162 results about "Automatic Identification System" patented technology

The invention belongs to the technical field of ocean detection, belongs to a multi-sensor fusion intelligent anti-collision method and system, comprises a sensing layer, a processing layer and an application layer, and provides an intelligent anti-collision and evidence recording ocean monitoring floating system integrating computer vision, target ranging, satellite positioning and ship automatic recognition system multi-sensor fusion. According to the invention, YOLOv8 target detection, Transform data fusion and a Kalman filtering algorithm are adopted, so that accurate detection and anti-collision early warning of ships and floating objects on the sea are realized. The system has an AIS failure processing mechanism and an evidence encryption storage function, and ensures reliable operation and data compliance under complex sea conditions.

The invention belongs to the technical field of ship fuel consumption prediction and big data analysis, and relates to an operating ship fuel consumption prediction method based on a deep learning model. The method comprises the following steps: 1, synchronously acquiring multi-source data from a ship automatic identification system, a cabin monitoring system and an ERA5 meteorological database, and preprocessing the multi-source data; step 2, inputting the preprocessed data into a pre-trained CNN-BiLSTM-Attention model to carry out ship fuel consumption prediction; wherein the CNN-BiLSTM-Attention model is composed of a convolutional neural network, a bidirectional long and short term memory network and a time attention module; the model adopts a composite loss function based on navigational speed-power-fuel oil physical constraint. The method is more excellent in the aspects of prediction precision, robustness and fitting effect. Compared with the prior art, the method has higher accuracy and reliability in the aspect of ship fuel consumption prediction.

The invention discloses a shipping logistics flow direction prediction method and system based on multi-source data fusion, and relates to the field of logistics prediction, and the method comprises the steps: constructing a shipping network diagram based on shipping data; acquiring trajectory data, marine meteorological data, port log data and cargo electronic shipping bill data of a ship automatic identification system in real time; carrying out logistics flow direction prediction on logistics in transportation, obtaining ship data in a future time period, and perfecting a shipping network diagram; carrying out logistics flow direction prediction on the logistics in berthing, and obtaining the remaining loading and unloading amount and the estimated completion time in a future time period; integrating the logistics transportation prediction model and the logistics loading and unloading prediction model, and constructing an end-to-end logistics flow direction prediction model; and correcting the model based on the error between the actual logistics flow direction data and the prediction data. The method has the advantages that the LSTM and Transform models are combined, the shipping logistics flow direction is accurately predicted, the transportation and loading and unloading efficiency is optimized, the prediction precision is improved, and the shipping logistics management efficiency and decision support are improved.

The invention provides a ship distance estimation method and device based on an AIS (Automatic Identification System) supervision signal, relates to the technical field of intelligent shipping and water traffic supervision, and solves the problems that the prior art depends on a single sensor or a complex multi-source fusion scheme, so that supervision blind areas, inaccurate distance measurement and system complexity are caused, and the cost is low. And effective distance estimation cannot be carried out on the AIS-free ship. The method comprises the following steps: acquiring and preprocessing AIS data and video data; obtaining an approximate predicted position of the ship at the current moment through the AIS trajectory prediction model, and obtaining a ship target detection frame of the current field angle through the target detection model; dividing the current field angle of the camera into equal credible areas and carrying out space-time matching; and the matched visual features and AIS distance information are fused, an AIS distance is used as a supervision signal to construct a distance regression module, and a target detection model is trained to output a ship distance estimation value. The method is used in the other ship distance estimation process of the shore-based and ship bridge view angle.

The invention relates to the technical field of communication, in particular to an AI situation awareness and emergency decision-making system for water area multi-source information fusion. Comprising a multi-source data acquisition module used for acquiring ship automatic identification system data, radar monitoring data, hydro meteorological data and video monitoring data in a water area; the data preprocessing module is used for carrying out noise reduction processing, time synchronization and format standardization on the collected multi-source data; the AI fusion analysis module is used for performing feature extraction and correlation analysis on the preprocessed multi-source data based on a deep learning model to generate a water area target dynamic feature vector; the situation assessment module is used for constructing a water area situation assessment matrix according to the dynamic feature vector and a preset assessment rule; and the emergency decision module is used for performing space-time alignment, feature fusion and conflict processing on the multi-source heterogeneous data based on the situation assessment matrix.

