48 results about "Network Convergence" patented technology

The application discloses a kind of face depth forgery detection methods based on multi-modal fusion, belong to artificial intelligence, computer vision and multimedia forensics field.For the generalization deficiency of existing detection method, audio and picture time sequence inconsistency problem, end-to-end detection scheme is proposed.The method fuses the time-aligned audio spectrum and continuous video frame mosaic picture in advance, forms multi-modal unified input to solidify audio and picture time sequence relationship.Global branch uses contrast learning pre-training visual Transformer, introduces modal bias and cross-modal attention bias to enhance feature distinction interaction;Local branch fuses local and global features through multi-scale region perception network, realizes adaptive weighted fusion by combining position coding, vector-level channel attention and scale attention, and is guided by region perception auxiliary loss to select discriminative features.The method provides an effective tool for content review, traceability forensics and identity authentication security.

The application discloses a kind of unmanned vehicle software information matching method, device and equipment and storage medium, it is related to data matching technical field, comprising: extracting the to-be-matched character of to-be-matched software information in to-be-matched software list and pre-set standard information base, construct the first feature for representing the pronunciation feature of to-be-matched character, the second feature for representing the structure feature of to-be-matched character and the third feature for representing the semantic feature of to-be-matched character of to-be-matched character, target feature is obtained using pre-set full connection network splicing first feature, second feature and third feature, and based on target feature, construct feature sequence, the feature sequence of to-be-matched character is matched, the similarity of different to-be-matched character is obtained, and the matching result of to-be-matched software information is determined based on similarity. By extracting three-dimensional features of sound, shape and meaning, the core features of characters are captured, and then complete character features are obtained by full connection network fusion, which significantly improves the matching accuracy and reliability.

This invention belongs to the field of communication network convergence technology and discloses a protective wavelength spacing configuration method and a QKD and OTN co-fiber transmission system. The system includes: a QKD transmitter, a synchronous negotiation transmission unit, an OTN transmitter, a first silicon photonics integrated wavelength division multiplexing chip, a common transmission optical fiber, a second silicon photonics integrated wavelength division multiplexing chip, a QKD receiver, a quantum optical detection unit, a synchronous negotiation receiving unit, an OTN service receiving unit, and a control and management module. The control and management module executes the protective wavelength spacing configuration method. Compared with existing technologies, this invention, through the protective wavelength spacing configuration method, can dynamically determine the isolation distance based on bandwidth, temperature drift, filter roll-off, and safety margin, reducing the probability of crosstalk and Raman scattering noise from adjacent channels entering the quantum receiving window. It also makes linked decisions based on bit error rate, key generation rate, receiver count rate, OTN bit error rate, optical power, chip temperature, and alarm information, improving the adaptive capability of the co-fiber system.

This invention belongs to the field of open-pit coal mine safety monitoring and data fusion technology, and specifically provides a multi-source data fusion method for open-pit coal mines based on BP neural networks. This method includes five steps: data acquisition, preprocessing, BP neural network fusion model construction, fusion inference and early warning application, and online model updating. Data acquisition covers geological, equipment status, and environmental parameters. Preprocessing includes data cleaning, noise suppression, feature selection, and standardization. The model construction adopts an input layer-double hidden layer-output layer structure, trained using the Adam optimizer and physical constraint loss function. Fusion inference outputs equipment status or slope displacement results and provides early warnings. The model is updated incrementally on a daily / weekly basis, and a full retraining is performed when the evaluation index degrades by more than 10%. This method solves the problems of weak adaptability and limited fusion accuracy in existing algorithms, and features high accuracy, strong robustness, good real-time performance, and practical applicability. It can improve the safety monitoring level of open-pit coal mines and reduce the incidence of disasters.

This invention discloses an automated test anomaly identification and scheduling recovery method based on machine learning, belonging to the field of automated testing and intelligent scheduling technology. The method includes: acquiring hierarchical sample encoding data and continuous time-series data through sensor networks and control systems; after preprocessing, generating two types of embedded representations using a hierarchical perceptual graph neural network encoder and a hybrid encoder of temporal convolution and multi-head self-attention, respectively; generating an aligned feature set through fusion of a cross-attention mechanism and a residual fully connected network; outputting anomaly risk levels using a machine learning model trained by federated distillation; marking tasks exceeding thresholds as pending recovery tasks; and using a scheduling recovery strategy library built based on a graph database to match the optimal recovery scheme according to anomaly type and equipment model and generate execution instructions, while simultaneously sending preventative check instructions through cross-site anomaly propagation analysis. This achieves accurate anomaly identification, rapid recovery, and risk prediction, ensuring the continuity and reliability of multi-site testing.

