2242 results about "Edge based" patented technology

The invention provides a thermal power equipment real-time monitoring method and system based on edge computing, and relates to the technical field of thermal power equipment real-time monitoring, and the method comprises the steps: deploying an edge computing node array to collect multi-source heterogeneous data of thermal power equipment, and carrying out the preprocessing through data screening, multi-scale adaptive filtering and wavelet packet decomposition, a conditional variation auto-encoder is used to extract features, a hierarchical attention mechanism and a deep feature fusion network are combined to generate mixed feature representation, refined distribution estimation and abnormal mode recognition are performed on equipment states, cooperative monitoring modeling is performed based on a multi-scale spatial-temporal feature fusion network and a hierarchical depth deterministic policy gradient network, and a multi-scale spatial-temporal feature fusion network is established. The real-time monitoring accuracy and efficiency of the thermal power equipment can be effectively improved, the equipment failure rate is reduced, and safe and stable operation of a thermal power plant is guaranteed.

The invention provides an intelligent inspection robot path optimization method and system based on an edge inference model, and the method comprises the steps: obtaining an environment feature topological graph of a target inspection region, and determining an initial edge inference model; incremental training is carried out on the initial edge reasoning model through the real-time environment perception data flow, and a dynamic reasoning model adaptive to the current environment characteristics is generated; performing priority scoring on each path node in the environment characteristic topological graph based on a dynamic reasoning model, and generating an initial optimization path sequence; triggering a feedback calibration mechanism of the dynamic reasoning model according to the environment perception data flow updated in real time, performing dynamic path node replacement on the initial optimization path sequence, and generating a final inspection path; and controlling the intelligent inspection robot to execute an inspection task according to the final inspection path, and continuously collecting new environment sensing data streams in the inspection task process to update a parameter set of the dynamic reasoning model. According to the invention, the adaptability and reliability of path planning to a complex dynamic environment can be improved.

The invention relates to the technical field of tunnel power supply and distribution, in particular to a tunnel modular prefabricated cabin power supply and distribution intelligent substation self-adaptive regulation and control system based on edge calculation. Comprising an edge calculation and AI decision-making unit which is used for realizing rapid acquisition, processing and instant decision-making of tunnel power supply and distribution multi-dimensional data, generating a power supply and distribution adaptive regulation and control strategy by deploying calculation resources and a machine learning algorithm at edge nodes close to a data source, and converting the strategy into an executable regulation and control instruction; a cloud platform collaborative management unit; and an intelligent sensing and internet-of-things unit. According to the invention, hierarchical decision control of the tunnel power supply and distribution system is realized by constructing a hybrid architecture of edge computing and cloud platform collaboration and a priority judgment mechanism; according to the invention, multi-modal data are integrated through the multi-protocol communication link module and the full-scene data fusion analysis module, and data association analysis is realized through Kalman filtering, D-S evidence theory and other algorithms.

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 relates to the field of Internet of Things data defense, in particular to an Internet of Things data security collaborative defense system based on edge computing. The technical problem to be solved by the invention is to provide an Internet of Things data security collaborative defense system capable of fusing lightweight cryptography, dynamic trust evaluation and cross-layer collaborative decision, and ensuring edge real-time response and global security situation awareness. The invention discloses an Internet of Things data security collaborative defense system based on edge computing. The system comprises a terminal equipment layer, an edge computing layer, a fog computing layer and a cloud collaborative layer which work cooperatively, the terminal device layer comprises a plurality of Internet of Things devices, and a lightweight security module is arranged in each device. According to the method, the effects of fusing lightweight cryptography, dynamic trust evaluation and cross-layer collaborative decision, and ensuring edge real-time response and global security situation awareness are achieved.

