205 results about "Automatic Identification System" patented technology

The invention discloses an automatic geological disaster recognition system and method based on multi-source remote sensing data, and particularly relates to the field of geological disaster recognition, and the system comprises a multi-modal remote sensing data acquisition module, a cross-domain physical fusion module, a spatio-temporal evolution decision module, a multi-cascade early warning decision module, an optimization control module and a visualization module. According to the geological disaster automatic identification system and method based on the multi-source remote sensing data, virtual features are generated through a cross-domain physical fusion module by using a domain adversarial network, the model generalization ability during cross-domain application is improved, physical association among the multi-source remote sensing data is deeply mined, and dependence on manual design rules is eliminated; through a three-layer processing chain technology composed of a spatial-temporal feature extraction layer, a dynamic graph evolution layer and a critical recognition layer, the capability of capturing disaster features in a complex geological environment is effectively improved, especially the recognition precision of precursor tiny deformation is improved, and the risk of missing report is reduced.

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 relates to a ship trajectory data mining and macro-micro fusion route prediction method and system, and the method comprises the steps: S1, extracting ship trajectory data near an inland waterway from the data of an automatic ship recognition system, and carrying out the cleaning, filtering, sorting and preliminary analysis; s2, performing clustering analysis by using the features of the ship trajectory data, preliminarily mining trajectory features in combination with manual annotation, and establishing a ship trajectory database; s3, on the basis of historical data, constructing a deep learning model aiming at the inland river driving key path nodes of the ship from a macroscopic level; s4, training a deep learning model combining a convolutional neural network and a long-short-term memory network based on historical data, and predicting a ship position track from a micro level; and S5, performing macro-micro fusion prediction on the new ship data to obtain a ship navigation path and trajectory prediction coupling result. According to the method, coupling of macro and micro prediction means is adopted, and a certain guarantee is provided for the precision of ship trajectory prediction.

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 provides a hardware fitting automatic identification technology system and method, and the system employs a cascaded depth separable convolutional network and a feature pyramid network to extract the multi-scale features of a hardware fitting, and improves the feature extraction capability through integrating a channel attention mechanism and a space attention mechanism. The system combines global features and local features, carries out global average pooling on a multi-scale feature map to obtain global features, and carries out grid division on a target coding feature map to obtain local region features. Through a pre-stored prototype feature library, similarities between the global features and the local features of the to-be-identified hardware fitting and the prototype features of each category are calculated respectively, and a final similarity score is obtained through multi-level fusion, so that accurate identification of the hardware fitting category is realized. The technical scheme can effectively improve the accuracy of hardware fitting recognition, and is especially suitable for hardware fitting recognition scenes with high appearance similarity.

The invention provides an unmanned aerial vehicle visual inspection and AIS data association method and device, and the method comprises the steps: carrying out the target detection of an image photographed by an airborne image sensor on an unmanned aerial vehicle in maritime cruise, obtaining the azimuth features of a ship in the image, and enabling the azimuth features to comprise the position information and the course information; according to a ship automatic identification system installed on each ship, acquiring AIS data of each ship; and matching the azimuth features of the ships in the image with the azimuth features in the AIS data of each ship, obtaining the AIS data corresponding to the image, and associating the image with the AIS data corresponding to the image. Visual inspection and AIS data association are realized, course information is additionally introduced on the basis of position feature matching, and the accuracy of visual inspection and AIS data association is improved.

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 belongs to the technical field of behavior prediction, and particularly relates to a ship anomaly detection method and device based on TCN trajectory prediction. The method comprises the following steps: S1, acquiring AIS data given by a ship automatic identification system in a set time period before the current moment; s2, predicting the position of the ship at the next moment based on a pre-trained time convolutional network; s3, determining an error between the actual position and the predicted position of the ship at the next moment; s4, when the error exceeds a threshold value, determining that the position is an abnormal point; s5, continuously appearing abnormal points serve as abnormal track segments, the time length of the abnormal track segments serves as a weight calculation factor, and the abnormal scores of the abnormal track segments are calculated; and S6, determining the abnormal level of the abnormal track segment according to the abnormal score and the plurality of abnormal degree intervals. According to the invention, the detection precision of ship abnormal behaviors is 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 relates to the technical field of automatic detection and early warning of weather radar gust front, and discloses a weather radar gust front automatic identification system based on deep learning, which comprises a radar data acquisition and preprocessing module used for acquiring radar echo data from weather radar equipment, carrying out format conversion on the data, and sending the converted data to a server; the radar reflectivity is converted from a polar coordinate format to a Cartesian coordinate format, and meanwhile, normalization processing of the radar reflectivity value is carried out; and the feature extraction and region candidate generation module is used for performing feature extraction on the radar image by adopting a deep convolutional neural network and generating a target region candidate box through a region candidate network, and the region candidate network comprises a classification branch and a regression branch. According to the method, rapid and accurate gust front identification is realized through a deep learning target detection and mask segmentation technology, the stability and robustness of detection are remarkably improved, false alarms and missing alarms are effectively reduced through dynamic alarm threshold calculation and trend analysis, and the accuracy and timeliness of alarm are improved.

