1701 results about "Edge based" patented technology

The invention discloses a distributed slope monitoring system based on edge cloud collaborative intelligent adaptive decision. The distributed slope monitoring system comprises a plurality of intelligent sensing unit ISU nodes deployed at key positions of a slope and a data processing and intelligent analysis unit, each intelligent sensing unit ISU node is used for transmitting data to an edge gateway in an ad hoc network wireless mode or directly uploading the data to a cloud platform and carrying out slope monitoring based on a local adaptive monitoring strategy; the data processing and intelligent analysis unit comprises an edge intelligent module, a cloud gateway and an edge gateway; the edge gateway serves as a middle layer and is used for protocol conversion, data aggregation, temporary storage and preliminary analysis; the cloud gateway is used for providing calculation and storage resources, training a more complex AI model based on historical and real-time data, and performing pattern recognition, prediction analysis and anomaly detection tasks; the edge intelligent module comprises a plurality of edge computing nodes and is internally provided with a lightweight AI reasoning unit, and the edge intelligent module is arranged on the edge side and used for implementing edge intelligent processing.

The invention discloses a power grid load prediction and scheduling optimization system based on artificial intelligence, particularly relates to the technical field of power system automation, and solves the technical problems of low power grid load prediction precision, poor scheduling strategy robustness and insufficient source grid load storage coordination in the prior art. Multi-source heterogeneous data space-time alignment is realized by constructing a data acquisition layer based on edge calculation, a load prediction result is generated by adopting an AI prediction module fused by a graph convolutional network and an attention mechanism, and a source-network-load-storage collaborative scheduling scheme is generated through a multi-target risk hedging optimization algorithm. And closed-loop optimization is realized by using digital twinborn pre-check and incremental learning. And finally, the load prediction accuracy, the scheduling decision reliability and the system adaptive capability in the new energy access environment are improved.

The invention relates to a cloud edge cooperative computing framework and processing method for multi-modal data stream fusion processing, and the method comprises the following steps: S1, carrying out the noise suppression based on an original data stream collected by an edge computing node through employing an improved Wiener filtering algorithm, achieving the signal denoising through the adaptive threshold wavelet transformation, and obtaining a cloud edge data stream; and a timestamp alignment technology is utilized to solve the problem of time delay difference of multi-modal data, and a space-time alignment purified data stream is generated. Through combination of the improved Wiener filtering algorithm and the adaptive threshold wavelet transform, the noise suppression efficiency of the original data stream is significantly improved, the timestamp alignment technology effectively solves the time delay difference of the multi-modal data, the generation of the space-time alignment purified data stream ensures that the subsequent processing has a unified time sequence benchmark, and the efficiency of noise suppression of the original data stream is improved. The space-time attention fusion network adopts a collaborative architecture effect of a bidirectional gating circulation unit and a lightweight 3D convolutional network.

The invention relates to a fabric flaw detection and traceability system based on edge calculation and computing power scheduling, which is suitable for intelligent quality control in a textile production process. The system comprises an acquisition unit, a modeling unit and the like. The acquisition unit acquires fabric images and environmental data through a multispectral imaging device and a process parameter sensor, and constructs time-aligned multi-modal feature tensors. The modeling unit extracts texture features by using unsupervised comparative learning in combination with fabric material characteristics, and generates potential texture fingerprint vectors. And the detection unit adopts a target detection network of a channel attention mechanism to identify fabric flaws and output positions, types and severity. The traceability unit analyzes correlation between defects and process parameters through time sequence causal reasoning, and constructs a causal atlas. And the optimization unit generates a process optimization vector according to the causal atlas and the risk score, and realizes visual display and edge control feedback, thereby constructing a real-time defect control and explainable traceability-oriented closed-loop quality management system.