The invention discloses a multi-modal data real-time identification and cooperative processing system based on edge computing and federated learning. The multi-modal data real-time identification and cooperative processing system comprises a cloud center coordination node, a plurality of edge computing nodes, a cross-modal encryption engine, a federated learning controller and a model updating verification module. The cloud center coordination node executes federated learning model aggregation and dynamic task allocation, and generates a cross-modal encryption strategy; and the edge computing node is configured with a multi-modal data acquisition module, a local model training unit and a co-processing gateway to realize multi-modal data acquisition and local processing. The system encrypts vision, acoustics and text data by using differentiated algorithms such as spatial confusion, frequency domain permutation and homomorphic encryption; the federated learning controller carries out multi-modal feature fusion, hierarchical encryption and dynamic networking at the edge node; and the model updating verification module performs aggregation updating after ensuring parameter consistency by using secure multi-party calculation. According to the method, real-time processing and privacy protection of multi-modal data are realized, and the data co-processing efficiency is improved.

Presented herein are systems and methods for the employment of machine learning models for image processing as may be performed by computing devices associated with an end user. A method may include obtaining video data comprising a plurality of frames including a document of a document type. The method may include executing an object recognition engine of a machine-learning architecture using image data of the plurality of frames, the object recognition engine trained to detect edges of documents. The method may include identifying, based on the edge detection, a plurality of boundaries for the document. The method may include validating, based on the plurality of boundaries, the document as the document type. The method may include transmitting via one or more networks, to a computer remote from the computing device, responsive to the validation of the type of document, the image data for the plurality of frames depicting the document.

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 the technical field of medical image segmentation, in particular to a few-sample medical image segmentation method based on edge perception multi-prototype learning, and the method comprises the steps: inputting support and query images into a feature encoder, and extracting support and query feature maps of different sizes; inputting into a local attention fusion prototype generator to generate a support foreground prototype; processing the support mask through dynamic corrosion operation to generate an inner boundary prototype; generating a multi-foreground local prototype through a multi-layer perceptron; local and global information is optimized through multi-scale feature extraction, and a multi-scale prototype is obtained; fusing to obtain a multi-prototype foreground prototype; dynamic calculation weighting is carried out on the multi-prototype foreground prototype by using a double-stage prototype optimization network, and automatic calibration is carried out; then prediction is carried out through a prototype prediction module, and finally collaborative optimization is carried out through a loss calculation module; the method can effectively solve the problem of edge detail loss involved in the background technology.

The invention provides an intelligent mechanical safety protection system based on industrial Internet of Things equipment, which comprises a dynamic topology modeling module, a risk propagation analysis module, a dynamic protection generation module and a collaborative triggering execution module, the composite topological graph comprises a physical layer and a logic layer; the risk propagation analysis module is used for analyzing a propagation path of the risk along a physical connection edge and a logic dependence edge based on the composite topological graph, and generating a risk propagation full-link sub-graph; the dynamic protection generation module is used for calculating a minimum necessary protection set based on the risk propagation path and generating a hierarchical response strategy; and the cooperative triggering execution module is used for sending a cooperative control instruction to the equipment in the minimum necessary protection set based on the hierarchical response strategy. By adopting the system, a risk propagation path under a complex scene can be automatically identified, and the safety protection level of equipment is improved.

The invention provides a smart campus safety early warning method and system based on edge computing and big data. The method comprises the following steps: collecting multiple paths of video data streams in real time through an edge computing node, performing frame sequence segmentation and spatial-temporal feature extraction, generating an initial behavior feature set, performing multi-dimensional correlation analysis on the initial behavior feature set based on a preset behavior semantic tag, extracting a spatial-temporal behavior feature vector corresponding to a target monitoring scene, and obtaining a spatial-temporal behavior feature vector; and transmitting the time-space behavior feature vector to a central server, inputting the time-space behavior feature vector into a target behavior recognition model, generating a behavior semantic description sequence corresponding to the video data stream, determining a real-time behavior monitoring result according to a matching result of the behavior semantic description sequence and a preset abnormal behavior rule base, receiving the real-time behavior monitoring result through an edge computing node, and sending the real-time behavior monitoring result to the central server. And adaptive adjustment is carried out on acquisition parameters of the video data stream based on a dynamic priority strategy. According to the invention, the real-time performance, the accuracy and the system sustainability of campus behavior monitoring can be improved.

