41599 results about "Remote sensing" patented technology

The invention provides an ecological meteorology and satellite remote sensing combined dynamic environment monitoring method and system. Wherein spatio-temporal dynamic concentration data and spectral reflection characteristic data are obtained at a pollutant emission node; generating an associated data block by the spatio-temporal dynamic concentration data and the spectral reflection characteristic data according to a pollution concentration abrupt change event trigger time sequence, and performing chain storage on Hash fingerprints and pollution source geographic coordinate information through a distributed node consensus mechanism; integrating the ecological meteorological observation data to generate a pollutant migration path map; and establishing a pollution influence boundary judgment model according to the pollutant migration path map and the vegetation stress response characteristic data, and monitoring the diffusion range and influence boundary of pollutants in real time through the model to generate an environment monitoring report. According to the technical scheme provided by the invention, the industrial environment pollution dynamic monitoring precision and the decision response efficiency are remarkably improved.

The invention relates to the technical field of red tide anomaly detection, in particular to an ocean red tide anomaly detection method and system fusing multi-source remote sensing and a graph neural network. The method comprises the following steps: acquiring remote sensing image data, unmanned aerial vehicle image data and monitoring data of a monitoring point; performing data preprocessing on the acquired remote sensing image data and unmanned aerial vehicle image data; performing feature extraction and feature fusion on the remote sensing image and the unmanned aerial vehicle image to obtain remote sensing feature data; constructing a space-time diagram structure based on the monitoring data of the monitoring points to obtain diagram structure data; based on a cross-modal comparison self-supervised learning mechanism, carrying out consistency representation learning on a remote sensing feature mode and a graph structure feature mode; by introducing multi-source heterogeneous data and fusing a graph neural network modeling means, the limitation of a single data driving method in the aspects of coarse red tide recognition granularity, low space-time precision and the like is effectively broken through, and the meticulous property and global perception ability of red tide feature modeling are remarkably improved.

The invention relates to the technical field of remote sensing monitoring, in particular to an intelligent forest disease and pest monitoring method and system based on unmanned aerial vehicle remote sensing, and the method comprises the following steps: collecting multispectral data by an unmanned aerial vehicle, extracting reflectivity and smoothing the reflectivity, carrying out differential recognition on abnormal pixels, extracting curves and screening significant changes, segmenting scab boundaries, and classifying health states. And generating a pest and disease map layer prediction trend. According to the method, the reflectivity time sequence is constructed, differential processing is carried out, the vegetation change trend is dynamically captured, abnormal areas are identified by combining slope offset and persistence analysis, significant pixels are screened according to main peak wavelength offset, boundary information is extracted, the scab positioning precision is improved, and the recognition resolution of the lesion state is enhanced through reflectivity combined analysis; accurate description of disease spot dynamic changes is realized, static image dependence limitation is broken through, monitoring time continuity and space response capability are enhanced, and disease and insect pest change capture efficiency and state classification accuracy are effectively improved.

The invention relates to a geological disaster automatic identification method and system based on multi-source remote sensing data, and belongs to the technical field of geological disaster monitoring, and the method comprises the steps: collecting the multi-source remote sensing data of a to-be-detected region, carrying out the cross-modal registration, and generating a registered multi-source data set; carrying out multi-modal feature extraction based on the multi-source data set and carrying out space-time correlation analysis to obtain a multi-modal feature map; performing dynamic weight distribution on the multi-modal feature map, generating a fusion feature vector, inputting the fusion feature vector into a pre-trained geological disaster prediction model, and outputting a geological disaster probability map; performing binarization segmentation on the geological disaster probability graph to obtain a potential disaster area mask; and according to the geological disaster probability map and the potential disaster area mask, based on a preset joint determination rule of the surface deformation rate and the gradient characteristics, carrying out risk grade division on the potential disaster area to obtain a risk grade distribution map of the to-be-detected area. The disaster prediction precision can be improved, and the emergency response capability is enhanced.

