254 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 discloses an automatic endoscope pipeline inner wall crack identification system based on video deep learning, and relates to the technical field of pipeline detection, and the system comprises an image acquisition module, an image preprocessing module, a pipeline inner wall model establishment module, a deep learning identification module and a result output module. The image acquisition module is used for acquiring images in the pipeline by utilizing the autonomous moving endoscope device, and a multi-wheel driving structure, a self-adaptive rubber track, a high-precision sensor and the like of the image acquisition module ensure that the acquisition process is stable; according to the invention, the automation level and accuracy of pipeline inner wall crack detection are obviously improved, the autonomous moving endoscope device is matched with three-dimensional illumination and multi-angle image acquisition to ensure that high-quality pipeline inner wall images can be obtained in a complex environment, and through calibration registration, noise suppression and contrast enhancement of the image preprocessing module, the pipeline inner wall crack detection accuracy is improved. And high-quality input data is provided for a deep learning model, so that the accuracy and reliability of crack identification are improved.

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 a ship traffic flow prediction method and device, electronic equipment and a readable storage medium. The method comprises the steps of collecting historical ship traffic flow data provided by a ship automatic identification system (AIS); performing time serialization and spatial dimension integration processing on the historical ship traffic flow data to construct a ship traffic flow time sequence three-dimensional tensor; performing feature coding on the three-dimensional tensor of the ship traffic flow time sequence, and extracting features with characterization capability; and performing extraction and fusion of time features and space features on the features with the characterization capability to generate a prediction result of the ship traffic flow. According to the invention, the distance graph, the interaction graph and the dynamic attention graph are constructed based on the space-time multi-graph convolutional network model to describe the spatial correlation among the nodes in the maritime traffic network, and the maritime traffic prediction and management are optimized.

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 embodiment of the invention provides a ship navigation track sensing method and system, electronic equipment and a storage medium, and belongs to the technical field of ship sensing enhancement. According to the method, first navigation tracks of multiple ships in a detectable range are obtained through a distance detection sensor to obtain a first data set, and second navigation tracks of the multiple ships are obtained through an automatic ship identification system to obtain a second data set; and then carrying out correlation analysis on the tracks of the first data set and the second data set based on a track point matching algorithm, and correspondingly removing the correlated first navigation track and second navigation track from the data sets according to a correlation result so as to reduce the subsequent calculation amount. The trajectory similarity between every two navigation trajectories in the first data set and the second data set is calculated, the first navigation trajectory and the second navigation trajectory which are matched with each other are determined according to the trajectory similarity, the first navigation trajectory which is matched with each other is corrected according to the second navigation trajectory, and therefore the accuracy of ship navigation trajectory detection 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 relates to a multi-ship route dynamic optimization method and device. The method comprises the following steps: firstly, constructing a water transportation network based on original ship automatic identification system data; then, on the basis of mapping nodes, arcs, passing points and route weights of the water transportation network, a mixed integer programming model is constructed, and an objective function and constraint conditions associated with dynamic optimization of routes of multiple ships are defined; and finally, solving based on the objective function and the constraint condition by adopting an improved adaptive large-field search algorithm to obtain a fleet path plan. And proposing a fleet route optimization mathematical model and a heuristic algorithm capable of processing a large-scale fleet. According to the model, various factors such as navigation characteristics, transportation requirements and time windows of ships are fully considered, and it is ensured that route planning not only meets economic benefits, but also can meet the requirements of safety, high efficiency and the like. Interference information in AIS data is deeply mined, the influence degree of interference on route planning is analyzed, and effective prevention and response measures are provided.

