93 results about "Automatic Identification System" patented technology

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 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 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 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 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 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.

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.

1. Name of the product in this design: Shipborne Automatic Identification Panel (FT-A5900). 2. Application of this design: Shipborne marine remote sensing automatic identification system for global satellite positioning system. 3. The key design feature of this product is its shape. 4. The image or photograph that best illustrates the design's key points: a 3D model.

A method of identifying a vessel as well as a vessel identifying device and a floating object and land installation including such a vessel identifying device. The vessel identifying device includes a processor operative to obtain a position of a vessel being sensed by a sensor, assign an identity to the vessel, which identity is assigned according to an identity assignment scheme of at least a part of a maritime tracking system, which maritime tracking system is an Automated Identification System (AIS), and provide an identification of the vessel according to the maritime tracking system, the identification including the assigned identity and the position of the vessel, and order a transmitter to transmit the identification of the vessel.

The invention relates to the technical field of industrial process automatic monitoring and control, and particularly discloses a phase-change absorbent phase-splitting tank phase-splitting interface automatic identification system. The system mainly solves the technical problems of inaccurate measurement, low reliability, dependence on artificial experience and incapability of realizing intelligent closed-loop control existing in a traditional split-phase interface identification method under complex working conditions of foam, an emulsion layer, medium characteristic fluctuation and the like. According to the invention, vision, waveform and pressure information is cooperatively acquired through the multi-modal data acquisition module; the intelligent data processing and fusion module performs intelligent analysis on the multi-source data by using a fusion algorithm and an AI model to generate accurate interface information; the interface dynamic prediction and decision-making module realizes trend prediction and intelligent decision-making based on time sequence analysis; the control signal output and execution module drives an execution mechanism to realize closed-loop control according to the decision result; and the man-machine interaction and data management module provides panoramic visualization and data management.

The invention discloses a ship automatic identification system information and video data fusion method, which comprises the steps of preprocessing AIS information, extracting and tracking a ship target from a video by using a YOLOX algorithm in combination with a ByteTrack algorithm and Kalman filtering, and generating visual data of historical and predicted trajectories; performing space-time alignment on the AIS data through a constant-speed model to generate a corresponding pixel coordinate sequence; constructing a timing constraint residual mechanism to calculate a similarity matrix of the visual trajectory and the AIS data, and iteratively calculating the minimum matching cost by using a screening rule and a Hungary matching algorithm to obtain an optimal matching pair; aIS information with the most matching times of each ship is screened out through a sliding window voting system, and stable and accurate fusion of the AIS and video data is achieved; the fusion accuracy can be improved, noise can be effectively resisted, and the anti-interference capability is high; and the calculation complexity is reduced, and error accumulation caused by overlong time span is reduced.

The present application belongs to the technical field of ship fuel consumption prediction and big data analysis, and relates to a kind of operating ship fuel consumption prediction method based on deep learning model.The method includes step 1: synchronously collecting multi-source data from ship automatic identification system, engine room monitoring system and ERA5 meteorological database, and pre-processing;Step 2: the pre-processed data is input into the pre-trained CNN-BiLSTM-Attention model for ship fuel consumption prediction;Wherein, the CNN-BiLSTM-Attention model is composed of convolutional neural network, bidirectional long short term memory network and time attention module;The model uses a composite loss function based on speed-power-fuel physical constraints.The present application performs better in terms of prediction accuracy, robustness and fitting effect.Compared with the prior art, the present application has higher accuracy and reliability in predicting ship fuel consumption.

The invention provides a boundary flow control method and system based on a busy water area macroscopic basic diagram, electronic equipment and a storage medium, and the method comprises the steps: building a water area macroscopic basic diagram which can accurately reflect the macroscopic law of mixed traffic flows of different ship types through collecting and processing the data of a ship automatic recognition system; determining an optimal accumulated ship number target when the overall navigation efficiency of the system is highest; delimiting an easily-congested key area in the target water area as a core water area, and establishing a ship traffic flow dynamic balance model taking the optimal target as a balance point for the core water area as a control object; a fuzzy self-adaptive PID controller is designed and applied, the PID controller can sense the deviation between the actual number of ships in a core water area and a target value and the variation trend of the deviation in real time, control parameters are intelligently adjusted on line through a built-in fuzzy inference engine, the optimal boundary flow control rate is dynamically calculated and output, and therefore the flow of the ships entering the core water area is accurately adjusted. The number of the ships in the water area is actively and stably maintained in the optimal interval.

