15761 results about "Radar" patented technology

Disclosed are a high-performance loosely-coupled multi-modal data fusion system for a smart driving environmental perception system and a vehicle-mounted device, comprising: a fusion detection model based on a modality-independent feature interaction strategy, which is configured for converting a LiDAR point cloud, a camera image, and a millimeter-wave radar point cloud into a unified bird's-eye view representation, and performing multi-modal fusion; and a fusion tracking model based on a motion-appearance feature cascaded coupling data association strategy, which is configured for performing subsequent trajectory tracking and matching according to multi-modal fusion feature information. A VoD data set and a K-Radar data set are selected for training, verifying, and testing the comprehensive performance of the models, and a TensorRT accelerated inference model is applied, then quantized, and deployed to a vehicle-mounted computational testing platform. The present invention is compatible with mainstream sensor deployment solutions, and achieves the efficient complementary fusion of multi-source heterogeneous sensor information, significantly improving the reliability, accuracy, and adaptability of vehicle-mounted perception systems, thereby effectively responding to extreme operating conditions such as complex traffic scenarios and inclement weather.

A radar device which is installed in a vehicle or the like needs to detect a target at a short distance from several tens of centimeters to several tens of meters and, therefore, has a problem that a reflected wave from the target may be reflected by a front case of the radar device and then reflected by the target again to be received as a secondary echo by itself. To solve this problem, it is an object of the present invention to provide a radar device again that can prevent multi-reflection to accurately measure a distance to a target in a short range. In a radar device according to the present invention, a surface of a frame body provided around a transmitting antenna and a receiving antenna is shaped so that a reflected wave from a target may not be returned in a direction in which a transmitting wave is transmitted from the transmitting antenna.

The invention discloses an intelligent driving multi-sensor fusion data processing system, which belongs to the technical field of fusion data processing and comprises a data preprocessing unit, a data fusion unit and a data post-processing unit. The data preprocessing unit normalizes the original data of the laser radar, the camera, the millimeter wave radar and the ultrasonic sensor through a standardization, cleaning, noise reduction and calibration synchronization module; the feature level fusion module deeply digs the characteristics of each sensor, and fuses geometric, visual, motion and close-range obstacle features by using a deep neural network; the decision-making level fusion module generates a driving instruction through weighted voting and fuzzy logic reasoning through classification and situation evaluation; and the data post-processing unit combines the vehicle and road condition optimization instruction, evaluates the risk, and stores the data feedback optimization system. The system can improve the sensing precision and decision reliability, enhances the expansion adaptability of the system, and provides guarantee for safe and efficient operation of intelligent driving.

An intelligent traffic management device for optimizing traffic flow at one or more intersections using reinforcement learning, comprising: a central control unit housed in a weatherproof enclosure and mounted on a structural support near an intersection; a sensor interface operatively connected to a variety of multimodal sensors, including at least radar-based vehicle detectors, inductive loop sensors embedded in the road surface, infrared pedestrian detection detectors, and high-resolution optical imaging modules; a powerful embedded computing unit consisting of a multi-core processor and a GPU configured for on-device inference; a reinforcement learning engine embedded in the computing unit, comprising a deep reinforcement learning model trained to map environmental states defined by traffic-related sensor inputs to optimized action policies for traffic light control; a signal actuation module electrically connected to traffic signal controllers, with action policies outputting signal phase durations and transitions in real time; a communications module supporting wireless connectivity and configured for device-to-device communication and cloud synchronization; a policy synchronization module configured to perform federated learning-based updates of model parameters by exchanging gradient weights or distilled traffic state vectors with neighboring intelligent traffic management devices deployed in a traffic network; where the reinforcement learning engine continuously observes traffic state vectors consisting of vehicle counts, average speeds, queue lengths, and pedestrian activity, and dynamically updates the policy to maximize cumulative traffic throughput and minimize average delay.

An agricultural vehicle includes a propulsion system configured to move the agricultural vehicle, a steering system configured to orient the agricultural, a navigation controller operably coupled to the propulsion system and the steering system, and a camera and a radar operably coupled to the agricultural vehicle. A computing device is operably coupled to the radar and the camera and includes instructions that, when executed by at least one processor, cause the computing device to receive image data from the camera, receive radar data from the radar and generate a radar point cloud based on the radar data, identify one or more objects in the image data using a trained neural network to generate labeled image, fuse the labeled image data with the radar point cloud to obtain fused data, and perform one or more navigation operations based on the fused data. Related agricultural vehicles and methods are also disclosed.

