68 results about "Network partition" patented technology

A decentralized mesh-network platform provides real-time governance, supervision, and audit of large-language-model persona agents in regulated environments such as healthcare. Each computing node hosts processor circuitry, memory, a network interface, and an integrated suite comprising a User-Prompt Interface, Metadata Filter & Enrichment Engine, Persona Role Constructor, Policy Constraint Engine, gating logic module, Supervisor Review Interface, Session Memory Governance Module, Audit Logging Subsystem, cryptographically linked audit ledger, and Fallback Control Module. Devices communicate over a peer-to-peer channel, exchange role-switch tokens, and merge local ledgers into a composite audit graph when connectivity returns. The architecture supports continuous compliance and resilient operation across institutional boundaries, network partitions, and device failures, enabling conversational interaction with heterogeneous medical data while maintaining rigorous credential lineage, policy oversight, and forensic traceability.

The invention relates to a heterogeneous AI computing virtualization architecture and slice enhancement method and system, and the method comprises the steps: evaluating the demands of an AI task for GPU resources through an analytic hierarchy process, and dividing the GPU resources into ultrafine-grained slice units according to an optimization model; predicting a resource demand through an ARIMA model, allocating slice resources through a minimum error sum of squares algorithm, and generating a resource allocation scheme; the method comprises the following steps: dividing a GPU into a plurality of independent hardware isolation units based on a resource allocation scheme and GPU hardware topology, and generating a hardware unit configuration table; establishing an isolation mechanism based on the identity information and the ACL, and establishing a software isolation environment; the method comprises the following steps: dividing network partitions, configuring a firewall rule to control network access authority, monitoring network traffic in real time, processing abnormal traffic, and generating a network partition mapping table; gPU resources are abstracted into a logic resource pool, a resource scheduling platform based on an intelligent scheduling algorithm is constructed, and resource scheduling of heterogeneous slices is carried out.

The invention relates to the technical field of thermoelectric pipe network inspection, and discloses a thermoelectric pipe network fault inspection system and method based on deep learning. The system comprises a pipe network partition module, a multi-mode sensing module, a fault prediction module, a grading response module and a dynamic control module. The pipe network partition module is divided into a high-temperature steam pipeline area, a medium-temperature hot water pipeline area, a low-temperature water return pipeline area and a key valve connecting area; the multi-modal sensing module deploys temperature, pressure and sonic sensor arrays in each area; the fault prediction module extracts spatial-temporal characteristics through a deep belief network model and outputs a fault probability value; the hierarchical response module triggers different levels of response instructions according to a comparison result of the fault probability value and a preset threshold interval; and the dynamic control module controls a moving path of the inspection robot and detection equipment parameters according to the response instruction. According to the system, accurate prediction and dynamic response of the thermoelectric pipe network fault can be realized, and the inspection efficiency and the pipe network operation reliability are improved.

The invention discloses a GIS (Geographic Information System)-based DMA (Direct Memory Access) intelligent partitioning method for a water supply network, relates to the field of data processing, and can solve the problem that a virtual partition boundary is misjudged or a boundary at a certain position frequently fluctuates due to the fact that a DMA intelligent partitioning method for the water supply network at the present stage cannot accurately distinguish sudden water taking abnormity and leakage abnormity. The method comprises the following steps: acquiring water flow state monitoring data; determining a water flow abnormal data set according to the water flow state monitoring data; according to the water flow abnormal data set, a water supply pipe network partition preliminary result is determined; wherein the water supply pipe network zoning preliminary result is used for representing a preliminary division range of each area in the water supply pipe network; and determining the DMA partition of the water supply network according to the preliminary partition result of the water supply network and the physical equipment information of the water supply network. The method is used for DMA intelligent partitioning and leakage monitoring of the water supply network.

