95 results about "Edge model" patented technology

The invention relates to a landslide grading early warning method based on multi-modal data change characteristics. The method comprises the following steps: establishing a mountain digital twinborn body; the method comprises the following steps: deploying a multi-node sensor network in a target area, configuring an edge computing unit, collecting geological data in real time, and screening the geological data based on mountain digital twin to form effective local data; constructing a lightweight multi-modal neural network model at each node, dynamically searching hyper-parameters by using an ant colony optimization algorithm, and generating an encryption model weight update quantity packet; the central server dynamically calculates node weights according to disaster feature vectors output by the digital twins, generates a global model through weighted aggregation, and directionally distributes and updates the global model; real-time monitoring data and a model prediction result are fused, millimeter-level disaster evolution simulation is executed through a variable step size physical engine, an advanced early warning signal is triggered when a deduced prediction risk exceeds a threshold value, and the edge model adaptability, federal aggregation precision and early warning advancement are remarkably improved.

The invention discloses a real-time translation recognition system under a cloud service framework, belongs to the technical field of real-time translation, and solves the problems that an existing translation system is insufficient in real-time performance, poor in scene adaptability, weak in privacy protection, slow in model evolution and the like. The dynamic model management engine obtains adaptive slices from a model slice factory according to scenes, equipment states and network quality and distributes the adaptive slices to edges, and the adaptive slices are distributed to a cloud-side collaborative reasoning system; the cloud-side collaborative reasoning system comprises a cloud-side collaborative reasoning system, a cloud-side collaborative reasoning system, a cloud-side collaborative reasoning system and a cloud-side collaborative reasoning system; the multi-modal perception engine fuses audio, images and dialogue history to generate a structured context vector and improve translation context fitting degree, the cloud edge cooperation engine takes an edge model as a core, processes different complexity tasks in combination with a cloud end, and constructs a data closed loop by incremental learning and a federation engine to realize model optimization and privacy protection; according to the system, the real-time performance, accuracy and safety are improved through cloud edge collaboration, dynamic adaptation and continuous learning, and the system is suitable for multi-scene real-time translation.

The invention discloses a fault diagnosis method and a fault diagnosis system for an electric valve actuating mechanism. The method comprises the following steps: synchronously acquiring signals through an anti-EMI (Electro-Magnetic Interference) multi-source sensor; adopting complex Morlet wavelet packet decomposition to extract a 1.2-2.4 kHz energy entropy minimum frequency band, and calculating a kurtosis index; separating the third harmonic of the current through variational mode decomposition, and calculating the total distortion rate of the third harmonic; a graph attention network with 12-dimensional features is constructed, and weighted fusion is carried out through a multi-head attention mechanism; the lightweight CNN outputs a fault type, and when the confidence coefficient is less than 0.9, a knowledge graph rule engine is triggered; and updating a threshold value based on a historical diagnosis clustering result, and aggregating edge model parameters by federal learning. The system comprises a wafer-level micro-strain sensing layer, an FPGA accelerated edge computing layer, a cloud platform supporting federated learning, and an AR maintenance guidance and block chain evidence storage module. The early fault detection rate is improved, the false alarm rate under strong EMI is reduced, and the average repair time is shortened.

The invention discloses a method for determining the demulsification speed of emulsified asphalt, and relates to the technical field of online detection and intelligent monitoring of road engineering materials, and by integrating a lightweight time sequence prediction model and a self-adaptive processing mechanism, the practicability and reliability of the determination of the demulsification speed of the emulsified asphalt are remarkably improved; in the aspect of portability, an edge model based on a gating circulation unit structure is adopted, so that the dependence on computing resources is reduced, the method can be operated on handheld equipment, the problem that traditional laboratory equipment is heavy is avoided, and construction personnel can conveniently and quickly deploy the equipment on site; the real-time performance is enhanced, the adaptive sliding window dynamically adjusts the window length and step length according to the data stability to ensure that the inference frequency is matched with the demulsification process, and the model can immediately respond to environmental changes such as temperature fluctuation and output a demulsification speed estimated value without obvious delay in combination with an online learning mechanism, so that the paving and compacting opportunities are guided; and construction interruption is reduced.