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 discloses a high-resolution radar echo extrapolation prediction method fused with satellite data, and the method specifically comprises the following steps: firstly, inputting historical radar echo sequence preprocessing at a previous T moment, including denoising, normalization processing and data set segmentation, and obtaining cleaned data; then, through a deterministic modeling method (SimVP), a fuzzy prediction sequence of a future T duration is obtained, then a variational auto-encoder (VAE) maps an original radar echo image and the fuzzy prediction sequence to a low-dimensional potential space, and two-stage diffusion modeling is carried out on the basis; in the first stage, a space-time converter (ST-Translator) is used to extract space-time evolution characteristics of radar echoes; in the second stage, satellite data at the corresponding time of the previous T moment is input, preprocessing including normalization processing, feature selection and data set segmentation is carried out, cleaned data is obtained, and the influence of the satellite data is dynamically adjusted in the diffusion process by adopting a multi-source fusion denoising network Fsrform so as to make full use of satellite information; and finally, inversely transforming output results of the two stages into a pixel space to obtain a high-resolution radar echo extrapolation prediction result of the future T duration. According to the invention, computing resource consumption can be effectively reduced, and the precision and detail fidelity of short temporary rainfall prediction are improved.

Disclosed is a time series InSAR tropospheric delay correction method for relieving atmospheric seasonal oscillation, so as to relieve the seasonal oscillation deviation caused by tropospheric delay. This method is based on the temporal and spatial characteristics of atmospheric delay. Firstly, based on the ERA-5 atmospheric grid data covering the study area, the ZTD negative exponential vertical profile function model is established. Fourier series function is introduced to establish the time series model for β and ZTDr, Finally, according to the radar incidence angle parameters, the delay is changed from zenith direction to radar line of sight direction.

The invention discloses a regional risk monitoring and early warning system based on space-air-ground multi-source data fusion, and relates to the technical field of disaster monitoring and early warning. According to the invention, comprehensive monitoring of regional risks is realized by fusing various data sources such as satellite remote sensing, unmanned aerial vehicle aerial survey and ground-based radar. The system can automatically process data, construct a high-precision three-dimensional real scene model, and identify a potential risk area based on a time sequence InSAR technology and a multi-source data fusion technology. And meanwhile, non-contact high-precision measurement is carried out on key facilities. An interactive monitoring and early warning platform is constructed, functions of deformation graph generation, target detection and risk division, data fusion processing, potential risk surface determination, stress-strain analysis and early warning information release are realized, and support is provided for regional risk prevention. The system has the characteristics of high automation, intelligence and visualization, and is suitable for various application scenes such as earthquakes, floods, landslides and the like.

The invention discloses an ultra-wideband laser radar inertial navigation cooperative SLAM (Simultaneous Localization and Mapping) method and system, which are suitable for robot positioning and mapping tasks in a GPS (Global Positioning System)-free environment. According to the method, high-frequency motion priori of an IMU, relative pose constraint of LiDAR and absolute ranging information of UWB are fused, a unified factor graph optimization model is constructed, and multi-sensor cooperative positioning is realized; in the system initialization stage, IMU bias calibration, UWB base station coordinate configuration and LiDAR initial attitude estimation are completed; in the operation process, IMU pre-integration is utilized to predict the pose, LiDAR point cloud registration is utilized to obtain the relative motion between key frames, UWB ranging is combined to construct a residual item, a self-adaptive weight mechanism is introduced to suppress ranging abnormity, and the fusion robustness is improved; the system supports geometric feature loopback detection, cross-frame constraints are constructed in combination with UWB to carry out closed-loop optimization, and an optimization result is used for real-time incremental updating of a global map; the method has the advantages of high positioning precision, strong anti-interference capability, wide application scene and the like.

The invention relates to a multi-source remote sensing image classification method based on a key band retrieval attention mechanism, and belongs to the technical field of remote sensing image processing. The method comprises the steps of firstly performing preprocessing and data set division on multi-source remote sensing data, then constructing a key band retrieval attention network, performing training and evaluation on a model by utilizing a training set and a verification set after division, and finally performing visual analysis on a model result by utilizing a test set. According to the method, the features of the hyperspectral image and the laser radar / synthetic aperture radar image are effectively extracted and fused, redundant information interference is effectively reduced, the retention rate of hyperspectral key information is improved, the complementary expression ability among multi-source data is enhanced, and the remote sensing image classification precision and calculation efficiency are remarkably improved. The method is suitable for application scenes of multi-source remote sensing data fusion and classification, can meet efficient intelligent processing requirements of complex earth surface information, and provides an accurate and efficient remote sensing image classification solution.