Disclosed in the present invention are a power transmission network emergency repair and restoration method and device based on a mobile power supply. The present application improves the power transmission network fault restoration efficiency by the method: reading a preset power transmission network black start strategy optimization model and parameter data acquired from a power transmission network to be restored, wherein the power transmission network black start strategy optimization model is constructed by setting an emergency repair resource constraint, a power transmission network operation constraint and a power transmission network partition constraint, with the optimization objective of minimizing power transmission network load loss; initializing the power transmission network black start strategy optimization model on the basis of the parameter data, and solving the power transmission network black start strategy optimization model by means of a solver to obtain a mobile power supply arrangement scheme and a unit restoration scheme of said power transmission network; and performing emergency repair on said power transmission network on the basis of the mobile power supply arrangement scheme and the unit restoration scheme. The present application can improve the power transmission network fault restoration efficiency and reduce power outage costs by allocating fault repair resources.

The invention relates to an automatic cluster recombination method, a distributed system and computer equipment. Determining a plurality of independent network partitions after the distributed system is subjected to network partitioning; for each independent network partition, determining first existence information of each cluster node in each independent network partition; determining a target local leader node and a target follower node from the cluster nodes of the independent network partitions according to a preset consensus recombination strategy based on the first existence information; establishing a first local consensus cluster corresponding to the independent network partition according to a following relationship between the target local leader node and the target follower node; according to the invention, automatic reorganization of cluster nodes during network partitioning is realized, so that each independent network partition can still maintain local consensus and service availability under the condition that global intercommunication cannot be realized, service is provided to the greatest extent, the availability is ensured, and the service continuity and the overall availability of the system after network partitioning are remarkably improved.

Systems and methods generate requests for encrypted (e.g., secured) credentials and data using one of a plurality of API plugins that are selected based on an access protocol for a network partition at which requested data is determined to be stored. Through the use of the dynamically-selected API plugins, the systems and methods may provide the aforementioned functions while being compatible with the different access protocols that may be encountered when providing central management across a diverse spectrum of user accounts, networks, and / or cloud platforms.

Systems and methods generate requests for encrypted (e.g., secured) credentials and data using one of a plurality of API plugins that are selected based on an access protocol for a network partition at which requested data is determined to be stored. Through the use of the dynamically-selected API plugins, the systems and methods may provide the aforementioned functions while being compatible with the different access protocols that may be encountered when providing central management across a diverse spectrum of user accounts, networks, and / or cloud platforms.

The invention relates to the field of intelligent equipment control, and provides a smart home cooperative control method based on the Internet of Things. The method comprises the following steps: analyzing historical state data of a smart home environment through machine learning to obtain an equipment association map, and encoding collected real-time state data to obtain a global state vector; clustering by combining the global state vector and the equipment association map to obtain a life scene mode set, extracting a trigger condition to form a library, and inputting the global state vector into a neural network to predict scene occurrence probability distribution; determining standard equipment state configuration corresponding to a target scene in the trigger condition formation library according to scene occurrence probability distribution, and performing multi-target optimization on standard equipment state control to obtain a cooperative control strategy list; and executing instructions in groups on the cooperative control strategy list according to the equipment network partitions, and performing monitoring compensation in the execution process. According to the invention, the scene intelligent control accuracy of the intelligent equipment is improved.

This invention provides a distributed transaction processing method for BOM data in a PLM system, belonging to the technical field of PLM systems. When initiating a BOM data modification transaction, this invention sends a preparation request to participating nodes and performs field-level lock acquisition. A majority vote determines whether the transaction should be committed or rolled back. Transaction logs are batch-aggregated into group commit log blocks according to time windows and copied to persistent memory via RDMA. A closure table is established for the BOM tree structure to store ancestor-descendant relationships. An artificial intelligence model is used to monitor node response latency and log write rate to dynamically adjust parameters. When a network partition is detected, nodes holding a majority lease continue processing the transaction. After partition recovery, data merging is performed using version vector conflict detection and a compensation transaction mode. This solves the problem of data consistency being difficult to guarantee due to network partitions when processing BOM data modification transactions in a distributed PLM system.

