79362results about "Transmission" patented technology

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.

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.

An online social wager-based gaming system is disclosed, featuring dynamic cross-provider game filtering and persistent cross-provider voice-interactive group play. The system allows for automated multi-seat group game reservations and incorporates a distributed ledger for bet verification. Features include a Group Connect module for dynamic group formation and live audio across various game providers, enabling real-time play. A Professional Companion Connect module facilitates live webcam or microphone gambling sessions with professional companions, under contractual agreements. The User Games module enhances personalization by integrating user-uploaded images into the gaming environment. A cryptocurrency / blockchain-based bet tracking system ensures transparency and security in managing bet transactions through a controlled digital wallet system. This integrated platform aims to provide a cohesive, interactive, and personalized online casino experience, addressing limitations of traditional solitary online gaming by fostering social connections and enhancing user trust and engagement.

By implementing a new bitstream for a Delta-Quantization Parameter (DQP), a decoder is able to implement parallel decoding of multiple coding units within a largest coding unit. In some embodiments, the DQP is placed immediately after the mode information of the first coding unit. In some embodiments, the DQP is placed after the mode information of the first non-skipped coding unit. In some embodiments, the DQP is placed after the first non-zero coded block flag.

Systems and methods for integrating generative artificial intelligence (AI) within Software-as-a-Service (SaaS) platforms to automate data operations, synchronize cross-platform workflows, and enable intent-based interactions. A platform displays table structures of items and characteristics linked to a common objective, provides input interfaces, and enrolls AI agents as credentialed users with read / write privileges. The system prompts agents with column types, structural relations, and role profiles to generate and execute editing instructions that progress workflow objectives, detect missing or inconsistent data, and notify users or request information as needed. Hierarchical access schemes permit multiple agent instances with inherited privileges and resource limits managed through an AI center. Agents can operate as autonomous team members, analyze outputs, and support natural-language explanation sessions. Additional embodiments coordinate inter-service updates, maintain deviation detection tools, and construct tailored products and platform elements. These capabilities improve robust automation, decision support, and operational efficiency in complex SaaS environments.

Systems and methods disclosed herein perform privacy-preserving evaluations of artificial intelligence (AI) agents. A first AI agent associated with a first entity obtains a machine-readable data structure defining one or more operative boundaries for a second AI agent associated with a second entity. The system generates a unique fixed reference value representing the machine-readable data structure by applying a first transformation operation set, and transmits the unique fixed reference value to a multi-agent storage to store the value. The system receives, via the multi-agent storage, a verification artifact from the second AI agent that indicates an observed value based on internal operational data of the second AI agent corresponding to the operative boundaries. The first AI agent determines a verification status of the verification artifact by comparing the unique fixed reference value with the observed value, and autonomously generates a verification record including a representation of the verification status.

The present invention discloses a third party registration processing method based on the IMS network, which comprises that a UE completes the registration on a S-CSCF entity; the S-CSCF entity starts a waiting timer; the UE and a P-CSCF entity initiate and complete a registration status subscribing respectively to the S-CSCF entity; the S-CSCF entity initiate a third party registration to an AS when the waiting time of the waiting timer is enough. Through the present invention, the S-CSCF entity performs a waiting after the UE is successfully registered, to make the subscribing success of the registration status of the P-CSCF and the UE take precedence over the third party registration and avoid the situation of conflicting data of the UE, the P-CSCF, the S-CSCF and an HSS, to make the UE, the P-CSCF and the S-CSCF effectively process the abnormal circumstance occurred when the third party registration fails.

The invention provides an intelligent short message scheduling method and device based on multi-dimensional dynamic optimization, and the method comprises the steps: obtaining the performance data of a plurality of short message channels, and calculating a channel health score based on a weight dynamic adjustment model; determining a scheduling strategy according to the priority identifier of the to-be-sent message, and performing channel screening and optimal matching; executing message sending and monitoring a sending state; terminal state detection is carried out on the failure message through operator base station signaling, and a decision tree model is applied to determine a retry strategy; and performing Huffman coding compression processing on the P2-level marketing messages which fail in retry, and performing batch sending in an idle window. According to the method, a comprehensive performance evaluation index and reward function model is also constructed, and parameter optimization is performed by applying a reinforcement learning algorithm. According to the invention, multi-dimensional dynamic channel scoring, intelligent retry decision making based on terminal state perception, batch processing with balanced cost-time efficiency and a closed-loop self-optimization system are realized, the short message delivery rate is obviously improved, and the invalid retry rate and the sending cost are reduced.