The invention provides an unmanned aerial vehicle infrared target positioning system fusing multi-model Kalman filtering and deep learning, and relates to the technical field of unmanned aerial vehicle navigation and positioning. The main body system comprises a data acquisition module, a multi-source data fusion module, a feature extraction enhancement module, a dynamic model optimization module, a target state estimation module, a positioning calculation module and a flight decision planning module, and the data acquisition module is provided with a high-resolution infrared sensor and accurately captures target infrared radiation information; a high-definition vision camera and a high-precision laser radar are integrated at the same time, environment data are collected in an omnibearing mode, and the data collection module can collect the environment data according to the environment complexity and the real-time change of the target motion state by adopting a self-adaptive sampling strategy based on fuzzy logic, particle swarm optimization and a self-adaptive threshold value. And the sampling frequency and the resolution of the sensor are dynamically and intelligently adjusted.

The invention discloses a multi-source multi-temporal-spatial feature fusion short and temporary rainfall prediction method (M4Cester), which realizes layered temporal-spatial feature extraction through a multi-space multi-time aggregator (MMA), and comprises a multi-scale patch embedding (MSPE) module, a cross-scale perception optimization (CPR) module and a multi-time self-attention (MTS) module. The first and second spatial features are respectively used for capturing local-global spatial features, dynamically balancing cross-scale information and mining a time sequence dependency relationship; meanwhile, a bidirectional bridging fusion module (MSFM) is designed, bidirectional alignment and enhancement of radar and satellite features are realized by using a cross attention mechanism, and modal difference is relieved through residual connection. Experiments on a weather data set in the Yangtze River Delta region show that the key success index (CSI) reaches 0.267 and the HSS reaches 0.372 in heavy rainfall (greater than or equal to 50dBZ) prediction by the method, which are obviously improved compared with the existing advanced model, particularly, the limitation of single-source data is effectively overcome in the prediction of a convection initiation (CI) event, and a high-precision solution is provided for short and temporary rainfall prediction.

The invention provides a method for reducing the multipath effect of a take-off and landing guide radar based on adaptive off-axis tracking, and the method comprises the steps: enabling the take-off and landing guide radar to transmit a signal, and transmitting the signal to a radar direction after receiving the signal; the radar receives the direct signal and the multi-path signal at the same time, and separates and establishes a spatial-temporal characteristic database of the multi-path signal; when the influence of the signal on the angle error exceeds a dynamic threshold value, starting an off-axis tracking mode; dynamically calculating the optimal off-axis angle, locking the elevation angle of the antenna, and performing closed-loop tracking of the azimuth; carrying out conjoint analysis, and identifying and eliminating false targets in real time; intelligent suppression is carried out on angle random errors; a real-time multi-path propagation model is constructed, and phase and amplitude compensation is carried out on a received signal. The method has excellent real-time performance and engineering realizability, and can meet strict requirements of aircraft precision approach guidance. The method has good expandability and adaptability, and provides reliable technical support for guaranteeing safe take-off and landing of the aircraft in severe weather and complex environments.

The invention belongs to the technical field of intelligent perception and mode recognition, and particularly relates to a human body behavior recognition method based on millimeter wave radar and multi-task learning. The method comprises the steps that millimeter wave radar point cloud data and RGB video streams are synchronously collected, and a five-dimensional point cloud scene is generated through three-dimensional analysis and dynamic target extraction; a hierarchical Point Transform network is constructed to extract radar space-time features, and human body key point features are generated by using visual auxiliary attitude estimation; radar and key point features are dynamically fused through a cross-modal attention mechanism, a multi-task joint optimization strategy is combined, behavior classification serves as a main task, attitude estimation serves as an auxiliary task, and a behavior recognition result is output. According to the method, through multi-task cooperation and cross-modal feature interaction, on the premise of ensuring privacy security, the accuracy and robustness of human behavior recognition in a complex scene are remarkably improved, and efficient and reliable technical support is provided for the fields of intelligent monitoring, human-computer interaction and the like.