This invention provides a method and system for optimizing downhole communication links based on self-organizing networks, relating to the field of communication link technology. The method includes acquiring communication node information, constructing a network topology, and determining network characteristic parameters; constructing a link quality prediction model to evaluate link quality; performing dynamic network partitioning and establishing a node priority ranking table to select the optimal transmission path; and real-time monitoring of link status, triggering node self-organizing reconfiguration when necessary. This invention can improve the connectivity, stability, and transmission efficiency of downhole communication networks, reduce communication latency, and enhance the safety of downhole operations.

This invention discloses an IoT-based underground pipe network zonal metering monitoring and control system, comprising the following modules: a zonal monitoring module for collecting zonal flow, pressure, water quality, and valve status data, and for collecting upstream flow and pressure data; a data preprocessing module for preprocessing the zonal monitoring data; a time-series feature modeling module for outputting zonal prediction data using an improved TimesNet model; an anomaly detection module for generating zonal anomaly detection results; a control distribution generation module for outputting zonal control distribution based on an improved generative flow network reinforcement learning model; a control parameter generation module for obtaining zonal control parameters and updating model parameters; and a control execution module for adjusting valve opening, water supply distribution ratio, and pressure setpoint. This invention integrates time-series feature modeling and generative flow network reinforcement learning to achieve refined monitoring and intelligent control of underground pipe network zonal areas.

The invention discloses an underground pipe network zoning metering monitoring and regulation system based on the Internet of Things, and the system comprises the following modules: a zoning monitoring module which is used for collecting zoning flow, pressure, water quality and valve state data, and collecting upstream flow and pressure data; the data preprocessing module is used for preprocessing the partition monitoring data; the time sequence feature modeling module is used for outputting partition prediction data by using an improved Times Net model; the anomaly judgment module is used for generating a partition anomaly judgment result; the regulation and control distribution generation module is used for outputting partition regulation and control distribution based on the improved generation flow network reinforcement learning model; the regulation and control parameter generation module is used for obtaining partition regulation and control parameters and updating model parameters; and the regulation and control execution module is used for adjusting the valve opening, the water supply distribution proportion and the pressure set value. According to the method, time sequence feature modeling and generative flow network reinforcement learning are fused, and fine monitoring and intelligent regulation and control of underground pipe network partitions are achieved.

The invention relates to a method and system for synchronizing configuration data in a plant, in particular to a method for synchronizing configuration data of components within an industrial plant and the synchronization thereof. The method comprises the steps of partitioning a network in an industrial plant into a first sub-network and a second sub-network, the sub-networks being connected by a network connection. Storing first configuration data on a first local server and via the network connection on a second local server, and storing second configuration data on the second local server and via the network connection on the first local server. Interrupting the network connection; and updating the first configuration data and the second configuration data on the first local server. Reconnecting the network connection; and, if the first configuration data on the second local server has not been changed, overwriting the first configuration data on the second local server by the updated first configuration data.

Methods, systems, and media for partitioning neural networks are provided. In some embodiments, a method comprises obtaining a training set. The method comprises training a plurality of neural networks using the training set, wherein neural networks differ based on dimensions of one or more layers of the neural networks and a location of a compression block positioned between a first set of layers of a neural network and a second set of layers of the neural network. The method comprises selecting a neural network based on hardware constraints of a system on which the neural network is to be implemented, wherein the first set of layers of the selected neural network are executed by one or more sensor devices of the system, and wherein the second set of layers of the selected neural network are executed by an aggregator computing device of the system.

This invention discloses a method and system for simulating the induced voltage of a GIS disconnect switch housing, relating to the field of power equipment and electromagnetic field simulation technology. The method includes: establishing a simulation model of the induced voltage of the GIS disconnect switch housing; performing network partitioning on the simulation model; setting boundaries and applying excitation; solving for the magnetic field strength distribution; constructing an equivalent circuit and calculating the induced voltage of the housing; and setting an early warning model to provide an equivalent early warning of the induced voltage. By establishing an accurate simulation model of the induced voltage of the GIS disconnect switch housing, combined with quantitative analysis of the electromagnetic field and equivalent circuit calculation, it is possible not only to accurately predict the changes in the induced voltage of the GIS disconnect switch under high-voltage environments, but also to dynamically evaluate the induced voltage through real-time monitoring and an early warning model. This achieves early warning of equipment status and effectively prevents equipment failures or safety hazards caused by excessive induced voltage.

