1063 results about "Flight data" patented technology

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 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 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 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 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.

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 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.

The invention relates to the technical field of unmanned aerial vehicle control, and discloses a wind disturbance compensation control method for a quad-rotor unmanned aerial vehicle, which comprises the following steps: tracking flight paths of the quad-rotor unmanned aerial vehicle under different wind conditions through a model prediction controller to obtain flight state data under different wind conditions; according to the flight state data and the kinetic equation under different wind conditions, a kinetic model of the four-rotor unmanned aerial vehicle is constructed, and a flight data set is obtained based on the kinetic model; determining the attitude correction of the unmanned aerial vehicle based on the optimized deep network learning model; according to the attitude correction amount of the unmanned aerial vehicle and the flight state data under different wind conditions, the motor basic thrust of the four-rotor unmanned aerial vehicle is obtained; and according to the basic thrust of the motor and the wind disturbance compensation force output by the optimized deep network learning model, obtaining the target thrust of the motor after wind disturbance compensation. The invention further discloses a wind disturbance compensation control device and system for the quad-rotor unmanned aerial vehicle.

The invention relates to an artificial intelligence unmanned aerial vehicle ground station control method, system and device and a medium, and belongs to the technical field of unmanned aerial vehicle control. Performing space-time alignment on the unmanned aerial vehicle sensor data, and performing normalization processing on a heterogeneous data format to generate a standardized space-time data packet; constructing a dynamic obstacle map based on the historical flight database and the standardized spatio-temporal data packet; inputting the standardized spatio-temporal data packet into a machine learning model, predicting the flight path deviation and the equipment fault risk of the unmanned aerial vehicle, and obtaining a prediction result set; based on a preset task target of the unmanned aerial vehicle, generating a corresponding control instruction set according to the dynamic obstacle map and the prediction result set; and converting the control instruction set into an unmanned aerial vehicle flight action sequence, and sending the unmanned aerial vehicle flight action sequence to the unmanned aerial vehicle for flight state adjustment. According to the invention, the safety and adaptability of the unmanned aerial vehicle system are improved.

The invention discloses a departure peak period-oriented flight driving and taking-off collaborative optimization method, which comprises the following steps of: firstly, accurately identifying an optimal release control rate based on historical flight data, secondly, improving the sliding time prediction accuracy by utilizing integrated learning, and then, establishing a departure flight collaborative scheduling model according to an actual operation rule so as to realize the departure flight collaborative scheduling. And establishing a conflict mechanism to set flight priorities. A solving algorithm adopts a hybrid algorithm combining a genetic algorithm and tabu search, effective combination of global search and local search is ensured, and falling into a local optimal trap is avoided. The scheduling scheme covers key indexes such as runway throughput and average taxiing time, the airport scene congestion condition is relieved, the operation efficiency and safety of an airport in the departure peak period are improved, flight delay is reduced, and the utilization rate of runway time slot resources is increased.

The invention discloses a pilot EBT training target competency identification method and system based on eye movement / flight data fusion driving, and the method comprises the steps: S0, obtaining eye movement data of a pilot in an EBT training process, constructing a GDETC network to achieve the spatial-temporal trajectory clustering of the eye movement data, and extracting the staring spatial-temporal trajectory of the pilot; s1, extracting competency assessment scores and error time-space point data from EBT competency assessment worksheets and flight data; and S2, constructing a VAE-STRF network, fusing the data extracted in the step S0 and the step S1, extracting a pilot key competency evaluation score matrix, establishing a pilot key competency evaluation model, and outputting the target competency of EBT training. According to the method, the target competency of the pilot needing to continue to be improved subsequently is recognized through the eye movement data in the EBT training process of the pilot, an existing manual evaluation system can be replaced, and effective decision support is provided for improving the training pertinence of the pilot.

The invention discloses an expressway site selection result evaluation method considering the influence of a low-altitude aircraft, and relates to the field of site selection result evaluation, and the method comprises the steps: building a hierarchical structure evaluation model after generating an expressway site selection evaluation index system based on the flight influence factors of the low-altitude aircraft, obtaining a highway site selection result according to the hierarchical structure evaluation model; constructing a three-dimensional airspace reachability model by using historical flight data of the low-altitude aircraft, predicting a flight route of the low-altitude aircraft, and judging the service satisfaction degree of the highway site selection result to the low-altitude aircraft in combination with service requirements; and designing an emergency scene based on the service satisfaction degree, judging the strain capacity of an expressway site selection result, evaluating the reasonability of the expressway site selection result according to the strain capacity, and determining a final expressway site selection result by utilizing a reasonability evaluation result. According to the invention, the service of the aircraft in the site selection area of the expressway can be fully guaranteed.

The invention discloses a method and system for generating an EBT scene of a pilot based on a text and flight data, and the method comprises the steps: extracting threat error items in the operation of the pilot from a multi-source text through employing a scene perception word frequency-inverse document frequency method, and generating text analysis; a method of combining time feature attention, convolutional self-encoding and a long short-term memory network is utilized to comprehensively analyze flight data of daily routes and simulator training, dynamic features and potential risk factors in pilot operation are extracted, and flight data analysis is generated; in combination with text data analysis, flight data analysis and a scene element library, training scene elements are generated by adopting a hierarchical rule structure condition generative adversarial network method, and a teacher performs screening and combination to generate a personalized training scene. Through multi-source data fusion and data-driven analysis, a pilot personalized training scene is generated, the pertinence and effectiveness of training are improved, the use of training resources is optimized, and the overall flight safety and training quality are improved.