This application relates to the fields of deep learning and intelligent transportation technology, and discloses a method and system for predicting ship travel services based on deep learning. The method includes the following steps: generating supply and demand time-series feature tensors, tidal current vector fields, and wind speed vector fields based on meteorological and hydrological forecast data, automatic identification system data, battery management system data, and travel order data; calculating a bidirectional equivalent navigation energy consumption matrix along the directed channel edge of the physical wharf node; mapping the updated feature tensor by the absolute value of the difference and the energy consumption threshold mask, and connecting the weight matrix after a discharge energy threshold truncation operation; generating a decay factor as a compensation feature tensor using the tidal current time-series gradient; inputting the supply and demand gap prediction value into the spatiotemporal graph neural network and issuing navigation deployment instructions. This invention adopts a technical solution of projecting the tidal current and wind speed vector fields onto the directed channel edge and calculating the bidirectional equivalent navigation energy consumption matrix between adjacent nodes, achieving the technical effect of constructing an asymmetric navigation topology network.

Embodiments of the present application provide a ship destination identification method, device, electronic equipment and readable storage medium, wherein the ship destination identification method comprises: screening at least one target sample ship from ship automatic identification system data; extracting ship voyage data of each target sample ship within a preset historical period; determining a historical destination information set; obtaining structured standard destination text data; converting the standard destination text data into a high-dimensional semantic vector, and clustering the semantic vector through a clustering algorithm to determine a port semantic class library representing different standard ports; obtaining a large language model; inputting destination field text data of a to-be-identified ship into the large language model; and identifying corresponding standard port information from the port semantic class library according to the destination field text data. Through the scheme of the present application, different expression modes of the same port are normalized, and the consistency of ship destination data is improved.

The invention relates to the technical field of communication, in particular to a dynamic and static secret key encryption technology and method applied to an encryption ship automatic identification system. Comprising a ship name, a call number, a ship length, a ship width, a ship type, a ship native place port and positioning antenna position information, and the ship AIS terminal module is configured to collect ship static data, dynamic data and navigation related data and broadcast the data through a very high frequency channel; the dynamic data comprises longitude and latitude coordinates of the ship, the speed to the ground, the course to the ground, the bow direction, the navigation state and the steering rate, and the navigation related data comprises the draft of the ship, the dangerous cargo type, the destination and the estimated arrival time; the core problem that a traditional AIS system is insufficient in data protection capacity in a dynamic threat environment can be solved.

The embodiment of the invention provides a ship target correlation parameter generation method and device, electronic equipment, a medium and a chip, and relates to the technical field of maritime affair supervision, and the ship target correlation parameter generation method comprises the steps: obtaining multi-source monitoring data of a target water area; determining at least one first detection target according to the satellite image data; determining at least one second detection target according to the ship automatic identification system data; determining at least one associated target result for the first detection target and the second detection target according to the first position data and the second position data; determining at least one multi-dimensional risk feature parameter for each associated target result; obtaining a preset weight coefficient; and performing weighted fusion calculation on the at least one multi-dimensional risk feature parameter according to a preset weight coefficient, and determining an associated feature parameter of the target ship corresponding to the associated target result. According to the scheme, the reliability of credibility judgment in the ship monitoring process is improved.