The invention relates to the technical field of urban gas transmission and distribution and pipe network safety monitoring, and provides a gas pipe network partition leakage detection and graded early warning method and system, and the method comprises the steps: collecting the gas amount data and multi-source monitoring data of a target partition, and generating a partition gas amount balance sequence and a leakage feature matrix; constructing a partition baseline trend model to carry out trend offset analysis, carrying out sudden anomaly detection on the operation state of the target partition, and respectively generating a corresponding trend leakage value and a sudden leakage value; establishing a leakage grading model in combination with a pipe network topological structure, and determining the leakage grade of the current target partition; leakage probability calculation is carried out on the pipe section, a leakage space positioning result is output, and corresponding early warning and linkage control are executed according to the leakage level and the leakage space positioning result, so that rapid deployment can be carried out under the actual operation condition of a gas company, and a role can be played; and the safe operation capability, the quick leakage discovery capability and the emergency response efficiency of a gas company on a medium-low pressure gas pipe network are improved.

The invention discloses a distributed reasoning task arrangement method based on semantic driving. The method comprises the following steps: constructing a domain ontology model and establishing semantic mapping; analyzing task requirements; executing data source retrieval of multi-level semantic fusion; generating an execution path based on multi-objective collaborative optimization; tasks are distributed, and transmission overhead is reduced by adopting model differential updating; and performing adaptive migration and network partition fault tolerance based on a check point and a two-stage commit protocol. According to the method, the technical problems of heterogeneous data semantic gaps, static scheduling and dynamic environment conflicts and state loss caused by coarse-grained fault tolerance are solved, efficient distributed reasoning of data immobility and calculation flow is achieved, and the method is suitable for the multi-source heterogeneous data fields of marine science, meteorological monitoring, industrial Internet of Things and the like.

Methods, apparatus, systems, and articles of manufacture are disclosed for state convergence associated with high availability application migration in a virtualized environment. An example apparatus includes at least one memory, machine readable instructions, and processor circuitry to at least one of execute or instantiate the machine readable instructions to identify a high availability slot in a virtual server rack including a first virtual machine (VM) associated with first configuration data that identifies the first VM as a protected VM, transmit second configuration data to a second VM that identifies the first VM as a nonprotected VM and the second VM as the protected VM, after a determination that a network partition is identified based on a failure of a request to retrieve the second configuration data from the second VM, and transfer data from the first VM to the second VM after causing the removal of the network partition.

This application provides an IP address network partitioning method, apparatus, device, and storage medium, belonging to the field of asset management technology. The method includes: converting a target IP address into an integrated IP address block, wherein the target IP address belongs to either IPv4 or IPv6; determining the host bit width based on a variable-length subnet mask, and logically right-shifting the tail address block within the IP address block according to the host bit width to obtain a bit value; comparing the bit value of the tail address block with a reference bit value of the tail address block in a preset network partitioning rule to determine whether the target IP address matches the network partitioning rule. This method eliminates code redundancy and branch prediction failures. It significantly improves cache locality and instruction pipeline efficiency, thereby enhancing the adaptability of the solution.

The present application relates to the technical field of underground pipe network monitoring and early warning, and particularly relates to an underground pipe network partition visualization and safety early warning method and system. The method comprises: obtaining a map service source, an underground pipeline service, GIS data, a BIM model, equipment data, historical monitoring data, historical maintenance records, inspection logs and accident reports, performing data alignment and data standardization processing to obtain pipe network topology structure information; performing key node, valve, connection relationship and monitoring coverage density correlation processing to obtain partition objects; performing connection processing to obtain multi-source heterogeneous monitoring data, and performing time sequence feature coding, spatial feature coding, semantic text data feature coding and cross-modal fusion processing to obtain fusion feature vectors; and then performing confidence interval, intelligent alarm instruction, mutation alarm instruction, maximum clustering distance and connection relationship joint processing to obtain correlation alarm state information. The present application can effectively improve the accuracy and timeliness of safety early warning.