The application is suitable for the field of communication network fusion technology, and provides a low-cost cellular signal regeneration system and method based on an optical network terminal, which comprises an optical network terminal mainboard and a system-level chip, an expansion slot arranged on the optical network terminal mainboard, and a cellular function sub-card which is plugged into the expansion slot and connected with the system-level chip through a PCIe channel; wherein the cellular function sub-card does not include an independent power management chip, an independent main control processor and an independent heat dissipation structure, the peak power consumption is not more than 2.5W, and the power supply and heat dissipation of the cellular function sub-card are uniformly provided and processed by the optical network terminal. Remote zero-configuration opening and safe access are realized by using a cloud collaborative service platform based on double certificates, and high reachability guarantee and graceful degradation service are combined to improve reliability. The application changes indoor cellular coverage from high-cost hardware sales to low marginal cost operator monthly value-added services, and realizes dual innovation of business model and technical architecture.

This invention discloses a data hierarchical storage optimization method based on intelligent scheduling under cloud-network convergence, relating to the field of input / output data processing technology. It includes a data dynamic heat calculation module, a data processing module, a hard disk module, and a dynamic decay factor calculation module. The data dynamic heat calculation module collects data access characteristics in real time and constructs a corresponding dynamic heat model. The dynamic heat model calculates the heat value of an object. When a data object is accessed, its heat value is calculated using the heat value calculation formula. Based on the obtained heat value, the data is stored in the optimal hard disk location through the data storage location allocation unit within the hard disk module. This invention, by constructing a multi-dimensional dynamic heat model, achieves intelligent and adaptive optimization of data storage hierarchical storage, ensuring efficient utilization of storage resources and extending hardware lifespan.

This invention relates to a method, system, and medium for intelligent identification of data meta-objects based on metadata. The method includes: acquiring raw metadata from multiple heterogeneous data sources and mapping it to a unified Meta-Entity structure; generating technical tags and business semantic tags for the attributes of each Meta-Entity structure using a multimodal semantic annotation model; fusing the business semantic tags with an enterprise domain knowledge graph using a graph attention network to obtain statistical feature vectors, structural feature vectors, and semantic features; fusing the statistical feature vectors and structural feature vectors using an MLP-GNN Hybrid model to obtain a unified feature vector; dynamically adjusting the weight coefficients of the statistical features, structural features, and semantic features based on reinforcement learning, so that the weighted identification score maximizes the comprehensive reward function; and writing the technical tags, business semantic tags, and weighting strategy into a data directory. This invention can complete model transfer in new business domains with a small number of labeled samples, achieving high identification accuracy.

A satellite networking control system of an air conditioner cluster under a space-air-ground integrated architecture, the sky layer satellite networking module includes at least three low-orbit communication satellites, the low-orbit communication satellites are connected bidirectionally through inter-satellite links, and a mesh networking structure is formed, the air layer relay forwarding module is carried on a high-altitude long-endurance unmanned aerial vehicle, the communication end of the air layer relay forwarding module is connected with the sky layer satellite networking module and a ground air conditioner cluster control module correspondingly, the ground air conditioner cluster control module includes a cluster controller, the signal input end of the cluster controller is connected with the signal output end of each air conditioner terminal in the air conditioner cluster correspondingly, and the signal output end is connected with the air layer relay forwarding module in communication.The linkage of the four modules, the sky layer satellite networking module provides global signal transmission support, the air layer relay forwarding module solves the air-space-ground link connection problem, the ground air conditioner cluster control module realizes terminal management and control, and the heterogeneous network fusion module guarantees smooth data interaction of each link.