The invention provides a dynamic graph neural network modeling method for space-time big data, and relates to the technical field of data processing, and the method comprises the steps: mapping a network function entity into a topology vertex and mapping a topology correlation characteristic into a weighted transmission link, and triggering a sequence through a signaling event to drive topology reconstruction, and generating a communication network topology model; inputting the communication network topology model into a dynamic graph neural network, executing state feature space aggregation of a topological vertex neighborhood through a spatial-temporal feature extraction layer, and fusing time evolution dependency of a historical topological sequence to generate a network node spatial-temporal state tensor; and based on the network node space-time state tensor, a particle swarm optimization algorithm is adopted to calculate a whole network risk level quantitative topology feature, and network resource strategy optimization is dynamically executed to suppress end-to-end risk conduction. The adaptive capacity of the network to the dynamic scene is improved.

The invention relates to a cloud edge cooperative computing framework and processing method for multi-modal data stream fusion processing, and the method comprises the following steps: S1, carrying out the noise suppression based on an original data stream collected by an edge computing node through employing an improved Wiener filtering algorithm, achieving the signal denoising through the adaptive threshold wavelet transformation, and obtaining a cloud edge data stream; and a timestamp alignment technology is utilized to solve the problem of time delay difference of multi-modal data, and a space-time alignment purified data stream is generated. Through combination of the improved Wiener filtering algorithm and the adaptive threshold wavelet transform, the noise suppression efficiency of the original data stream is significantly improved, the timestamp alignment technology effectively solves the time delay difference of the multi-modal data, the generation of the space-time alignment purified data stream ensures that the subsequent processing has a unified time sequence benchmark, and the efficiency of noise suppression of the original data stream is improved. The space-time attention fusion network adopts a collaborative architecture effect of a bidirectional gating circulation unit and a lightweight 3D convolutional network.

The invention provides a self-adaptive network topology dynamic reconstruction method and system based on deep reinforcement learning, and relates to the technical field of deep reinforcement learning, and the method comprises the steps: obtaining the topology state information, service flow distribution information and historical reconstruction records of a current network; extracting topological correlation characteristics among nodes through graph convolution operation, and generating fusion state representation in combination with service flow information; inputting the fusion state representation into a deep reinforcement learning model to identify bottleneck nodes and redundant links, and outputting a reconstruction action candidate set; searching and evaluating the long-term cumulative income of the candidate actions through a Monte Carlo tree, and screening an optimal reconstruction action sequence; a graph coloring algorithm is utilized to allocate time slots and process resource conflicts, and a resource-feasible topology adjustment scheme is generated; and extracting a network evolution rule through tensor decomposition, and constructing a topological optimization association mapping graph. According to the method, the network bottleneck can be intelligently identified, the network topology structure is dynamically optimized, and the network performance and the resource utilization rate are effectively improved.

A system for adaptively caching network communication protocols enhances efficiency across heterogeneous device environments through a multi-level cache architecture with device-capability-based tiers. The system collects endpoint telemetry data including device capabilities and operational constraints to classify endpoints and generate context-aware protocol variants optimized for specific device types. Protocol optimization opportunities are determined through structural analysis of message patterns and state transitions. The system performs protocol deduplication by identifying functionally equivalent variants and maintaining canonical representations to reduce cache redundancy. Cache synchronization across distributed nodes uses enhanced Merkle tree structures with protocol normalization processing. The system predicts communication needs based on historical patterns, network context, and endpoint constraints, enabling proactive cache management tailored to device capabilities. Integration with event-driven data communication systems enables seamless protocol selection and translation while maintaining compatibility between diverse endpoint types, from high-performance servers to resource-constrained IoT devices.

The invention discloses a digital intelligent switch cabinet state comprehensive sensing system based on AI, and the system comprises a multi-source data collection module, a data preprocessing and synchronization module, an edge calculation feature extraction module, an AI intelligent fusion recognition module, an expert rule diagnosis module, and a cloud comprehensive evaluation and decision module. Various types of sensors are deployed to respectively acquire environmental parameters, electrical parameters and partial discharge signals generated in the operation process of the switch cabinet to form an original multi-modal data stream. The method has the advantages that the recognition precision and response speed of the complex operation state of the switch cabinet are improved, hidden faults under multi-modal data mismatch can be effectively found, and the misjudgment and missed judgment risks are reduced. Meanwhile, a closed-loop diagnosis system is constructed, intelligent evaluation and interpretable feedback of fault types, positions and trends are achieved, scientificity and reliability of operation and maintenance decisions are enhanced, and the method is suitable for intelligent upgrading of an electric power system.