Disclosed is a computer-implemented method and system for training a subject-specific machine learning model to infer inherent subject features from recorded or live video data. The system preprocesses the visual and audio channels, converting audio to text, and employs multiple pre-trained extraction models to generate feature embeddings. Ground truth data is obtained to guide training, where weights are assigned to produce and combine predicted feature values. Model performance is optimized by minimizing error. The trained feature extraction models are deployed on an edge device, while the subject-specific model resides in the cloud. A lightweight edge model, derived via knowledge distillation and model compression, supports local inferencing with reduced reliance on cloud resources. Synchronization ensures iterative updates for sustained accuracy.

The invention discloses an image processing method and device, a medium and a computer program product, and relates to the technical field of semiconductor manufacturing. The image processing method comprises the following steps: acquiring target image samples of a plurality of wafer samples through a scanning electron microscope, and obtaining a first contour image label of each target image sample; based on the target image sample, a first contour image result is generated through a generator of a preset image edge model, iteration training is carried out on the preset image edge model through pixel difference loss, adversarial loss and edge supervision loss until a convergence condition is met, and a target image edge model is obtained; the generator of the target image edge model can output an edge contour image based on the target image of the target wafer. Through multi-dimensional constraints of pixel difference loss, edge supervision loss and adversarial loss, the preset image edge model is trained in the direction of generating an actual edge contour, and the accuracy of the edge contour image is improved.

The invention discloses a low-altitude aircraft intelligent identification system based on an edge network, and belongs to the technical field of intelligent identification. Comprising a data acquisition module used for acquiring time sequence sensor data; the knowledge base construction module is used for constructing a physical rule knowledge base; the penalty value generation module is used for generating a physical violation penalty value; the model generation module is used for obtaining a physical rule embedded model; and the result output module is used for outputting an aircraft intelligent identification result conforming to the physical motion law. According to the method, the physical rule knowledge base is introduced into the edge network, and the lightweight model is trained by using the dual-target collaborative loss function, so that the effect of still outputting a physically consistent aircraft recognition result under the condition of limited computing power is achieved; the problem that in the prior art, a marginal small model generates jumping, over-limit or abnormal tracks due to lack of motion law constraints is solved.

The invention relates to an intelligent dam safety state prediction method and system based on a time sequence large model, and the method comprises the steps: constructing a space-time embedding mechanism comprising dynamic graph position coding and adaptive wavelet position coding through fusing sensor time sequence data, environment factor data and unstructured text data; and cross-time and cross-dam knowledge migration is realized by adopting a multi-scale memory enhancement encoder, and potential causal factors are identified through a causal decoupling decoder, so that the model interpretability is improved. And the prediction result is processed by the physical constraint output layer to ensure that the engineering mechanics law is met. The system supports collaborative deployment of an edge end and a cloud end, the edge end processes data in real time and generates preliminary prediction, and the cloud end operates a complete model and periodically updates edge model parameters. The method is suitable for real-time prediction and grading early warning of the health state of the dam structure, and is widely applied to intelligent water and electricity, infrastructure monitoring and AI-driven predictive maintenance systems.

An edge device provides an agentic framework to manage apps and artificial intelligence (AI) models used by the apps. An app and app metadata are downloaded from a cloud of servers to the device. The app metadata describes requirements of the app for AI models to be used by the app. The agentic framework performs a search in an on-device database that stores the app metadata and model metadata of edge models installed on the device. The search is performed to determine whether one of the edge models satisfies the requirements of the app. Following the search, the agentic framework sets a given edge model already installed on the device as a target model of the app, where the target model satisfies the requirements of the app. The agentic framework then directs the app to use the target model in response to a request for service.

The invention discloses a low-altitude unmanned aerial vehicle autonomous cruise method and system based on cloud edge basic model collaboration, and the method comprises the following steps: S1, collecting an environment RGB image through an airborne monocular camera of an unmanned aerial vehicle, and carrying out the preprocessing of the image, and obtaining a preprocessed gray image; s2, a neural scheduler based on deep reinforcement learning generates a scheduling instruction according to the environment data and the network state reasoned by the navigation model at the previous moment; s3, generating a preliminary flight instruction; s4, generating an optimized flight instruction; s5, generating a structured flight instruction; and S6, the unmanned aerial vehicle executes the preliminary flight instruction or the optimized flight instruction or the structured flight instruction. According to the invention, through dynamic on-demand cooperation and intelligent scheduling of the end-edge-cloud three-level model, resource consumption and delay are substantially reduced, and high-robustness and high-safety autonomous cruise of the unmanned aerial vehicle in a complex open environment is realized.