Systems and methods are disclosed for condition identification. One or more processors may receive a member data object with indicators and dimensions, access a member-specific graph network with nodes representing attributes and weighted edges indicating associations, modify the nodes and edges based on the member data object, generate a multi-modal graph database by combining the modified member-specific graph network and a disease graph network, apply the multi-modal graph database to an attention-based graph neural network (GNN) that identifies associations between nodes by dynamically allocating attention weights to edges, generate an embedding data object with node identifiers and vectors representing features and relationships, select a target node associated with condition data, apply the embedding data object to a classification layer that outputs predicted conditions for the target node, and generate the probability of predicted conditions appearing in the target node.

The invention relates to the technical field of visual inspection, in particular to a multi-mode industrial product dynamic defect detection system based on edge calculation. According to the method, by introducing a multi-modal image construction and enhancement mode, three-dimensional acquisition and enhanced expression of detail information of the surface of the wind power blade are realized, and by means of inter-modal image synchronization and a space registration mechanism, the consistency extraction capability of defect information under different sensor view angles is improved; through combination of density change trend analysis and direction mutation identification means, surface micro cracks, edge damages and other structural changes can be accurately identified in continuous frames, and further through multi-source discrimination and mutual elimination comparison of abnormal indexes, environmental interference and misjudgment risks are effectively eliminated, and the accuracy of the detection result is improved. Therefore, a stable defect track area with direction consistency and distribution continuity is screened out, accurate detection and partition identification of the surface defects of the wind power blade under the dynamic condition are achieved, and the reliability and precision of defect positioning are remarkably improved.

The invention discloses a digital production plan scheduling method and system, and belongs to the technical field of optimal scheduling, and the method comprises the steps: constructing a distributed storage architecture based on edge computing nodes; a central coordinator is adopted to realize cross-node data synchronization through an improved Raft consensus algorithm, multi-version concurrency control is realized based on a vector clock, and a global consistent data view is established; a visual scheduling platform is built based on a Vue3 framework, and man-machine interaction is realized by adopting a Canvas and WebGL collaborative rendering framework; establishing a dynamic coordinate conversion model based on bilinear interpolation, designing a space mapping function containing distortion compensation, establishing a multi-thread coordinate service based on WebWorker, and realizing submillimeter-level bidirectional mapping of pixel coordinates and physical coordinates; constructing a three-dimensional space-time analysis model fused with the multi-dimensional features; and all the units are subjected to feature fusion through residual connection, and finally a scheduling scheme with a confidence coefficient weight is output. The method and the device have the effect of meeting various scheduling requirements.

The invention discloses an urban rainwater pipe network blockage risk early warning method and system based on edge calculation, and relates to the technical field of urban drainage monitoring. The problems that existing pipe network blockage detection lags behind, and the early warning precision is insufficient are solved. Dynamic hydraulic parameters and sediment migration state data of a pipe section are collected in real time through edge calculation equipment deployed at a pipe network node, the hydraulic state deviation rate is calculated, and the local blockage risk is rapidly recognized; when the deviation rate exceeds a threshold value, a dynamic sensing network is established by the trigger nodes, and a pipe network hydraulic topological relation is established by integrating the liquid level and flow velocity characteristics of the upstream and downstream nodes; executing distributed collaborative analysis based on the topological relation, identifying an abnormal attenuation area, calculating a sediment dynamic equilibrium index, and generating a blockage diagnosis parameter; and further combining real-time rainfall intensity to predict an overflowing capacity attenuation curve, generating a multi-stage early warning instruction according to an attenuation slope, and distributing the multi-stage early warning instruction to an operation and maintenance terminal, thereby realizing accurate early warning and active regulation and control of the blockage risk of the rainwater pipe network.

The invention provides a real-time link anomaly detection and sampling method and device based on edge AI driving, and is applied to the technical field of artificial intelligence. Segmenting and aggregating the original link tracking data in each time window through a dynamic time window division mechanism to generate a statistical feature vector of link topology in the time window, the original link tracking data including a link topology relationship, a service calling level and a performance index; inputting the statistical feature vector into an anomaly detection model obtained by pre-training for analysis to obtain a path anomaly probability score; and dynamically adjusting the sampling strategy of the next time window according to the path anomaly probability score. Through edge AI calculation nodes, dynamic time window division, segmented aggregation and dynamic adjustment of a sampling strategy, the real-time performance of anomaly detection and the reliability and practicability of anomaly detection are improved, adaptive sampling is realized, and the sampling precision and efficiency are ensured.