The application discloses an application and computing power network fusion method and system, and electronic equipment. A user only needs to input scene information of an application to match computing power network resources meeting the application, and a charging method and rules based on different computing power network resources are provided. The user selects an application computing power network resource fusion charging model to realize charging according to the computing power network resources used by the application, reduces the difficulty of consumers in purchasing and using the computing power network resources, and is beneficial to the development of the computing power network application and the east-data-west-computing project.

This invention relates to the field of image processing, specifically to an image compressed sensing reconstruction method based on a hypergradient denoising network. Building upon traditional block-by-block image sampling and initial reconstruction, this invention maps the compressed sensing iterative denoising reconstruction model into an end-to-end denoising network using the concept of depth unfolding. Residual dense blocks are introduced into the denoising network as denoising operators to improve the quality of image denoising and reconstruction. Furthermore, a hypergradient strategy is used to optimize the iterative denoising model, accelerating network convergence. This invention overcomes the limitations of manually setting regularization terms by learning noise information through denoising operators during the iterative denoising process, flexibly representing the prior information of the image, and accurately and efficiently reconstructing the original image.

This invention relates to a battery pack balancing control method and system. The method includes: Step S1: treating the battery pack as an intelligent agent network and converting the battery pack balancing problem into a Markov game that can be handled by the DRL algorithm; Step S2: generating online expert data using the FCS-MPC algorithm; Step S3: training the intelligent agent network and a hybrid network based on different intelligent agent networks using the FCS-MPC-QMIX algorithm, and dynamically fusing the expert data into the objective functions of the intelligent agent network and the hybrid network during training to accelerate network convergence; Step S4: using the trained intelligent agent network and the hybrid network to achieve battery pack balancing control. This invention can provide balancing control for distributed battery energy storage systems.

The application belongs to the technical field of the Internet of Things and old-age health care, and particularly relates to a cognitive impairment chronic disease health management system and method based on the Internet of Things. The method is applied to a smart old-age care scene and comprises the following steps: a data acquisition step, in which an active acquisition and passive acquisition combination is adopted; a data transmission step, in which a 5G, WiFi and LoRa multi-network fusion transmission mode is adopted; an intelligent analysis step, in which a weighted fusion algorithm is adopted to fuse multi-source data and identify cognitive function abnormality features; a grading evaluation step, in which a four-dimensional weighted scoring model is used to calculate grading evaluation; and a grading intervention step, in which corresponding intervention measures are executed according to the grading evaluation result. The application realizes full-process automation of cognitive impairment chronic disease management, improves early screening coverage, delays disease progression, reduces care burden and improves the quality of life of the elderly.

The application discloses a multi-node situation unification method and system based on an information diffusion mechanism, and the method comprises the following steps: node information fusion: each node performs multi-node data association and data fusion on the information detected by the node and the information of other nodes received by the node through a network, and completes target unification batch and target position estimation; information diffusion transmission: each node diffuses and transmits the node information fusion result in the local field of the node; network fusion weight calculation: network fusion weights are calculated according to node related information; target position fusion estimation: the fusion estimation of the target position is completed according to the network fusion weights and node measurement weights; consistency decision: whether the iteration termination condition is met is decided according to the iteration number and the multi-node target position deviation. The application can overcome the long information transmission chain of the traditional situation unification method, the complex conflict resolution process, has good robustness, and has faster situation unification speed and better situation unification effect.

The present application relates to the technical field of data processing, in particular to a time series filling method and system based on dual-channel retrieval enhancement and frequency domain feature fusion, which solves the problems of single retrieval dimension, ignoring frequency domain periodic characteristics and weak anti-retrieval noise interference capability in the prior art. The method comprises: constructing a dual-channel module of Euclidean distance time domain retrieval and FFT frequency domain retrieval to obtain similar segments, performing deep extraction of frequency domain features such as direct current removal, logarithmic compression and projection, fusing multiple features through an adaptive gating network, combining a Context-Dropout strategy for robust training, and inputting the fused features into a backbone network to generate a filling sequence; the system corresponds to the function modules of each step. The present application improves the time series filling precision and periodic recovery capability, and can guarantee the lower limit of filling performance when the retrieval quality is poor, and can be widely applied to sensor data processing, Internet of Things data cleaning and financial time series analysis scenes.