The invention discloses a ship navigation oil consumption estimation method based on artificial intelligence, and particularly relates to the field of navigation oil consumption estimation, and the method comprises the steps: obtaining a ship navigation state, carrying out manual operation, carrying out the external environment data, carrying out the preprocessing, carrying out the dimension reduction through a feature screening network, generating navigation features, carrying out the distillation training of a lightweight model, and monitoring the actual oil consumption and an estimation error. And circularly updating. According to the ship navigation oil consumption estimation method based on artificial intelligence, through a feature screening network, the data dimension of an input model is reduced, the edge model calculation amount is reduced, the reasoning real-time performance is improved, and the system resource utilization rate and stability are guaranteed; knowledge of the first neural network model is migrated to the lightweight second neural network model, so that the fuel consumption pre-estimation reasoning process responds more quickly, and the real-time requirement is met; by collecting a new data dynamic optimization model, implementing a rollback mechanism and distinguishing short-term fluctuation and long-term degradation, it is guaranteed that the method continuously adapts to ship navigation state changes, and high estimation precision is maintained.

The invention discloses a lightweight image recognition method and system for power plant safety, and relates to the field of image recognition, and the method comprises the steps: constructing a multi-scale adaptive perception image anomaly detection model, and recognizing the violation behaviors of power plant operators in power plant real-time video data through the image anomaly detection model; constructing a multi-stage binocular stereo area distribution model to match the feature points in the binocular image of the power equipment, and calculating the safety distance between the power plant operating personnel and the electrified body based on the matching result; and constructing an edge equipment reasoning model of lightweight knowledge distillation in combination with illegal behaviors and safety distances of power plant operating personnel, and monitoring potential safety hazards of the power plant in real time by using the edge equipment reasoning model. According to the method, the teacher model with high expression ability is deployed on the cloud for training, semantic information and structural knowledge are transmitted to the student model on the edge device through the dynamic adaptation strategy, and the recognition ability of the edge model in a computing power limited scene is remarkably improved.

The invention discloses a multi-modal data processing method and system, computer equipment and a readable storage medium, and the method comprises the steps: carrying out the data processing of an edge-end model and a cloud model, enabling the data processing results of the edge-end model and the cloud model to approach through a semantic and feature weighted total loss function, and carrying out the optimization of the edge-end model; performing adversarial training by taking the optimized edge-end model as a generator and the cloud model as a discriminator, and further improving the edge-end model; when the improved edge-end model is superior to the cloud model, the edge-end model and the cloud model exchange roles, and the edge-end model guides parameter updating of the cloud model; and carrying out data processing on the obtained multi-modal data according to the improved edge end model. Multi-level distillation and adversarial distillation break through traditional distillation bottlenecks from longitudinal knowledge deep extraction and transverse model dynamic optimization, so that the cross-modal feature fusion precision of multi-modal power grid data is guaranteed, and the overall performance of the system is continuously improved through competitive collaborative learning of an edge cloud model.

The invention relates to the crossing field of image processing and artificial intelligence, and discloses a computer image recognition method and system based on machine learning, and the method comprises the steps: fusing a convolutional neural network with a self-adaptive attention mechanism, and constructing a multi-scale feature extraction network; a dynamic adversarial data enhancement strategy is adopted to process the input image to generate an adversarial sample, and the adversarial sample and the style migration image jointly form an enhanced data set; adopting a hierarchical transfer learning framework, initializing source domain pre-training model parameters based on meta learning, and learning a feature mapping relationship between a source domain and a target domain through a domain adaptive network; constructing a probability prediction model based on a variational auto-encoder and a Bayesian Transform, and outputting an image recognition result and a corresponding confidence index; carrying out incremental learning by adopting a federal learning algorithm based on differential privacy, and regularly issuing updated knowledge to the edge model; according to the invention, the image recognition performance and practicability are significantly improved.

