficient in robustness under complex environment disturbance, unreasonable in safety redundancy distribution and lack of foresight and adaptive ability. Therefore, the invention provides an unmanned aerial vehicle attitude control method, and the method comprises the steps: dynamically up-regulating or down-regulating the long-term disturbance baseline and risk level of each grid and the neighborhood thereof through the consistency deviation between the attitude error standard deviation in a certain time window and the expected error of a corresponding gear, enabling the disturbance atlas and risk distribution to be continuously self-corrected along with the flight data, and achieving the unmanned aerial vehicle attitude control. Therefore, a real environment can still be accurately reflected after long-term operation, risk identification misjudgment and control strategy mismatching caused by aging or distortion of an environment model are reduced, and attitude control robustness and overall task completion efficiency of the unmanned aerial vehicle during cruising in a complex scene are improved.

The invention relates to the field of golf, in particular to a portable multi-eye golf real-time detection and measurement method and equipment and electronic equipment. The method comprises the following steps: carrying out coupling calibration on a laser line direction and a camera coordinate system, and establishing a reference direction of measurement; acquiring a ball position based on the reference direction, judging whether the ball position is within a preset ball hitting range or not, and if yes, adjusting each camera and each auxiliary lighting device to be in a preset first working mode according to the ball position and a preset ball hitting mode; the ball hitting process is monitored, state information at different moments is screened according to a preset comparison rule, multi-view image data associated with state change moments are stored, and the state information comprises club state information, ball state information and environment state information; according to the multi-view image data and a preset image processing algorithm, calculating a three-dimensional space position including a sphere, a ball rod and an environment; and according to the three-dimensional space position and a simulation algorithm, acquiring flight data of the sphere and outputting the flight data.