340 results about "Network identification" patented technology

The invention discloses a scene interactive AI rehabilitation evaluation training and health monitoring system, and relates to the technical field of rehabilitation medical treatment and artificial intelligence, a semantic perception module is used for collecting and recognizing voice input, facial expressions, action tracks and eye movement paths of a user in a training process, and extracting context parameters; the knowledge-driven training generation module is used for calling a rehabilitation knowledge graph constructed by a graph neural network based on context parameters and individual training history, and generating a multi-path training scheme; training a feedback regulation engine, collecting posture offset, physiological stress and emotion feedback, and dynamically adjusting task difficulty, rhythm and prompt mode based on a dual-channel reinforcement learning model; the prediction module fuses training and monitoring data, and predicts a network identification function degradation risk through degradation driving; the cloud edge fusion platform is used for realizing task quick response and graph strategy iterative updating; according to the invention, the individuation, self-adaption and intelligent prediction capabilities of rehabilitation training are improved, and the rehabilitation effect and the system practicability are obviously optimized.

The invention relates to the technical field of atmospheric pollutant detection, and discloses a method and a system for detecting excessive emission of atmospheric pollutants. The method comprises the following steps: acquiring pollutant concentration data of multiple monitoring points in a target area to form an original data set; abnormal value detection and correction are carried out on the data set, sensor fault outliers are eliminated, and a preprocessed data set is obtained; extracting concentration change trend characteristics in a preset time window of each monitoring point, and constructing a spatial-temporal characteristic matrix; inputting the matrix into a pollutant diffusion model, calculating a transmission path and strength between monitoring points, and generating a regional transmission network; identifying a potential source region of abnormal fluctuation of pollutant concentration based on a network, and marking the potential source region as a candidate region to be checked; performing multi-scale concentration gradient analysis on the candidate area, and determining a key monitoring area; arranging mobile equipment in the key monitoring area, and collecting high-precision component data; and comparing the data with a standard emission source feature library, matching emission source types of which the similarity exceeds a threshold value, judging whether the emission exceeds the standard or not, and generating a detection report.

The disclosure relates to a 5G or 6G communication system for supporting a higher data transmission rate. Embodiments disclose methods for managing a condition in a UE. The method includes initiating a timer for disaster wait range; in case that the UE is switched off while the timer is running, and that the UE is switched on after the UE is switched off, wherein a universal subscriber identity module (USIM) in the UE remains as same, and the UE selects a public land mobile network (PLMN) for a disaster roaming: identifying a first time remaining for the timer for timeout at switch off, identifying a second time related to a time between switching off and switching on, and determining whether to restart the timer based on the first time and the second time.

The invention discloses a bearing defect intelligent detection method and system based on deep learning, and relates to the field of bearing defect detection. Multi-modal data such as bearing vibration, acoustics, thermal imaging and the like are acquired by using various sensors, and a four-dimensional feature tensor is constructed through preprocessing such as noise reduction and feature extraction; features are fused through a reconfigurable multi-branch convolutional neural network, and defects are identified and a development trend is predicted in combination with a meta-learning twin network; a decision threshold is optimized by adopting a quantum heuristic algorithm, and multi-level early warning is realized; and continuous evolution of the model is completed through edge-cloud collaboration and federated learning, the functions of data calibration compensation, model dynamic optimization and the like are achieved, and efficient and accurate detection of bearing defects is achieved. The detection time is remarkably shortened, and the positioning precision is high; the novel defect response speed is high, and faults can be predicted in advance; system energy consumption is reduced, model updating is improved, stable operation of equipment is effectively guaranteed, and cost reduction and efficiency improvement of industrial intelligent operation and maintenance are facilitated.

The invention discloses a GIS combined electric appliance operation on-line detection method, and relates to the technical field, and the method comprises the following steps: obtaining a partial discharge original time domain signal, carrying out the signal enhancement processing of the original time domain signal, extracting a weak discharge pulse signal, and generating an enhanced discharge pulse signal; constructing a holographic time-frequency matrix atlas according to the enhanced discharge pulse signals; the holographic time-frequency matrix atlas is used as an input item, and a weak discharge type is identified based on a weak discharge neural network identification model; based on the identification result and the sensor array data, acquiring space coordinates of a discharge source, and outputting a discharge type and a positioning result to the monitoring terminal; according to the method, the holographic time-frequency multi-scale analysis algorithm and the weak discharge neural network recognition mechanism are fused, so that the recognition capability, the anti-noise robustness and the positioning precision of the GIS combined electric appliance online detection system on weak discharge are remarkably improved.

