4139 results about "Network layer" patented technology

In example embodiments, an industrial technology stack for an industrial environment includes a set of computational resources and a set of layers executed by the set of computational resources, the set of layers including a governance layer, an enterprise layer, an offering layer, a transaction layer, an operations layer, a network layer, a data layer, and a resource layer. In example embodiments, the industrial technology stack may include one or more artificial intelligence models for implementing one or more components of one or more layers of the set of layers.

The invention provides an information network security self-defense method and system based on trusted computing, and the method comprises the steps: constructing a trusted computing platform which comprises the steps: deploying a trusted platform module TPM, building a trusted software stack TSS, and implementing a trusted network connection TNC; designing a self-defense architecture, wherein the self-defense architecture comprises deployment of a network layer, an application layer and a data layer defense system, integration of an intelligent sensing system and establishment of a central control and decision center; network traffic and user access behaviors are monitored, and embedded attack behaviors are preprocessed; performing anomaly detection on the network flow and the user access behavior by applying a machine learning algorithm; calculating a real-time risk score based on the anomaly detection result, and generating a security situation awareness report; and based on the security situation awareness report, defining a threat level and a response level, and executing automatic defense.

An artificial intelligence driven system of systems may include a layered architecture for providing transaction support to various types of enterprises. A governance layer implements automated governance and policy enforcement through specialized governance modules utilizing generative AI technology. An enterprise layer supports enterprise functions by integrating management and control platforms with digital infrastructure. An offering layer creates and manages system offerings via content generation, personalization, and smart product modules. A transactions layer enables automated transaction orchestration through API integration, execution, and fulfillment modules. An operations layer manages AI systems through generation, training, verification and orchestration modules. A network layer provides adaptive networking capabilities through routing, protocol selection and communication modules. A data layer processes fused data from multiple sources using machine learning and AI systems. A resource layer manages computing, storage, and other resources through specialized resource modules.

A small-size vehicle detection deep learning model based on feature fusion of multi-scale modules is provided, which solves the problem of small-size vehicle image detection. The model includes a Backbone network, a Neck layer and a Head network, wherein a C2f_DCNv3 module based on the combination of deformable convolution v3 (DCNv3) and a cross stage feature fusion (C2f) module and an SPPF_LSKA module based on the combination of a spatial pyramid pooling fast (SPPF) layer and a large separable kernel attention (LSKA) module are introduced into the Backbone network; a C2f_SCConv module based on the combination of spatial and channel reconstruction convolution (SCConv) and a C2f module is introduced into the Neck layer; and a multi-scale kernel detection (MSK_Detect) module is introduced into the Head network.

The invention discloses a network threat multi-modal detection method based on a large model, and belongs to the technical field of network security, and the method comprises the steps: collecting three types of heterogeneous data of NetFlow flow of a network layer, a system call chain sequence of a host layer and a protocol load of an application layer, and carrying out the desensitization processing and feature coding to generate a unified tensor format; the method comprises the following steps: through network security threat intelligence and MITRE ATTamp; performing supervision fine tuning on the large model by using a CK attack chain sample, and constructing a network threat identification special model; cross-device behavior characteristics are extracted through a model self-attention mechanism, and a dynamic behavior map is constructed; and finally, comprehensively evaluating an attack mode matching degree, a node vulnerability mean value, historical alarm association and an attack path risk by adopting a weighted fusion algorithm, and triggering a high-confidence alarm when a comprehensive score exceeds 0.8. According to the method, through multi-modal data fusion and dynamic graph analysis, the detection precision and response efficiency of the complex attack chain are improved.

The invention discloses a mechanical equipment state monitoring method and system based on multiple sensors, and the method comprises the five core steps: multi-modal data collection and preprocessing, dynamic feature fusion, adaptive threshold diagnosis, digital twin fault tracing and predictive maintenance decision. All-domain coverage of equipment is realized through a three-layer sensor network architecture, the problems of data synchronization and interference resistance are solved by utilizing a temperature and vibration integrated sensor, deep fusion and anomaly detection of multi-source data are realized in combination with an attention mechanism, a Gaussian mixture model, a three-dimensional convolutional neural network and the like, and finally a precise maintenance strategy is generated through digital twinning and reinforcement learning. The multi-sensor-based mechanical equipment state monitoring system comprises a sensor network layer, an edge computing layer, a cloud platform layer and a man-machine interaction layer, supports federated learning to protect data privacy, improves real-time diagnosis capability through edge-cloud collaboration, and enhances a reality interface to realize intelligent operation and maintenance interaction.

