371 results about "Data delivery" patented technology

The invention relates to the technical field of underground power tunnel design data processing, in particular to an automatic reinforcement design method based on an instantiation reference algorithm, which comprises the following steps: constructing a multi-level parameterized model, orthogonally classifying structure types and shapes, and associating geometric features, mechanical attributes and reinforcement rules for each type of structure to form a structured database. A geometric boundary of the special-shaped structure is defined by adopting a B spline curve or an NURBS curved surface, and dynamic association with a reinforcement rule base is achieved through a topological relation mapping module. The instantiation reference algorithm calls a reinforcement template library according to load conditions, and optimizes the diameter, spacing and reinforcement ratio of reinforcements in combination with an elastic mechanics principle to generate an initial scheme. And generating a standard drawing and a synchronous statistical table through a CAD conversion model. According to the method, diversified special-shaped structure reinforcement design is supported, and a whole-process closed loop of parametric modeling, dynamic rule expansion and construction data delivery is realized.

The invention relates to a data delivery risk early warning and intelligent management and control method and system. The method comprises the following steps: collecting flow characteristic data, user behavior data and environment characteristic data of data delivery in real time; establishing a dynamic baseline model, and generating reference range data of normal delivery behaviors based on historical data and industrial standards; performing anomaly detection on the user behavior data and the environment characteristic data by using a dynamic baseline model, and generating a risk early warning signal; when a risk early warning signal is received, identifying a risk type, and generating a corresponding risk level; and matching management and control actions in a preset strategy library based on the risk type and the risk level, executing the management and control actions, monitoring a feedback effect, and continuously optimizing dynamic baseline model parameters. According to the method, through dynamic reference generation, multi-dimensional data fusion and a continuous optimization mechanism, the problem of pain points of a traditional model in adaptability, comprehensiveness and maintenance cost is solved.

The invention relates to a vehicle-mounted edge computing network scheduling method based on information age, and belongs to the field of mobile edge computing. The method is oriented to a vehicle-mounted edge computing network in which a fixed server and a vehicle-mounted mobile server coexist, and aims to minimize the long-term average information age of the system by selecting a device node combination scheduled by the server in each time slot. The position constraint, the link conflict constraint and the server computing resource constraint of a vehicle-mounted mobile server are considered, an optimization problem is modeled as a Markov decision process, and a deep breach Q network is improved by using a bifurcation structure and an action mask to obtain a scheduling strategy of an equipment node. According to the method, the bifurcation structure and the action mask are introduced into the deep breach Q network, so that the problem solving complexity is reduced, and the data delivery freshness of the vehicle-mounted edge computing network system is guaranteed.

An unmanned aerial vehicle (UAV) agent for forming a two-tier hybrid smart agriculture network architecture is provided. The UAV agent is configured to support two communication protocols, a short-range communication protocol and a long-range communication protocol. The agriculture sensor gathers agriculture data such as soil temperature and participates in a first-tier short range wireless communication network to send the collected data to at least one UAV agent. A UAV agent participates in one or more first-tier short-range communication networks to pick up sensor data from agriculture sensors in one or more clusters and also participates in a second-tier long-range communication network to route the collected sensor data to at least one cloud server. The tasks in both tier networks are formulated as optimization problems to achieve optimal data delivery and solved by using multi-agent reinforcement learning (MARL), which is implemented by the invented Focus Coordination Multi-Agent Deep Deterministic Policy Gradient (FC-MADDPG) algorithm.

The invention relates to a data delivery feedback effect-based delivery parameter intelligent optimization method and system, and the method comprises the steps: setting an initial delivery parameter, and collecting the multi-modal feedback data in real time through a distributed sensor network after data delivery; constructing a space-time attention neural network, performing feature extraction on the multi-modal feedback data, and generating a dynamic interest migration vector; inputting the dynamic interest vector into a group game reinforcement learning framework, and calculating and outputting an optimal putting strategy set based on a Nash equilibrium theory; inputting the optimal putting strategy set into a digital twin environment for strategy verification, and outputting a verification result; and combining the verification result with the initial delivery parameter to generate an optimized delivery parameter, and carrying out iterative optimization on the delivery parameter. The method and the device have the effect that the data delivery strategy is highly matched with the market demand and the user behavior.