The invention relates to the technical field of power distribution network operation optimization, and discloses an intelligent power distribution network distributed power scheduling system based on edge computing, which comprises a data acquisition and preprocessing module, an edge computing and local scheduling module, an SDN network control module, an application layer global scheduling module and a scheduling execution and control module, according to the invention, by introducing the edge computing node, local preprocessing of data and generation of a preliminary scheduling strategy are realized, delay of data transmission to the central server is significantly reduced, and the system can dynamically adjust the scheduling priority in combination with the improved particle swarm optimization algorithm, so that the scheduling efficiency is improved. Three core indexes of renewable energy utilization rate, power distribution network loss and voltage stability are comprehensively considered, and a more accurate and efficient local scheduling strategy is generated. And meanwhile, the SDN network control module ensures that high-priority data can still be efficiently transmitted when the network is congested by calculating a link priority coefficient, so that the real-time performance and the reliability of the system are further improved.

The invention relates to the technical field of smart community data management, in particular to a smart community metadata interaction method and system based on an edge computing framework, and the method comprises the steps: carrying out the dynamic storage of multi-dimensional data through a hierarchical rolling cache; generating a uniform resource vector through sequential progressive mapping; community semantic tags are added for the uniform resource vectors through extensible tag slots, connection is established according to association rules, and a resource semantic graph oriented to community services is constructed; receiving a resident task request, constructing a multi-factor weighted scoring engine based on the resource semantic graph, and generating a task data packet with a priority label; a two-channel hybrid scheduling network is constructed, a first channel outputs resource availability and node association degree, and a second channel outputs time sequence characteristics and load prediction parameters; and fusing dual-channel output to obtain task-node correlation and generate an optimal matching strategy. According to the method, task collaboration and resource adaptive management are realized through semantic resource modeling and hybrid scheduling.

The invention discloses an oral tooth lesion AI auxiliary diagnosis system, which belongs to the field of artificial intelligence and comprises an image acquisition module, a three-dimensional modeling module, a lesion marking module, a dual-channel feature extraction module, a cross-modal diagnosis reasoning module, a lesion evolution trend prediction module and a dynamic risk level generation module. The image acquisition module utilizes multi-frequency structured light and a polarization camera to cooperatively acquire oral images; the three-dimensional modeling module is used for reconstructing an upper and lower jaw three-dimensional structure based on edge constraint splicing point clouds and registering images to form double-view fusion data; the lesion labeling module fuses expert labeling and weak supervision pseudo labels to generate joint labels; the dual-channel module extracts skeleton and texture features; the reasoning module realizes cross-modal semantic coupling through an image-text co-occurrence graph; the evolution prediction module models a lesion change path based on the time reversal causal network; and the risk module outputs a five-level risk and re-injects the embedded vector to strengthen prediction. The beneficial effects are that diagnosis intelligence and clinical decision support level are obviously improved.

The invention provides a resource allocation method and system based on edge cloud, electronic equipment and a storage medium. The method comprises the steps that a data acquisition module acquires environment information in an edge cloud environment; wherein the environment information comprises application load information, equipment performance information and user behavior information; the data analysis module analyzes and processes the environment information to obtain an application load prediction result, an equipment capability evaluation result and a behavior analysis result, and determines an initial resource allocation strategy according to the application load prediction result, the equipment capability evaluation result and the behavior analysis result; optimizing the initial resource allocation strategy to obtain a target resource allocation strategy; the resource allocation module allocates the computing resources according to the target resource allocation strategy; and the monitoring feedback module monitors the use state of the computing resources in real time and feeds back the use state to the data analysis module, so that the data analysis module adjusts the target resource allocation strategy according to the use state.

