134809results about "Transmission" patented technology

Various aspects of the present disclosure generally relate to wireless communication. In some aspects, a network entity may receive one or more criteria for an artificial intelligence or machine learning (AI / ML) model monitoring operation. The network entity may perform, for an AI / ML model, the AI / ML model monitoring operation based on a first distribution of inference data associated with the AI / ML model satisfying the one or more criteria. The network entity may perform, for the AI / ML model, an action based on the AI / ML model monitoring operation. Numerous other aspects are described.

The invention discloses a vehicle-mounted Beidou positioning deviation self-calibration system fused with inertial navigation information, and relates to the technical field of navigation and positioning. The method is used for solving the problems of positioning deviation accumulation and signal failure in a complex environment. The system realizes space-time alignment of inertial navigation and Beidou observation data through timestamp interpolation and coordinate system conversion, and constructs a multi-source synchronous data stream. A difference value between an inertial navigation calculation speed and a Beidou Doppler speed is analyzed based on a vehicle incomplete constraint model, and a weight factor is generated in combination with a confidence threshold to suppress abnormal observation. A geometric matching error is calculated through a high-precision lane map curvature and a vehicle steering angle, and candidate tracks are screened to generate transverse calibration parameters. The noise covariance is dynamically adjusted through a tight coupling filtering algorithm, the mode is switched to a lane constraint calibration mode when a Beidou signal is interrupted, inertial navigation errors are compensated through error boundary constraint, and stable output is achieved. Multi-source data deep fusion and adaptive robust calibration are realized, and system robustness and positioning continuity are improved.

An electronic device according to various embodiments comprises: a memory including instructions; and a processor that is electrically connected to the memory and executes the instructions. When the instructions are executed by the processor, the processor may: acquire a first traffic feature vector and a second traffic feature vector on the basis of traffic data generated in a wireless network device during a plurality of time intervals; generate a combined feature vector by combining the first traffic feature vector and the second traffic feature vector; and acquire predicted traffic data, to be generated in the wireless network device, on the basis of the combined feature vector. Various other embodiments are possible.

The invention provides a power distribution network communication network fault diagnosis and self-adaptive reconstruction method, which comprises the steps of formulating corresponding layering and partitioning strategies aiming at different network scales and service types, including modular management of a large-scale multi-layer network and centralized response of a small-scale network; the faults of various networks can be effectively positioned and isolated; recognizing a reconstruction object after the fault positioning is completed, and formulating a network reconstruction scheme according to the recognized reconstruction object and the current state of the network, including but not limited to replacing a fault node, reconstructing a connection or modifying a route by starting a standby resource; after the reconstruction scheme is executed, the data collection and analysis period is adjusted according to the fault frequency, and the reconstruction interval is controlled to reduce redundant network structure adjustment and potential network fluctuation, including active detection and passive monitoring of the power distribution network and bidirectional monitoring of the network condition.

The invention discloses a data center intelligent operation and maintenance method and system based on an industrial internet of things, belongs to the field of image processing calculation, and aims to solve the problems of privacy protection deficiency, serious manual dependence and weak energy efficiency and safety. Full-link dynamic optimization is realized through an edge calculation layer and an adaptive clustering network, edge nodes are integrated with a wavelet transform noise filtering technology, dynamic denoising is performed on multi-band sensor signals of temperature, vibration, current and the like, heterogeneous device time sequence data are aligned in combination with a dynamic time warping algorithm, sampling frequency differences of multiple devices are eliminated, and the multi-band sensor signals are obtained. A weight formula is put forward, a cluster head is dynamically elected based on node residual energy, data similarity and a load coefficient, a transmission path of a high-delay area is optimized in combination with a multi-hop routing protocol, the packet loss rate and the signal strength are evaluated in real time, redundant coding is started for a high-packet-loss link or the high-packet-loss link is switched to a low-power-consumption mode. And synchronizing the dynamic topology state with the digital twin platform through an open platform communication unified architecture protocol.

Systems, methods, and apparatuses for providing optimization of network resources. The system is operable to monitor the electromagnetic environment, analyze the electromagnetic environment, and extract environmental awareness of the electromagnetic environment. The system extracts the environmental awareness of the electromagnetic environment by including customer goals. The system is operable to use the environmental awareness with the customer goals and / or user defined policies and rules to extract actionable information to help the customer optimize the network resources.