A match table is used to match a concatenation of a source address of a data packet and a consecutive message sequence number (MSN) to a match data structure. The matched concatenation is inserted into the match data structure based on a source and a running counter for a MSN to each source. The currently active PDC associated with the source is attached or an empty PDC is atomically created and attached. In response to a packet arriving before the semantic layer has posted a recv( ), A canary value “failed match” is atomically inserted into the match data structure and a request to wait (RTW) message is sent to the source. In response to the semantic layer posting the recv( ), the “failed match” entry in the match data structure is identified, the “failed match” entry is atomically updated with match information, and a request to send (RTS) message is sent to the source.

The invention relates to the technical field of block chain and smart home data management, discloses a smart home data contribution quantification and excitation method and system based on a block chain identifier, and aims to solve the data source problem of vertical large model training in the smart home industry and guarantee data security and personal privacy. The method comprises the steps of equipment identifier registration and binding, home local data processing and desensitization, data contribution degree quantitative evaluation, intelligent contract incentive execution and standardized data delivery and evidence storage. The system comprises a block chain identifier management module, a home edge data center, a data contribution quantitative evaluation module, an intelligent contract incentive engine and a large model training data delivery interface. According to the technical scheme, accurate tracing of data sources and clear definition of contribution subjects can be achieved, the family computing power is activated, the risk of data privacy disclosure is reduced, high-quality and scenarized credible data sources are provided, and marketization circulation and value conversion of smart home data elements are promoted.

Apparatus and methods are provided for data collection general framework for AI-ML model training. In one novel aspect, the UE server sends the data collection request / triggering to UE through the application layer without network involvement with the general frame for data collection including data collection triggering, data collection configuration, measurements, and data delivery. In one embodiment, the assistance information is received from the RAN node with unicast or groupcast. In another embodiment, the UE delivers assistance information with the collected AI-ML model related data to deliver to the UE server. In one novel aspect, the UE server initiates a data collection request destined to the one or more UEs and receives one or more data collection responses and AI-ML model related data and assistance information through a data tunnel connected with the wireless network.

A wireless device receives one or more parameters for a store and forward (SF) mode for an application, and determines whether to send a packet of the application based on at least one of the one or more parameters or that the network operates in the SF mode. The wireless device sends, to a base station, a message comprising at least one of a non-access stratum (NAS) message comprising the packet, or a value of at least one of the one or more parameters.

The invention relates to a method for enhancing data transmission reliability of a time-sensitive network, and belongs to the technical field of industrial control. The method comprises the following steps: searching all alternative paths in a TSN network, and calculating a working path Pw based on path reliability; selectively deploying a frame replication and frame elimination node FN according to the node reliability; extracting an alternative redundant path set, and determining a redundant path combination Pr in the alternative redundant path set according to the redundancy; and outputting the combination FN of the frame copy node and the elimination node, and the optimal main redundant path combination P. The method comprises the following steps of: firstly, selectively deploying nodes with frame copying and eliminating functions by utilizing the thought of segment protection, and inclining protection resources to other paths while meeting the reliability requirement, so that the efficient utilization of the resources is realized; secondly, by considering the influence of link and node attributes on data delivery reliability, an optimized redundant path is extracted under the condition that the requirement of data redundancy is met, so that the reliability of data transmission is improved.

The invention provides an advertisement data delivery matching method and device based on position information. Relates to the technical field of computers. The method comprises the following steps: determining a high-value region distribution diagram; determining the position and the moving speed of the current advertisement putting device; determining second advertisement type information of the current advertisement putting device based on the position of the current advertisement putting device and the high-value area distribution diagram; and performing advertisement putting matching based on the second advertisement type information of the current advertisement putting device and the moving speed. Accurate matching and advertisement putting can be performed according to multi-dimensional information such as the real-time position, the motion state and the value of the user and the user characteristics, so that the pertinence and the effectiveness of advertisement putting are improved; and the advertisement putting strategy is optimized, and the advertisement putting effect is continuously improved.

A terminal includes: a reception unit that receives data via a downlink channel scheduled using common downlink control information common to a plurality of terminals in a data delivery to the plurality of terminals; and a control unit that performs a specific operation as an operation for receiving at least one logical channel from among a logical control channel and a logical traffic channel related to the data delivery to the plurality of terminals, depending on whether to support a specific function for receiving the at least one logical channel.