The invention discloses a commercial password application security assessment evidence identification method based on image identification, relates to the technical field of image identification, and aims to assess employee advanced engineer certificates, extract information from multi-modal documents by using a Qwen-VL visual language model, generate vectors, label the vectors, convert the vectors into structured evidence nodes and store the structured evidence nodes in a graph database. Establishing various edges according to different associations, and constructing a preliminary evidence network; identifying certificate nodes, and clustering to form a certificate entity group; a correlation score is calculated by searching the support evidence through forward traceability, a correlation score is calculated by searching the contradiction evidence through reverse traceability, the credibility of the certificate entity group is dynamically adjusted, and the influence is propagated according to the updated credibility; and screening abnormal certificates lower than a threshold value, analyzing causes and contradictory points, listing abnormal information, visually displaying key evidences, the contradictory points and associated paths, and generating text description.

The invention discloses a smelting process monitoring system of a medium-frequency electric furnace and a process guidance method of the smelting process monitoring system. The system comprises an edge acquisition terminal and a cloud platform, the edge acquisition terminal comprises an intermediate frequency voltage transmitter used for acquiring a voltage signal at the output side of the intermediate frequency electric control cabinet and converting the voltage signal into a first analog signal; the intermediate-frequency current transmitter is used for collecting a current signal at the output side of the intermediate-frequency electric control cabinet and converting the current signal into a second analog signal; the analog quantity conversion module is used for converting the first analog signal and the second analog signal into digital signals; the intelligent thermodetector is used for measuring the temperature of the molten metal in real time and sending temperature data; the cloud platform is used for receiving and processing the digital signal and the temperature data and storing high-frequency sampling data; identifying a smelting stage through an LSTM network; power control is optimized on the basis of a Transform model and a GAN model; and integrating an expert knowledge base and providing process guidance. According to the system, comprehensive digitization and real-time accurate sensing of the smelting process are achieved; and the timeliness and scientificity of decision making are improved.

The invention provides an aluminum melting furnace door flue gas automatic control dust removal air volume system, and relates to the field of aluminum melting furnace flue gas purification treatment, and the system comprises a data acquisition and processing unit which is used for acquiring flue gas state data and furnace door state images at an aluminum melting furnace door, and identifying the current flue gas state grade and furnace door opening and closing degree of the aluminum melting furnace; determining a target concentration control threshold value; the air volume distribution unit is used for generating the total air volume adjustment amount by using an increment inversion and gain scheduling algorithm based on the flue gas state grade and the target concentration control threshold value, and performing air volume distribution processing on the multiple execution mechanisms by using a redistribution scaling pseudo-inverse method; and the air volume control unit is used for generating control instructions of the multiple execution mechanisms based on the air volume distribution result, sequentially transmitting the control instructions to all the execution mechanisms and executing air volume control. According to the invention, through combination of deep learning network identification and dynamic adjustment, real-time matching of the dedusting air volume and the flue gas volume is realized, and the operation cost of an enterprise is remarkably reduced.

The invention discloses an intelligent data quality repairing method based on dynamic rule evolution, which belongs to the technical field of data quality management, and comprises the following steps: constructing a knowledge graph based on physical storage structure information and service logic of structured data; performing deep learning on the knowledge graph by using a graph neural network, and dynamically generating a global topology view based on a learning result; in combination with a historical damage mode and a global topology view, an optimal structured data scanning path is generated by utilizing reinforcement learning, and association anomalies of abnormal partitions in an optimal path scanning result are identified based on a graph neural network; the method comprises the following steps: constructing a multi-modal association sub-graph based on association anomaly, repairing structural defects in the multi-modal association sub-graph through a correct physical structure reversely deduced by a graph neural network, and carrying out credibility scoring on a repairing result to form a structured data management closed loop. The method can adapt to continuously evolved data modes and novel anomalies, continuously improves the robustness and autonomy of the system to deal with complex data problems, and reduces the long-term operation and maintenance cost.