The invention relates to the technical field of data fusion, and discloses a multi-source heterogeneous data fusion method and system based on edge computing, and the method comprises the steps: obtaining a heterogeneous data stream, carrying out the data type recognition and data feature extraction, and obtaining an original feature set; according to the original feature set, unifying feature dimensions and adjusting a time reference to obtain time sequence vector data; according to the time sequence vector data, filling the feature value of the missing time point to obtain a multi-modal feature; performing block storage on the multi-modal features, verifying the synchronism of adjacent modals, distributing modal synchronization weight coefficients, and finally generating a fusion feature matrix; according to the fused feature matrix, identifying and filtering redundant feature dimensions, establishing a feature association map, and executing feature merging to obtain a simplified feature matrix; according to the simplified feature matrix, feature importance scores are calculated, sorting weight coefficients are arranged and distributed in a descending order according to the scores, and a multi-modal fusion semantic vector is generated. The method improves the accuracy of data analysis and decision.

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.

The invention relates to the technical field of logistics management, in particular to an intelligent logistics terminal equipment collaborative management and control system based on AI edge computing, and the system comprises an edge data fusion and state recognition module which is deployed at an edge node, collects multi-source data of an operation state, environment perception, a communication link and the like, and generates an equipment state representation vector through fusion; the intelligent prediction and task scheduling decision module uploads the state vector to a cloud, predicts task completion capability and fault probability, and generates a task scheduling decision packet; the scheduling strategy issuing and edge execution collaboration module issues a scheduling packet through a multi-protocol gateway, and an edge node completes task distribution, communication switching and resource scheduling and caches a key strategy. According to the invention, the real-time sensing of the state of the logistics terminal equipment, the intelligent prediction and resource optimization of task scheduling, and the quick response and fault-tolerant control in a fault scene are realized, and the operation efficiency, the intelligent level and the stability of the system are remarkably improved.

The invention relates to the technical field of communication machine room safety protection, and discloses a communication machine room intelligent fire monitoring and fire extinguishing system based on edge computing, which comprises a fire parameter acquisition module, a temperature characteristic analysis module, a smoke dynamic detection module, a multi-source fusion judgment module, a regulation and control parameter generation module and the like. The fire parameter acquisition module acquires parameters such as temperature gradient, smoke concentration and equipment current, judges fire risk and triggers cooperative monitoring; the temperature characteristic analysis module extracts temperature distribution characteristics and quantitatively evaluates hot spot abnormity; the smoke dynamic detection module analyzes the smoke diffusion trend; the multi-source fusion judgment module is combined with multiple parameters to generate a fire extinguishing or optimizing signal; and the regulation and control parameter generation module is matched with the strategy to generate injection and ventilation parameters. The system also corrects a risk index through an equipment coupling analysis module, and forms closed-loop control by using a dynamic feedback execution module. According to the system, multi-source data fusion monitoring and intelligent fire extinguishing are realized, and the fire prevention and control accuracy of the communication machine room is improved.

The invention relates to a distributed large two-layer network intelligent routing method and system based on edge cloud nodes, and belongs to the technical field of computer networks. Aiming at the problems of slow routing decision response and low fault recovery efficiency of the existing network in a dynamic environment, the invention provides a method for periodically collecting link bandwidth utilization rate, transmission delay and other state information through an edge cloud node, uploading the state information to a central cloud platform to generate a global routing strategy and issuing the global routing strategy to the edge node, and triggering fault switching in combination with local dynamic threshold monitoring. According to the technical scheme, bidirectional communication between edge nodes and a central platform, a neural network model of spatial-temporal feature fusion and a path priority generation mechanism of multi-objective optimization are included. The method and the system are suitable for a large-scale distributed network environment, and network resource utilization rate and service continuity can be improved.