The invention relates to the technical field of power converters, in particular to a power converter real-time fault diagnosis method and system based on edge calculation. According to the method, operation data, including electrical parameters, thermal parameters and the like, of the power converter are obtained firstly, aging and quality are ensured through synchronous filtering, limitation of a traditional single parameter is broken through, tiny abnormity and instantaneous fluctuation can be captured, and redundancy is avoided; according to the equipment structure, edge computing nodes are deployed near a data source, the transmission distance is shortened, delay is reduced, electromagnetic interference is avoided, accuracy is improved, resources with excellent computing power are matched, and local real-time monitoring is supported. Next, an edge computing node lightweight algorithm is used for localization analysis, normal data and abnormal data are quickly distinguished, identification time consumption does not need to be reduced by transmitting to a cloud end, it is ensured that abnormality is found immediately according to misjudgment and sensitivity improvement of a dynamic baseline, and finally, thermal radiation information is obtained in combination with abnormal data, and a fault is accurately positioned by associating a device structure and parameters. And refining to an element level to shorten the troubleshooting time, and analyzing the trend to realize predictive maintenance.

The invention belongs to the technical field of intelligent regulation and control, and discloses an aquaculture pond water quality remote intelligent regulation and control system based on edge calculation. The method aims at solving the problems that in traditional water quality regulation and control, due to data transmission delay, model simplification neglects the coupling effect, and equipment regulation and control lack collaboration. Water quality parameter data are obtained in real time through a parameter acquisition module, a micro-coupling analysis module constructs a parameter micro-coupling matrix at edge nodes, a short-term fluctuation relation is analyzed, a dynamic regulation and control weight is generated, and then a local regulation and control instruction is formed. And the uploading coupling prediction module uploads the data to a cloud end, constructs a long-term coupling prediction model and generates a global regulation and control strategy. And finally, the edge node generates a final regulation and control instruction in combination with the local instruction and the global strategy, and optimizes the working states of the oxygenation equipment, the bait casting equipment and the water circulation equipment. Real-time accurate regulation and control are realized through edge calculation, and the risk of abnormal water quality is reduced; the efficiency is improved through equipment collaborative optimization, and resource waste is reduced.

The invention discloses a hospital Internet of Things equipment intelligent monitoring and fault early warning system based on edge computing, relates to the technical field of equipment monitoring and early warning, and aims to solve the technical problem that fault discovery lags in a high-real-time scene of an existing intelligent monitoring and fault early warning system. The system is used for collecting various data of hospital Internet of Things equipment and sending the data to a preset storage position, and comprises a local database, a data processing module and an abnormal data judgment unit; the Internet of Things base station is used for being connected with Internet of Things equipment, collecting equipment data and achieving intelligent monitoring and fault early warning through an algorithm model, and the algorithm model is constructed based on a core algorithm and rules; and the hospital Internet of Things edge computing platform is used for carrying out edge computing management on the data sent by the Internet of Things base station, feeding back an analysis result and storing the data. The method has the advantage of improving the fault discovery speed of the Internet of Things equipment.

The invention discloses a power distribution network fault identification driving accurate isolation method based on an edge computing architecture, and relates to the technical field of power system automation, and the method comprises the following steps: S001, collecting voltage and current signals at each edge computing node of a power distribution network, analyzing the signal fluctuation amplitude and propagation time delay of different nodes based on the same fault event, and obtaining a fault event; and calculating a fault identification judgment difference degree between the nodes. According to the method, the identification credibility is quantified and abnormal node judgment is corrected through fault identification judgment difference analysis and topological constraint modeling; self-adaptive identification logic optimization is realized in combination with historical and real-time data; control priority and pre-simulation analysis are introduced, so that the feasibility and safety of the isolation action are improved; and finally, a closed-loop mechanism integrating recognition, simulation and feedback is constructed, and the fault response accuracy, coordination and operation stability of the power distribution network under a distributed architecture are remarkably improved.