The invention belongs to the technical field of image recognition, and provides a remote sensing image building recognition method and system based on multi-neural network integration, and electronic equipment. The method comprises the steps of original edge network construction, two-class standard building data set construction, multi-class model parameter training, positioning information acquisition, remote sensing image acquisition, region related information extraction, main building type determination, airspace rule matching, parameter and rule lowering, model parameter updating and image recognition. According to the invention, the original edge network is constructed, so that the requirements on model parameters and computing power are reduced; through the trained multi-class model parameters, region related information extraction and main building type determination, adaptive matching of the model parameters according to different scene types is realized, the building identification accuracy is improved, and the volume of the edge model is reduced; through airspace rule matching, movement or flight of edge equipment is limited, and potential risks of personnel and buildings are reduced.

Disclosed is a computer-implemented method and system for training a subject-specific machine learning model to infer inherent subject features from recorded or live video data. The system preprocesses the visual and audio channels, converting audio to text, and employs multiple pre-trained extraction models to generate feature embeddings. Ground truth data is obtained to guide training, where weights are assigned to produce and combine predicted feature values. Model performance is optimized by minimizing error. The trained feature extraction models are deployed on an edge device, while the subject-specific model resides in the cloud. A lightweight edge model, derived via knowledge distillation and model compression, supports local inferencing with reduced reliance on cloud resources. Synchronization ensures iterative updates for sustained accuracy.

An agentic framework is provided on a device that interacts with a cloud and runs apps and artificial intelligence (AI) models. An app and app metadata are downloaded from the cloud to the device. The app metadata describes requirements of the app for AI models to be used by the app. A search is performed in a database on the device that stores the app metadata and model metadata of edge models that are installed on the device to identify a target model among the edge models that satisfies the requirements. The target model is downloaded from a collection of downloadable models in the cloud in response to a determination that the target model is not already installed on the device.

This invention discloses a large-scale visual-language model collaborative inference method for cloud-edge-device systems. Its features include: deploying a high-performance cloud-based LVLM model and a cloud-based archive in the cloud; deploying an edge-based LVLM model and an edge-based archive at the edge, wherein the edge LVLM model incorporates a retrieval-enhanced generation algorithm, and the edge archive and the cloud archive can dynamically interact in terms of knowledge; a task scheduler evaluates the complexity of received query tasks based on a task allocation algorithm, and allocates the query tasks to the edge for local processing or to the cloud for processing based on the complexity evaluation results. This invention can improve the inference efficiency within cloud-edge-device systems while maintaining high accuracy, effectively solving the technical problem of low inference efficiency caused by limited computing resources, intermittent communication windows, and bandwidth limitations of edge devices.

The invention discloses an overhead line system dropper defect identification method based on lightweight YOLOv12s. The method comprises the following steps: acquiring an overhead line system dropper image to be identified; and inputting a to-be-identified catenary dropper image into the pre-trained YOLOv12s-Lite, and outputting a catenary dropper defect detection result. The YOLOv12s-Lite is a lightweight network obtained by carrying out lightweight improvement on the original YOLOv12s; the lightweight improvement comprises the following steps of: replacing a backbone network in the original YOLOv12s with the OfficientNetV2s; an efficient multi-scale attention module EMAttention is introduced into a neck network in the original YOLOv12s. According to the method, the model complexity is remarkably reduced while the model defect identification precision based on the YOLOv12s algorithm is maintained, lightweight detection of catenary dropper defect identification is realized, and technical reference is provided for edge end model deployment.

When retraining an AI model used for object detection processing on edge devices to add identifiable classes, the aim is to improve the class identification accuracy of the AI ​​model while reducing the workload on the user. [Solution] The information processing device comprises an annotation processing unit that performs annotation processing on an input image using a large-scale AI model that performs object detection processing and is configured to output region information indicating an object detection region even for objects that cannot be classified as a class; a reception processing unit that accepts corrections from the user regarding the annotation results from the annotation processing unit; a large-scale model retraining processing unit that performs retraining processing on the large-scale AI model using the annotation information corrected by the user; and an edge model retraining processing unit that performs retraining processing on an edge model, which is an AI model used in an edge device that performs object detection processing on captured images, by knowledge distillation using the retrained large-scale AI model as the training model.