This invention discloses a human resource intelligent analysis method based on machine learning using multi-source data, relating to the fields of human resource management and artificial intelligence. The method includes: acquiring employee static profiles and individual historical data; generating individual basic potential vectors through machine learning model encoding; constructing a multimodal organizational network graph integrating organizational structure and collaborative relationships; applying a graph embedding model to learn and generate network fusion latent vectors, and grouping employees accordingly; collecting temporal interaction metadata for specific groups, inputting it into a temporal analysis model to construct a dynamic knowledge influence profile; and collecting skill acquisition data and organizational needs, generating personalized skill growth prediction maps through a temporal prediction model. This invention, by integrating multi-source data and revealing implicit network structures, achieves dynamic, multi-dimensional, and forward-looking assessment of talent, improving the scientific rigor and accuracy of human resource decision-making.

The application provides a gas equipment operation data anomaly identification processing method and system, relates to the technical field of data processing, and comprises the following steps: receiving, in real time, equipment operation data streams uploaded by cell gas Internet of Things terminals, performing time stamp alignment, noise filtering and missing value interpolation processing on the equipment operation data streams, and obtaining a standardized time series data set; extracting synchronous parameter sequences of a pressure regulating station outlet convergence node, a supporting public building pipe network convergence node and a high-rise residential end table front node from the standardized time series data set, and constructing a three-dimensional working condition response surface; performing curvature differentiation and topological equivalent domain division on the three-dimensional working condition response surface, and obtaining a pipe network hydraulic and thermal coupling compensation amount. The application improves the accuracy, real-time performance and fine level of gas equipment operation data anomaly identification.

This invention discloses a hyperspectral image classification method based on multi-scale self-supervised contrastive learning. The method comprises a network structured as follows: a contrastive learning feature extraction module suppresses unimportant features in image patches and filters out useful features; when the number of labels is small, a self-supervised mode is used to learn features; a deep and shallow multi-scale feature learning module constructs hierarchical scales to extract multi-scale information from hyperspectral images; a spatial-spectral reconstruction module constructs spectral and spatial branches, using separation, filtering, and reconstruction to combine deep and shallow features, reducing model parameters and complexity, and enhancing feature representation capabilities; the network output is then average-pooled and fed into a classifier to obtain the result. This invention acquires rich spatial-spectral features through the multi-scale feature learning module, spatial-spectral reconstruction module, and contrastive learning feature extraction module, broadening the application scenarios for images with few labels, accelerating network convergence, and thus improving classification accuracy.

This application provides a method, apparatus, and device for network feature fusion representation and extraction. The method includes: acquiring network feature data, which includes at least device feature data, topology feature data, and traffic feature data; encoding the network feature data and converting it into structured data; and fusing the structured data to obtain a network feature fusion representation. This application characterizes the network from three different perspectives: device features, topology features, and traffic features, encompassing both static and dynamic characteristics. Furthermore, structured data can carry various forms of device feature data, topology feature data, and traffic feature data, making the fusion feature representation more comprehensive. Therefore, the network fusion feature representation can comprehensively describe a network and uniquely identify it.

This invention discloses a Hapke hyperspectral autoencoder unmixing method for spectral variations. Addressing the problem of spectral variations in hyperspectral images, this method combines the Hapke physical model of spectral imaging with a deep network to construct a unmixing network architecture for the Hapke model. This effectively reduces the impact of spectral variations on endmember extraction, resulting in more accurate endmembers and abundances. The core of this method is to construct a deep network to learn the spectral variation parameters in the Hapke model. Based on the autoencoder unmixing network architecture, this invention extracts abundance features through convolutional autoencoder layers, while the weights of the decoding layer are endmember features. To reduce the impact of spectral variations on endmembers and abundances, the Hapke physical model is fused with the network, and a parameter estimation network is designed to learn the spectral variation parameters, which are then used as illumination-topography attention applied to abundances. Simultaneously, these parameters are substituted into the Hapke model, transforming endmember features into single-scattering albedo. Combined with reflectance and albedo spatial losses, iterative optimization of endmember abundances is achieved. Numerical experiments show that the proposed unmixing method has good unmixing performance and significantly improves the accuracy of endmember estimation.