This application provides a bandwidth control method and an apparatus. One example method includes: A service enabler architecture layer data delivery SEALDD server obtains bandwidth limit information from a first server, where the bandwidth limit information includes at least one of bandwidth limit information of the first server or bandwidth limit information of a first user. The SEALDD server determines, based on the bandwidth limit information, bandwidth allocated to a first SEALDD client, where the first SEALDD client is a client corresponding to the first user. The SEALDD server sends, to the first SEALDD client, information about the bandwidth allocated to the first SEALDD client.

The invention discloses a reinforcement learning routing optimization method considering a dynamic underwater sound environment and node mobility. The method comprises the following steps: performing parameter initialization on an underwater sensor network; monitoring and updating underwater environment parameters and node positions in real time, and dynamically updating a network topology connection state; during data transmission, constructing a multi-dimensional state vector, selecting a routing action by adopting an epsilon-greedy strategy, and generating a routing decision; if the data packet transmission fails when the routing action is executed, ending the iteration, otherwise, calculating a multi-target reward value; updating the Q value table to optimize the routing decision; and monitoring network performance indexes in real time, adaptively adjusting reinforcement learning parameters of the intelligent agent for next iteration, and outputting an adaptive routing selection model until an iteration termination condition is met. According to the method, time-varying channel and node mobility challenges can be intelligently dealt with, and multi-objective optimization balance of energy consumption, delay and stability is realized while the data delivery rate is remarkably improved.

A distributed control system (DCS) of an industrial process plant includes a data center storing a plant information model that includes a description of physical components, the control framework, and the control network of the plant using a modeling language. A set of exposed APIs provides DCS applications access to the model, and to an optional generic framework of the data center which stores basic structures and functions from which the DCS may automatically generate other structures and functions to populate the model and to automatically create various applications and routines utilized during run-time operations of the DCS and plant. Upon initialization, the DCS may automatically sense the I / O types of its interface ports, detect communicatively connected physical components within the plant, and automatically populate the plant information model accordingly. The DCS may optionally automatically generate related control routines and / or I / O data delivery mechanisms, HMI routines, and the like.

The invention discloses a meteorological data source credibility proving method based on a block chain, and provides a new data source credibility proving mechanism by utilizing the characteristics of tamper resistance, forgery resistance and loss resistance of the block chain and combining signature verification and hash operation algorithms in cryptography. According to the mechanism, credibility and data delivery validity verification can be accurately carried out on delivery data recorded by a data provider; according to the patent, transaction / shared data is converted into a pre-agreed intermediate format (a character set, a coding mode and an XML / JSON data structure expression mode) and is stored in a file form. And after receiving the data, the data user converts the data into a self system format according to self system requirements, so that the correctness of data information expression is ensured. Through the overall transmission of the data packet, the accidental missing and tampering of the data in the transmission process between the two handover parties are effectively avoided.

Embodiments described herein comprise an advanced Software as a Service (SaaS) platform addressing challenges including trust, governance, quality, data accuracy, and transparency, ensuring full visibility of enterprise data warehouse data pipelines. These embodiments seamlessly integrate with cloud-based and legacy data platforms, enhancing visibility through monitoring data quality, tracking metadata changes, and generating exceptions for review. Data quality management allows for custom script creation to validate pipelines and business rules. Cloud-native connectors facilitate integration with one or more cloud-based services streamlining data delivery processes. Personalized notifications aid administrators in responsive action, while data profiling capabilities identify quality issues and provide distribution insights. Actionable data intelligence for privacy, security, and governance is enabled through data classification. Automation features encompass data quality checks, metadata monitoring, profiling, and classification, orchestrated by a flexible scheduler. The architecture leverages a scalable serverless design with independent microservices, dynamically scaling based on demand.

A method and apparatus for delivering power and data over optical fiber. The Power over Fiber (PoF) optical power and data delivery system, includes a PoF transmitter and a PoF receiver that are connected by an enhanced capacity PoF optical cable. The PoF transmitter is configured to convert electrical power to optical power, encode data into light signals, and supply the optical power and light signals to an output of the PoF transmitter. The PoF receiver is configured to receive the optical power and light signals from the optical cable input, decode the light signals into data, and convert the optical power to electrical power. The PoF receiver is further configured to interface with an Ethernet powered device and supply electrical power to the Ethernet powered device.