The invention discloses a vulnerability patch identification method and device for open source software silent repair, and belongs to the field of silent security patch identification. In order to solve the problems of difficulty in vulnerability semantic understanding and difficulty in patch intention identification in an existing silent security patch identification method, a dual-encoder structure is adopted to perform joint modeling on patch codes and submitted messages, and a message enhancement method of a large language model and a vulnerability repair mode library retrieval enhancement method are combined to realize the silent security patch identification method. And automatic identification of CVE-free warning development submission is realized. According to the method, the repair intention and technical details when a developer submits the patch can be accurately grasped, the recognition performance and generalization ability of the model are effectively improved, a full-process automatic recognition system is constructed, timing detection of the silent patch in a target software library is supported, and manual intervention and expert resource consumption are reduced.

The invention, which relates to the technical field of intelligent traffic and unmanned aerial vehicles, discloses an unmanned aerial vehicle-based automatic identification system for a thrown object on a highway, comprising: an unmanned aerial vehicle cluster deployment module which adopts a Mesh ad hoc network and covers a lane with a width of 150-250 m; the multi-modal data acquisition module is used for acquiring infrared light, visible light, a depth map and point cloud data; the image preprocessing module is used for downsampling, filtering and denoising and enhancing the contrast ratio; the thrown object recognition module is fused with multi-modal features, the confidence coefficient threshold value is 0.6-0.7, and suspected targets are verified through point cloud clustering; the three-dimensional modeling and positioning module is used for fusing binocular and laser radar data; the intelligent decision-making and scheduling module is used for generating priorities in combination with the traffic flow and planning an inspection path; and the linkage processing module is used for pushing information through 4G / 5G, and linking the information board and the road administration vehicle for processing. The method improves the monitoring coverage rate and the recognition accuracy, shortens the response time, reduces the positioning error, optimizes the processing efficiency, and guarantees the high-speed traffic safety.

The invention relates to the technical field of ship group intelligent control, and discloses a ship motion trail optimization control system based on deep learning, which comprises control devices deployed on ships, receiving navigation state information of adjacent ships through a ship automatic identification system, generating asymmetric virtual energy-saving potential fields, superposing the asymmetric virtual energy-saving potential fields to form a total potential field, and outputting the total potential field; according to the method, a ship group spontaneously forms a dynamic navigation manifold with relatively low overall energy consumption, and through the asymmetric potential field design of bow repulsive force and stern gravitational force, linkage course correction caused by traditional collision avoidance is converted into collaborative energy-saving motion, so that the energy-saving effect of the ship is improved. Meanwhile, navigation safety is ensured in combination with a post-arbitration mechanism, and environment self-adaptive compensation is achieved through micro-disturbance inversion ocean currents.

The invention discloses a ship refined perception full-voyage track fitting and optimization method, which comprises the steps of drawing historical data acquired by an AIS (Automatic Identification System) to obtain a full-voyage-segment ship route track diagram, and dividing the full-voyage-segment ship route track diagram into a training set and a test set, the training set being used for generating an input image and a training label; based on the input image and the training label, loading historical weight parameters at the same time, and training to obtain a deep learning track separation model; testing set data is processed and then input into a deep learning track separation model, the model outputs an image, track data is cut and divided into a straight line stage and a curve stage, and track fitting is carried out in a segmented mode; combining the segmented tracks to obtain an overall track of the ship route; comparing the difference between the fitted curve and the actual curve, calculating a precision index, and if the precision is improved, iteratively updating a weight parameter of the deep learning trajectory separation model; otherwise, repeatedly training the iteration model until the precision index meets the requirement. According to the invention, the track features can be effectively identified, and the track fitting precision is effectively improved.

The invention belongs to the field of transformer pile head recognition, and particularly relates to a distribution transformer pile head automatic recognition system which comprises a data integration module, a data processing module, a system control module, a waveform parameter calculation module, an excitation circuit analysis module, an equivalent inductance conversion module and a looseness recognition algorithm module. According to the scheme, the high-cost limitation of a traditional vibration sensor or 3D modeling is broken through, and remote monitoring of looseness of the transformer pile head can be achieved through equivalent excitation inductance conversion and waveform similarity analysis without additional hardware transformation based on voltage and current data collected by an existing digital platform (a resource middle platform, a marketing middle platform and the like) of a power grid. Compared with the traditional scheme in the industry, the method has the advantages that the hardware transformation cost is reduced by more than 70%, the problem of missing inspection of manual inspection is avoided, the average recognition time of the pile head loosening fault is shortened to be within 15 minutes from 4 hours of traditional manual inspection, the monitoring efficiency is remarkably improved, and non-intrusive and low-cost accurate sensing of the state of the transformer is realized.