The invention relates to a flow media bandwidth adapting system, which comprises flow server, media provider, and mobile device, wherein the flow server comprises server flow protocol module, bandwidth analyzing module, bandwidth selecting module, document analyzing module and media sending module; the mobile device comprises terminal flow protocol module, wireless module, bandwidth detecting module, buffer module and play module; the mobile device via wireless module and bandwidth detecting module timely reports received bandwidth state and media receiving state to the server flow protocol module; the server flow protocol module sends said information to the bandwidth analyzing module to find the media bandwidth and sending speed, to be sent by the media sending module. The invention can automatically adjust the sending speed and code bandwidth of media document via the network state and media receiving state, to improve relative play effect.

The invention discloses an intelligent substation communication link fault accurate positioning method and system, and the method comprises the steps: obtaining a configuration file and equipment state data, carrying out the processing of the configuration file and the equipment state data, and generating a standardized link feature vector and a marking data set; constructing a hybrid deep learning model, and optimizing parameter configuration of the hybrid deep learning model by adopting an optimization algorithm to obtain a parameter-optimized hybrid deep learning model; training by using a real fault sample in combination with a virtual fault sample generated by a generative adversarial network, optimizing a time sequence prediction capability through an echo state network, and generating a fault positioning model; in combination with the link state data, outputting a fault link positioning result and confidence evaluation through multi-stage confidence evaluation and topological correlation analysis; and carrying out virtual-real corresponding verification in combination with the configuration file, carrying out parameter optimization on the fault positioning model, and outputting a fault positioning system. The problems that the fault positioning precision is low, the response speed is low, and complex fault scenes cannot be processed are solved.

The invention relates to the technical field of agricultural intelligent irrigation, and discloses a high-standard farmland intelligent irrigation system based on Internet of Things and data analysis, and the system comprises a soil moisture content sensing module which collects data through a multi-source sensor to construct a three-dimensional soil moisture content distribution model, and generates a soil moisture content characteristic spectrum; the soil moisture content prediction module generates water demand prediction data based on the soil moisture content characteristic spectrum, the meteorological data and the crop growth stage; the irrigation strategy module fuses terrain elevation and pipe network pressure parameters to generate an irrigation control map; the equipment state monitoring module collects water pump current waveform and other data to generate equipment health degree parameters; the pipe network optimization module optimizes pipe network topology and generates an adjusting instruction; the multi-source data fusion module generates fusion evaluation indexes by using an evidence theory, an entropy weight method and the like; and the intelligent execution module generates an execution control instruction accordingly. All the modules cooperate to achieve precise irrigation, and the utilization efficiency of water resources and the intelligent level of farmland management are improved.

Various embodiments of the technology described programmatically access a user query intended for a Large Language Model (LLM), analyze the user query, and determine prompt-enriching information that is combined with the user query to generate an enriched user query that is ultimately communicated to the LLM. In this manner, additional prompt-enriching information or context is added to the user query before being communicated to the LLM so that the additional prompt-enriching information, along with the user query, can be tokenized to better guide the LLM to a more accurate answer without modifying weights, parameters, or training of the LLM. Certain embodiments have the technical effect of improved accuracy relative to existing approaches by enriching user queries with prompt-enriching information to generate an enriched user query that is passed to the LLM. Based on the enriched user query, certain embodiments reduce the likelihood of hallucinations present in the LLM response.

A method is described. The method comprises: extracting one or more contents related to an organization from one or more data sources based on one or more job role names and one or more contextual inputs; determining, using an artificial intelligence engine, at least one of an organizational structure, one or more roles, one or more responsibilities, one or more hierarchical relationships, one or more access levels, one or more service actions, and one or more departments of the organization from the one or more contents; and generating, using the artificial intelligence engine, one or more job descriptions based on at least one of the organizational structure, the one or more roles, the one or more responsibilities, the one or more hierarchical relationships, the one or more access levels, the one or more service actions, and the one or more departments.

The method and systems for automatically identified currently viewing website domain name or a uniform resource locator (URL), receiving, by the server, said automatically identified website domain name or the uniform resource locator (URL), a request or an invitation for initiating a communication including live chat, puts the request for initiating a communication including live chat from the first user and the second user in a communication including live chat queue of the website or the uniform resource locator (URL) associated account, for available or identified or relevant agents to pick up, routing request or invitation for initiating a communication including live chat from the first user and the second user according to a pre-set rules and start a communication including live chat by available agent by selecting particular request for initiating a communication including live chat from the queued communication including live chats.