The invention relates to the technical field of wind power generation, and discloses a wind power plant energy management method based on laser radar wind measurement, comprising the following steps: S1, deploying laser radar equipment, and collecting real-time data of wind speed, wind direction and turbulence intensity in a wind power plant; s2, constructing a three-dimensional dynamic model of a wind field, and modeling wind speed, wind direction and turbulence distribution in combination with laser radar data and historical meteorological data; s3, performing short-term prediction on the future wind speed by using the deep learning model, and outputting minute-level to hour-level wind energy prediction data; s4, optimizing a dynamic scheduling strategy of the wind turbine generator based on the prediction data; and S5, docking an energy storage system, optimizing charging and discharging behaviors of energy storage equipment, and realizing smooth power output. According to the invention, by fusing the laser radar wind measurement data, the deep learning prediction model and the intelligent optimization algorithm, high-precision wind condition monitoring, dynamic scheduling optimization and power output stability improvement of the wind power plant are realized.

The invention discloses a tunnel deep-buried drainage ditch rapid blasting excavation method based on vertical drilling, which comprises the following steps: step 1, detecting the geology in front of a tunnel by using vertical drilling coring, geological radar and TSP seismic wave technology, and constructing a three-dimensional model; 2, generating an asymmetric hole net layout based on a geological model and AI, and setting a drilling-free vibration isolation belt; the design of dense inside and sparse outside is adopted, and vibration propagation is blocked; thirdly, a three-stage air spacer is arranged at the bottom of the blast hole, segmented charging is implemented, the explosive energy release time is prolonged, and the single-segment explosive quantity peak value is reduced; fourthly, an electronic detonator is adopted for millisecond detonation, and peak vibration is weakened through the waveform superposition offset effect; 5, arranging fiber grating sensors in the tunnel, collecting data in real time and transmitting the data to the cloud AI platform; dynamically adjusting parameters based on the monitoring data; and 6, evaluating a blasting effect through a three-dimensional laser scanner, generating an evaluation report in combination with data, updating a BIM model database, and continuously optimizing an AI algorithm.

The invention discloses a robot dynamic environment adaptive sensing and navigation system based on a three-dimensional laser radar, relates to the technical field of robots, and solves the technical problems that comprehensive environment information is difficult to obtain, and the weight is difficult to adjust by fusing weather types and sensor confidence coefficients. Comprising the following steps: generating original point cloud data by combining a bionic compound eye type laser radar with a silicon photon integrated chip; marking point clouds based on a KITTI data set, performing preprocessing, training a Transform model to output a dynamic obstacle mask, and filtering background point clouds; a weather detection model is constructed, the weight is dynamically adjusted according to the weather type and the sensor confidence coefficient, and position and attitude estimation is fused; laser radar point cloud constructs a geometric map, a camera depth map generates a dense map, and semantic tags are mapped to generate an environmental semantic map; and converting the environmental semantic map into a three-dimensional grid map, and planning an obstacle avoidance path based on the grid map by using an A * algorithm.

The invention provides an anti-unmanned aerial vehicle defense method and system based on radar and audio, and the method comprises the steps: constructing a three-dimensional airspace monitoring network through radar sensors and audio capture devices which are disposed at different heights and positions, carrying out the time-space synchronous multi-modal information fusion, and obtaining signal audio alignment data; a real-time behavior track of the target in a three-dimensional space is generated through a space positioning algorithm, a behavior profile modeling technology is combined with a machine learning algorithm to obtain a behavior profile with unique identification, the behavior profile is analyzed to obtain an analysis result, and a threat level judgment model is constructed according to the analysis result; the threat level judgment model is utilized to evaluate the intention and risk level of the abnormal behavior of the unmanned aerial vehicle to generate a threat evaluation result, and a self-adaptive threat evaluation engine is utilized to generate a target defense scheme; according to the technical scheme provided by the invention, the precision and reliability of target positioning and the intelligent level are improved, and the capability of coping with complex threats is enhanced.

The invention discloses a millimeter wave radar human body tumble detection method based on multi-feature fusion, and the method comprises the steps: obtaining an original point cloud frame from each radar, and carrying out the timestamp calibration and space coordinate system conversion; performing noise filtering, ground segmentation and human body point cloud extraction on each frame of point cloud; dividing the preprocessed point cloud into a static cluster and a dynamic cluster; filtering false dynamic clusters; extracting a residual dynamic cluster set, and identifying and tracking the human body dynamic clusters in continuous frames by adopting a tracking algorithm; extracting a corrected time sequence feature from the tracked human body cluster; inputting the time sequence characteristics into a pre-trained deep time sequence network, learning a falling time sequence dependency relationship, and outputting an abnormal index; and performing multi-source fusion with the abnormal index to obtain a comprehensive index to judge whether to trigger an alarm. The method can adapt to a complex home environment, improves the detection accuracy and real-time performance, and reduces the false alarm and missing alarm.