The invention provides a ship port entering and leaving yaw early warning identification method and system which are applied to the technical field of shipping informatization, ship traffic management and maritime safety monitoring, and the method comprises the steps: obtaining a channel center line and a yaw threshold value, receiving the data of a ship automatic identification system in real time, determining a track direction, determining a reference channel section, and determining a yaw threshold value; the included angle between the track direction and the reference channel section is calculated, and an early warning signal is generated when the included angle is larger than the threshold value. According to the scheme, the hysteresis quality of manual monitoring can be effectively avoided, and the problems that early warning is not timely and the accuracy is low in the prior art are solved.

The invention relates to the field of computer data processing and artificial intelligence, and discloses a mobile phone APP intelligent code scanning and AI automatic identification system which comprises a mobile terminal, an internet network and a local server. The mobile terminal is provided with an acquisition detection module, and a mobile phone end code scanning module automatically controls light supplement when the image brightness is lower than a threshold value. A server-side AI intelligent recognition module executes Gaussian filtering, binarization and geometric correction preprocessing on a code scanning image, and a text is extracted by using a deep convolutional neural network and a bidirectional long-short-term memory network. And the system further executes semantic analysis by traversing the business template library, judges the attribution of key fields by using a spatial Euclidean distance, and generates structured JSON data. The statistical module calculates a real-time production efficiency index, and the remote viewing module associates and displays a process optimization suggestion when the data is abnormal. According to the invention, high-precision identification, business semantic understanding and intelligent decision support of code scanning data are realized.

The embodiment of the invention provides a ship control method and device, electronic equipment, a readable storage medium and a chip, and the method comprises the steps: determining a first distance range and a second distance range corresponding to a target ship; determining an obstacle tracking detection result obtained based on perception fusion in the first distance range; determining information parameters of a plurality of ships in the ship automatic identification system within the second distance range; determining a sailing ship according to the obstacle tracking detection result and the information parameters; determining a relative position type of at least one sailing ship corresponding to the target ship; determining a traffic flow value of the current driving water area of the target ship according to the information parameters; and according to the relative position type and the traffic flow numerical value, switching a driving mode of the target ship, wherein the driving mode comprises a tracking mode and a ship following mode. According to the scheme, the safety of tracking or following the ship by the automatic driving ship in the inland river environment is improved.

The invention discloses a geological feature-based regional landslide hidden danger automatic identification system and method, and relates to the technical field of intelligent sensors. The geological feature-based regional landslide hidden danger automatic identification method comprises the steps of S1, acquiring and preprocessing multi-source monitoring data and geological constraint data, and constructing a landslide monitoring database; s2, based on the weighted sensitivity and residual accumulation of the multi-source monitoring data, analyzing the credibility of the physical property parameters of the slip band; s3, spatial coupling analysis is carried out through multi-source deformation and geometric parameter data, and sliding band form recognition and connectivity adjustment operation are dynamically optimized; s4, dynamic instability analysis is carried out through deformation rate and sliding surface geometric fusion data, and the slip band instability trend is quantified; and S5, performing comprehensive judgment on the stable state of the monitoring unit by fusing the three-dimensional sliding surface probability body and the spatio-temporal evolution index. The problems of inaccurate slip zone parameter inversion, low slip surface judgment precision and early warning lag in regional landslide hidden danger identification are solved.

The invention belongs to the technical field of building engineering intelligent detection, and particularly relates to an unmanned aerial vehicle-based fabricated building defect automatic identification system, which comprises a data acquisition module, a data registration module, a sleeve positioning module, a feature extraction module, a defect identification module and a report output module. A sequential three-dimensional data field is constructed, environmental interference influences are eliminated through preprocessing, visible light image features, infrared thermal imaging features and three-dimensional point cloud geometric features are extracted in parallel, internal and external comprehensive diagnosis of the grouting full state is achieved, grouting insufficient cavities, later-stage contractile cavities and grouting vertical depth deficiency defects are accurately recognized, and the grouting quality is improved. Dynamic tracking, accurate quantification and traceability are synchronously carried out, the problems of single dimension, poor space-time continuity, weak environmental adaptability, difficulty in accurate quantification and traceability of defects and the like of existing prefabricated building sleeve grouting fullness state detection are effectively solved, and the intelligent reliability of quality control is improved.