The present invention relates to the technical field of underground power tunnel design data processing, and in particular to an automatic reinforcement design method based on an instantiation reference algorithm. By constructing a multi-level parametric model, the structure type and shape are orthogonally classified, and geometric features, mechanical properties and reinforcement rules are associated with each type of structure to form a structured database. The special-shaped structure uses B-spline curves or NURBS surfaces to define the geometric boundaries, and is dynamically associated with the reinforcement rule library through a topological relationship mapping module. The instantiation reference algorithm calls the reinforcement template library according to the load conditions, and optimizes the steel bar diameter, spacing and reinforcement ratio in combination with the principles of elastic mechanics to generate an initial scheme. Standard drawings and synchronous statistical tables are generated by CAD conversion models. The present invention supports the design of reinforcement for diversified special-shaped structures, and realizes a full-process closed loop of parametric modeling, dynamic rule expansion and construction data delivery.

The invention relates to a wireless network scheduling method based on information age and data importance, and belongs to the technical field of wireless networks. According to the method, for a wireless network in which a plurality of sensors transmit state updating data packets to a central node under unreliable multiple channels, the average weighted sum of information age and received data of a system is minimized under the condition that the processing capacity of the central node and the number constraint of the wireless channels are met. An original optimization problem is decoupled into link selection between sensor nodes and channel allocation between scheduling nodes, and a state space and an action space for solving the problem are reduced by adopting an approximate optimal solution sorting method and a deep Q network, so that a scheduling scheme consisting of a main strategy and a sub-strategy is obtained. According to the method, joint optimization of information age and data importance of multiple sensors under multiple channels is considered, so that the freshness of wireless network data delivery is ensured, and reliable transmission of important data is ensured.

Techniques and systems are disclosed for managing the registration and communication of Internet-of-Things (IoT) devices with a Lightweight M2M (LwM2M) server using both Internet-Protocol (IP) and non-IP data bearers. The system registers the IoT client device with the LwM2M server using an IP data bearer to enable communication over the IP data bearer. The system monitors for a first condition, which includes the reception of a Monitoring Event (MONTE) event or a failure to receive a registration update message within a period corresponding to the registration lifetime of the IoT client device. Upon determining that the first condition is satisfied, the system evaluates device or network data to determine if a second condition is met and, if so, transmits a communication to the IoT client device instructing it to register with the LwM2M server using the non-IP data bearer.

The invention is suitable for the technical field of processors, and particularly relates to a speculative wake-up-based data forwarding optimization system and method and related equipment. According to the method and the device, the data forwarding flag bit and the data forwarding pipeline number are set for the to-be-executed instruction, and whether the to-be-executed instruction needs to be subjected to data forwarding or not is judged according to the data forwarding flag bit; if yes, the instruction to be executed needs to obtain the corresponding source operand from the front instruction, data forwarding needs to be carried out, and the source operand is read from the corresponding pipeline of the assembly line according to the serial number of the data forwarding pipeline; and if the data forward flag bit is the second data forward flag bit, the instruction to be executed does not need to acquire the source operand from the front instruction and is directly read from the physical register, so that resource conflicts are reduced. Compared with the prior art, the method has the advantages that the logic level of the data forwarding behavior can be effectively reduced, and the running efficiency and performance of the processor are improved.

The invention relates to a mixed channel industrial internet of things scheduling method based on information age and throughput, and belongs to the technical field of industrial wireless networks. According to the method, for a multi-node industrial Internet of Things system in which a fast unreliable channel and a slow reliable channel coexist, the overdue probability that the information age of the system exceeds a threshold value is minimized under the condition that the limited bandwidth constraint and the minimum throughput demand of all nodes are met. An original optimization problem is decoupled into a node link scheduling sub-problem and a scheduling node channel selection sub-problem, a channel parameter driven region division mechanism, random network distillation and deep reinforcement learning are adopted to cooperatively solve the two types of sub-problems, and a multi-node scheduling scheme formed by two sub-strategies is formed. According to the method, joint optimization of information age and throughput of multiple sensors under a mixed channel is considered, strict freshness of industrial wireless network data delivery is guaranteed, and differentiated throughput requirements of all nodes are ensured.