The invention discloses a supply chain full-process traceability system based on digital twinning and block chains, and belongs to the technical field of digital twinning and block chains, and the system comprises a data collection layer which obtains original data from each link in real time, and carries out the preprocessing of the original data through an edge computing device; the twinborn model building layer is used for building digital twinborn models corresponding to all links of the supply chain respectively and generating link-level optimization parameters through simulation analysis; the block chain network layer is used for verifying and storing shared data and key traceability information among the digital twin models; the collaborative optimization module is used for dynamically adjusting a cross-link collaborative strategy based on a multi-agent reinforcement learning algorithm; and the data traceability layer responds to an external traceability query request and generates a full-link visual traceability map. By means of the collaborative algorithm based on multi-agent reinforcement learning, automation and intellectualization of information interaction among all link models are guaranteed, and personalized and multi-terminal traceability service can be provided for users.

The present application relates to the technical field of batteries, and discloses a thermal runaway risk prediction method and apparatus, a device, and a storage medium. The method comprises: processing, by at least two neural network layers in a target prediction model, thermal runaway risk parameters layer by layer, wherein the target prediction model is obtained by pre-training on the basis of state vectors of a plurality of time steps, so that the memory capability of the model for past state sequences can be enhanced, and thus the model can better learn the dynamic characteristics of an energy storage battery system, and captures a complex temporal association relationship among multiple variables, thereby improving the accuracy of thermal runaway risk prediction and reducing the safety risk of the energy storage battery system.

The invention provides a network risk analysis method and system based on a multi-level game model, and relates to the technical field of network risk analysis, and the method comprises the steps: constructing a multi-level dynamic game model, and based on the constructed multi-level dynamic game model, employing a multi-level game equilibrium solving algorithm to solve the multi-level dynamic game model. Decomposing the multi-layer game problem into a plurality of sub-game problems corresponding to different network layers, respectively solving each sub-game problem by using a reverse induction method, and carrying out iterative optimization until a preset condition is met to obtain a multi-layer game equilibrium solution; and inputting the obtained multi-layer game equilibrium solution into a pre-trained multi-layer risk assessment model, outputting a risk assessment value, when the risk assessment value exceeds a preset risk early warning threshold, triggering a strategy adjustment mechanism, updating a defense strategy by recalculating the multi-layer game equilibrium solution, and finally obtaining an optimal defense strategy combination under risk minimization.

The invention discloses an electric vehicle parking lot fire prevention and control system based on multi-source information fusion. A sensing layer captures the environment state of an electric vehicle parking lot and the operation parameters of a vehicle battery in real time through a multi-mode sensor network; the network layer adopts an intelligent fusion network architecture to realize communication between the sensing layer and the data processing layer; the data processing layer is used for performing fusion analysis on the environment state and the operation parameters, judging a fire risk level and generating a dynamic control instruction; the data processing layer is composed of a multi-dimensional analysis engine, and a hierarchical progressive intelligent analysis architecture is adopted to realize multi-level research and judgment of fire risks; and the application layer receives the dynamic control instruction generated by the data processing layer, and realizes full-process closed-loop management of fire prevention and control through a multi-mode terminal device. The method not only improves the fire prevention and control precision of the electric vehicle parking lot, but also has good interpretability, and can be directly applied to a fire prevention and control system of the electric vehicle parking lot.