The invention relates to the technical field of intelligent sorting of decoration garbage, and discloses an automatic recognition system for recyclable components of decoration garbage based on multi-modal feature fusion. The system comprises a multi-modal data acquisition module, a cross-modal feature fusion module, a recyclable component discrimination module and a self-adaptive identification strategy optimization module. The multi-modal data acquisition module constructs a framework based on a sample library, synchronously acquires visible light images, near infrared spectrums and three-dimensional point cloud data and outputs a feature set; the cross-modal feature fusion module performs time domain, frequency domain and space dimension comparison on the feature set and a standard library to generate a hierarchical feature incidence matrix; the recoverable component discrimination module inputs the matrix into a spatial semantic analysis network, generates a spatial thermal distribution diagram in combination with spatial distribution parameters and position information, and locates an enrichment region; and the adaptive module configures parameters according to the parameters, starts multi-modal synchronous acquisition for a high-probability region, and applies a characteristic disturbance test to adjacent types of samples.

The invention relates to the technical field of ship-to-anchor management, and discloses a ship-to-anchor management method for AIS identification deviation and navigation area coverage blind areas, and the method comprises the steps: recognizing the navigation deviation of ship navigation data through employing a ship kinematics model, taking the navigation deviation as AIS signal deviation, and carrying out the dynamic correction of an AIS signal; calculating the AIS signal receiving efficiency of each ship navigation area grid, marking the ship navigation area grid with the coverage blind area, and adaptively optimizing the AIS signal distribution strategy of the ship in the ship navigation area grid with the coverage blind area; and constructing a ship-to-anchor management strategy of the ship navigation area grid. According to the method, accurate correction and optimal distribution of the AIS signals are realized, the problem of positioning errors existing in traditional AIS signals is solved, navigation area coverage blind area identification is carried out, a ship-to-anchor management system adaptive to AIS signal deviation and AIS signal receiving efficiency in a navigation area is constructed, and the safety management level and the communication guarantee capability of the navigation area are remarkably improved.

A method and device for imputing missing values in dual-directional automatic identification system (AIS) data based on deep learning are provided. The method includes constructing a deep-dual-directional chained imputation (DDDCI) model including a forward model and a backward model and predicting a missing value at a prediction time point t, which is to be imputed for AIS data, by using a forward prediction value predicted through learning by the forward model and a backward prediction value predicted through learning by the backward model.

The invention relates to the technical field of maritime traffic monitoring, and discloses a ship lapping behavior identification method and system based on identity and trajectory feature analysis, and the method comprises the steps: accessing a ship automatic identification system (AIS) in real time, and obtaining original target data through a radar and a Beidou sensor; performing fusion processing on the multi-source data, and selecting, marking and fusing target identity information through preset black and white lists and storing the fused target identity information; taking the track appearing earliest as an output track, and adopting an extended Kalman filtering algorithm to correct motion track data in real time and extract features; and marking the static target as a candidate to-be-put-in target through timed traversal, and analyzing the track feature difference between the candidate to-be-put-in target and the surrounding target to realize putting-in behavior recognition. According to the method, the multi-source data fusion technology and the dynamic trajectory feature analysis are creatively combined, the problem that the multi-source ship data fusion precision is insufficient is effectively solved, the accuracy and the real-time performance of offshore abnormal behavior recognition are remarkably improved, and the method has important application value for maintaining offshore safety supervision.

The invention provides an intelligent law enforcement evidence obtaining method and system based on comprehensive information integration, and relates to the technical field of data processing, and the method comprises the steps: synchronously obtaining the original data of a photoelectric evidence obtaining device, a navigation radar, a ship automatic recognition system and a unification device through a multi-source data collection interface, and protocol analysis and standardization processing are carried out to form a unified structured data stream. Space-time fusion of the radar track and the AIS information is realized based on the data stream, a fusion target object is generated, and a dynamic situation target set is constructed; utilizing the abnormal behavior model to automatically identify suspicious behaviors and outputting an alarm; identifying a target type and a board number in combination with the photoelectric video stream, and updating or newly adding a ship file; meanwhile, a photoelectric guiding instruction is generated through azimuth calculation, and automatic target tracking and video collection are achieved; and finally, performing time correlation and integration on the multi-source data, generating a law enforcement evidence chain and automatically outputting an evidence obtaining report. The efficiency of the law enforcement evidence obtaining process can be improved.