The invention relates to a coaxial multi-rotor unmanned aerial vehicle control method based on a Koopman operator, and the method comprises the steps: constructing a deep neural network based on the Koopman operator, and mapping a state variable to a high-dimensional observable space through an encoder; in a high-dimensional observable space, state variables are predicted through a linear connection layer based on dimension rising state variables and rotor rotating speeds; the decoder restores the prediction result to an original state space; training a deep neural network by using historical flight data to obtain codec parameters and a weight of a linear connection layer, and constructing a linear dynamic model of the observation function and the unmanned aerial vehicle; and the prediction controller predicts state variables of future multiple steps of the unmanned aerial vehicle by using a linear dynamics model, and enables the unmanned aerial vehicle to track a given trajectory by minimizing a cost function to obtain optimal control input. Compared with the prior art, the method has the advantages that complete data-driven modeling is realized, and the method can adapt to unmanned aerial vehicles with different sizes, loads and flight conditions.

The invention provides an A320 model original QAR data frame structure compiling engine and method, and belongs to the field of civil aviation. The engine adopts a multi-node transverse deployment and distributed computing architecture, and comprises four modules: an original file reader, an original data standardizing device, a data converter and an engineering value data writing device, the original file reader is used for reading an original RawData file sent by external aviation software; the original data standardizing device is used for converting an original RawData file into a new standardized RawData file; the data converter decodes the new standardized RawData file according to a decoding rule to obtain plaintext engineering value data; and the engineering value data writer writes the plaintext engineering value data into different storage media. The method is suitable for large-scale, multi-source and heterogeneous flight data processing scenes, and the data processing efficiency and the system performance can be remarkably improved.

The invention discloses a fan blade transportation and installation system and method and a flight device, and belongs to the technical field of intelligent transportation of fan blades. The system comprises a flight transportation module, an intelligent navigation control module, an installation auxiliary module and a scheduling module; the flight transportation module is used for suspending and loading the fan blades, and suspending, flying and transporting the fan blades according to a preset flight route; the intelligent navigation control module is used for monitoring a flight path, collecting flight data and environment data in a flight process, predicting potential obstacles according to the flight data and the environment data, and optimizing a flight strategy; the mounting auxiliary module is used for aligning and fastening the blades and the fan hub; the scheduling module is used for determining a flight plan according to the meteorological data, the performance of the flight transportation module and the priority of the transportation installation task. By combining a plurality of transportation, installation and dispatching modules, efficient and safe transportation and installation of the fan blades are achieved, and the transportation and installation difficulty and cost are reduced.

The invention provides a multi-modal pulse fusion network prediction framework for aero-engine gas path performance parameters, and belongs to the technical field of aero-engine gas path performance parameter prediction and fault diagnosis. The method comprises the following steps: firstly, acquiring flight data of an aero-engine, and preprocessing data of each sensor; secondly, constructing a multi-modal pulse fusion neural network model comprising a time sequence data pulse processing module, an image data pulse processing module, a multi-modal feature fusion module and a prediction output module; and finally, training the multi-modal pulse fusion neural network model, and predicting gas path performance parameters by using the trained model. According to the method, the leakage integration distribution spiking neuron and the attention mechanism are introduced, the depth feature extraction and fusion of the time sequence data set and the image data set are realized, the complex nonlinear correlation between the data is effectively captured, the accuracy of the prediction result is remarkably improved, and the real-time prediction requirement in the operation process of the aero-engine can be met.

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 path planning method and system for navigation guidance of a low-altitude aircraft, and the method comprises the steps: calculating a probability density value of pre-processed prior flight data based on kernel density estimation, mapping the normalized probability density value to a grid map of a rasterized environment model, generating a flight path safety experience distribution map, and carrying out the navigation guidance of the low-altitude aircraft. And expanding the boundary of the obstacle area based on the safety distance constraint matrix, generating a restricted area matrix, generating an initial path according to the bidirectional A *-KDE algorithm cost function, the flight line safety experience distribution map and the restricted area matrix, removing redundant nodes from the initial path, and carrying out smoothing processing to obtain a flight route. The flight route is obtained by using the prior flight data, the deviation between the flight route and the actual navigation path can be reduced, the obstacle area boundary is expanded based on the safety distance constraint matrix, the obstacle safety distance is fully considered, and the risk that the low-altitude aircraft collides with the obstacle can be reduced.

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 belongs to the technical field of aircraft design, and particularly relates to a static pressure source position error correction method, which comprises the following steps: performing theoretical appearance static pressure source position error correction analysis based on current numerical simulation and flight test state points to obtain a static pressure source pressure coefficient Cp, a flight test static pressure source pressure coefficient Cp0 and an actually measured static pressure source pressure coefficient Cp1; static pressure source position error correction data optimization is carried out, a static pressure source position error correction relation Cp'during real flight of the theoretical appearance is obtained, and static pressure source position error correction is carried out; appearance measurement, CFD simulation and test flight data comprehensive application are realized; correcting the position error of the static pressure source into a function of Mach number, angle of attack and height; and not only can the requirement of the CCAR25 part for the air pressure height be met, but also the requirement of RVSM operation in AC-21-13 for the error of a height measurement system can be met. The method can be used for atmospheric data correction of transportation aircrafts, and atmospheric data correction of other aircrafts can be used for reference.