1444 results about "Multiple node" patented technology

The invention relates to the field of supply chain scheduling decisions, in particular to a supply chain intelligent scheduling decision optimization method and system based on big data driving. The method comprises the following steps: collecting supply chain heterogeneous data streams, extracting abnormal disturbance events, carrying out nonlinear disturbance response mining, and constructing a supply chain disturbance response field; identifying dynamic state parameters of all nodes of the supply chain, analyzing correlation characteristics among the parameters, performing multi-node logistics state transfer evolution, and generating logistics state transfer characteristics of each node; and multi-node delay evaluation is carried out based on the supply chain heterogeneous data flow, global distribution mapping is carried out, and an upstream and downstream logistics state delay characteristic spectrum is constructed. According to the invention, adaptive optimization collaboration between multiple nodes and multiple targets is realized, so that the stability, flexibility and economic benefit of the whole supply chain system are effectively improved.

The invention relates to the technical field of distributed computing, and discloses a distributed computing power scheduling method and system based on dynamic load balancing. The method comprises the following steps: acquiring real-time resource state data of a plurality of nodes in the distributed system and demand characteristics of a task to be allocated, generating a dynamic task allocation strategy, allocating the task to a target node for execution, monitoring a resource state of the target node in real time, and triggering task reallocation when the resource state deviates from a preset threshold value. The system comprises a monitoring acquisition module, a task analysis module, a strategy generation module, a scheduling execution module and a resource abstraction module. According to the method, tasks and node resources can be accurately matched, the resource utilization rate is improved, the real-time performance and reliability of the tasks are guaranteed, the overall performance of a distributed system is optimized through heterogeneous resource unified scheduling and a resource reserving and recycling mechanism, and the method is suitable for various distributed computing scenes.

A wireless communication method and system enabling dynamic multi-path data transmission and reception across heterogeneous radio access networks. The method includes selecting between sequential, concurrent, or redundant multipath transmission strategies based on real-time measurements of link quality parameters, including signal-to-interference-plus-noise ratio (SINR), reference signal received power (RSRP), delay, and spectrum availability. The system further enables adaptive transmission power control, modulation scheme selection, and carrier configuration based on physical layer feedback, minimizing interference and optimizing spectral efficiency. Peer-to-peer communication between terminals is supported without base station mediation, reducing RAN load. Multi-base station connectivity is enabled, allowing terminals to simultaneously transmit through multiple nodes and aggregate at the core network. The architecture supports real-time topology control, dynamic link adaptation, and transmission parameter adjustment based on physical-layer constraints.

The invention relates to the technical field of intelligent customer service control, and discloses a federated learning driven customer service robot cooperative control method and system. The method comprises the following steps: deploying a local intention recognition model at a plurality of nodes, collecting a user dialogue stream, extracting a semantic behavior track fragment, and generating a behavior feature vector set containing a time sequence and context association; the federal cooperative controller performs periodic aggregation, constructs a cross-node feature alignment mapping table based on trajectory similarity, and generates a global behavior feature distribution map; calculating node feature offset, screening high-contribution-degree nodes in combination with a sparse activation threshold, and allocating aggregation tasks; a knowledge distillation compression model is used at the high-contribution-degree nodes, weight updating parameters are extracted, compensation coefficients are added, and an encrypted updating package is generated; and the federal cooperative controller carries out heterogeneous fusion on the encrypted packet, reconstructs a global intention decision tree and carries out segmentation and distribution, so that efficient cooperation and optimization are realized, and privacy protection and service adaptability are considered.

The invention discloses an industrial equipment fault detection method fusing a complex relation and space-time dependence, and belongs to the technical field of industrial anomaly detection, and the method comprises the steps: constructing a plurality of adjacent matrixes, carrying out the weighted fusion to form an enhanced adjacent matrix, and comprehensively and accurately describing the complex multi-dimensional relation between industrial equipment; designing a spatial-temporal feature extraction module, extracting spatial features in parallel by using a graph convolutional neural network and a random graph attention network, extracting time features through time convolution and a multi-head attention mechanism, and dynamically fusing the spatial-temporal features by means of a gating mechanism to generate graph-level features; a state judgment layer composed of a plurality of node-level binary classifiers and a voting mechanism are adopted to comprehensively judge classification results of all nodes, so that the stability and reliability of judgment of the overall state of the industrial control system are enhanced, and the risk of misjudgment is reduced; the problems of equipment relation modeling and multi-dimensional information fusion are effectively solved, features are extracted and fused more accurately, and the accuracy and adaptability of anomaly detection are improved.

The invention relates to the technical field of data monitoring, and discloses an intelligent data monitoring method and a monitoring platform, and the intelligent data monitoring method comprises the steps: constructing a dynamic heterogeneous hypergraph model, and representing the complex relation and time sequence evolution characteristics among multi-source data nodes; based on a dynamic heterogeneous hypergraph model, realizing high-order relationship representation and calculation, and obtaining embedded representation of nodes and relationships; performing heterogeneous graph information transmission and aggregation by utilizing embedded representation of nodes and relationships, and capturing complex interaction characteristics among multiple nodes; according to the complex interaction characteristics, an abnormal propagation rate model is established, and accurate prediction of an abnormal diffusion path is realized; based on the abnormal propagation rate model and the abnormal diffusion path, multi-level collaborative anomaly detection is implemented, and collaborative faults across subsystems are identified; according to the invention, the abnormal propagation path can be predicted in advance, the abnormal prediction accuracy is improved, and the system fault response time is advanced.