The invention relates to the technical field of electrical operation supervision, in particular to a power operation order compliance automatic identification system and method. The power operation ticket compliance automatic identification system comprises a data import module which is used for receiving and analyzing structured operation ticket text data and associating and importing a video file of a corresponding operation execution process; the multi-modal data processing module comprises a voice recognition unit and a video analysis unit; the compliance analysis module is used for comparing and analyzing the output results of the voice recognition unit and the video analysis unit with the operation ticket text data and judging the compliance of the operation process; and the report generation module is used for generating a structured compliance review report. Through cooperative work of the multi-modal data processing module and the compliance analysis module, automatic analysis of an operation order execution process is realized, the problems of missed judgment and misjudgment caused by subjective factors are avoided, and objectivity and fairness of an examination result are ensured.

The invention relates to the technical field of artificial intelligence and computer vision crossing, and discloses a fabric weave structure automatic identification system, which comprises a data acquisition module, a production monitoring module, a production data analysis module, an evaluation module, an adjustment module, an execution module, a feedback module and an upgrading module, precise integration of raw material data and historical process data is realized by establishing a data acquisition module, multi-dimensional data acquisition is realized by a production monitoring module, quantitative calculation of key parameters is realized by a production data analysis module, multi-dimensional performance evaluation is realized by an evaluation module, targeted process optimization is realized by an adjustment module, and real-time parameter adjustment is realized by an execution module. The feedback module realizes closed-loop data circulation, and the upgrading module realizes continuous evolution of the system, so that the system has full-process intelligent management and control capability, the limitation of traditional two-dimensional analysis is effectively broken through, and high precision, high adaptability and high dynamic response of fabric weave structure identification are realized.

The invention discloses a full-leg ship yaw early warning method based on a camera array and an AIS, and the method comprises the steps: deploying a monitoring system based on the camera array in a target water area, and collecting the image data of the target water area; performing laser scanning on the target water area through the unmanned aerial vehicle to obtain laser scanning information; fusing the image data with the laser scanning information to obtain a video monitoring track of the ship; track matching is carried out on track data obtained by an automatic identification system (AIS) and a video monitoring track of the ship; after track matching is completed, weights are distributed according to the reliability of all the data sources, and tracks of all the data sources are fused; and on the basis of the fused trajectory, target water area danger grade division and ship yaw early warning are realized. According to the invention, blind areas monitored by a single camera are effectively compensated, the ship target identification capability is improved, and high-precision space positioning of ship tracks in a navigation channel is realized. And ship multi-source trajectory fusion is realized by monitoring ship trajectory change at the edge of the camera array.

The invention belongs to the technical field of navigation safety analysis, and particularly discloses an intelligent navigation mark and AIS (Automatic Identification System) data collaborative navigation safety improvement method, which comprises the following steps: acquiring a navigation channel image through a navigation mark visual sensor, preprocessing the navigation channel image, and generating an initial detection frame through multi-scale detection; an NMS threshold value is dynamically configured according to the physical size of the ship, and a final detection frame with a physical size label is output; extracting ship visual feature vectors, distributing feature updating strategies for multiple ships according to physical sizes, predicting positions through historical tracks when the ships move out of a visual field, and triggering re-identification; and finally, a visual tracking result and real-time AIS data are fused, and a channel risk early warning and navigation strategy is generated. According to the method, the problems of missing of a front-end sensing layer, missing detection caused by the fact that an NMS mechanism is not involved, shielding of target re-identification design and re-identification trigger logic blank and the like in the prior art are solved in a targeted manner, and the accuracy and reliability of navigation safety guarantee are comprehensively improved.

The embodiment of the invention provides a port piloting method and system for a target ship. The method comprises the following steps: receiving propulsion and control system data, automatic identification system data, environment perception data and ship motion attitude data of the target ship sent by a shipborne terminal; according to the propulsion and control system data, the environmental perception data and the ship motion attitude data, determining a real-time control performance boundary of the target ship through a ship digital twin model; fusing the real-time maneuvering performance boundary, the automatic identification system data and the environment perception data, and constructing a dynamic risk field taking the target ship as the center; taking the dynamic risk field and the ship digital twin model as constraints, performing multi-target rolling optimization, and generating a piloting instruction sequence of a target ship; and sending the piloting instruction sequence to a pilot intelligent terminal for display. According to the embodiment of the invention, the fundamental conversion from static empirical piloting to dynamic data-driven and self-adaptive intelligent piloting can be realized.