The invention relates to the field of electronic communication, and provides a multi-protocol dynamic switching method and system based on an FPGA. The method comprises the following steps: carrying out regional division on an FPGA chip through a layout planning tool to obtain a static logic region and a plurality of reconfigurable logic partitions; performing hanging processing on the current protocol module based on the protocol switching trigger signal, and writing running state data of the current protocol module into a state cache unit to obtain protocol context snapshot data; according to the target protocol type, bit stream loading is carried out on the corresponding reconfigurable logic partition through configuration of an access port, and a reconfiguration protocol module is obtained; and reading protocol context snapshot data, and performing state recovery on the reconstruction protocol module to obtain a target protocol data path. According to the invention, rapid protocol switching is realized, the utilization rate of hardware resources is improved, and the system deployment cost is reduced.

The embodiment of the invention relates to the technical field of artificial intelligence, and provides a linkage method and system of an apartment network and intelligent equipment, and the method comprises the steps: collecting network operation data and equipment operation data of a target apartment in real time; based on the equipment operation data, determining currently achievable candidate service scenes of the target apartment; fusing the network operation data and the equipment operation data into a joint operation map of the target apartment through the space-time diagram attention network; a multi-agent depth deterministic strategy gradient algorithm is adopted to carry out network resource allocation and equipment control decision making on the joint operation map, scene adaptive optimization is realized in combination with candidate service scenes, and a linkage strategy of a target apartment is obtained; and generating a decision instruction based on the linkage strategy, and adopting the decision instruction to realize remote control of the network equipment and the intelligent equipment. Through deep fusion of the space-time diagram attention network and multi-agent reinforcement learning, dynamic collaborative management and control of the network and equipment are realized, and the resource utilization rate and the service response speed are improved.

The invention belongs to the field of civil aviation, provides a civil aircraft field service operation scheduling control system, and aims to solve the problem of low cooperative efficiency of multiple links of ground service scheduling. File management is based on an Ali cloud OSS development interface, supports multi-format files, and realizes functions of permission access, encryption and the like; communication adopts an MQ / MQTT architecture to construct an IM system, and supports multi-type message transmission and http network communication; data processing packages a multi-language database interface, and internal and external network secure intercommunication is realized through a gatekeeper agent. Flight functions cover and guarantee node management and control, dynamic updating, cross-department communication, real-time flight data butt joint, task distribution of unified operation according to work types, and support of multi-terminal such as a web terminal and a handheld terminal and nailing access. Ground service multi-scene requirements can be met, and the operation efficiency and the management level are improved.

Embodiments of the subject application relate to methods and apparatuses for a delay status report associated with a protocol data unit (PDU) set. According to an embodiment of the subject application, a user equipment (UE) includes a processor and a transceiver coupled to the processor, and the processor is configured to: determine delay information associated with one or more protocol data unit (PDU) sets received from an upper layer of the UE, wherein the delay information includes at least one of: a remaining PDU set delay budget; a PDU set buffer delay; a PDU set delay budget offset; or first timing when receiving a PDU within the one or more PDU sets from the upper layer; and transmit a report including the delay information, wherein the report is at least one of: a delay budget report (DBR); a delay report (DR); an arriving timing report (ATR); or an enhanced buffer status report (EBSR).

The invention discloses a disaster intelligent Internet of Things multivariable time sequence anomaly detection method and device, and relates to the technical field of Internet of Things anomaly detection, and the method comprises the steps: S1, constructing an initial anomaly detection model; s2, acquiring a training data set; s3, performing optimization training on the initial anomaly detection model by using the training data set to obtain an optimized anomaly detection model; s4, acquiring real-time monitoring data; s5, analyzing the real-time monitoring data by using the optimized anomaly detection model to obtain a detection result; the dynamic gated expansion convolutional network DGDC solves the problems of rigid structure and parameter explosion of a traditional TCN. Dynamic expansion rate scheduling enables a receptive field to expand in an exponential level along with the number of layers, and second-level burst and week-level periodic characteristics can be captured at the same time; the parameter quantity is reduced by 60%-70% through depth separable convolution, and the efficiency and precision of local feature extraction are both superior to those of an existing convolution module by combining the suppression effect of a gated linear unit GLU on noise features.