The invention discloses a chromatographic data optimization processing method for a liquid chromatograph, and relates to the technical field, and the method comprises the following steps: collecting an original signal flow, carrying out preliminary peak detection, and generating an original peak list; extracting modal exclusive characteristics of each chromatographic peak; converting the modal exclusive features into a unified multi-dimensional semantic feature vector; calculating a time interval weight, and calculating a semantic relevancy weight based on the multi-dimensional semantic feature vector; correcting the time interval weight, generating a network edge weight, and constructing a peak correlation topology network by taking the multi-dimensional semantic feature vector as a node; identifying node clusters connected with high edge weights, and aggregating the node clusters into candidate compound entities; tracking a node signal intensity change track, and adding a conflict mark; conflict resolution arbitration is carried out on the candidate compound entities with the added conflict marks, dominant detector evidence is output, and an analysis report is generated. According to the method, the problems of poor data collaboration of multiple detectors, inaccurate peak identification and association and isolated data processing flow are effectively solved.

The invention provides a sheep abnormal behavior real-time monitoring method and system based on the Internet of Things, and relates to the technical field of abnormal monitoring, and the method comprises the steps: obtaining multi-source data information of sheep, including a position coordinate sequence, motion sensor data, a gait rhythm sequence, a colony house environment parameter sequence and a group topological relation; the influence of environmental constraints on group movement is quantified by constructing a group behavior field intensity model; based on an individual physiological energy consumption backstepping mechanism, an energy metabolism imbalance index is identified by using a deep residual network; through Granger causal relationship and conditional independence analysis, behavior causal separation of true and false anomalies is realized; and performing cross-cycle abnormal evolution path prediction in combination with historical behavior data, and generating a graded early warning conclusion. According to the invention, the false anomaly and the real health anomaly caused by environmental factors can be effectively distinguished, and the monitoring accuracy and the early warning timeliness are remarkably improved.

This invention discloses a method for acoustic emission signal recognition, comprising the following steps: collecting acoustic emission signal data generated during axle operation; preprocessing the collected signal data; importing the preprocessed signal data into a network recognition module, and using a pre-trained improved one-dimensional CNN network model in the network module to identify and analyze the imported signal data; and outputting the recognition result. This invention also discloses a model training method for training the improved one-dimensional CNN network model. This invention is applicable to the field of train fault diagnosis, enabling the identification of acoustic emission signals from train axles after acquisition and real-time monitoring during axle operation. It can extract higher-level signal features from the original acoustic emission signals through a deep network architecture and possesses good fault identification capabilities.

The invention discloses a complex number network SAR (Synthetic Aperture Radar) identification method based on an improved rednet network, which relates to the technical field of SAR image processing and identification, and comprises the following steps: step 1, constructing a training set and a test set of complex number SAR images; 2, extracting multi-dimensional statistical characteristics and performing principal component analysis; step 3, constructing a CV-SE attention module, including amplitude operation, compression operation, activation operation and indexing; 4, constructing a fusion feature complex number recognition network, wherein the fusion feature complex number recognition network comprises two branches and a fusion module; 5, training the fusion feature complex number recognition network; step 6, testing the fusion feature complex number recognition network; the complex network SAR recognition method based on the improved rednet network solves the problems that in the prior art, it is difficult to directly extend a real value activation function to a complex domain, and the recognition rate is not high.

The invention discloses a provincial and regional power transmission network cooperative control method, system and device and a medium, and relates to the technical field of power transmission, comprising the steps of monitoring a network in real time, identifying fault nodes and analyzing propagation, evaluating an influence range and resource availability, optimizing standby resource scheduling, and generating a calling priority in combination with load balancing. Cross-level permission allocation is adjusted through load difference analysis, a standby path is analyzed to determine a convergence acceleration sequence, real-time flow and sequence are combined, flow is reallocated to balance loads, resource scheduling parameters are dynamically adjusted, a resource calling and path switching sequence is iteratively optimized, and historical data feedback is utilized to realize resource scheduling. And generating a final resource allocation and path switching scheme. According to the method, a provincial and regional power transmission network cooperative control closed loop is constructed, the fault recovery speed, the resource utilization efficiency and the system toughness are improved, and the problems of response lag, resource waste and insufficient adaptability of a traditional method are effectively solved.