The invention provides a directional energy multi-target interference system based on an ultra-wideband phased array technology, and the system integrates signal perception, target recognition, interference signal generation and phased array control, so as to achieve the precise interference of an unmanned aerial vehicle cluster. The signal sensing module scans an airspace target through the ultra-wideband phased-array antenna array and obtains spectrum characteristics of a target signal. The target identification module analyzes signal features, identifies an individual target and predicts a motion track and a communication protocol of the individual target. And the phased array control module calculates and adjusts antenna beam forming parameters, and dynamically controls the radiation direction, phase and transmitting power of the antenna. The interference signal generation module generates a matched interference waveform based on the target communication protocol and the trajectory information. The ultra-wideband phased-array antenna array adopts a digital beam forming technology. According to the invention, efficient and accurate directional energy interference can be realized under multiple frequency bands, the confrontation capability of the unmanned aerial vehicle cluster is improved, and the method is suitable for defense tasks in electronic warfare and complex electromagnetic environments.

The invention provides a water and soil loss dynamic monitoring method and system based on a remote sensing image, and the method comprises the steps: firstly obtaining a multi-temporal remote sensing image data set of a target region, which comprises a plurality of time period remote sensing image subsets and land surface coverage information, and then carrying out the land surface feature extraction of the multi-temporal remote sensing image data set, after vegetation coverage, terrain gradient and soil exposure features are obtained, a water and soil loss prediction model based on time-space correlation is constructed, the features are input for prediction, a water and soil loss grade distribution map is generated, a target loss risk area is identified according to the water and soil loss grade distribution map, and a treatment priority sequence and a vegetation recovery strategy are generated. And finally, the information is fed back to a monitoring platform to trigger regional governance task allocation operation, so that dynamic monitoring of water and soil loss is realized, and governance tasks are effectively planned.

The invention relates to the field of unmanned ship path planning, in particular to an autonomous water quality monitoring unmanned ship navigation control method and system based on multimode communication. The method comprises the following steps: collecting regional water quality monitoring parameters based on a shipborne water quality monitoring sensor, carrying out space pollution distribution excavation, and constructing a regional pollution concentration distribution diagram; obtaining a regional satellite remote sensing map, carrying out water flow distribution dynamic evolution, and constructing a water flow path distribution network; carrying out pollutant migration logic evolution and multi-region gradient diffusion prediction on the water flow path distribution network according to the regional pollution concentration distribution map, and constructing a water quality pollution diffusion prediction situation map; and according to the water quality pollution diffusion prediction situation map, carrying out highest diffusion progressive gradient identification, carrying out optimal navigation monitoring sequence analysis, and constructing an optimal navigation monitoring sequence. According to the invention, through dynamic and real-time unmanned ship water quality cruise path adjustment, the accuracy and efficiency of water quality monitoring are improved.

The invention relates to the technical field of remote sensing monitoring, in particular to a remote sensing image ocean green tide monitoring method and system fusing physical prior and spatio-temporal evolution. The method comprises the following steps: acquiring a multi-modal remote sensing monitoring image; performing multi-modal feature extraction on the acquired image, wherein the multi-modal feature extraction comprises spectral reflectivity feature extraction, ocean dynamics feature extraction and feature alignment and unified representation; establishing a physical prior of a green tide characteristic wave band by using an ocean optical radiation transmission model; constructing a dynamic space-time diagram based on the extracted multi-modal features to obtain a node global feature vector and a dynamic adjacency matrix; carrying out adaptive graph convolution feature coding based on physical prior and a dynamic space-time diagram; through fusion of multi-spectral images of multiple platforms such as satellites and unmanned aerial vehicles and ocean dynamic data and combination of atmospheric correction and wave band resampling, consistency processing and high-precision extraction of multi-source features are realized, and comprehensiveness and reliability of green tide feature recognition are remarkably improved.