The invention relates to the technical field of edge calculation application and partial discharge detection, in particular to a partial discharge multichannel signal real-time synchronous acquisition method based on edge calculation. According to the method, the functions of multi-channel signal acquisition, time synchronization, feature analysis, hierarchical storage and the like are integrated at edge nodes, asynchronous acquisition, unified timestamp marking and data synchronous fusion of multiple types of partial discharge signals are realized, and extraction of multi-dimensional feature parameters such as time domain, frequency domain and energy and intelligent event discrimination are locally completed. For abnormal signals, the system realizes classified storage and remote uploading after encryption and compression processing; and for normal signals, dynamic management is carried out through circular caching. According to the method, the accuracy and efficiency of multichannel signal synchronous acquisition are remarkably improved, the data safety and the system adaptive capacity are enhanced, and the method is suitable for real-time online monitoring and intelligent diagnosis of partial discharge of power equipment.

The invention relates to the technical field of underground pipe gallery intelligent monitoring, and discloses an underground pipe gallery data real-time processing system based on edge computing, which comprises a dynamic sensing primitive library abstracting pipeline pressure and video monitoring multi-source data into primitive units containing associated weights, the primitive recombination module dynamically adjusts the coupling relation between primitives through a function according to the rainfall environmental parameters, so that the association weight of the video texture and the pressure data is adaptively enhanced; according to the system, through a dynamic coupling mechanism driven by environment feedback, leakage gradual change characteristics which are difficult to capture by a traditional fixed threshold are effectively identified; the edge collaborative network realizes cross-node knowledge migration, and automatically triggers high-precision sampling of adjacent nodes when local abnormality occurs, so that a self-organizing diagnosis cluster is formed. According to the invention, through a composite architecture of dynamic primitive recombination and edge collaboration, the problems of response lag and high false alarm rate of a traditional monitoring system are avoided, and the technical span from passive monitoring to active prediction of the underground pipe gallery is realized.

The invention relates to the technical field of data management, in particular to a low-altitude economic flight data management method and system based on edge computing, and the system comprises edge computing nodes deployed on an aircraft and a ground base station, a sensor for collecting multi-source data, a module for preprocessing the data, and an annular data queue caching mechanism. The system comprises a composite biological feature code generation module, a distributed storage edge node, an abnormal fluctuation feature extraction module, an equipment health state and trend analysis and calculation module, a virtual power supply manager, a main power supply, a standby power supply, an abnormal behavior monitoring module and a defense strategy library. After preprocessing and caching, feature codes are generated and encrypted distributed storage is carried out, dynamic resource adjustment is realized through state evaluation and a digital twinning and optimization algorithm, a neural network is utilized to manage a power supply, data security is guaranteed in combination with abnormal monitoring, and a whole-process management system is formed.

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 provides an intelligent management method and system for hospital human resources, and the method comprises the steps: collecting the dynamic position, track and regional thermal distribution data of medical staff in real time through an infrared tracking technology and a building thermal sensor, carrying out the space-time coupling analysis based on an edge calculation node, recognizing the thermal load aggregation characteristics of a high-flow region of a patient, and carrying out the real-time collection of the thermal load. Calculating a medical care response time efficiency threshold value; dynamically matching the on-duty medical care skill labels with the patient demand portraits, and generating a department elastic scheduling priority sequence; a cross-department collaborative scheduling link is constructed in combination with the threshold and the priority, and multi-department qualification mobile medical resources are allocated; and according to the real-time load and burst flow fluctuation characteristics, generating a dynamic scheduling scheme through multi-dimensional efficiency verification, and synchronizing the dynamic scheduling scheme to the terminal. According to the technical scheme provided by the embodiment of the invention, the utilization rate of medical staff can be optimized, and the regional thermal load gathering risk is reduced.

The invention discloses an e-commerce live broadcast real-time interaction quality evaluation system based on edge calculation, and relates to the technical field of e-commerce live broadcast, and the system comprises a multi-modal interaction data collection module which collects multi-modal interaction data of a live broadcast stream in real time through a distributed edge calculation node cluster, and constructs a multi-dimensional quality feature vector, the multi-modal interaction data comprises a video coding parameter, an audio quality index, user interaction behavior data and network transmission state data; according to the invention, the distributed edge computing node cluster collects the multi-modal interaction data of the live stream in real time, the lightweight space-time attention neural network model carries out data fusion processing, the deep reinforcement learning network generates a quality optimization scheme, and the adaptive fuzzy inference system corrects the optimization scheme in real time. And dynamic parameter adjustment is carried out in combination with network bandwidth fluctuation and a terminal device resource state, so that the effect of accurately and comprehensively evaluating the e-commerce live broadcast interaction quality in real time is achieved.