A system and method for neural network weight compression with intrusion detection capabilities that optimizes model storage and transmission while providing security. The system analyzes weight characteristics to identify statistical properties within different neural network layers, generates optimized encoding schemes based on the analysis, and creates reference distributions for security verification. The compression process employs a multi-resolution approach that produces a progressive representation with base and enhancement layers, enabling flexible deployment across diverse computing environments. Security markers and statistical fingerprints can be embedded throughout the encoded representation, allowing for detection of unauthorized modifications during transmission or deployment. The system monitors encoded weight streams, measures distribution divergence against reference baselines, and generates alerts when statistical anomalies indicate potential tampering. This approach achieves superior compression ratios while maintaining model performance and providing robust protection against increasingly sophisticated attacks targeting neural network weights.

The invention relates to the technical field of streaming media decoding, and discloses a streaming media decoding method and device for a network high-definition player and a storage medium, and the method comprises the steps: carrying out the multi-thread parallel collection and storage of real-time temperature data, network bandwidth jitter data and video code rate fluctuation data of a decoding chip in the network high-definition player; obtaining a streaming media dynamic feature data set; performing classification training to obtain an inter-frame correlation prediction model; constructing a multi-stage streaming media caching system based on the inter-frame correlation prediction model; monitoring a cache state, a network state and a chip state in the multi-stage streaming media cache system in real time to obtain a decoding event sequence; according to the decoding event sequence, the decoding parameter is adjusted in real time, and the streaming media decoding control instruction is obtained.The collaborative optimization of a network layer, a decoding layer and a display layer is achieved, the playing starting delay is reduced, and the overall stability of a network high-definition player is improved.

The invention relates to the technical field of bridge detection, in particular to a bridge defect automatic identification and inspection method based on deep learning, which comprises the following steps: S1, multi-modal data acquisition: acquiring multi-modal data of the surface and internal structure of a bridge; s2, multi-modal data preprocessing: generating a space-time aligned enhanced data set; s3, feature fusion and extraction: generating a fusion feature spectrum; s4, adaptively optimizing the characteristic spectrum: adjusting the connection weight between network layers according to the real-time environment parameters; s5, defect positioning and type identification: positioning space coordinates of defects and synchronously outputting probability distribution of defect types; and S6, defect analysis and report generation: generating an inspection report including defect evolution trend analysis based on the result of defect positioning and type identification. According to the invention, the bridge inspection efficiency is improved, the labor cost is reduced, and the intelligent upgrading of the bridge management field is further promoted.

The Internet can be configured to provide communications to a large number of Internet-of-Things (IoT) devices. Devices can be designed to address the need for network layers, from central servers, through gateways, down to edge devices, to grow unhindered, to discover and make accessible connected resources, and to support the ability to hide and compartmentalize connected resources. Network protocols can be part of the fabric supporting human accessible services that operate regardless of location, time, or space. Innovations can include service delivery and associated infrastructure, such as hardware and software. Services may be provided in accordance with specified Quality of Service (QoS) terms. The use of IoT devices and networks can be included in a heterogeneous network of connectivity including wired and wireless technologies.

Provided is a process, including: accessing, with a processor of an embedded computing device, immutable executable code stored in read-only memory of the embedded computing device; executing, with the processor of the embedded computing device, instructions of the immutable executable code that retrieve, from the read-only memory, a network-layer address of a tamper-evident, immutable data repository and an application-layer address of firmware of the embedded computing device stored in the tamper-evident, immutable data repository; executing, with the processor of the embedded computing device, instructions of the immutable executable code that, using the network-layer address and the application-layer address, download the firmware of the embedded computing device from the tamper-evident, immutable data repository; and executing, with the processor of the embedded computing device, instructions of the immutable executable code that store the downloaded firmware in re-writeable memory of the embedded computing device.