The embodiment of the invention provides a ship waiting time length prediction method and device, electronic equipment, a readable storage medium and a chip, and the method comprises the steps: determining navigation data in a ship automatic recognition system; determining a first area corresponding to a ship lock passing lock chamber according to the selected ship lock; determining a historical lockage record; determining a sample ship passing through the first area according to the historical lockage record; the sample ships are classified, and at least one ship type is determined; according to the selected ship lock, determining a second area corresponding to anchoring and waiting for the lock; determining the gate waiting duration of the plurality of sample ships corresponding to the second area; determining a gate waiting parameter corresponding to each ship type according to the gate waiting duration; determining a target ship and trajectory parameters corresponding to the target ship; and according to the trajectory parameters and the gate waiting parameters, determining the predicted gate waiting duration and the predicted gate passing time of the target ship. Through the scheme of the invention, the ship waiting time length prediction precision and the ship lock scheduling efficiency are improved.

The application discloses a kind of based on OCR's scrap steel recycling pound single automatic identification system, including customer pound single configuration module for defining pound single pound single rule according to the pound single picture uploaded by customer;OCR training module is used to obtain the character in the pound single picture uploaded by customer is corrected and stored;OSS object storage module is used to store the pound single picture uploaded by customer;Pound single OCR image-text recognition module is used to obtain pound single picture and process, text information in pound single picture is identified based on OCR image recognition engine, and text information is converted into structured pound single data according to the pound single rule of customer pound single configuration module;Relational database storage module is used to store the pound single data obtained by pound single OCR image-text recognition module;Manual check module is used to provide pound single picture, pound single data to artificial and recheck.Pound single structured data information is identified automatically according to pound single picture, and rechecked by artificial, improve pound single recognition efficiency and accuracy.

The embodiment of the invention provides an airline reconstruction method and device among multiple ports, electronic equipment, a readable storage medium and a chip, and the method comprises the steps: obtaining a preset port sequence and a port event database; screening out a sample ship list conforming to a port sequence in a set observation time period from the port event database; based on the sample ship list, extracting ship automatic identification system data of at least one sample ship from a starting port to a destination port; processing the data of the ship automatic identification system, and identifying a plurality of navigation track segments; fusing and reconstructing the plurality of navigation track sections, and determining a continuous route from the starting port to the destination port; and determining a route reconstruction path from the starting port to the destination port according to the plurality of continuous routes. Through the scheme of the invention, the intelligent and scientific level of shipping management is improved while the calculation precision of the air route is enhanced.

This invention discloses an automatic identification system for digital instrument testing, comprising an image acquisition module, an image preprocessing module, a character recognition module, a data judgment module, a data processing module, and a certificate generation module. The invention acquires the reading window image of the digital instrument under test through the image acquisition module, and automatically extracts and parses the displayed digital content by combining image preprocessing and character recognition algorithms. This achieves non-intrusive reading without requiring any hardware or software modifications to the tested digital instrument, making it applicable to most digital instruments. The invention connects the image acquisition module locally to a computer, avoiding the risks of privacy violations and data leaks associated with using webcams. Furthermore, the invention ensures standardized operating procedures and accurate data collection by performing automated logical verification on the identified digital information. This invention achieves automated and standardized reading, improves the testing efficiency of digital instruments, and eliminates human error.

This invention discloses a lightweight automatic instrument identification system, comprising: a data acquisition unit including an instrument image field acquisition device for real-time acquisition of instrument images; a processing unit including a front-end module, an instrument preprocessing module, an instrument alignment processing module, and an instrument identification module for processing the instrument images to identify instrument data within them; a transmission unit including a transmission module and an instrument data module for remotely transmitting the identified instrument data from the instrument images to a database in the management department; and an operation and maintenance unit for providing remote operation and maintenance services to the data acquisition unit, processing unit, and transmission unit. This lightweight automatic instrument identification system can acquire and automatically identify data displayed on the instrument panel in real time, enabling real-time acquisition, identification, transmission, and remote operation and maintenance of data displayed on the instrument panel, providing data support and information services for modern enterprise management.