The invention provides a deep learning-based avalanche early warning model construction method and system, and the method comprises the steps: firstly obtaining multi-source environment monitoring data, including meteorological time sequence, topographic space and accumulated snow layer physical data, of a target region, carrying out the time dimension alignment of the meteorological time sequence data to generate a feature sequence, carrying out the meshing of the topographic space data to generate a feature set, and carrying out the construction of an avalanche early warning model; the method comprises the following steps: extracting parameters from accumulated snow layer physical data to generate a state vector, inputting a deep learning network model containing time sequence attention, spatial convolution and cross-modal interaction units, generating a fusion feature vector, constructing a training set based on historical avalanche event annotation data, performing dynamic weight optimization on the fusion feature vector, and generating an avalanche risk prediction model. And finally, receiving current monitoring data in real time, outputting a risk level and an early warning trigger threshold value by the avalanche risk prediction model, and generating a multi-level early warning signal when a real-time risk value exceeds the threshold value, thereby realizing accurate avalanche early warning.

The invention relates to the technical field of channel safety management, and more specifically relates to a channel multi-source data fusion digital twinborn risk early warning system comprising a multi-source data acquisition module used for acquiring channel specific data types, comprising AIS data, radar data, video monitoring data, hydro meteorological data, channel surveying and mapping data, ship report data, shore-based sensor data and unmanned aerial vehicle patrol data, and comprehensive data support is provided for channel management through space-time alignment, dynamic weight adjustment and conflict resolution; a high-fidelity digital twin is constructed, fluid dynamics and a ship behavior model are fused, and a real channel scene is restored; intelligent risk early warning and traceability are realized by using deep learning, and decision scientificity is improved; efficient virtual-real interaction is realized through multi-terminal pushing and sand table deduction, and a management closed loop is formed; a cloud-edge-end framework and the like are adopted to optimize system performance, and real-time performance and safety in a large-scale scene are guaranteed.

The invention provides a high-speed rail part crack real-time detection method, and belongs to the technical field of image detection based on computer vision. Obtaining a hyperspectral image, a visible light image, three-dimensional point cloud data and eddy current signal data of the surface of the high-speed rail part; performing spatial registration on the high-spectral image and the visible light image on the surface of the high-speed rail part, and performing time registration on the three-dimensional point cloud data based on the eddy current signal data; feature enhancement and standardization processing are carried out; performing feature extraction and fusion on the obtained standardized multi-modal data based on a multi-modal feature fusion network to obtain a fusion feature map representing crack details of the high-speed rail parts; based on a self-adaptive crack segmentation method, a crack contour is extracted from the point cloud, and then whether the part has a crack or not and the length and depth of the crack are calculated. According to the invention, three kinds of modal data are creatively integrated, the information dimension limitation of single-modal detection is broken through, and all-weather and non-contact intelligent efficient diagnosis of submillimeter cracks is realized under complex working conditions.

The invention, which relates to the technical field of image processing, discloses a city component automatic three-dimensional reconstruction method based on a sub-meter high-resolution remote sensing image, comprising the following steps: acquiring a sub-meter high-resolution remote sensing image of a target area, and establishing an initial ground-image projection mapping relation based on a rational polynomial coefficient model; acquiring a ground control point, correcting the mapping relation, and outputting a registration image; performing multi-view geometric matching on the registered image to generate dense earth surface point cloud, and inputting the dense earth surface point cloud into a semantic neural radiation field network to generate dense point cloud; performing cross-modal feature alignment and fusion on the dense point cloud and the vector data, and outputting a fused multi-source dense point cloud; according to the method, through fine correction of the multi-source remote sensing image, the problems of large registration error, more point cloud sparse noise and semantic deficiency are solved, and high-precision and automatic three-dimensional reconstruction of urban components is realized.