The invention discloses a GNSS (Global Navigation Satellite System) navigation anti-interference antenna for an intelligent boat and an anti-interference method thereof, which belong to the technical field of GNSS radio navigation and comprise an antenna array, an anti-interference signal processing unit, an AIS (Automatic Identification System) receiving module and a data fusion processing module. The anti-interference signal processing unit forms space null in an interference coming direction through a self-adaptive digital beam forming algorithm so as to suppress and suppress interference; and the data fusion processing module performs kinematics cross validation on the external reference ship information acquired by the AIS receiving module and the GNSS positioning result of the ship so as to accurately identify deception interference and output an alarm. According to the invention, through dual mechanisms of suppression suppression and cheating verification, the reliability and safety of radio navigation positioning of the intelligent boat in a complex electromagnetic environment can be significantly improved.

The application belongs to the technical field of intelligent detection of building engineering, and particularly relates to an automatic defect identification system for fabricated buildings based on an unmanned aerial vehicle, which comprises a data acquisition module, a data registration module, a sleeve positioning module, a feature extraction module, a defect identification module and a report output module. The system collects multi-source heterogeneous data through the unmanned aerial vehicle, constructs a time-sequenced three-dimensional data field, eliminates the influence of environmental interference through pretreatment, extracts visible light image features, infrared thermal imaging features and three-dimensional point cloud geometric features in parallel, realizes comprehensive diagnosis of the internal and external filling state of grouting, accurately identifies insufficient hollows of grouting, late shrinkage hollows and defects of insufficient vertical depth of grouting, simultaneously carries out dynamic tracking, accurate quantification and tracing, effectively solves the problems that the existing detection of the filling state of sleeve grouting of fabricated buildings is single in dimension, poor in space-time continuity, weak in environmental adaptability and difficult to accurately quantify and trace defects, and improves the intelligent reliability of quality control.

The invention discloses a coal pillar damage threshold prediction and crack automatic identification system based on deep learning. The system comprises six units including a multi-dimensional heterogeneous coal pillar data acquisition and fusion unit, an optimization hypergraph neural network feature extraction unit and the like. The system integrates elastic modulus, Poisson's ratio and other multi-source data through a multi-dimensional heterogeneous coal pillar data acquisition and fusion unit, constructs a hypergraph structure by using an optimized hypergraph neural network to extract implicit features, establishes parameter and damage threshold nonlinear mapping based on a multi-dimensional heterogeneous coal pillar data analysis model, and completes accurate calculation through a specific formula. Through cooperation of all unit sub-modules, deep analysis of coal pillar crack image features is realized, and finally, cracks are automatically identified in combination with image features and a damage threshold prediction result. The system overcomes the problems that traditional multi-source data fusion is difficult and the model prediction precision is low, provides an intelligent monitoring means for coal mine safety production, and has technical advancement and application value.

This invention relates to a method for estimating the azimuth angle of high-frequency over-the-horizon radar signals based on an optimized layout of multiple orthogonal dual magnetic antennas. The method includes: constructing a compact optimized layout adapted to a shipborne platform using two centrally co-located and orthogonally placed magnetic antennas as basic units; and proposing an azimuth angle estimation method based on this layout: first, using information from the ship's Automatic Identification System (AIS) to perform amplitude and phase calibration on the received signal; then, normalizing the signal's magnitude; finally, using an ideal steering vector and a multi-signal classification algorithm to estimate the direction of arrival (DOA) of the target signal. This method inherits the target detection performance advantages of magnetic antennas while effectively adapting to the spatial constraints of shipborne platforms. The proposed azimuth angle estimation method effectively eliminates the influence of environmental interference on signal reception, achieving azimuth estimation for weak target signals, and providing key technical support for the practical application of small shipborne high-frequency over-the-horizon radar.

The utility model relates to the technical field of lifesaving devices, in particular to a life jacket position indicating lamp with an AIS (Automatic Identification System) function, which comprises a sealing shell, a lamp holder, a lamp holder, a lamp holder and an AIS (Automatic Identification System) module, the sealing shell comprises an upper cover and a lower cover, and the upper cover and the lower cover are hermetically mounted together; the water falling signal induction module comprises a controller, the controller is provided with two water touch terminals, the two water touch terminals are not in contact with each other, and when the two water touch terminals fall into the sea at the same time, the two water touch terminals are communicated through seawater, so that when a person wearing the device falls into the sea, the water touch terminals are not in contact with each other; signals generated when the two water touch terminals are communicated with seawater are transmitted to the controller, and then the controller transmits personnel falling-into-water signals to the outside. The controller is arranged in a cavity between the upper cover and the lower cover, and the water contact terminal penetrates through the lower cover and is arranged outside the lower cover. According to the device, people falling into the sea can be quickly searched and rescued.