The invention discloses a medical image super-resolution reconstruction method based on multi-level attention guidance, and the method comprises the following steps: S10, constructing a deep learning network model based on a generative adversarial network architecture, which comprises a generator and a discriminator; the generator is based on an improved U-Net architecture, a hierarchical attention module and a dual-path feature processing module are configured in an encoder and a decoder of the generator, the hierarchical attention module adopts different attention strategies according to network levels to consider structure and texture, and the dual-path feature processing module separates and processes low-frequency and high-frequency information; the generator further comprises a multi-level feature fusion module for integrating the multi-scale features of the decoder, and an attention guide up-sampling module for final enhancement and dimension raising. The discriminator adopts a spectrum normalization U-Net architecture and uses multi-scale features for matching; s20, training the network model by adopting a composite loss function comprising pixels, adversarial, perception and total variation loss; and S30, inputting the low-resolution image into the trained model, and outputting a high-resolution image. According to the method, through deep fusion of multi-level attention and multi-scale feature processing, the image restoration quality can be remarkably improved, the texture detail definition can be enhanced, the anatomical structure accuracy can be ensured, and the noise robustness can be improved.

This disclosure is directed to UE measurements and measurement report of serving cells, and configuration for the cell measurement and report to effectuate a fast serving-cell change during UE mobility within lower network layers, such as layer-1 or layer-2 of the wireless communication protocol stack. This disclosure is further directed to signaling procedures associated with the fast serving-cell change for the UE as triggered by the network based on the cell measurement and report, and various lower layer configurations and operations for effectuating an actual fast serving-cell change at and for the UE.

The invention relates to the technical field of part detection, and discloses a part defect automatic detection method based on machine vision. The method comprises the following steps: firstly, acquiring three-dimensional geometric parameters of a target part, and matching a historical defect sample set in a visual sample library according to the three-dimensional geometric parameters; performing defect type clustering division on the set to obtain a plurality of defect type subsets; processing the subsets one by one to execute multispectral feature extraction, and obtaining a reference detection area and a defect diffusion range parameter corresponding to each defect category; utilizing defect diffusion range parameters to configure the scanning step length of the multi-stage detection network layer, and generating a plurality of scale defect feature maps; and finally, performing cross-level association fusion on the feature maps to generate a fusion defect feature map, and outputting the fusion defect feature map as a final detection result. According to the method, three-dimensional geometric features and historical data are combined, and the comprehensiveness and accuracy of part defect detection are improved through multispectral extraction, adaptive scanning and feature fusion.

The invention relates to the technical field of computers, in particular to an automatic resource allocation method for bearing a neural network, and the method comprises the following steps: S1, analyzing the calculation precision demand of each network layer according to the hierarchical structure and calculation characteristics of the neural network; s2, predicting hardware resources of processing unit computing power and memory bandwidth required by each computing stage; s3, automatically adjusting the resource allocation of the task according to the resource prediction result of the S2; s4, dynamically adjusting a calculation precision mode according to the resource load; and S5, after the task is completed, the allocated hardware resources are released, and a resource use feedback report is generated. According to the method, by intelligently analyzing the calculation precision requirement and the hardware resource condition of the neural network task, dynamically adjusting the calculation precision mode and optimizing resource allocation, the utilization efficiency of hardware resources is remarkably improved, the calculation complexity is reduced, and resource waste and bottleneck problems are effectively avoided.

The invention discloses a metal defect identification method based on YOLOv8, and the method comprises the steps: carrying out the semi-automatic labeling of a collected sample data set through a LabelMe semi-automatic labeling tool, and carrying out the detection feedback through an existing YOLOv8 model. Carrying out manual labeling correction on a labeling sample with a relatively poor detection result through a LabelImage labeling tool; replacing a feature fusion part in an original YOLOv8 network with a combination of a weighted splicing operation, a feature enhancement layer and a gating mechanism based on a bidirectional feature pyramid network idea, and performing adaptive fusion on features of different scales; inserting a GCNECA module into the last layer of the backbone network of the original YOLOV8 network; the method comprises the following steps of: inserting a TransformerSE module behind a P3 layer in a neck network layer of an original YOLOV8 network; a MultiScaleLSTMSimAM module is inserted behind a P5 layer in a neck network layer of an original YOLOV8 network, so that the detection effect of large target metal defects is enhanced. According to the method, the metal defect identification capability in a natural scene is enhanced, and higher robustness, accuracy and efficiency are achieved.