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 discloses a data-driven ship abnormal behavior online detection and early warning method and system. The method comprises the following steps: carrying out data cleaning on original ship AIS (Automatic Identification System) trajectory data; compressing the cleaned trajectory data by adopting a trajectory data compression algorithm, calculating trajectory similarity with all ships based on a time sequence similarity algorithm, and constructing a trajectory similarity measurement matrix; performing clustering analysis on the trajectory similarity measurement matrix by using a trajectory clustering algorithm to obtain a clustering result; backtracking the cleaned trajectory data based on a clustering result, and determining a ship navigation area by using a boundary extraction algorithm; utilizing a historical average ship trajectory prediction algorithm to generate a ship prediction trajectory under a time length threshold value; and in combination with the ship navigation area and the ship prediction trajectory, ship abnormal behavior detection is realized through angle difference change analysis and matching of the ship trajectory point and the ship navigation area, and early warning is carried out. According to the invention, real-time, efficient and accurate judgment and early warning of the ship trajectory can be realized.

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 provides a ship collision monitoring and early warning method and system for dams and navigation facilities, and relates to the technical field of shipping traffic. The method comprises the following steps: respectively acquiring images, messages and radar point cloud data through a camera, a ship automatic identification system and a millimeter wave radar at a main monitoring point and an auxiliary monitoring point, projecting the radar data to an image plane to form a radar image after time-space synchronization processing, sending the radar image to a ship identification network, and identifying a two-dimensional surrounding frame and a type of a ship; then, obtaining a three-dimensional bounding box and a type of the ship by utilizing epipolar geometric processing, comparing the three-dimensional bounding box and the type with message data, and issuing third-level early warning when the three-dimensional bounding box and the type are inconsistent with the message data or no related type exists; meanwhile, the future navigational speed and course of the ship are predicted according to a plurality of three-dimensional bounding boxes of the ship, and if the future navigational speed and course exceed a set threshold value or the risk of impacting a dam exists, secondary early warning is issued; if the ship triggers third-level and second-level early warning at the same time, first-level early warning is issued. According to the scheme, the accuracy of early warning and the completeness of an early warning mechanism can be greatly improved.

The invention discloses a port ship comprehensive analysis and entry and exit situation prediction method, which comprises the following steps of S1, drawing a port area, and obtaining real-time data and historical data of an AIS (Automatic Identification System) in the port area; s2, preprocessing the data in the S1 to obtain dynamic data and static data of each ship; s3, constructing a visual port model, generating a map containing a port area on a display screen, marking the position of each ship on the map in real time, and generating a corresponding mark; and S4, constructing a ship comprehensive analysis model, selecting a corresponding mark on the map, displaying the corresponding historical track and predicted track on the map, and generating an information table of the corresponding ship. According to the invention, data can be simplified and visually displayed in front of management personnel, the state of each ship is intelligently analyzed, and the working difficulty of the management personnel is reduced.

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 micro-grid multi-energy collaborative scheduling, and discloses a port micro-grid and ship energy system collaborative enhancement method and system. According to the specific implementation scheme, the method comprises the steps that the real-time request power supply amount of a port and the actual power supply amount of reverse power supply of a ship in the extreme weather are predicted according to ship automatic identification system data, actually measured extreme weather data and the state of a port power system; according to the real-time request power supply amount and the actual power supply amount, calculating the minimum operation cost and the toughness coordination index of the port, and determining the electricity purchase price; according to the electricity purchase price and a maximum income objective function of the ship, determining a protocol power supply amount of reverse power supply of the ship; and determining the real-time power supply amount of the port power generation equipment according to the real-time request power supply amount and the protocol power supply amount. According to the invention, the robustness of the coordination strategy can be improved.

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.