The invention discloses a wide area network sub-domain construction method for sensing a topological structure. The method comprises the following steps: generating a multi-time clustering result as a reference sub-domain set through K-means clustering; establishing an intimacy measurement model based on co-occurrence frequency and geographic distance, calculating intimacy between nodes, and determining a pairing set and a pairing matrix in combination with a threshold value; a node fusion algorithm is adopted, a pairing set is iteratively selected to form a preliminary partition A according to the limiting conditions that sub-regions are fully connected and the number of nodes does not exceed a threshold value, and then unallocated nodes are classified into the sub-regions which are highest in intimacy and conform to the limitation; through a local search algorithm, edge nodes of each sub-region in the preliminary partition are redistributed to neighborhood sub-regions, the routing transmission efficiency of a neighborhood partition result is calculated in a traversal manner, iteration is carried out until a solution which is locally optimal or close to the optimal efficiency of a centralized network theory is found, and the solution is a final network partition result, so that efficient network node partition is realized, and the efficiency of the network node partition is improved. And meanwhile, the routing transmission efficiency equivalent to the theoretical optimal efficiency of a centralized network is achieved.

Disclosed herein are systems and method for testing distributed systems using injected network partitions. A method may include monitoring communication between a plurality of computing devices in a distributed system to identify each communication link that exists between two respective computing devices in the distributed system. The method may include generating a communications list comprising a plurality of computing device pairs and injecting a network partition in at least one pair of the plurality of computing device pairs. The method may include detecting whether a performance degradation greater than a threshold performance occurs in response to the network partition. In response to detecting the performance degradation greater than the threshold performance, the method may include generating and transmitting a security report indicative of the performance degradation and the at least one pair of the plurality of computing device pairs causing the performance degradation.

This invention relates to the field of analysis and early warning, and discloses an IoT-based regional water supply network leakage analysis and early warning system. The system includes a partitioning module, a fusion analysis module, and an early warning module. The system collects data through sensor arrays deployed in the network partitions. The fusion analysis module executes a negative pressure wave localization algorithm and an acoustic correlation analysis algorithm in parallel, generating first and second localization results respectively. By calculating the Euclidean distance between the two results and comparing it with a preset tolerance, it determines whether to output localization information or trigger model reverse verification. During verification, a dynamic hydraulic model takes real-time data as input, iteratively fits and outputs a virtual leak location, and outputs the final result after a second distance verification. The early warning module generates tiered early warning information based on the analysis results and displays it visually. This invention, by combining multi-algorithm fusion verification with hydraulic model reverse verification, significantly improves the accuracy and reliability of leakage localization and effectively reduces the false alarm rate.

The invention discloses an interactive operation optimization method and system considering meteorological distributed resource characteristic packaging, and the method comprises the steps: constructing a P2P power transaction system based on a block chain technology; considering meteorological distributed resource characteristic packaging, and constructing a power transaction clearing model; the electricity transaction clearing model is operated in the P2P electricity transaction system to simulate a transaction clearing process, and an electricity transaction clearing result is obtained; performing grid division on the power network system to obtain a network division result; dynamically reconfiguring network partitions according to the network division result; establishing a distributed operation management model in combination with a power transaction clearing result; and carrying out optimization solution on the distributed operation management model by adopting a Lagrangian double multiplier rapid solution algorithm to obtain an optimal result. According to the invention, the efficiency of large-scale power distribution network operation and management is improved.