The invention discloses a new energy automobile valve body assembly monitoring platform based on machine vision. The new energy automobile valve body assembly monitoring platform is used for achieving intelligent assembly quality monitoring of a new energy automobile valve body. The method comprises the following steps: firstly, dividing assembly stages based on process specifications, deploying a multi-source sensor network to collect monitoring data with stage labels, and preprocessing to generate a multi-source data set; secondly, identifying a component through a pre-trained target detection network, and extracting space and time sequence features to construct a whole-process feature set; constructing a three-dimensional model by synchronously utilizing the point cloud data, and measuring and calculating derivative features such as contact surface fitting degree and stress field distribution to generate an enhanced feature set; a double-layer topological graph of the assembly stage and the features is constructed, and the quality score and the defect probability are analyzed through a graph neural network; and finally, performing defect diagnosis on the unqualified valve body traceability abnormal stage to judge whether accidental assembly is abnormal or not, and if not, performing management and control. The quality control requirement of new energy automobile valve body assembly is met, and the automobile valve body assembly monitoring accuracy and intelligence are improved.

The embodiment of the invention discloses a causal inference method for traffic accident data processing and prediction model optimization, and the method comprises the steps: constructing a multi-source heterogeneous accident database, and constructing a standardized feature matrix; based on the standardized feature matrix, extracting a feature constraint rule through a decision tree, and generating a minority class anti-fact sample conforming to domain knowledge in combination with a heuristic algorithm to form an equalized data set; inputting the equalized data set into a double-path model system for training, expanding a feature space by a data path to improve the characterization capability of a minority class, and embedding an interpretability constraint module into an interpretation path to obtain a trained model; based on the trained model, constructing a visual interpretation system comprising a feature importance map and a causal association network; and based on a visual interpretation system and the standardized feature matrix, constructing a multi-level causal network identification core influence factor, and quantifying the causal effect of intervention measures through anti-fact simulation to form a priority intervention list.

The invention belongs to the technical field of infrared weak and small target detection, and particularly relates to a multi-frame infrared weak and small target detection method based on improved YOLOv8, which comprises the following steps of: 1, acquiring and constructing an infrared image data set; 2, constructing a YOLOv8 network model; step 3, training an improved YOLOv8 network identification model; and 4, inputting the test set for testing and evaluation, and evaluating the overall detection effect of the network by using the recall rate, the accuracy rate, the average precision mean value, the model parameter scale and the floating point operand per second. A user-defined DIFA module is introduced to the end of the network Backbone, the module adopts a double-branch structure, a receptive field is expanded through cavity convolution, an attention mechanism is introduced to suppress redundant features, an original information flow is kept through residual splicing, feature enhancement is finally achieved, and the accuracy of feature enhancement is improved. The problems of false alarm and missing detection caused by small target size, similarity with background texture and low resolution of an infrared weak target in an infrared image are solved.

The invention discloses a building full life cycle management method and system based on AI and BIM, and particularly relates to the technical field of building information, and the method comprises the steps of component information access and semantic standardization, spatio-temporal data collection and registration and multi-objective optimization, version monitoring and difference influence evaluation and influence management and control. Component attributes are automatically extracted through natural language processing, and precise mapping between the BIM model and field sensing and detection data is achieved; the graph neural network and the long and short term memory network are combined to identify collision risk and performance deviation, and a multi-target optimal solution optimization scheme is generated through an evolutionary algorithm; a graph neural network is utilized to extract difference sub-graphs, and change influence scores are quantified in three dimensions of progress, cost and safety, so that a basis is provided for auditing and backspacing; people, machines, materials and early warning are dispatched based on the change influence score, and the risk management and control and resource utilization efficiency in building full life cycle management is improved.

Identification and resolution of anomalies over a network include obtaining at least a set of media characteristics associated with media data transmitted from a first entity to a second entity over the network and contextual information associated with at least one of the first entity or the second entity. A first operational score associated with the first entity is determined based on the obtained set of media characteristics and the obtained contextual information. The first operational score is indicative of operating conditions associated with the first entity for the transmission of the media data over the network. Based on a comparison of the first operational score with a threshold, a set of anomalies associated with the first entity is identified. A set of operations to resolve the set of anomalies is determined. The first entity is controlled to execute the determined set of operations on the media data.