The invention discloses a wind turbine generator blade crack defect detection method based on a feature recombination network, and the method comprises the steps: obtaining a to-be-detected wind turbine generator blade image, inputting the to-be-detected wind turbine generator blade image to a pre-trained feature recombination network model, and obtaining a crack defect recognition result of the to-be-detected wind turbine generator blade image. According to the method, multi-scale feature extraction is carried out on the wind turbine generator blade image through the small target feature extraction capability of the backbone network layer by using an optimized and constructed feature recombination network model, and then more crack detail information in the multi-scale features is reserved by means of feature recombination and feature expansion of the neck network layer. The context information of the small target is fully acquired for feature fusion, and then the crack defect identification result of the wind turbine generator blade image is acquired through the detection head, so that the crack defect of the small target is detected more accurately.

The invention discloses a large language model acceleration system and method based on binary quantization, and the system comprises an extraction and quantization unit, a grouping packaging and splicing unit, an operation realization unit, and an inverse quantization and output unit, and comprises the specific steps: extracting parameters needing matrix / vector multiplication in a model; performing binary quantization processing on parameters of the model matrix / vector multiplication operation; splicing the grouped binary quantized parameters into single integer data bit by bit according to an order when matrix / vector multiplication is calculated by adopting a grouping and packaging algorithm; matrix / vector multiplication which is equivalent to original model reasoning and is friendly to hardware is executed; the result is converted back to the floating point data type through an inverse quantization algorithm, a final calculation result of the large language model in the matrix / vector multiplication operation is obtained, the final calculation result is transmitted into a subsequent network layer, and calculation acceleration for the matrix / vector multiplication operation in the large language model is completed. According to the method, the accuracy is ensured, and meanwhile, the speed is increased during large language model reasoning.

The invention provides a deep learning large model training method and system for heterogeneous equipment, belongs to the technical field of model training, and aims to solve the problem that a heterogeneous GPU cluster cannot be effectively utilized when a traditional scheme is used for training a deep learning large model. Different network layers of a deep learning large model to be trained are divided into a plurality of stages, forward propagation and back propagation calculation of all network layers in each stage is executed by independent virtual equipment, and meanwhile, utilization of GPU resources of different structures is coordinated in combination with a proposed hybrid parallel training strategy, so that efficient model training is realized.

The invention provides a decoupling type image style migration method based on a diffusion model, and relates to the field of image processing, and the method comprises the steps: constructing an image style migration model based on content and style decoupling, the image style migration model at least comprises a content extraction module, a style extraction module, a projection network and a style migration module, and the model decouples contents and styles from a feature level and a network level at the same time; constructing a training set covering a plurality of data types; training the image style migration model through the training set and the loss function; based on the trained image style migration model, the content image and the style image are input, the stylized result image with the target content and the target style is generated, and the method has the advantages that the style migration effect is improved, and more interpretability is achieved.

An electronic circuit system implementing and executing machine learning inference engines. While ML inference engines are based on (architectures and parameters defined by) configured, trained and tuned machine learning models, our design has the novel ability to support data driven, on-the-fly-reconfigured model runs. Reconfiguration and tuning operations include dynamic computational graph modifications, define-by-run alterations, changes to network depth (number of layers) and width (neurons per layer), and adjustments to weights, biases, plus activation function parameters. Neural networks supported include Feed-Forward, RNN, CNN, and Hopfield architectures, plus Ensemble, Federated, Cooperating, Adversarial, and Swarm collections. Decision Trees and Forests are also supported, as are more esoteric approaches such as ART and KAN. Our invention is capable of running both standalone and cooperatively, the cooperative processing being local and / or remote / cloud based, interfacing with telemetry applications to feed data, and machine learning software to feed new or updated models.

The invention discloses a storage optimization method and device, electronic equipment and a storage medium, and relates to the technical field of data processing.According to the storage optimization method and device, the electronic equipment and the storage medium, the occupied space of caches is compressed by adopting retention and selection strategies, and when the caches are allocated to each network layer, the cache allocation efficiency is improved. According to the method, key value caches from a low layer to a high layer are distributed in a gradually decreasing mode, so that keyword elements are prevented from being omitted in the low layer, unimportant lexical elements are prevented from being stored in the high layer, and then the target attention value is obtained by utilizing the attention focusing degree of the input lexical elements and the cached lexical elements. The keyword elements which have the most influence on the input lexical elements are selected from the cache lexical elements to be cached, then representative representative lexical elements are selected from the remaining lexical elements to be stored, and only the lexical elements which have the influence on the model processing data can be stored. And the technical effect of compressing the occupied space of the KV cache so as to provide the capability of processing more requests is achieved.