The invention discloses a road video event rapid detection method based on edge calculation, particularly relates to the field of computer vision and image processing, and is used for solving the problem of motion evidence distortion caused by frame-level sequential disorder and content gaps in a road monitoring video. The method comprises the following steps of: reading a video clip, constructing a time sequence correction graph according to inter-frame content similarity and boundary continuity, rearranging to generate a corrected sequence, outputting a frame consistency index, estimating a stable motion field and a continuity score on the corrected sequence, and writing back an image stabilization amplitude correction time anchor point; generating an event evidence sequence according to the stable motion field, combining track breakpoints according to a time sequence correction graph, initiating backtracking revision to return candidate event segments, and if consistency verification is executed on candidate events, performing cross-level revision on the time sequence correction graph and image stabilization amplitude to trigger the stable motion field to re-estimate and output a confirmation event; and issuing a trigger result and establishing a mapping index as a new fragment to initialize priori acceleration time sequence correction and judgment convergence.

The invention discloses a traffic flow real-time optimization method and device for vehicle-road collaborative edge calculation, and relates to the technical field of intelligent traffic, and the method comprises the steps: carrying out the real-time collection of a vehicle-mounted unit and roadside equipment in a target road section, and generating a multi-source heterogeneous traffic data set; performing space-time fusion processing on the multi-source heterogeneous traffic data set, and extracting a dynamic traffic flow parameter set; traffic flow optimization is carried out based on the dynamic traffic flow parameter set, and a local optimization instruction set is constructed; and issuing the local optimization instruction set to a vehicle-mounted unit and roadside equipment in the target road section to realize real-time distributed optimization of traffic flow. The technical problem that the traffic flow optimization response is not timely in the prior art is solved, and the technical effect of improving the real-time performance and precision of traffic regulation and control is achieved by fusing the multi-source data of the vehicle-mounted unit and the roadside equipment and performing real-time processing and optimization instruction generation based on edge calculation.

The invention relates to the technical field of road repair state monitoring, in particular to a road repair state monitoring method and system based on edge calculation. The method comprises the following steps: acquiring planning information of a repaired road, deploying 3D laser scanning vibration meters at a plurality of repairing points, collecting pavement vibration signals in different time periods, and performing disordered distribution time domain feature analysis to obtain a vibration signal disordered distribution time domain graph; then, structure weakening behavior simulation is carried out based on the graph, periodic structure weakening data is quantified, and a pavement water damage cumulative gradient is obtained through water damage cumulative gradient estimation; and finally, in combination with the periodic structure weakening data and the water damage cumulative gradient, carrying out service life evaluation on the restoration state, and sending an evaluation result to the terminal. According to the invention, the road repair state monitoring technology is optimized, so that the road repair state monitoring technology is more perfect.

The invention relates to the technical field of enterprise office collaboration, and discloses an enterprise series intelligent office collaboration data processing system based on edge computing. The system comprises a collaborative data acquisition module, a collaborative feature analysis module, an edge task scheduling module and a dynamic storage optimization module. The collaborative data acquisition module is deployed at a local edge node of an enterprise, captures a collaborative operation behavior flow of the multi-source office terminal in real time, and extracts space-time distribution features to generate an original collaborative feature vector; the collaborative feature analysis module analyzes the coupling strength of related data through a multi-dimensional correlation analysis engine, quantifies the frequency fluctuation amplitude of a communication session, and fuses to generate a collaborative efficiency quantized value; the edge task scheduling module dynamically divides task load levels based on the value, and matches execution priorities to generate a distributed calculation task allocation strategy; and the dynamic storage optimization module responds to the strategy, monitors the throughput delay of the storage nodes, reconstructs a high-frequency access data storage index, and generates a cross-node storage topology configuration.

The invention provides an agricultural machinery multi-source data fusion and collaborative operation method based on edge calculation. The method comprises the following steps: step 1, dividing a multi-source sensor group into three units to collect data; 2, constructing a data fusion and collaborative operation model through four parts of space-time calibration, dynamic weight data fusion, a real-time decision engine and multi-agricultural machinery collaborative operation; 3, realizing practical application of the data fusion and collaborative operation model in the step 2 through edge-cloud collaboration; and 4, performing test detection on the data fusion and collaborative operation model. According to the invention, real-time collection and fusion of agricultural machine multi-source data are realized through an edge computing architecture, and the data transmission delay is significantly reduced; the heterogeneous computing architecture design greatly reduces the power consumption of edge nodes, and meets the long-time operation requirement; and the edge-cloud collaboration mechanism supports incremental updating and exception verification of model parameters, so that the reliability and the expandability of the model are ensured.