Embodiments of the present application relate to the technical field of 5G cloud-network convergence, and provide a link scheduling method and system, an electronic device, and a storage medium. The method is applied to an SDN controller and at least comprises: converting, into second QoS policy information, first QoS policy information allocated by a 5G core network element, and forwarding the second QoS policy information to each forwarding node in an IP transmission network; receiving a QoS guarantee response fed back by each forwarding node, and sending the QoS guarantee response to the 5G core network element, so that the 5G core network element respectively detects the latency of a plurality of channels in a 5G network and the IP transmission network, records latency information, and feeds back same; and on the basis of the latency information of the plurality of channels in the IP transmission network, selecting a minimum-latency path to generate a minimum-latency forwarding result and feed back same to a terminal, so that the terminal determines, on the basis of minimum-latency dedicated bearer information and the minimum-latency forwarding result fed back by a QoS controller, a minimum-latency wireless channel from the terminal to a cloud platform for transmitting a video stream, thereby implementing end-to-end minimum-latency link scheduling from the 5G network to the cloud on the basis of the video stream.

This invention discloses a trunking communication system based on a 4G wireless network, belonging to the field of trunking communication technology. It is used to start the device and select an architecture for connection, monitor the group call signal corresponding to the group information in real time, and send the group call signal to the terminal startup module. After receiving the group call signal, the terminal startup module sends the group call signal to all online devices in the group, compresses the voice data, and transmits the voice data based on the PoC protocol. Online devices monitor the multicast address in real time, obtain the call end signal, and send the call end signal to the online devices to stop voice data transmission. This invention achieves seamless switching between private and public networks through multi-network convergence technology, improving the reliability and stability of communication. By adopting the PoC protocol and utilizing 4G / 5G networks for trunking communication, communication efficiency is improved. At the same time, the optimized network transmission protocol reduces communication costs, demonstrating significant advantages in emergency communication and public safety fields.

The application discloses a 6G all-in-one network management computing power session method and system, and belongs to the technical field of mobile communication.The technical problem to be solved by the application is how to realize flexible scheduling and on-demand allocation of computing power and network fusion resources in a 6G all-in-one network environment without changing network devices, and to build a mobile network environment supporting the computing power as a service mode for users.The technical scheme adopted is that the method is to deploy an algorithm network fusion perception network element, an algorithm network fusion control scheduling network element and an algorithm network fusion service arrangement network element on the core network side, so that the improved core network has the functions of communication, perception and computing power integration;and the computing power application request index is analyzed and identified, the computing power service arrangement is performed, the computing power and network Qos configuration mapping is performed, the algorithm network fusion resources are decided and scheduled, and the distributed collaborative computing task is issued, so that the computing power session creation is completed, so as to meet the different computing power requirements of mobile terminals.

This invention discloses a high-performance network device with multi-protocol fusion, comprising an application layer, a network fusion layer, a data link layer, and a physical layer connected in sequence. The application layer is configured to perform high-speed data interaction with the network fusion layer via the AXI-Stream protocol. The network fusion layer is configured to identify and parse the input data packets, converting data frames from Interlaken, Aurora, Ethernet, and FC protocols into a unified AXI-Stream format and providing error detection flags to the upper layer. The data link layer is configured to generate, encapsulate, and encode frame structures according to the frame format requirements of different network protocols based on the standardized data stream output by the network fusion layer. The physical layer includes a high-speed SerDes module for implementing serial-to-parallel conversion. The high-performance network device with multi-protocol fusion provided by this invention significantly improves system parallelism, transmission bandwidth, and reliability, while reducing CPU processing burden and transmission latency by implementing multi-protocol fusion and a unified interface at the hardware level.

The present disclosure relates to the technical field of cloud-network convergence, and provides a service processing method, a capability exposure platform, a computer readable storage medium, and an electronic device. The service processing method is applied to a capability exposure platform; the capability exposure platform is used for providing a plurality of service interfaces; different service interfaces correspond to different service capabilities in a cloud-network integrated scheduling scenario; and the cloud-network integrated scheduling scenario is used for scheduling cloud-network basic resources. The method comprises: receiving a cloud-network integrated service invocation request sent by a capability invoker by invoking a specific service interface, the cloud-network integrated service invocation request being used for requesting to invoke a specific service capability associated with the specific service interface; and forwarding the cloud-network integrated service invocation request to a capability provider, and upon receiving a request response result returned by the capability provider, returning the request response result to the capability invoker. The present disclosure allows for provision of a more flexible and efficient cloud-network integrated scheduling service for a capability invoker.