The invention discloses a gold wire bonding abnormal point automatic identification system and method based on a neural network architecture, and relates to the technical field of image processing. A detection image of a product is obtained through shooting of an electron microscope and input into the backbone network, and features of different scales are extracted through multiple convolution and pooling operations; then, in a hybrid encoder, through a series of convolution and up-sampling operations, adjusting the size of the feature map to carry out feature fusion; and then, through down-sampling operation, splicing with the previously fused feature map, and carrying out feature fusion again. And finally, inputting the three feature maps with different scales into a decoder, and carrying out target detection, thereby completing automatic identification of the gold wire. The product image is processed through the improved RT-DETR model, and automatic gold wire identification and defect marking are realized, so that an operator can quickly position according to the defect marking and complete the gold wire refilling operation, and the production efficiency is 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 provides a maritime photovoltaic maritime navigation monitoring system, and relates to the technical field of maritime safety monitoring. The maritime photovoltaic maritime navigation monitoring system comprises a radar system, a video monitoring system, a ship automatic identification system, an electronic fence system, a very high frequency communication system, a hydro meteorology acquisition system, an acoustic optical driver isolation system and a processing device, the processing equipment is used for respectively acquiring first navigation data of the target ship detected based on the radar system, second navigation data of the target ship detected based on the video monitoring system and third navigation data of the target ship detected based on the ship automatic identification system; and fusing the first navigation data, the second navigation data and the third navigation data to determine target navigation data of the target ship. According to the invention, the comprehensiveness and accuracy of offshore safety monitoring can be improved.

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 provides a ship autonomous positioning precision enhancement method under a GNSS interruption condition, and belongs to the technical field of ship intelligent navigation autonomous positioning. The method comprises the following specific steps: acquiring the latitude and longitude of a target ship through an automatic ship identification system (AIS); obtaining the distance and orientation of the target ship relative to the ship through a radar; based on the latitude and longitude of the target ship and the distance and orientation of the target ship relative to the ship, obtaining an electronic plotting ship position of the ship; the electronic plotting ship position of the ship and navigation positioning parameters output based on an inertial navigation system (INS) are fused through a volume Kalman filtering algorithm to serve as the ship position after precision enhancement. According to the invention, the electronic plotting ship position of the ship is obtained through AIS information and radar information; and the electronic plotting ship position of the ship is used for performing precision enhancement on the ship position output by the INS, so that the ship position after precision enhancement is obtained. The problem that the positioning precision of an existing INS / GNSS integrated navigation system is reduced due to the fact that measurement data are lost under the condition that the GNSS is interrupted is solved.

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 discloses a real-time track fusion algorithm and system based on a radar and an AIS (Automatic Identification System) and application, and particularly relates to the technical field of shipping monitoring and management, which is used for carrying out batch elimination processing on targets subjected to batch elimination. And for the associated tracks, association judgment is performed on the associated radar tracks and AIS tracks in the latest time window in a manner of sliding the time window, and if the number of unassociated points exceeds a misassociation threshold, it is judged that the associated tracks are misassociated, and de-batching is performed on the associated tracks. The method comprises the following steps: calculating the correlation degree of a radar track to be correlated and an AI track to be correlated in pairs to obtain a correlation matrix, setting a coarse correlation time window, and performing correlation degree calculation on track points in the time window; and merging the regional associated tracks. And on the basis of an incidence matrix obtained by coarse correlation, through a KM algorithm, a radar and AI target combination with the highest correlation degree is screened out to serve as a correlation track.

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 invention provides a boundary area monitoring method and system for ship operation, and the method comprises the steps: determining a boundary area of an oil field area, and determining a monitoring range based on the boundary area; adopting an AIS (Automatic Identification System) to identify a ship in a monitoring range, and obtaining AIS data information of the ship; acquiring meteorological and hydrological data information of the monitoring range; performing ship operation prediction according to the ship AIS data information and the meteorological and hydrological data information to obtain ship prediction information; performing boundary area monitoring analysis on the ship prediction information in combination with an oil field area, determining whether ship operation has safety risks or not, and obtaining a monitoring analysis result; and performing security risk warning based on a monitoring analysis result. According to the invention, comprehensive real-time monitoring of the oil field area is realized, and safety risk warning can be carried out in time when safety risks exist in ship operation, so that ship operation adjustment can be carried out in time aiming at ships, safety accidents are avoided, and potential safety hazards are reduced.

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.