The invention discloses a trusted space-based data delivery method and device and a medium, and the method comprises the steps: determining a production library and a delivery library for data delivery, and constructing a one-way data transmission channel between the production library and the delivery library; acquiring to-be-delivered data in the production library, and performing multi-level encryption on the to-be-delivered data; transmitting the encrypted to-be-delivered data to a delivery library through a one-way data transmission channel to obtain delivered data, and testing the integrity of the delivered data; and when it is determined that the delivered data is complete, deleting the copy of the data to be delivered in the production library. Security protection measures are implemented in the whole process from data extraction, transmission to delivery, the whole process of data delivery is safe and controllable through multiple technical means such as a trusted computing space, a one-way transmission channel, encryption verification, access control, an auditing mechanism and data destruction, and reliable data delivery guarantee is provided for the field with high security requirements.

A SEALDD server may receive a request to establish a SEALDD flow, wherein the SEALDD flow facilitates a transfer of VAL data between a VAL client endpoint and a first VAL server endpoint, and wherein the request comprises at least one of VAL client endpoint information and first VAL server endpoint information. The SEALDD server may establish a SEALDD flow associated with the first VAL server and associated with at least one VAL client endpoint. The SEALDD server may subscribe to a core network and receive a notification from the core network indicating a UE mobility event associated with a UE upon which the VAL client is hosted. The SEALDD server may determine to transfer the VAL client to a second VAL server. The SEALDD server may transfer the SEALDD flow to a different SEALDD server associated with the second VAL server.

The embodiment of the invention discloses a digital delivery method based on a gas overground pipeline, and the method comprises the steps: collecting the basic information of a to-be-delivered project, and generating a gas planar graph according to the basic information; the method comprises the following steps: designing a VR scanning scheme, scanning a space environment where a to-be-delivered project is located according to the VR scanning scheme to obtain a 720-degree space three-dimensional model, and processing data in the 720-degree space three-dimensional model to obtain a three-dimensional high-precision model; uploading the gas planar graph and the three-dimensional high-precision model to a cloud platform; the cloud platform adds labels to important information on the three-dimensional high-precision model and the gas planar graph, wherein the important information comprises information of directions of a gas vertical pipe, a pressure regulating cabinet, a gas meter, an electromagnetic stop valve, gas utilization equipment and a gas pipeline; and completing delivery through the cloud platform. According to the method, the gas facility safety operation and maintenance guarantee capability of the gas overground pipeline is improved, and the data delivery accuracy of the gas overground pipeline is improved.

The invention discloses a multi-user data delivery method for locking an application scene, and the method comprises the steps: firstly segmenting a high-value data set into a plurality of data subsets, then dynamically generating a symmetric secret key, segmenting the secret key into a plurality of share secret keys, encrypting each piece of data by using the secret key, and transmitting the encrypted data to a server; then, an access strategy is made for the share of each data user according to the attribute provided by each data user, a share key distributed to the data user is encrypted, meanwhile, the encrypted share key is uploaded to a server, the data user downloads a ciphertext, the secret key is recovered by utilizing the share key of the data user and the share keys of other data users, and the data user sends the secret key to the server; and all the users decrypt the own data and then complete a specific application scene in a federal learning modeling mode. According to the invention, the problem of security control when the data is delivered to a plurality of data users for use is solved, 'special data and special purpose 'is realized, and the economic benefits of data providers are guaranteed.

Apparatus and methods are provided for data collection general framework for AI-ML model training. In one novel aspect, the UE server sends the data collection request / triggering to UE through the application layer without network involvement with the general frame for data collection including data collection triggering, data collection configuration, measurements, and data delivery. In one embodiment, the assistance information is received from the RAN node with unicast or groupcast. In another embodiment, the UE delivers assistance information with the collected AI-ML model related data to deliver to the UE server. In one novel aspect, the UE server initiates a data collection request destined to the one or more UEs and receives one or more data collection responses and AI-ML model related data and assistance information through a data tunnel connected with the wireless network.