The invention discloses a network equipment integrated monitoring method and system. The method comprises the following steps: collecting multi-source heterogeneous data, constructing a protocol compatible layer, and supporting multi-protocol adaptation; data fusion and intelligent analysis: constructing a dynamic topology, analyzing an equipment configuration file, and generating a network topological graph; performing time sequence prediction according to a root cause analysis model, and predicting an abnormal trend; mining association rules, analyzing historical data, and extracting fault association rules; constructing an equipment fault knowledge base under the assistance of a knowledge graph, and accelerating root cause positioning; self-adapting an alarm threshold, analyzing historical data distribution, and dynamically adjusting the threshold; visual decision making and automatic processing are carried out, a 3D topological map is provided, and layered display is supported; and performing fault grading processing, comprehensively calculating a fault influence degree score, mapping to a fault grade and a work order type according to an influence degree score interval, and formulating a dynamic work order generation rule. A protocol compatible layer is constructed by deploying a lightweight agent program, multi-protocol adaptation is supported, and various network devices can be fully covered.

The invention discloses a cloud monitoring service operation and maintenance dynamic optimization system and method based on an AI intelligent agent, and relates to the technical field of cloud computing intelligent operation and maintenance. The method is used for solving the problems of hybrid cloud cross-layer data splitting, hidden fault association failure and operation and maintenance action conflict. Heterogeneous data of a physical layer, a virtual layer and an application layer are collected through a containerized probe, and a standardized cross-layer index is constructed through layered tagging and dynamic time sequence alignment. And constructing a fault propagation map through incremental correlation analysis and a dynamic time window, and identifying space-time coupling nodes. And based on the directional disturbance verification causal relationship, combining with the resource scheduling and fault chain incidence matrix fitting coupling degree index, and generating a root cause positioning instruction. And optimizing an action priority based on a propagation cost gradient and an asymmetric game strategy, and updating the model and the rule through a feedback closed loop. According to the invention, cross-layer data fusion and accurate fault positioning are realized, and the cloud service stability and the resource utilization rate are improved.

The invention discloses an Internet of Things multichannel earthquake early warning emergency linkage system, belongs to the technical field of earthquake early warning, and aims to solve the problems of insufficient real-time performance, reliability, scene coverage and safety in the prior art. The sensing layer collects data cooperatively through various sensors, eliminates environmental interference and ensures high-precision seismic wave detection, the edge calculation layer filters noise in real time by adopting an advanced algorithm, and confirms seismic events through multi-dimensional comparison, so that the local processing efficiency is remarkably improved, the cloud load is greatly reduced, a cloud platform fuses multi-source data, and the seismic detection efficiency is improved. A machine learning model is combined to dynamically optimize an early warning threshold value, the accuracy of earthquake type classification and intensity prediction is improved, full-link optimization from data acquisition to intelligent decision making is realized, a multi-channel distribution module is designed, instructions are transmitted according to priority levels, and urban and rural full-scene coverage is ensured through multiple communication technologies.

The invention relates to the technical field of computer programming languages, and particularly discloses a coal-fired power plant safety monitoring system and method. The system comprises a multi-modal data fusion platform, a federal learning agent network and a digital twinborn simulation engine. The edge computing node carries out unified acquisition and feature extraction on multi-source heterogeneous data through multi-protocol conversion, quantum noise suppression and a preprocessing chipset; the multi-modal data fusion platform realizes semantic mapping based on a knowledge graph, and fuses time-space correlation characteristics of data such as an infrared image and gas concentration by adopting a time-space encoder and a cross-modal attention mechanism. According to the method, the problems of early warning delay and high false alarm rate caused by low multi-source data decentralized processing efficiency and insufficient nonlinear correlation analysis in a traditional scheme are solved, efficient data fusion, complex risk accurate prediction and automatic safety response are realized, and the real-time performance and reliability of a coal-fired power plant monitoring system are remarkably improved.