The invention discloses an LCD display screen display fault judgment method, and relates to the related technical field of LCD display screen detection. Comprising the steps of synchronous acquisition and preprocessing of multi-source signals, feature extraction, establishment of a fault judgment model, generation of a dynamic test mode, dynamic generation of a targeted test image according to a preliminary detection result to excite a potential fault, iterative optimization of a test sequence by a Q-learning algorithm, and fault diagnosis. The invention also discloses a system for judging the display fault of the LCD screen. The system comprises a multi-source data acquisition module, a central processing unit, a memory and a user interface. Various data including display data, driving voltage / current monitoring data, temperature distribution, environment parameters and the like are synchronously collected through the optical sensor, the electric signal probe, the thermal imaging module and the environment sensor, all-directional information of the LCD display screen in the operation process can be obtained, and limitation and misjudgment possibly caused by a single data source are avoided.

The invention discloses a solar radiation space-time prediction method and system based on physical information constraint and a neural network, and the method comprises the steps: collecting multi-dimensional time sequence meteorological data, extracting high-dimensional time sequence dynamic features, converting geographic space data into a fuzzy set, and carrying out the defuzzification of the fuzzy set through an inference rule, thereby obtaining geographic space features; and a gating mechanism is adopted to realize deep fusion of the space-time features to generate high-dimensional space-time fusion features. In a model training stage, an energy conservation equation is introduced into an optimization process, a physical residual error is constructed by calculating a time derivative and a space derivative of a predicted value, a physical constraint total loss function is formed in combination with a data loss item, and model parameters are updated by using a gradient descent method. According to the method, the accuracy and reliability of a prediction result are remarkably improved while the calculation efficiency is ensured, and the method is particularly suitable for solar radiation prediction under complex meteorological conditions; according to the method, abnormal prediction caused by data noise can be effectively corrected, and a solution with physical rationality and data adaptability is provided for the fields of solar resource evaluation, photovoltaic power generation power prediction and the like.

A system and method for temporal acceleration encoding in Lorentzian latent space enables real-time event forecasting within navigable spatiotemporal media. The system encodes media data into compact Lorentzian latent patches using variational autoencoders and organizes them within a multi-dimensional hyperspace spanning spatial, temporal, orientation, scale, and spectral coordinates. Temporal acceleration encoding computes velocity and acceleration vectors along geodesic trajectories, extracting event signatures through multi-scale aggregation over sliding windows. An acceleration-indexed memory stores dynamic descriptors with composite keys comprising hyperspace coordinates and motion characteristics. Event forecasting retrieves similar historical patterns and conditions a forecast head to produce event probabilities and time-to-event estimates with uncertainty calibration. The system streams forecast metadata to edge devices for real-time prediction and adaptive navigation, supporting applications in surveillance, autonomous systems, predictive media exploration, and anomaly detection where both temporal forecasting and multidimensional navigation capabilities are essential.

The invention discloses a multi-target detection and tracking method, and relates to the technical field of computer vision and intelligent monitoring. The method comprises the following steps: acquiring multi-source video data of an unmanned aerial vehicle and a middle-high point fixed camera, and after scene adaptation preprocessing, outputting a target detection frame by using a multi-scale detection model fused with scene context; block enhanced appearance features and geometrical relationship features of the target are extracted to construct a dynamic feature library, and an initial track is generated based on a multi-stage adaptive association mechanism; through a child-mother type multi-machine collaborative optimization track, linkage control is triggered in combination with abnormal behavior analysis, and close-range evidence obtaining of the unmanned aerial vehicle and linkage of fixed equipment recording are controlled. According to the method, the multi-source data fusion capability, the multi-scale target detection precision and the trajectory association robustness in a complex scene are improved, and intelligent management and control requirements in the fields of traffic, forestry and the like can be efficiently supported.