The invention provides a vehicle surrounding obstacle detection method and system based on a multi-modal sensor, and relates to the technical field of intelligent driving, and the method comprises the steps: obtaining ultrasonic radar point clouds and millimeter wave radar point clouds at two sides of a vehicle, and image data at the rear side of the vehicle; the radar point clouds are clustered, and the spatial position, the movement speed and the reflection section characteristics of an obstacle are extracted; performing target detection on the image data, and outputting to obtain a bounding box and texture features of an obstacle; generating preliminary multi-modal data, inputting the multi-modal data into a collision detection model, respectively entering a radar branch network and a visual branch network to extract point cloud spatial-temporal features and image semantic features, and performing weighted fusion on the point cloud spatial-temporal features and the image semantic features through a Transform mechanism to obtain a collision detection result; outputting the three-dimensional position, the movement track, the type label and the collision risk probability of the obstacle; and when the collision risk probability exceeds a preset threshold value, triggering a corresponding early warning or control instruction.

The invention provides a multifunctional health monitoring and early warning system based on radar and vision fusion, and relates to the technical field of intelligent early warning, and the system comprises a feedback receiving module which receives a radar feedback signal and an image feedback signal; the monitoring identification module traverses the target monitoring area to determine monitoring data, and carries out monitoring identification; the abnormity judgment module is used for carrying out fusion analysis on the first monitoring data set and the second monitoring data set, carrying out bidirectional abnormity judgment and generating an early warning instruction; and the risk analysis module uploads the abnormal judgment result to a cloud platform for risk analysis, and sends early warning information to a remote terminal according to the risk level. According to the invention, the technical problem that the accuracy of early warning information is affected due to high false alarm rate caused by environmental infection due to dependence on a single sensor in health monitoring in the prior art can be solved, risk assessment is carried out by combining radar and visual fusion, the accuracy of anomaly recognition is improved, and the accuracy of early warning information is improved. And the early warning real-time performance and accuracy are improved.

The invention relates to the technical field of data processing, in particular to a multi-source target trajectory fusion method and system based on deep learning. Comprising the following steps: constructing a space-time alignment training set containing radar echo data, satellite telemetry data and truth value trajectory data; designing a multi-channel deep learning network architecture which comprises a radar echo data feature extraction channel and a satellite telemetry data feature extraction channel, and establishing dual-channel feature association through an attention mechanism; optimizing network parameters through back propagation to obtain a weight prediction model; radar echo data and satellite telemetry data are received in real time, and space-time alignment of sampling data is achieved through a sliding window mechanism and coordinate system conversion; inputting the preprocessed double-source features into the trained weight prediction model, and generating a radar-telemetering fusion weight coefficient; and performing weighting processing and optimization on the double-source trajectory coordinates, and outputting a fused target motion trajectory. The problem that a traditional single-source track measurement system is insufficient in precision in a complex environment is solved.

The invention discloses an expressway tunnel monitoring method, system and device and a medium, and relates to the technical field of tunnel monitoring. Tunnel environment data are collected in real time through a multi-source heterogeneous sensor array, and the data at least comprise video images, millimeter wave radar point cloud, laser radar three-dimensional coordinates, temperature and humidity and CO concentration parameters. According to the system, an improved YOLOv7 algorithm is used for carrying out target real-time detection, an optical flow method is combined to predict the motion trail of a target object, a tunnel dynamic characteristic spectrum is constructed, real-time monitoring of the tunnel environment is achieved, by deploying a risk level assessment engine, the system can extract space-time correlation characteristics, and the risk level assessment efficiency is improved. A comprehensive risk index is generated in combination with a fuzzy logic decision tree, and when the risk index exceeds a dynamic threshold value, the system triggers a grading early warning mechanism, links tunnel emergency equipment, synchronously generates an emergency plan and pushes the emergency plan to an operation and maintenance terminal to guarantee tunnel operation safety.