The invention relates to the field of rail transit control systems, and discloses a rail transit intelligent control system and method based on the Internet of Things, and the system comprises a sensing layer which is integrated with a visual sensor, a thermal imaging sensor, a sound sensor, a pressure sensor, a temperature and humidity sensor, a CO2 sensor and other sensors, the sensing layer collects various data of the rail transit system in real time, and the sensing layer performs joint optimization processing on sensor data by adopting deep learning and reinforcement learning algorithms through an adaptive data fusion method so as to realize high-precision real-time monitoring, data quality screening and anomaly detection; and the network layer comprises a wireless communication module and adopts an intelligent routing mechanism based on real-time network load prediction. A self-adaptive data fusion method is adopted by the sensing layer, and the sensing capability of the rail transit system is greatly improved through intelligent data processing, so that the system can realize real-time and accurate response to a complex traffic environment.

The invention discloses a land-sea-air-space holographic sensing and collaborative decision-making system based on multi-mode edge intelligence. The system comprises a sensing layer which is used for carrying out three-dimensional monitoring on a ship driving process, the sea, the sky, a shore base and a space formed by the ship driving process, the sea, the sky, the shore base and the sea, the sky and the shore base; the network layer is used for constructing a global communication basis, preprocessing monitoring data, judging whether the ship has an abnormal condition or not, and realizing data transmission of the ocean, the sky, a shore base and a space formed by the ocean, the sky and the shore base; and the application layer is used for judging whether an abnormal condition exists in the ship driving process or not based on the data of the ocean, the sky, the shore base and the space formed by the ocean, the sky and the shore base transmitted by the network layer, performing collision early warning decision based on the abnormal condition, and performing dynamic channel capacity prediction. And navigation guidance and route tracking in extreme weather are carried out based on environmental risk early warning, underwater obstacle collision is avoided, and early warning information and decision information are fed back to a sensing layer.

The invention relates to a method for realizing power grid hidden fault diagnosis processing based on a graph convolutional network. The method comprises the following steps of performing data acquisition and preprocessing; constructing a power grid topological graph; a GC-LSTM model is constructed; carrying out model training and inference; and fault classification and positioning are carried out. The method and the device for realizing power grid hidden fault diagnosis processing based on the graph convolutional network, the processor and the computer readable storage medium are high in efficiency and accuracy: power grid topological structure information is extracted by utilizing the GCN, and time sequence dynamic data is processed by combining with the LSTM, so that high-precision identification and positioning of hidden faults are realized. The expandability is high, the number of network layers, input characteristics and adjacent matrixes can be flexibly expanded or cut, and the method is suitable for power grids and data sampling frequencies of different scales. The self-adaptive capacity is high, static equipment attributes and real-time dynamic electrical quantity data are fused, and good diagnosis performance can be kept for the conditions of power grid topology change, load change, equipment aging and the like.

The embodiment of the invention discloses a lithium battery charge state estimation method and system. The method comprises the following steps: acquiring battery operation data including current, voltage, temperature and battery state; inputting the battery operation data into a state of charge estimation model to perform SOC estimation of the lithium battery so as to obtain an estimation result; outputting the estimation result; wherein the state of charge estimation model is obtained by training a basic model formed by a TCN network layer, a GRU network layer, a Transformer network layer and a full connection layer which are connected in sequence by using a plurality of pieces of battery operation data as a sample set and adopting forward propagation, a back propagation algorithm and hyper-parameter adjustment and optimization to train the TCN network layer, the GRU network layer, the Transformer network layer and the full connection layer. By implementing the method provided by the embodiment of the invention, the accuracy of SOC estimation can be improved under the condition of data missing.