An example operation may include one or more of ingesting data from one or more websites via one or more application programming interfaces (APIs) and storing the data within a data store of a host platform, displaying one or more prompts on a user interface of a user profile page within a software application hosted by the host platform, receiving one or more responses to the one or more prompts, generating a travel itinerary based on execution of an artificial intelligence (AI) model on the data from the one or more websites, the one or more prompts, and the one or more responses, and displaying the travel itinerary via the user interface of the user profile page of the software application.

The invention discloses a construction engineering risk assessment method and system for multi-source anomaly monitoring, and particularly relates to the field of construction engineering risk assessment, comprising the following steps: analyzing equipment-to-component node mapping and return delay characteristics based on multi-source monitoring data received in an idle task period; establishing a cross-device time alignment model and a pseudo-synchronization section time migration trajectory, identifying a potential abnormal path set through a time delay drift trend, and providing a time feature basis for subsequent structure anomaly reasoning and risk assessment; deducing a node triggering sequence and a time causal chain based on the final pseudo-synchronization segment index matrix and channel path mapping; multi-source monitoring data is received in an idle task period, and a cross-device time alignment model is established, so that the problem of time sequence dislocation caused by mechanisms such as cache delay and packet loss re-sending is effectively identified, and historical abnormal data is prevented from being misjudged as a future risk from the source.

The invention relates to the technical field of computer networks, and discloses a computing power network real-time performance evaluation method based on an analytic hierarchy process, and the method comprises the steps: collecting a node operation state and task demand data through a sensor, and generating a local performance index in combination with an edge quantum algorithm; simulating a future network state by using digital twinning, and fusing to generate a multi-dimensional performance data set; the AHP weight is dynamically adjusted based on resource deviation and a geological classification model, high-frequency updating is started for high load / fault, and the weight range is expanded for low load; introducing a risk assessment algorithm to quantify a performance-cost-carbon effect conflict level, and triggering resource recovery, optimization prompt or single index suggestion; scheduling strategies are triggered in a grading mode according to evaluation results, and active intervention is started in combination with anomaly detection; the AHP weight is dynamically updated through reinforcement learning, and quantum-classical hybrid algorithm parameters and block chain verification weight are automatically optimized. The real-time response efficiency and the resource utilization rate of the computing power network can be improved.

An example operation may include one or more of storing a database of payment card data, receiving an identifier of a product from a digital wallet on a user device, identifying one or more payment cards stored within the digital wallet on the user device, determining the benefits that will be obtained by using each of the one or more payment cards to purchase the product via execution of an LLM on the identifier of the product and the database of payment card data; and displaying a chat message within a chat window on the user device with a description of the determined benefits that will be obtained.

Disclosed in the present invention is a four-network integration architecture for an unmanned swarm system. The four-network integration architecture has the capabilities of heterogeneous platform resource pooling, intelligent dynamic computing power allocation, and timely decision planning, so as to maximize the overall benefit. The present invention focuses on abstracting and integrating independent submodules to form a mesh topology of a swarm. The present invention designs a four-network integration architecture for an unmanned swarm system, which comprises a computing power network, a perception network, a decision network and a communication network as core modules. The structure aims to achieve efficient cooperation of all parts in the swarm, thereby improving the overall performance and adaptability of the system. The system integrates environmental perception, a swarm network modeling component, a knowledge base and a resource pool, providing an intelligent environmental perception strategy and a network modeling strategy for the interior of the swarm. Therefore, the perception of environments, tasks and networks by nodes can be facilitated, thereby completing establishment of intelligent networks, so as to ensure the characteristics of the stability and flexibility of networks.

A system and method for creating complex, immersive, and interactive digital content is disclosed. The system integrates advanced artificial intelligence, multi-modal input processing, cloud-based shared environments, and immersive hardware to generate, optimize, and deliver rich interactive experiences. The platform supports content mashups, custom scenario generation, and adaptive AI behaviors, enabling the creation of unique and engaging digital environments across various media formats.

An example operation may include one or more of training an artificial intelligence (AI) model to recommend numerical goals based on execution of the AI model on historical profile data and contextual data that is associated with the historical profile data, storing profile data of a user profile within a data store of a software application, receiving a request via a user interface of a user profile page of the software application, wherein the request comprises context of a user of the user profile, determining a numerical goal for the user profile based on execution of the AI model on the profile data of the user profile and the context, and displaying the numerical goal via the user interface of the user profile page within the software application.