The invention belongs to the technical field of medical artificial intelligence, discloses an aMCI risk automatic identification system based on a polysleep electroencephalogram, and aims at solving the problems that an existing aMCI identification technology is high in subjectivity, high in cost and difficult to popularize. The system comprises a data input module, a PSG parameter automatic extraction module, an aMCI risk prediction module and a result visualization output module which are in communication connection in sequence. The data input module receives PSG original data and preprocesses the PSG original data; a PSG parameter module calls YASA, Spindler Toolbox and K complex wave detection methods, and nine core parameters are automatically extracted; the risk prediction module outputs aMCI risk probability through LASSO regression and column diagram models; and the result module generates a PDF report, is in butt joint with a medical platform and stores historical data. The system realizes full-process automation, predicts AUC to be more than 0.95, adapts to multiple scenes, and provides an accurate and economical tool for early recognition of aMCI.

This invention discloses a salient target detection method based on SAR / AIS multimodal asymmetric fusion, belonging to the field of intelligent remote sensing image processing technology. Focusing on the synergistic utilization of SAR (Synthetic Aperture Radar) and AIS (Automatic Identification System) multimodal information, and based on ship distribution density analysis of long-time-series AIS data, robust feature extraction methods for SAR images, and asymmetric multimodal fusion strategies, a salient ship detection method system is proposed. By introducing an improved deep coding model, a modality-adaptive feature reshaping perception module, and a feature attention guidance module, a salient ship detection workflow suitable for complex maritime scenarios is constructed, achieving efficient fusion of SAR / AIS multi-source data and automatic extraction of salient features of ship targets, thus improving the accuracy and engineering application capabilities of ship detection.

The invention relates to a trajectory prediction model and method in a coastal commercial fishing boat encounter scene, and the method comprises the steps: extracting ship marching information from a historical record according to a timestamp, carrying out the preprocessing of automatic identification system (AIS) data, and extracting encounter trajectory data from the data; visualizing the extracted encounter trajectory data according to different densities in unit areas, and selecting a ship encounter interaction feature data set according to the densities; executing a pooling function on the hidden state of the adjacent ships of which the aggregation of the target ship is within a predefined spatial distance threshold, integrating the pooled information into the input of a long short-term memory (LSTM) network, constructing a ship encounter trajectory prediction model based on Encounter-LSTM, and reconstructing a loss function Loss; training and finishing a ship encounter trajectory prediction model based on the data set; and utilizing the trained ship encounter trajectory to predict the trajectory in the encounter scene. The future track of the ship can be effectively predicted through extraction and training of multi-ship encounter track data.

The invention belongs to the technical field of artificial intelligence, and discloses a method for establishing a ship trajectory prediction model, which comprises the following steps: acquiring data including ship position, course, speed, ship draft and ship type parameters in real time through a ship automatic identification system, introducing a ship motion model by taking Transform containing a multi-head self-attention mechanism as a basic framework, and establishing a ship trajectory prediction model; the attention weight is indirectly corrected through the influence exerted on the total loss function by the residual constraint of the theoretical physical quantity and the predicted physical quantity; and through multiple rounds of training, based on the corrected attention weight matrix, the improved Transform model can generate prediction conforming to ship dynamics. The method overcomes the problems that pure data driven model prediction is poor in reliability and interpretability, and a large amount of training data is needed, and is especially suitable for scheduling of port and inland river shipping. The invention also provides a recording medium storing the program of the method and a system comprising the medium.

The invention provides an AIS (Automatic Identification System) and video associated positioning method, which comprises the following steps of: receiving a ship broadcast signal in real time by using AIS receiving equipment, and analyzing to obtain identity information, geographic position data and a corresponding generation timestamp of a ship; the method comprises the following steps: acquiring real-time video stream data through camera equipment in a monitoring sea area, identifying a ship target in the video stream data, and determining a pixel coordinate position of the ship target in a video picture and a corresponding acquisition timestamp; converting the geographic position data of the ship into a theoretical projection position in a visual field range of the corresponding camera equipment based on the parameters of the camera equipment by adopting a coordinate conversion algorithm; the Euclidean distance between the theoretical projection position and the pixel coordinate position of the ship target in the video picture is calculated, and the time synchronization error between the generation timestamp and the collection timestamp is calculated; and if the Euclidean distance is smaller than a preset association threshold value and the time synchronization error is within a preset tolerance range, determining that matching succeeds, and establishing an association relationship between the ship identity information and the ship target.