A method for shoreline segmentation in complex environments based on the perspective of an unmanned surface vessel is provided. A visible light image, a thermal infrared image and a raw radar echo image of a shoreline are obtained. The visible light image and the thermal infrared image are subjected to fusion and feasible region segmentation to obtain an all-weather two-dimensional image information of the shoreline, and an echo image including tiny features is obtained based on the raw radar echo image. An extraction region is constrained and shoreline features are enhanced based on the all-weather two-dimensional image information and the echo image to obtain a multi-feature point cloud dataset for shoreline segmentation.

A transmission tower deformation monitoring device based on a phased array radar technology relates to the field of irregular contour metering, and comprises a map construction module used for constructing a regional digital map; the boundary determination module is used for near-field region boundaries; the signal acquisition module is used for acquiring a near-field echo signal of a near-field region through a narrow beam and acquiring a far-field echo signal of a far-field region through a wide beam; the calibration table construction module is used for constructing a signal intensity comparison table; the data processing module is used for determining effective monitoring data; the error calculation module is used for performing error calculation on the effective monitoring data to obtain a phase error and an amplitude error; and the parameter determination module is used for determining iron tower deformation parameters. According to the invention, the accuracy of transmission tower deformation monitoring can be improved.

The invention relates to the field of monitoring and early warning, in particular to a dynamic beam radar monitoring and linkage early warning method and system for a layered side slope of an expressway, and the method comprises the steps: carrying out the layered scanning of a layered geologic structure of the side slope, synchronously obtaining a phase coherent echo signal, carrying out the multi-threshold scattering point extraction, and carrying out the multi-threshold scattering point extraction; through analysis of a prior constraint model and a geological vegetation recognition network, interference signals caused by vehicle passing are eliminated to obtain a space-time coherent scattering point set, the space-time coherent scattering point set is input to a deformation calculation assembly line, differential interference measurement, adaptive atmospheric disturbance correction and slope structure parameter inversion are executed, and a displacement data stream is generated. And carrying out space-time correlation analysis on the layered inclination angle time sequence data and the meteorological and hydrological data, updating a slope stability evaluation index through a dynamic baseline, and generating an early warning instruction. According to the invention, a technical system from interference suppression and precise calculation to decision closed loop is formed, high reliability of road slope monitoring results is ensured, and intelligent support is provided for traffic safety and emergency management and control.

The invention discloses a wounded rescue life detection and positioning method based on deep learning. The method comprises the steps that S1, multi-view RGB-D images, laser radar point clouds, millimeter wave vital sign radar data and infrared thermal image data are collected and subjected to time synchronization; s2, outputting a pixel-level semantic mask; s3, generating a semantic point cloud of the wounded; s4, forming a metric-semantic double-map data set; s5, generating vital sign scores and writing the vital sign scores into a metric-semantic double-map data set; s6, updating wounded dynamic anchor point attributes in the measurement-semantic double-map data set; and S7, based on the updated metric-semantic double-map data set, calculating the three-dimensional coordinate and rescue priority sequence of each wounded dynamic anchor point, and generating an obstacle avoidance optimized navigation path for avoiding the risk semantic region. The rescue efficiency and safety are effectively improved.

The invention provides a sea target detection and identification method based on combination of radar and a photoelectric system. The method comprises the following steps: firstly, acquiring a target track by using radar equipment and AIS equipment respectively, fusing the target track, and then realizing accurate prediction and tracking of a ship target motion track by using Kalman filtering; and then, guiding a photoelectric system to accurately capture a target ship image by using the track information, and detecting and identifying the ship in the image by using an image processing and deep learning algorithm. According to the invention, the accuracy of target detection and identification can be greatly improved, and stable tracking and accurate identification of a target ship in a complex marine environment are ensured.

The invention discloses a battlefield target behavior prediction method capable of being guided by micro-physical representation and a thinking chain, and the method comprises the steps: obtaining satellite images, radar / communication detection and open source text multi-source time sequence data, completing the entity recognition, relation extraction and event detection, and constructing a dynamic space-time knowledge graph; the maneuverability, sensor detection, weapon range and terrain accessibility mechanism are micronized to serve as a physical consistency constraint embedded prediction model; generating an intention-action-result causal priori chain and parameterizing the causal priori chain into a computable structure; performing multi-branch long-time-sequence situation deduction, and outputting a future target behavior track and a scene probability; and evaluating and explaining by integrating the causal confidence coefficient, the physical consistency and the data goodness of fit, and giving a key event probability and situation evolution report. According to the method, unified modeling of semantic causal and physical constraints is realized, and the method has explainable, verifiable and robust prediction capabilities, and is suitable for target behavior prediction and command information system decision support in a complex environment.