The invention discloses a base station facility abnormity intelligent monitoring method and system, and the method comprises the steps: employing a multi-scale wavelet fusion pulse neural network to construct a dynamic signal perception topology according to the real-time data of a base station physical layer, and generating a space-time aligned base station full-dimension feature tensor; inputting the full-dimensional feature tensor of the base station into a fault mode confrontation distillation module, reconstructing a normal working condition manifold of the base station based on a physical constraint variational auto-encoder, and outputting an abnormal feature vector with a fault fingerprint identifier; fault propagation graph modeling is carried out on the abnormal feature vectors, and a base station fault root cause topological graph is generated in combination with back propagation credibility verification; and inputting the base station fault root cause topological graph into a toughness self-healing strategy generator, and finally outputting a base station facility self-healing strategy set meeting real-time constraint. By utilizing the embodiment of the invention, the accuracy and the real-time performance of abnormity monitoring can be improved, and the operation reliability and the communication service quality of the base station are improved.

A system and method are provided for providing advisory notifications to mobile applications. The method includes interfacing the server device with at least one endpoint within an enterprise system and storing a model trained by a machine learning engine to automatically determine advisory notifications relevant to client data sets stored by the endpoint(s) and / or the at least one endpoint. The method also includes determining a current state of a client account, using the model to determine an advisory notification for the client account based on the current state, referring to a set of rules to determine when to provide the advisory notification in the mobile application, and in what portion of the mobile application to display the notification; and sending the advisory notification via the communications module to a client device to display the advisory notification in the mobile application.

The invention provides a network topology intelligent generation method and system based on deep learning and topology analysis, and relates to the technical network intelligence field, and the method comprises the steps: obtaining historical topology data, carrying out the feature extraction based on time sequence division, constructing a graph neural network model, training, and evaluating the evolution trend and stability of a network topology structure. And constructing a deep reinforcement learning model to generate an optimization strategy, and carrying out iterative optimization until a network topology structure meeting requirements is generated. The network structure can be adaptively optimized, the network performance is improved, the operation and maintenance cost is reduced, and the network stability is enhanced.

A method to share remote DMA (RDMA) pointers to a key-value store among a plurality of clients. The method allocates a shared memory and accesses the key-value store with a key from a client and receives an information from the key-value store. The method further generates a RDMA pointer from the information, maps the key to a location in the shared memory, and generates a RDMA pointer record at the location. The method further stores the RDMA pointer and the key in the RDMA pointer record and shares the RDMA pointer record among the plurality of clients.

The invention belongs to the technical field of data transmission, and particularly relates to an SD-WAN low-delay data transmission method and system based on edge computing, a plurality of edge computing nodes are deployed in an SD-WAN architecture, and the edge computing nodes are distributed on a network edge side, close to terminal equipment or a branch mechanism and have data preprocessing and local computing capabilities; monitoring network state parameters of each link in the SD-WAN in real time, wherein the network state parameters comprise delay, bandwidth, packet loss rate and computing resource utilization rate of edge nodes; routing to-be-transmitted data to the target edge node, and if the node has local processing capability, executing data preprocessing or caching operation; otherwise, forwarding the data to an adjacent edge node or a cloud data center; a distributed cache strategy is adopted, when network congestion is detected according to data access frequency and timeliness requirements, non-real-time data is temporarily stored in a local cache, and asynchronous transmission is executed after a link is recovered, so that the method has the effect of providing higher-quality and more reliable network service for a user.

The invention provides a multi-domain computing resource aggregation method and system based on a virtualized user network, and the method comprises the steps: firstly collecting a real-time resource state data set of a computing resource node in a multi-domain environment, covering a computing power load rate and the like, and then calling a virtualized resource mapping model to carry out cross-domain resource feature extraction, the method comprises the following steps: generating a multi-dimensional resource feature vector containing dynamic load fluctuation and other features, performing resource association analysis on the feature vector based on an intelligent aggregation strategy network, determining a collaborative matching weight, aggregating dispersed nodes into a virtualized resource pool, and finally performing dynamic adaptation on aggregation resources in the virtualized resource pool. And generating a resource scheduling topological structure matched with the target business demand, and deploying the resource scheduling topological structure to a multi-domain computing environment to trigger a resource collaborative service, so that multi-domain computing resources can be effectively aggregated, and the resource utilization efficiency and the service matching degree are improved.