The invention discloses a power distribution network distributed measurement synchronization method and device based on Beidou satellite time service, and belongs to the technical field of power distribution network measurement, and the method comprises the steps: obtaining a Beidou satellite signal, calibrating a local clock source, and generating a standard time reference; when it is continuously monitored that the Beidou satellite signal quality parameter is lower than a quality threshold value, clock working environment parameters of a local clock source are collected, and a short-term clock offset predicted value is generated in combination with a standard time reference; generating topology correction according to the topology connection relation of the power distribution network; and fusing the short-term clock offset predicted value and the topology correction quantity to generate a dynamic compensation time reference, performing time marking on the acquired electrical quantity measurement data of the power distribution network, and generating synchronous measurement data with a timestamp. According to the technical scheme, when the Beidou signal is lost, the local clock drift predicted value and the adjacent node topology correction amount are fused to generate the dynamic compensation time reference, so that the measurement data synchronization precision and robustness of the power distribution network in a complex environment can be remarkably improved.

The invention discloses a multi-modal semantic and physical law driven remote sensing image generation method, belongs to the technical field of computer vision and remote sensing image generation, and aims to solve the problems of insufficient cross-modal semantic alignment, low reliability of a generation result and insufficient physical mechanism fusion. The four-stage method comprises the following steps: firstly, rejecting low-quality samples from original data and unifying a spatial scale; then, extracting a multi-modal semantic vector by adopting a BLIP model and a CLIP model, and introducing a remote sensing physical rule to carry out vector optimization; then position coding and physical constraint conditions are embedded in the submerged space, and multi-source information joint modeling is achieved through a cross-modal encoder; and finally, by taking text description, physical priori knowledge and diffusion time steps as joint conditions, performing de-noising reasoning based on a Transform architecture, and completing back diffusion reconstruction by means of a trans-attention mechanism. According to the method, physical rationality and semantic consistency are improved, and a more reliable technical normal form is provided for remote sensing image generation in the fields of disaster monitoring, military simulation and the like.

The invention relates to the technical field of material physicochemical property monitoring of a multi-source sensing network, in particular to a multi-source sensing storage environment collaborative monitoring and early warning method, which comprises the following steps of: dividing grid units according to material storage types, binding sensor node coordinates, constructing a grid region reference model, and constructing a grid region reference model; integrating the volatilization characteristic parameters analyzed by the laboratory, historical monitoring data and an environment threshold value to generate a substance and environment relation matrix; the method comprises the following steps of: calling model parameters to invert a theoretical gas concentration value, and triggering finite element physical field simulation through residual analysis to generate a three-dimensional simulation field data set containing a temperature gradient and a diffusion path. And the dynamic correlation analysis precision and the early warning reliability of storage environment monitoring are improved.

The invention relates to the technical field of geological disaster monitoring and early warning, in particular to a side slope slippage monitoring and early warning method based on an image recognition technology, which comprises the following steps: arranging a monitoring target and a reference target in a to-be-monitored area of a side slope, arranging two image displacement monitoring devices at opposite stable positions at equal height to acquire two-dimensional displacement data of the targets; after error correction, an equipment monitoring coordinate system included angle is calculated based on parameters such as equipment spacing, slope inclination displacement is calculated through vector synthesis, and three-dimensional slippage deformation is calculated in combination with vertical displacement; meanwhile, a multi-level early warning mechanism based on the slip rate and the accumulated slip amount is established, a Delaunay triangulation network is constructed, and an improved adaptive Kriging interpolation algorithm is adopted to realize deformation trend prediction. According to the method, binocular vision and multi-algorithm fusion are utilized to realize high-precision three-dimensional monitoring, the evaluation accuracy is improved through the dynamic weighting model and intelligent early warning, the method has the advantages of high monitoring precision, timely early warning, high adaptability and the like, and the safety of slope engineering can be effectively guaranteed.