The application discloses a database high-availability cluster brain split protection method and device and medium, belongs to the technical field of database high-availability cluster, and is used for solving the technical problems that the existing PostgreSQL high-availability cluster scheme is prone to cluster brain split, mis-switching and data inconsistency problems in network partition and communication anomaly due to the lack of network topology awareness, communication quality quantification, state cross-verification and hierarchical isolation strategy. The method comprises the following steps: after triggering the master selection process of the current cluster, detecting the communicable nodes in the current cluster; performing multi-dimensional quality perception on the communicable nodes to obtain corresponding node state parameters; performing weighted calculation according to the node state parameters to obtain a weighted value; if the weighted value reaches a first preset threshold, performing visibility consensus verification on each communicable node; after the visibility consensus verification is passed, determining a candidate master selection area based on the regional awareness master selection strategy; and selecting a new master node in the candidate master selection area according to the hierarchical response strategy.

The application relates to a heterogeneous AI computing virtualization architecture and slice enhancement method and system, which comprises the following steps: evaluating the demand of an AI task for GPU resources through an AHP method, dividing GPU resources into super-fine-grained slice units according to an optimization model; predicting resource demand through an ARIMA model, and allocating slice resources through a least error square sum algorithm to generate a resource allocation scheme; dividing a GPU into a plurality of independent hardware isolation units based on the resource allocation scheme and a GPU hardware topology to generate a hardware unit configuration table; establishing an isolation mechanism based on identity information and an ACL to establish a software isolation environment; dividing network partitions, configuring firewall rules to control network access permissions, monitoring network traffic in real time and processing abnormal traffic to generate a network partition mapping table; abstracting GPU resources into a logical resource pool, constructing a resource scheduling platform based on an intelligent scheduling algorithm, and performing resource scheduling of the heterogeneous slice.

The application provides a water supply pipe network burst monitoring method, and relates to the field of burst detection. The method comprises the following steps: acquiring flow data and monitoring point pressure values of a water supply pipe network partition; predicting whether a flow mutation event occurs in the partition based on an instantaneous flow value; screening suspicious monitoring points from multiple monitoring points of the partition based on pressure values of each monitoring point in the partition; if the partition includes at least two suspicious monitoring points, a dynamic coverage radius is assigned, a dynamic coverage area with the suspicious monitoring points as the center points is generated based on the dynamic coverage radius; the overlapping area is determined based on a spatial index, the burst risk level is determined according to the overlapping area and the pressure value, and the position information of the coverage area with the burst risk level greater than the set level is output. The method realizes accurate positioning and risk assessment of the burst area by monitoring the flow and pressure data, calculating the dynamic coverage area and calculating the area overlapping risk, and significantly improves the detection accuracy and response efficiency.

The invention relates to the technical field of electric vehicle charging systems, in particular to a centerless multi-terminal-based electric vehicle charging system, which comprises a plurality of independent terminals serving as nodes, and is characterized in that only one main node exists in the system at the same moment, and other nodes are common nodes; when the main node is unavailable, each common node in the system selects a new main node through an election algorithm, and the new main node is used for taking over system management responsibilities; when the network partitions appear, the original main nodes continue to manage the network partitions where the original main nodes are located, and the common nodes of the other network partitions respectively select respective main nodes through an election algorithm so as to manage the corresponding network partitions; and when the at least two network partitions are combined, the main nodes of the at least two network partitions select a unique main node to manage the combined network partitions, and the other main nodes are degraded into common nodes. According to the method, the main node can be dynamically selected to ensure high availability and stable operation of the system.

The invention provides a network configuration method, device, equipment and medium, which are applied to the technical field of computers, and the method comprises the following steps: acquiring IB network card information of a bare metal server; in response to the IB network partition creation request, obtaining a partition key of a newly created IB network partition; and in response to the bare metal instance creation request, creating a bare metal server instance for the bare metal server, and in the process of creating the bare metal server instance, performing IB network configuration on the bare metal server instance according to the network card identifier and the partition key. According to the embodiment of the invention, the automatic configuration of the IB network through the bare metal platform is realized, and the manual configuration risk and the management complexity are reduced.