An apparatus and / or a method for providing dual connectivity (DC) in an electronic device which may include multiple SIMs. An electronic device may include: a first SIM comprising first subscriber identity information; a second SIM comprising second subscriber identify information; a wireless communication circuit; and at least one processor operatively connected to the first SIM, the second SIM, and the communication circuit, wherein the at least one processor: connects to a first MCG network or a first SCG network, based on the first subscriber identity information when data communication using the first subscriber identity information is configured; identifies a state of communication with the first MCG network; restricts addition of a second SCG network, based on the second subscriber identity information when the state of communication with the first MCG network is a connected state; and allows addition of the second SCG network, based on the second subscriber identity information when the state of communication with the first MCG network is in an idle state. Other embodiments may be also possible.

The invention relates to a grassland obstacle visual detection and identification method for a mowing robot, and belongs to the technical field of image identification. According to the method, firstly, a traditional neural network is used for carrying out obstacle recognition and elimination on a grassland image during mowing operation to obtain a preliminary screening image, and then the possibility that different sub-regions in the preliminary screening image contain unobvious obstacles is represented in a multi-dimensional mode based on image texture differences, edge trend differences and color distribution differences between the unobvious obstacles and a grassland background. Therefore, the parameters of the lightweight neural network are adaptively and accurately adjusted, so that the adjusted network is prevented from losing weak and sparse characteristic signals of an unobvious obstacle when facing a preliminary screening image possibly containing the unobvious obstacle, and the unobvious obstacle recognition with both recognition efficiency and recognition accuracy is realized. Finally, when the grassland obstacles in the mowing operation are recognized through the lightweight neural network, the obstacle recognition accuracy is remarkably improved.

The invention relates to the technical field of computer authority management, and discloses an office authority transfer management system for mobile office, which comprises an authority abstraction layer module used for converting original authority representation of each terminal platform into unified and standardized intermediate state representation; the permission authorization service module is used for supporting permission verification, granting and revocation operations in an offline state; the scene sensing module is used for constructing a multi-dimensional scene feature space and identifying key features related to safety in a current scene through a multi-head attention network; the permission adjusting module is used for realizing a continuous permission control strategy from strict limitation to high freedom; the behavior monitoring module is used for recording permission use behaviors in an offline state and detecting potential abnormal operations; according to the method, the working continuity and the user experience in a mobile office environment are improved, and meanwhile, the enterprise data security is effectively guaranteed through a permission progressive release mechanism and a permission re-evaluation mechanism triggered by environment change.

The invention discloses an ancient building parameterized three-dimensional modeling method and system based on point cloud and texture features, and relates to the technical field of computer vision and three-dimensional reconstruction. According to the invention, high-precision cross-modal registration is carried out on the point cloud and texture of the ancient building; intelligent component segmentation is realized by fusing geometric features and texture features of the ancient building and adopting an improved density sensitive DBSCAN algorithm; identifying the category of the component by using a PointNet-CNN hybrid network, and generating an optimal component parameter through parameter template matching and LM optimization solution; a BIM component is generated through layered LOD modeling, and a damaged area is repaired by adopting a Pix2Pix-HD network; the components and BIM metadata are integrated to construct a complete model, and closed-loop feedback correction is achieved through geometry / texture double-precision verification and error type diagnosis; the problems of precision-efficiency-cost imbalance, texture-geometric splitting and component intelligence are solved, and technical support and data basis are provided for high-precision, high-efficiency and low-cost digital archiving, research, monitoring and repairing of ancient buildings.

The invention discloses a dynamic network access control system under a zero-trust architecture, which relates to the technical field of network security, and comprises a multi-dimensional trust evaluation module, a self-adaptive micro-segmentation engine, a strategy decision execution module and a risk perception feedback module, the multi-dimensional trust evaluation module calculates a comprehensive trust score based on five-dimensional features of identity, equipment, network, application and data; the self-adaptive micro-segmentation engine dynamically generates network micro-segments based on a graph diffusion algorithm; the strategy decision execution module adopts deep reinforcement learning to generate an access decision; the risk perception feedback module identifies abnormity based on the LSTM network and adjusts trust parameters through closed-loop feedback, the four modules are deeply coupled and cooperated, refined dynamic access control is realized, the occurrence rate of security events is reduced by more than 85%, and an innovative solution is provided for enterprise network security.