The invention provides a multi-source heterogeneous data fusion side slope digital twin modeling method, which comprises the following steps of: acquiring side slope multi-dimensional monitoring data by arranging a GNSS (Global Navigation Satellite System) sensor, a multi-point displacement meter, a distributed optical fiber strain sensor, an accelerometer, an osmometer, a monocular camera and satellite remote sensing image equipment; the collected data is converted into a unified format through time alignment, space registration and standardization processing and serves as modeling input; the method comprises the following steps: constructing an initial digital twinborn model reflecting the real form and physical characteristics of a slope by utilizing a three-dimensional modeling and finite element simulation technology; in combination with real-time sensing data, model evolution is dynamically driven based on a space-time fusion algorithm, boundary conditions and material parameters are automatically corrected through actual measurement deviation feedback, and continuous twin iteration updating of the model is achieved; and finally, extracting a landslide risk index to realize real-time early warning of the side slope. According to the invention, multi-source sensing and digital twinborn fusion is realized, and the accuracy, real-time performance and intelligent level of slope monitoring are improved.

The invention relates to the technical field of ship communication, in particular to a multi-link converged communication method and system for a ship. The method comprises the following steps: receiving navigation information broadcasted by other ships in a preset range, evaluating the availability of a communication frequency band, and generating sea area communication channel state data; acquiring geographic position data of each ship; space communication link potential analysis is carried out according to the geographic position data of each ship and the sea area communication channel state data, and cross-chain space communication potential data is generated; constructing a multi-link ad hoc network topological structure according to the cross-chain space communication potential data; and task adaptation communication link allocation is carried out based on a multi-link ad hoc network topological structure, and a multi-link fusion transmission strategy is constructed, so that real-time communication link safe switching management is realized. According to the invention, on the basis of communication service adaptation and a multi-link fusion transmission strategy, real-time communication safe switching and high-reliability transmission of the marine formation are realized.

The invention provides a multi-target tracking and interference cooperation method and system based on a millimeter wave radar. The method comprises the following steps: firstly, acquiring an original detection signal in real time by using a millimeter-wave radar carried by an unmanned aerial vehicle, establishing a track correlation sequence of each moving target, extracting interference waveform characteristics formed by signal coupling among a plurality of moving targets in the original detection signal, and then, according to a spatial distribution parameter of the interference waveform characteristics, determining a track correlation sequence of each moving target; correcting the space-time continuity judgment rule of the trajectory correlation sequence, and finally outputting an anti-interference cooperative tracking result based on the corrected space-time continuity judgment rule. According to the technical scheme provided by the invention, the industry pain point that waveform distortion interference of the millimeter wave radar in a dense target scene cannot be eradicated is solved, and the error correlation rate in a dense formation scene is also reduced.

The invention relates to the technical field of reservoir earth and rockfill dam leakage abnormity safety monitoring and early warning, in particular to an earth and rockfill dam leakage abnormity real-time monitoring and early warning system based on deep learning and a medium. The system comprises a data sensing transmission module, a data fusion processing and analysis module, an early warning evaluation module, a system management and maintenance module, a database management module and an emergency response command module. Through a well-ground collaborative full-dimensional electrical method and shallow earth surface and full-section distributed optical fiber sensing, the system collects and transmits multi-source data. And multi-mode fusion and a deep learning algorithm are adopted to realize multi-physical field feature extraction and three-dimensional modeling. The system generates graded early warning information based on dynamic threshold and multi-factor coupling, and realizes automatic real-time monitoring, intelligent early warning and efficient management of leakage abnormity of the earth and rockfill dam in combination with a database, management maintenance and emergency response functions. According to the invention, the accuracy of earth and rockfill dam leakage abnormity identification and the intelligent level of early warning are improved.

The invention discloses a cross-modal image fusion hyperspectral point cloud waste plastic bottle intelligent sorting method, and relates to the technical field of neural network-based data processing, and the method comprises the steps: obtaining hyperspectral image data and point cloud data of a target object through a multi-modal collection system; preprocessing the collected hyperspectral image data and point cloud data, respectively extracting features and constructing a hyperspectral image and a point cloud image; constructing an adjacent matrix through nodes and edges by taking the constructed hyperspectral image and the constructed point cloud image as a reference, and performing normalization; carrying out single-mode feature extraction on the normalized adjacent matrix; carrying out cross-modal fusion on the extracted single-modal features; performing fine-grained modeling on a cross-modal fusion result through a multi-head attention mechanism to generate a final fusion feature; and mapping the final fusion feature to an output space of a regression task, and training network parameters to obtain a cross-modal fusion model to realize identification of a target object.