The invention provides precast pile upper surface modeling equipment. The precast pile upper surface modeling equipment comprises a rack, one or more trowelling mechanisms and one or more blunt edge modeling mechanisms. The trowelling mechanism is mounted on the rack; the blunt edge modeling mechanism is mounted on the rack, and in the working advancing direction, the blunt edge modeling mechanism is located behind the trowelling mechanism; the blunt edge modeling mechanism comprises at least one pair of blunt edge modeling assemblies with adjustable combination width in the transverse direction, and the two blunt edge modeling parts of the same pair of blunt edge modeling assemblies are oppositely arranged. A longitudinal walking mechanism is arranged at the bottom of the machine frame to drive the machine frame to move longitudinally, so that a trowelling mechanism and a blunt edge modeling mechanism on the machine frame are driven to move longitudinally, and in the moving process, the trowelling mechanism and the blunt edge modeling mechanism conduct trowelling operation and blunt edge modeling operation on the upper surface of the precast pile. Due to the fact that the combination width of the blunt-edge modeling assemblies in the same pair is adjustable in the transverse direction, the blunt-edge modeling mechanisms in the same pair can adapt to the width changes of the thick cavity section and the thin cavity section of the long mold cavity of the modeling mold in the longitudinal direction.

The invention discloses a traffic video edge analysis system based on Yolov5-Edge and Prewitt algorithms, and the system achieves the real-time collection, intelligent recognition and encrypted transmission of a vehicle-mounted video through the construction of a layered architecture of an object-side equipment layer, an edge calculation layer and a cloud service layer. According to the system, a lightweight Yolov5-Edge model and a Prewitt edge detection algorithm are fused in edge equipment, multi-channel video parallel processing is realized under finite computing power, and the video inspection efficiency is effectively improved; the frame rate and the number of processing paths are dynamically adjusted through the resource monitoring and load control module, and the real-time performance and stability of AI reasoning are guaranteed; a data management and encryption mechanism is adopted, only abnormal data are stored at an edge end and clouded through RSA and AES encryption modes, and security and compliance of sensitive behavior information in the transmission and storage process are ensured. The system can identify and classify abnormal behaviors of drivers and passengers without depending on a high-performance server.

The invention discloses a coal pile weight calculation and dynamic weighing method based on an intelligent sensor. The method comprises the following steps: sparsely deploying intelligent sensing nodes at the bottom of a coal pile and acquiring initial data; constructing a double-loop model of an edge online sequence extreme learning machine and a cloud physical information neural network; the edge model processes the real-time data stream and outputs a fast weight estimation value and a virtual observation residual error; the cloud model reconstructs full-field stress distribution by using the data and calculates a virtual sensing value; adjusting an edge model learning target through virtual value feedback between double rings; carrying out integration on the reconstructed stress field to obtain accurate total mass and local mass, and carrying out visualization; and monitoring residual features to trigger early warning and adaptively adjust physical parameters of the model. According to the method, high-precision, continuous, dynamic and self-adaptive direct weighing of the mass of the coal pile is realized under the condition of low-cost sparse sensing deployment, and the contradiction among dynamics, precision and engineering feasibility of a traditional method is effectively solved.

The invention relates to the technical field of an edge AI computing system of a graph neural network, and discloses a behavior space-time prediction method and system of an edge AI chip. The method comprises the following steps: extracting customers and shelf entities in real time based on a video stream, and constructing a dynamic space-time node set; generating a sparse heterogeneous space-time high-order correlation structure based on the node set; on the basis of the structure, through a topological graph neural network subjected to knowledge distillation, behavior prediction and loss prevention response of topology perception are executed; in order to solve the problems that traditional unstructured data processing is high in memory occupation, a traditional graph structure is difficult to capture multi-body interaction, edge model behavior discrimination precision is low and loss prevention response lags, edge chip resource constraints are adapted through model quantitative pruning, dynamic sparse pruning and multi-dimensional loss prevention evidence fusion, and the edge chip behavior discrimination precision is improved. The subtle behavior distinguishing precision and the real-time response speed are improved, and precise loss prevention and shelf layout optimization of an unmanned sales scene are achieved.