The present application relates to the technical field of irrigation control automation, in particular to a kind of irrigation area flow full node measurement method based on remote sensing interpretation and Internet of Things fusion, in the present application, through remote sensing interpretation plot pixel determination gray scale difference and reconstruct boundary connected structure, combined with multi-scale residual network identifies low reflection water area, effectively improve the identification precision of water area in complex background, make the boundary of identified area have closed integrity, through combining plot center and actual node coordinate error analysis, realize the spatial binding of node and canal section, optimize the spatial fragmentation and response delay problem caused by traditional segmented irrigation information acquisition mode, through the structural association between disturbance information by graph neural network, extract water pressure response consistent path combination, improve the ability of connectivity identification, water pressure evolution capture, path closed loop mining and scheduling node identification in distributed irrigation system.

An example operation may include one or more of storing a hash of a service set identifier (SSID) of a wireless network via an apparatus, receiving a probe request message transmitted from a network device, wherein the probe request message comprises a hash value, determining that the hash value within the probe request is a valid SSID based on the hash of the SSID of the wireless network stored in the storage device, and controlling the network interface to transmit a probe response with a plain text name of the SSID to the network device in response to the determination.

This invention discloses an artificial intelligence-based method for managing and controlling power grid water dispatch data, specifically relating to the field of power grid water dispatch data management technology. The method involves collecting multi-source heterogeneous water dispatch data and its metadata to construct a unified format dataset; generating a credibility score based on metadata and data features to form a credibility matrix; constructing a multi-dimensional quality assessment tensor and extracting the dominant factor subspace; inputting this tensor into a clustering perceptual neural network to identify abnormal data, outputting an abnormal dataset and abnormal labels; combining a large model enhanced by RAG retrieval with historical similar data to complete abnormal data repair; constructing a data quality improvement report using the difference vectors before and after repair and label information; and finally pushing the report to a data management platform and generating a traceable rectification work order. This method achieves automatic identification, intelligent repair, and feedback tracking of power grid water dispatch data, improving data credibility, governance efficiency, and security, and is suitable for data management needs in complex scenarios under new power systems.

The application discloses a point cloud analysis and reconstruction method and system based on a cloud CAD platform, belongs to the technical field of point cloud reconstruction, and is used for solving the technical problems that the existing point cloud data reconstruction mode depends on manual operation, is low in efficiency, poor in precision and consistency, and difficult to adapt to complex scenes and automation requirements. The method comprises the following steps: receiving a point cloud data file and determining a processing mode; determining a corresponding irregular sampling region according to the processing mode, and performing signed distance field prediction on the point cloud data file in the irregular sampling region to obtain unsigned distance field and gradient information; generating a sliding window, identifying local boundary information in the sliding window through a boundary prediction neural network, and performing splicing to obtain global boundary information; dividing a face region of the point cloud data file according to the global boundary information to obtain different types of faces; performing targeted fitting processing on each type of face, and solving a boundary representation relationship between the faces to reconstruct a CAD model.

This invention discloses a method for preparing and intelligently identifying microscopic stealth codes by integrating artificial intelligence and hyperspectral imaging, belonging to the field of anti-counterfeiting technology. The method comprises two parts: preparation and identification. During preparation, photochromic materials are doped into a substrate to form microscopic code points. A hyperspectral feature library is established, and a digital twin initial model containing partial differential equations is constructed, followed by pre-training of a physical information neural network. During identification, measured hyperspectral data of the code points to be tested are collected. Combined with environmental parameters and historical data, the expected state is predicted using the trained physical information neural network. The authenticity is determined by comparing the measured data with the expected state. This invention embeds physical laws into the identification process through a digital twin model and a physical information neural network, achieving proactive prediction of the evolution of the microscopic stealth code state, significantly improving the accuracy, robustness, and anti-counterfeiting capability of anti-counterfeiting identification.