The invention provides a high-speed magnetic levitation suspension system control method and system based on edge calculation and Transform prediction, and the method comprises the steps: constructing a Transform prediction model with a space-time attention mechanism and an autoregression mechanism based on obtained local low-delay calculation resources and train real-time sensing data, and deploying the Transform prediction model in a vehicle-mounted edge calculation unit; performing short-term high-precision prediction on the gap, the acceleration and the disturbance trend at a plurality of sampling moments in the future through a Transform prediction model to obtain a prediction result; processing actuator current saturation and gap safety threshold hard constraints in a limited prediction domain by using a model prediction controller, and solving an optimization control sequence in real time in combination with a prediction result; and overlapping a control barrier function as a safety filter of the model prediction controller, correcting the optimized control sequence to obtain an optimal control sequence, and controlling the high-speed magnetic suspension system. According to the method, the cloud communication delay and jitter are reduced, and the robustness and security of the system under uncertain disturbance are improved.

The invention provides an underground equipment fault real-time diagnosis method and system based on edge calculation, and relates to the technical field of coal mine safety production, and the method comprises the steps: collecting multi-modal data through a distributed sensor network, extracting multi-scale time sequence features, projecting the features to a Lie group manifold space, constructing a coupling mapping relation matrix, obtaining fusion features, and carrying out the real-time diagnosis of an underground equipment fault; and constructing a causal directed acyclic graph based on a topological connection relationship and a Granger causal coefficient, executing Bayesian probabilistic reasoning, determining an execution strategy in combination with entropy similarity matching, and performing deep time-frequency analysis and causal chain verification. High-precision real-time diagnosis of equipment faults in an underground complex environment is realized, and the fault early warning accuracy is improved.

The invention discloses a metering box abnormal power consumption behavior diagnosis method and system based on edge calculation, and belongs to the technical field of power monitoring, and the method comprises the steps: collecting the voltage, current and time mark data of a metering box in real time through an edge end, synchronously obtaining the physical state information of a box door state and the like, generating a space-time feature matrix through an electric parameter space-time coupling analysis method, and carrying out the calculation of the time-space feature matrix; inputting an electric parameter feature mask pruning algorithm to obtain an optimized feature set, triggering an edge-sensing sleep wake-up linkage device when current abnormity is monitored, and collecting magnetic field data; the edge end calculates a total-branch electric energy difference value, combines the magnetic field data and a box door state, and constructs an electricity consumption abnormity classification determination tree through a metering error-electricity stealing behavior coupling diagnosis method to distinguish electricity stealing behaviors; an intermittent abnormal trajectory splicing algorithm is adopted to process fragmented abnormal data to generate an electricity stealing trajectory, and the electricity stealing trajectory is fed back to an electric parameter space-time coupling analysis method to dynamically update the space-time feature weight; according to the invention, the abnormity diagnosis precision and real-time performance are improved, and the energy consumption is reduced.

The invention discloses a wind generating set fault prediction method and system based on edge computing, and the method comprises the steps: collecting the vibration, temperature, rotating speed and other parameters of a wind generating set in real time through a data collection module deployed by edge computing equipment, carrying out the preliminary screening and packaging through a built-in data processing unit, and synchronizing to a constructed digital twinborn model; establishing an edge calculation fault analysis model based on model data, identifying potential fault features, comparing a physical unit operation state with a digital twin model simulation state parameter by parameter, performing preliminary diagnosis in combination with the fault features, evaluating a unit health state according to a fault mechanism model, and outputting a fault prediction result and part information. The system is correspondingly provided with six units including a data acquisition interaction unit, a data processing unit and a model construction updating unit, and all the units work cooperatively, so that accurate prediction and health management of faults of the wind generating set are realized.