The application discloses a data blood relationship analysis method and device and a computer readable storage medium. The method comprises the following steps: obtaining job blood relationship data based on a directed graph corresponding to a scheduling task in a scheduling system, and obtaining a first application program identifier based on an execution log corresponding to the scheduling task; when a structured query language (SQL) statement is detected, obtaining field blood relationship data corresponding to the SQL statement and a second application program identifier corresponding to the field blood relationship data; determining a job blood relationship subgraph based on the job blood relationship data and a Neo4j point-edge model, and determining a table field blood relationship subgraph based on the Neo4j point-edge model and the field blood relationship data; and connecting the job blood relationship subgraph and the table field blood relationship subgraph based on the first application program identifier and the second application program identifier. The application can obtain blood relationship data of different types of data systems, adapt the data blood relationship analysis method to different types of data systems, and improve the expansibility of the data blood relationship analysis method.

According to the self-adaptive control method for the maintenance robot based on curvature edge feature reconstruction and cascade active-disturbance-rejection control, multi-source sensing is constructed, and image information is collected through sensors such as a high-resolution RGB camera and a depth camera. A real-time adaptive Canny edge detection method is adopted to process an RGB image so as to extract an edge, point cloud data of a depth camera is converted to a robot-based coordinate system through point cloud coordinate conversion, registration is completed, and meanwhile, the principal curvature and the normal vector of each point are estimated. And realizing automatic generation and dynamic adjustment of a spraying path based on the edge model, including spray gun attitude calculation and distance speed flow control. The system adopts a three-stage cascade controller architecture, an outer ring generates an expected position instruction, a middle ring performs position compensation to output a speed instruction, and an inner ring realizes speed and current regulation and integrates a hardware protection mechanism. The invention aims to improve the spraying precision, efficiency and coating quality of the robot and realize high-quality spraying operation on the surface of a complex workpiece.

The application discloses an emulsified asphalt demulsification speed determination method, relates to the online detection and intelligent monitoring technical field of road engineering materials, and remarkably improves the practicability and reliability of emulsified asphalt demulsification speed determination through integration of a lightweight time sequence prediction model and a self-adaptive processing mechanism; in terms of portability, an edge model based on a gated recurrent unit structure is adopted, calculation resource dependence is reduced, the method can be run on a handheld device, the problem of clumsiness of traditional laboratory equipment is avoided, and construction personnel can be conveniently and rapidly deployed on site; real-time performance is enhanced, a self-adaptive sliding window dynamically adjusts window length and step length according to data stability, the inference frequency is ensured to match the demulsification process, an online learning mechanism is combined, the model can instantaneously respond to environmental changes such as temperature fluctuations, and a demulsification speed estimation value can be outputted without obvious delay, so that paving and compaction time is guided, and construction interruption is reduced.

The invention discloses a micro-morphology measuring and modeling method for a cutting edge of a light shielding ring of a light shielding cover, and belongs to the technical field of precision measurement and three-dimensional modeling. The method comprises the following steps: firstly, acquiring three-dimensional point cloud data of a cutting edge of a light blocking ring through a laser scanning confocal microscope; carrying out denoising processing on the data by adopting wavelet threshold denoising and five-point secondary smoothing filtering; the data precision is improved by combining clamp installation optimization and inclination correction technologies; data pruning is achieved through curvature analysis, and a cutting edge feature area is extracted; missing data are repaired by using cubic B-spline interpolation; and finally, a high-precision cutting edge model is established through trapezoid fitting, arc fitting and NURBS curved surface reconstruction methods. According to the method, the problem that the micro-morphology representation accuracy of the cutting edge of the light shielding ring of the light shielding cover is insufficient in the prior art is solved, the accuracy and efficiency of the morphology representation of the cutting edge are remarkably improved, and technical support is provided for the surface morphology design of a high-precision light shielding cover.

The invention discloses an application construction and management system based on session type interaction and an implementation method thereof, belongs to the technical field of application construction management, and aims to unify multiple expressions of the same intention through small-sample causal association generation of a large language model and digest ambiguity of the same sentence through scene causal dependence intensity calculation. Semantic enhancement is realized by fusing semantic vectors of text and causal atlas features, richer intention and scene associated information can be contained, and accurate semantic input is provided for subsequent steps; diversified samples are generated through digital twinning, so that the recognition accuracy of an edge end model on unseen overexpression can be effectively improved; by deploying a lightweight large language model, sub-second response is realized, and the problem of cloud transmission delay is solved; by implementing a federal incremental learning and differential privacy synchronization mechanism, the user privacy is protected, and the real-time performance of a model semantic rule is ensured.