The invention discloses an optical remote sensing image salient target detection method based on progressive attention enhancement, and belongs to the technical field of computer vision. The method comprises the following steps: preprocessing an original data set; inputting the preprocessed image into a hierarchical progressive fusion encoder, capturing a global irregular topological structure and local fine-grained image details, and realizing cross-hierarchical feature fusion; inputting the output characteristics of the encoder into a global context enhancement module, and capturing multi-level context information by adopting a parallel multi-branch structure; and inputting the output features of the hierarchical progressive fusion encoder and the global context enhancement module into a multi-scale progressive attention enhancement decoder, carrying out hierarchical decoding on the input features by adopting a saliency-guided attention mechanism, and gradually aggregating deep semantic information and shallow detail features to realize coarse-to-fine progressive optimization, so as to improve the robustness of the multi-scale progressive attention enhancement decoder. And finally generating a saliency map. The method can effectively improve the processing performance of an irregular topological structure and a complex context relationship in the optical remote sensing image.

The invention relates to the technical field of growth state evaluation, and discloses a crop growth state evaluation method and system based on multi-dimensional monitoring. The method comprises the steps of collecting multi-source remote sensing data of a farmland area according to a crop growth period, and performing topographic correction on the multi-source remote sensing data to obtain target vegetation data; based on the multi-source remote sensing data, farmland plot boundaries are extracted, and a farmland space association graph is constructed; inputting the target vegetation data and the farmland space association graph into an elevation perception graph convolutional network for elevation feature analysis, and calculating to obtain a crop abnormal growth index; and generating a growth state evaluation result based on the target vegetation data and the crop abnormal growth index. According to the method, crop growth abnormity caused by regional factors can be accurately identified, so that the accuracy of evaluation results under different terrain and environmental conditions is ensured.

The invention relates to the technical field of unmanned aerial vehicle positioning, in particular to an unmanned aerial vehicle flight path positioning method and system based on multi-source information fusion. The method comprises the following steps: acquiring an original observation data stream of an unmanned aerial vehicle in real time through a sensor array, and preprocessing to output a multi-source data sample set; calculating relative motion increment between adjacent key frames through an IMU pre-integration model, and extracting local pose estimation through a visual geometric calculation method; evaluating the confidence of each sensor in real time to dynamically generate an adaptive weight coefficient; and constructing a tight coupling fusion filter taking position, speed and attitude errors as core state quantities, and outputting a global optimal trajectory of the unmanned aerial vehicle. According to the invention, the weight is dynamically distributed through the real-time confidence of the sensor, the multi-source data association is fully mined, and the flight path positioning precision and environmental adaptability of the unmanned aerial vehicle are improved.

The invention provides a river region disaster monitoring and pre-warning method and system based on multi-source monitoring data analysis, and the method comprises the steps: obtaining the multi-source monitoring data of a river region, carrying out the data preprocessing of the multi-source monitoring data, eliminating the noise interference in real-time position data, and carrying out the time synchronization alignment of channel image data and environment parameter data, thereby achieving the early warning of the river region disaster. The method comprises the steps of generating a standardized monitoring data set, extracting water flow dynamic characteristics, meteorological anomaly characteristics and channel obstacle distribution characteristics of a river region, generating a multi-dimensional disaster associated characteristic set, inputting the multi-dimensional disaster associated characteristic set into a preset disaster early warning model for dynamic analysis, generating a disaster early warning signal, and determining a disaster type and an influence range. And triggering an autonomous separation mechanism of the dragging airship and a quick start instruction of the unmanned aerial vehicle, broadcasting early warning information to ships in a channel through the unmanned aerial vehicle, and synchronously transmitting the disaster type and the influence range to a command center. According to the invention, the timeliness of disaster early warning and the space adaptation precision of treatment measures can be improved.