The invention provides a ship unmanned aerial vehicle cabin multistage fire alarm system and method based on edge computing, and the system comprises an intelligent shipborne system and a shore-based cloud system, the intelligent shipborne system is disposed in a ship cabin, and has the functions of data collection, data abnormity perception, fire monitoring and multistage early warning; the method is composed of three levels of data acquisition, edge processing and fire decision making. The shore-based cloud system is a cloud end of a shore-based system arranged on the land, has the functions of transmitting and receiving distress signals, storing remote data and tracing large fire accidents, and is composed of a communication unit, a storage unit and an alarm unit. The intelligent ship-borne system is connected with the shore-based cloud system through the satellite communication technology, infrared thermal imaging and multi-source sensing fusion is achieved, the device has the edge processing capacity, the multi-level fire judgment logic and the shore-based remote traceability function, and the device is suitable for the closed, complex and unattended ship engine room environment.

The invention provides a ship video concentration and intelligent retrieval method and system based on edge nodes, and belongs to the technical field of edge computing, and the method comprises the steps: S1, collecting ship video data; s2, an edge node generates a concentrated video and metadata through an improved Gaussian mixture model, an improved DeepSORT algorithm and a dynamic concentration proportion adjustment algorithm; s3, synchronously concentrating the index information of the videos and the metadata by each edge node through a publishing-subscribing mode; s4, training a lightweight intelligent retrieval model through a knowledge distillation algorithm; s5, calling the lightweight intelligent retrieval model to match the corresponding concentrated video clip, and returning a retrieval result; and S6, collecting feedback information of the user on the retrieval result, and performing incremental optimization on the lightweight intelligent retrieval model through an elastic weight consolidation algorithm. According to the invention, efficient monitoring management is realized through edge localization processing, cross-node cooperation and closed-loop optimization architecture, and the actual requirements of ship safety monitoring and operation management are met.

The invention discloses an unmanned aerial vehicle real-time surveying and mapping data fusion method and system based on multi-modal edge calculation, and the method comprises the steps: carrying out the modeling based on the physical characteristics of a sensor, and constructing a multi-source sensor dynamic time synchronization model; dynamically calibrating a space coordinate system of the multi-source sensor through a space registration algorithm under kinematics constraint on the basis of edge computing equipment; based on a multi-modal data fusion algorithm of adaptive weight distribution, obtaining a sensor confidence coefficient and an environment characteristic dynamic adjustment fusion weight; real-time three-dimensional reconstruction is carried out based on an edge computing device, and feature level fusion of point cloud and image data is completed at an unmanned aerial vehicle end; and constructing a distributed edge caching mechanism, and performing data life cycle management based on an LRU algorithm. The method has the advantages that the dynamic synchronization model of the sensor is innovatively constructed, the real-time three-dimensional reconstruction architecture is based on edge calculation, and the space-time consistency of surveying and mapping data in a complex environment is remarkably improved.

The invention discloses a multi-scale SAR (Synthetic Aperture Radar) image ship detection method and system based on edge enhancement and diffusion denoising, and mainly solves the problems that the existing SAR ship detection method is sensitive to noise and poor in small target feature extraction capability. According to the implementation scheme, the method comprises the following steps: obtaining an SAR image, carrying out standardized preprocessing, inputting the SAR image into a deep convolutional neural network, extracting a multi-scale feature map, and carrying out dynamic channel fusion enhancement on a low-layer feature map in the multi-scale feature map to obtain a fused high-quality feature map; performing differential edge enhancement on middle and high-level feature maps in the multi-scale feature map, and inputting the enhanced feature map and the fused feature map into a diffusion model detection head for training; and inputting a to-be-detected SAR image into the trained diffusion model detection head, outputting a preliminary target bounding box and a category confidence coefficient, performing score screening and non-maximum suppression operation on the preliminary target bounding box and the category confidence coefficient, and generating a final ship target detection result. According to the method, the precision and robustness of SAR image ship detection are remarkably improved, and the method can be used for ocean monitoring and military reconnaissance.