This application discloses a radar echo extrapolation method based on temporal motion decomposition and dominant fusion, relating to the field of spatiotemporal sequence prediction technology. The method proposes an innovative network fusion paradigm, specifically including: acquiring implicit long-term / short-term temporal features and explicit global / transient motion features during processing using stacked hierarchical recurrent networks; then, innovatively constructing query vectors using temporal features and key-value vectors using motion features, and fusing them using a heterogeneous cross-attention mechanism to form a fusion architecture "dominated by an implicit state modeling network and referenced by an explicit motion modeling network." Based on this, through a series of collaborative designs such as directional decoupling, reference confidence estimation, and long-term feature constraints on short-term features, noise in long-term / short-term motion features is effectively suppressed, enhancing system stability. This method can improve the problems of blurred texture details and attenuation in high-value echo regions in predicted images.

This invention discloses a DQN parameter optimization system and method based on Voronoi boundaries, belonging to the field of reinforcement learning-based wireless communication handover application technology. This invention continuously acquires user location information to determine whether to perform a handover, thereby reducing unnecessary information interaction and simplifying the handover process. Furthermore, to further alleviate the problem of frequent user handovers, this invention combines the Voronoi model with deep reinforcement learning (DQN) to construct a novel reinforcement learning-driven handover framework. This framework can adaptively optimize the Voronoi handover boundary to better adapt to complex communication environments, thereby effectively reducing the user handover rate and increasing the user data rate. In addition, this invention introduces a self-feedback mechanism into the reinforcement learning-driven handover framework, which can better correct the action output of the DQN network and accelerate network convergence.

This invention relates to a method, device, and medium for constructing a deep reinforcement learning network model for UAV control. The method constructs a value network for UAV flight actions into a short-sighted network and a long-sighted network, and also constructs a policy network. Based on sampling of UAV flight action scenarios, training samples are constructed, network parameters are initialized, the short-sighted network is trained, and the policy network is pre-trained. The pre-trained short-sighted network is used to perform gradient boosting on the policy network, training until it converges or reaches a preset number of iterations. The trained network model is then fine-tuned and serves as the UAV flight action control model. Compared with existing technologies, this invention splits the value network into a short-sighted network and a long-sighted network, which on the one hand improves the reusability of training samples and reduces the number of interactions between the UAV and the environment; on the other hand, pre-training accelerates the network convergence speed and further reduces training costs.

This invention relates to the field of intelligent control technology for the operating status of air compressor groups, and discloses a method and system for intelligent control of the operating status of air compressor groups. The method collects real-time operating data of the air compressor group, extracts energy efficiency, load, and pipeline pressure fluctuation characteristics, and generates a sequence of state feature vectors through orthogonalization processing; it uses a temporal attention network to fuse time decay and operating condition correlation, encoding and outputting hidden state vectors; it generates optimal control actions through a policy network based on deep reinforcement learning; and it combines feedforward and feedback control to generate composite control signals to drive actuators, achieving intelligent control of the air compressor group. This method improves energy efficiency, stabilizes pipeline pressure, reduces equipment start-up and shutdown frequency, and extends equipment lifespan.

This invention provides a risk assessment method, a risk assessment device, a computer device, and a storage medium, relating to the field of artificial intelligence. The method includes: acquiring business data and extracting information from the business data to obtain business information corresponding to each business type and multiple user nodes; establishing association information between user nodes based on the business information corresponding to each business type, and generating a multi-layer topology network based on the association information; performing network fusion on the multi-layer topology network to obtain a vector matrix corresponding to the multi-layer topology network; detecting and filtering user nodes based on the vector matrix corresponding to the multi-layer topology network to obtain target user nodes; and performing risk prediction on user node groups associated with the target user nodes to obtain risk assessment results corresponding to the user node groups. This allows for rapid and accurate detection of fraud targets, effectively curbing insurance fraud and preventing losses to company interests.