The invention, which belongs to the technical field of intelligent breeding equipment, discloses an underwater bionic robotic fish school cooperative breeding environment dynamic monitoring and regulation system comprising a bionic robotic fish body core module, a group cooperative control core module and an intelligent regulation core module. The bionic robotic fish module simulates a real fish swimming mode and carries various water quality sensors to autonomously cruise, so that interference to cultured fishes is reduced; the group cooperation module utilizes a cluster intelligent algorithm and an underwater acoustic communication technology to realize coordinated movement of multiple robotic fishes and full coverage of a monitoring area; the intelligent adjusting module is based on an abnormal detection algorithm, and integrates a miniature oxygenation device, a pH adjusting device and the like to achieve precise regulation and control of the local environment. By the adoption of the system, dynamic sensing and active regulation and control of the culture environment are achieved, a bionic monitoring regulation and control solution is provided for modern aquaculture through intelligent cooperation of the robot fish school and the management system, and the system has the important value of improving the culture efficiency and improving the growth environment.

A dynamic, topology-aware load balancing in a distributed ML inference system (100), consisting of: a topology and monitoring module configured to detect, map, and dynamically update the network topology by capturing real-time latency, bandwidth, and connectivity metrics across distributed nodes; a module for capturing node performance, configured to evaluate and record the computing power, hardware configuration, accelerator availability, and storage capacity of each node; a workload characterization module configured to analyze incoming inference requests with respect to computational complexity, latency sensitivity, memory requirements, and model type; a dynamic load balancing engine configured to assign inference tasks to the optimal nodes based on topology data, node capabilities, and workload characteristics; an adaptive feedback and optimization module configured to capture performance metrics for task execution and refine task allocation strategies based on historical results; a fault tolerance and recovery module configured to detect errors, redirect tasks, and maintain uninterrupted service; and an orchestration and integration interface module configured to provide APIs and orchestration hooks for seamless deployment in heterogeneous computing environments; The system dynamically adjusts the load distribution to respond to changes in network conditions, fluctuations in resource availability, and workload requirements to improve efficiency, minimize latency, and maximize throughput in distributed ML inference environments.

The invention discloses an electric power communication network visual operation and maintenance method based on digital twinning, and the method comprises the steps: carrying out the coupling mapping of a global data mirror image and multiple sources, collecting data through a reconfigurable sensing array, and outputting a three-domain digital mirror image through time alignment; semantic topology reconstruction is carried out, and a dynamically growing semantic topology is generated by using a graph semantic engine; based on explainable causal chain fault tracing, an event-causal-countermeasure knowledge network is constructed; performing multi-target balancing operation and maintenance decision making, and screening an optimal strategy by using a three-dimensional Pareto model; immersive visualization and interactive intervention are performed, and a cross-platform engine is injected to realize multi-dimensional display. According to the method, through multi-source data accurate mapping, topology dynamic adaptation, causal interpretable traceability and scientific decision making, the problems of data islands, topology lagging and low decision making efficiency in existing operation and maintenance are solved, and the operation and maintenance accuracy and efficiency of the electric power communication network are improved.

The invention discloses an intelligent air conditioner water chilling unit control method and system based on predictive optimization. Operation data and weather forecast data are collected to establish a dynamic response reference, building cold load sudden change opportunity is recognized, the mismatching relation between magnetic suspension frequency and wet bulb temperature is detected, and energy-saving opportunity recognition data is generated to determine a cooling starting judgment table; identifying equipment response delay characteristics by using a dynamic response reference, extracting time sequence advantage parameters to determine dislocation configuration among multiple pieces of equipment, and generating coordination control parameters; cloud-local transmission delay analysis is carried out according to the coordination control parameters, and a coordination control sequence is generated through buffer opportunity identification and delay compensation; mechanical refrigeration suppression data is generated through energy efficiency mode classification, free cooling potential mining is implemented to form a cold source optimization factor, and an emergency response strategy is generated; an emergency response strategy is used to identify a multi-device linkage trigger critical zone, a prediction correction feedback network is constructed, a global collaborative optimization instruction is generated, and the system operation efficiency and the control precision are improved.

The invention discloses a vehicle-mounted cross-protocol data time sequence cooperative processing method, which comprises the following steps: establishing a unified time reference, and providing an accurate space-time coordinate system for heterogeneous network nodes by electing a global master clock and constructing a clock relation model and a periodic correction mechanism; systematic cognition and management of communication resources are realized, and a verified protocol capability table is generated by automatically identifying protocol types and variants; constructing an automatic mapping system from demand to execution, performing formalized packaging on a target scene by introducing a unified intermediate model, generating a cross-protocol mapping rule based on the model and a protocol capability table, and then forming a global scheduling table in combination with a unified time reference; the cooperative communication of all nodes is finally realized by using the global scheduling table, and the end-to-end delay is constrained within the preset range, so that the problem that the end-to-end time sequence in the vehicle-mounted heterogeneous network is uncontrollable is solved, and the controllability and reliability of the time sequence of the vehicle-mounted network are improved.