The embodiment of the invention provides an industrial real-time data transmission guarantee method and system based on a TSN. The method is applied to the field of industrial communication and intelligent scheduling, and comprises the steps of obtaining industrial field network node information, completing TSN topology deployment and time synchronization, extracting communication characteristics and historical load data of various task flows, constructing a unified time sequence, and inputting a prediction model to generate a load prediction result. And in combination with task parameters, a scheduling strategy list and a prediction result, inputting a TSN digital twin network model to carry out analogue simulation, evaluating performance indexes under multiple strategies, selecting an optimal scheduling strategy and issuing the optimal scheduling strategy to TSN switching equipment, thereby realizing accurate scheduling control configuration of various task flows. According to the scheme, intelligent evaluation and automatic optimization issuing of the scheduling strategy are realized, the real-time performance and the deterministic communication capability of an industrial network are remarkably improved, the resource utilization efficiency and the adaptability of the scheduling decision are enhanced, and the key task flow is ensured to be reliably transmitted on time.

One or more data streams may be used for controlling multi-system interaction. For example, a data stream may be received, and a surgical option associated with a surgical instrument may be selected based on the data stream. A control signal associated with the surgical instrument may be generated based on the selected surgical option. A surgical device may receive an external data stream from a source external to the surgical system. The device may derive, based at least on the external data stream, decision contextual information. The device may select a surgical option associated with a surgical instrument based on the decision context information. The device may generate a visual indication of the decision context information associated with selecting the surgical option. The device may generate a control signal associated with the surgical instrument based on the selected surgical option.

A system is provided for managing Internet of Things (IoT) networks. The system includes a learning module configured to employ machine learning models with hyperparameters optimized through a hyperparameter optimization process; wherein the process includes evaluating a set of hyperparameters against a performance metric to select optimal hyperparameters that enhance the adaptability and efficiency of dynamic membership functions within an adaptive fuzzy logic engine (AFLE).

The invention provides a power station equipment state real-time monitoring and diagnosing method and system based on cloud edge collaboration, and the method comprises the steps: adjusting a data collection period dynamically determined based on an adaptive sampling frequency adjustment algorithm, and collecting a vibration signal, a temperature signal and a current signal through a multi-source heterogeneous sensor array disposed in a power station equipment body; carrying out preprocessing by utilizing the edge computing node, generating a compressed feature vector, and uploading the compressed feature vector to a cloud end through an MQTT protocol; a multi-modal data fusion analysis module is started through a cloud, a three-dimensional evaluation matrix of the equipment health state is constructed in combination with historical operation data and environmental parameters of the equipment, and a calculation task distribution strategy between an edge calculation node and the cloud is adjusted in real time according to an evaluation result of the three-dimensional evaluation matrix. Abnormal mode recognition based on a deep residual network and fault source tracing double-channel analysis based on a physical model are executed, fault types and fault reasons are diagnosed, and the accuracy and timeliness of fault diagnosis are guaranteed.

The invention discloses an infrastructure risk operation optimization management system based on AI driving, and belongs to the field of computer data processing and commercial management, and the system comprises a multi-modal causal twinning construction module which integrates on-site multi-modal data streams to construct a dynamic space-time causal map; the risk evolution deduction module is used for performing anti-fact simulation based on a causal atlas to construct a prospective risk model; the collaborative configuration optimization module is used for solving an optimal collaborative defense strategy according to the risk model; the instruction analysis and digital prescription generation module is used for analyzing the defense strategy into a job digital prescription for a specific risk scene; and the intervention efficiency attribution and evolution correction module performs attribution analysis according to the execution effect of the digital prescription and adaptively updates the causal atlas. According to the method, a comprehensive method of constructing a dynamic causal map for risk deduction, coupling resource constraints for collaborative optimization and performing closed-loop feedback on a correction model is adopted, and active prediction, accurate intervention and continuous learning optimization of capital construction risks can be realized.