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 discloses a distribution network low-voltage equipment state intelligent analysis method and system based on edge computing, and relates to the technical field of distribution network equipment state detection.The method comprises the steps that mesh network topology between edge computing nodes is constructed, and task allocation weights between the nodes are set; constructing a fault knowledge graph based on the multi-dimensional state features of the edge computing nodes; collaborative calculation is carried out based on the edge calculation nodes, calculation results of the edge calculation nodes are fused based on a weighted voting mechanism of a consistency algorithm, and a state evaluation result is obtained. According to the method, the multi-dimensional state features and the edge computing technology are combined, and intelligent analysis of the distribution network low-voltage equipment state is achieved. A mesh network topology is constructed based on electrical characteristics and environment characteristics, and efficient allocation of computing resources is realized through comprehensive evaluation of load complementation characteristics and state evaluation. The accuracy of fault propagation path identification is improved through double constraints of a feature association propagation chain and an equipment physical connection relationship.

The invention discloses a multi-source equipment health collaborative operation and maintenance method in a complex environment based on edge federation, and relates to the technical field of equipment collaborative operation and maintenance. Vibration acoustic emission current waveforms are mapped to a unified time-frequency grid at an edge gateway, and an encrypted sparse index is generated and uploaded; training a global model by combining a graph regular base network with a gradient direction and a distribution distance, and injecting fault information by using a new working condition protection door; after being issued by a sparse adaptation layer of double-temperature-zone distillation and random projection compression, fine adjustment is carried out on site under few samples through temperature gradual fusion and reversible orthogonal mapping, and only unit gradient direction and health labels are uploaded; the center adopts entropy constraint Bayesian filtering to fuse information to generate a health index, a maintenance schedule and a spare part plan are formed by integer programming according to confidence intensity mapping risk popularity, a result is differentially pushed and audited, and a closed loop of collection, learning, evaluation and decision is realized.

The invention discloses a remote monitoring and guiding system for rehabilitation training in the orthopedics department, particularly relates to the technical field of medical rehabilitation intelligent monitoring, and is used for solving the problems that in an existing remote rehabilitation system, timing sequence dislocation exists in action recognition and regulation and control instruction execution, and joint movement safety early warning is delayed. According to the method, localized time sequence alignment processing is performed on joint movement data and electromyographic signals based on edge computing nodes, a joint linkage action time sequence difference is generated, and an abnormal action risk level is judged in real time by combining an electromyographic signal pre-activation feature and a conflict detection mechanism of a standard action intention; historical abnormal records are matched through the cloud platform to generate a hierarchical regulation and control instruction set, and progressive safety intervention is triggered by the patient end equipment according to the dynamic deviation of the joint activity; through a collaborative mechanism of edge side action stage accurate analysis, electromyographic signal feed-forward verification and multi-stage instruction dynamic binding, millisecond-level response of joint linkage abnormity in staged rehabilitation training is realized, and the risk of joint over-limit activity is effectively avoided.

The invention discloses a network intrusion detection method and system based on edge attention learning, and a storage medium, and the method comprises the steps: converting an original network flow into a network flow diagram, and constructing a training diagram and a test diagram under the condition that a coarse-grained label and a fine-grained label are reserved; edge embedding representation is obtained through edge feature reservation, adaptive weight distribution and multi-layer feature extraction of the training graph and the test graph; based on the edge embedding representation, performing coarse-grained detection to identify a basic attack category, and performing fine-grained classification by using multi-scale feature fusion related to global graph attributes; and adversarial training: through initializing adversarial disturbance and optimizing disturbance based on loss function gradient iteration, superposing final disturbance into the training graph, and based on loss function back propagation updating, obtaining a trained network intrusion detection model. The method provided by the invention can effectively capture the depth characteristics of the key attack and maintain the stable detection performance in the confrontation environment.