The invention discloses an industry return on investment real-time acquisition method based on multi-source heterogeneous data, and relates to the technical field of data analysis and processing, and the method comprises the following steps: normalizing a multi-source timestamp and retaining metadata information based on a UTC reference, an adjustable gain and a power exponent; in the streaming processing, reordering of out-of-order data and marking of expired data are realized by setting a buffer queue and a watermark threshold value; when null value fields or cross-source conflicts are detected, interpolation and conflict processing are carried out, and complete data subjected to consistency correction are output; then, an external high-precision reference or a cross-correlation function is used for further fine tuning the timestamp at a millisecond level, and if the adjustment amplitude exceeds a safety boundary, the timestamp is marked as suspicious; and finally, the corrected data is distributed to multiple nodes according to a fragment mapping strategy, and fault-tolerant consensus and difference repair are triggered under fault duration judgment, so that high-precision time sequence alignment and high availability in a massive concurrent scene are kept, data loss or precision attenuation caused by time sequence inconsistency and node faults is avoided, and the method can be widely applied to high-frequency analysis.

The invention relates to the technical field of cluster video stream processing, and discloses a video stream real-time coding and decoding transmission method under a cluster. The method comprises the following steps: firstly, acquiring video stream coding parameter text data, link state time sequence data and equipment performance index data of multiple nodes of a target cluster to form a transmission link data set; semantic analysis is carried out on the coding parameter text data to obtain a coding semantic feature vector, dynamic fluctuation features are extracted from the link state time sequence data to obtain a link fluctuation feature vector, and cross-modal fusion is carried out to generate a fusion transmission feature set; generating an abnormal association degree score set by using a pre-trained multi-layer sensing network model, and obtaining an abnormal source node and an equipment defect type by combining root cause tracing according to the abnormal association degree score set; and finally, generating a dynamic optimization strategy and feeding back to the transmission control system to trigger parameter calibration. According to the method, the abnormal root cause can be accurately traced, the transmission parameters are optimized, and the cluster video stream transmission quality is improved.

The invention discloses a micro-grid dynamic scheduling method based on deep learning, and the method comprises the steps: fusing industrial Internet of Things collection and GIS positioning, and constructing a multivariable original spatio-temporal data set covering multiple nodes; extracting multi-scale features through multi-resolution wavelets and Fourier transform, combining the multi-scale features with a dynamic adjacency matrix, and realizing feature adaptive distribution and nonlinear dynamic modeling by using multi-scale attention gating, graph convolution and a time sequence neural network model; the micro-grid load and state prediction accuracy, the system generalization ability and the abnormal response level can be effectively improved, and powerful support is provided for intelligent scheduling and abnormal analysis.

The invention discloses a digital content tracing system based on a block chain technology, and particularly relates to the technical field of block chains. The tampering risk of the digital content in the distributed storage process is evaluated by analyzing the multi-node data synchronization behavior; the transaction overstock risk judgment module evaluates the transaction overstock and local congestion hidden danger according to the transaction verification rate in the propagation process; and the reliability evaluation module judges the reliability of the digital content tracing chain in combination with the tampering risk and the local congestion hidden danger. The abnormal risk assessment module analyzes operation behavior abnormity through the intelligent contract log, and the conflict possibility assessment module detects consistency deviation of circulation records; and the potential risk assessment module synthesizes the multi-dimensional risk, generates a potential risk assessment result of the tracing chain, and determines whether to trigger an on-chain responsibility node tracing verification mechanism, so that potential hidden dangers in the tracing chain can be comprehensively identified, and an efficient and reliable solution is provided for block chain digital content tracing.

The invention relates to the technical field of edge computing, in particular to a safety production monitoring method based on edge collaboration, which comprises the following steps: collecting multi-node clock pulses, calculating adjacent time differences to form a pulse difference parameter set, carrying out statistical distribution, setting a dynamic tolerance boundary, and constructing an effective deviation parameter set; the method comprises the following steps of: generating a weighted smooth deviation result by combining clock stability and link delay weighted normalization, sorting monitoring data according to a timestamp to identify duplication abrupt change exception, constructing a cross-node exception time sequence by associating nodes and time information, and judging a safety state transition result of equipment. A time difference parameter is constructed through multi-node reference pulse arrival time, a dynamic deviation constraint is formed by combining distribution characteristics, clock stability and link measurement weighted smoothing are introduced, cross-node time reference consistency is kept, operation data unified time sequence rearrangement is carried out, a sudden change event reliably reappears in a time window, and an abnormal identifier is associated with a node identity. And forming a cross-node event evolution link.

The invention provides a local cache resource optimization method and system combined with a node state, and relates to the technical field of cloud storage optimizing.The method comprises the steps that firstly, a state index set, including indexes such as resource use, security levels, survivability and transmission cost, of multiple nodes in a distributed network is obtained; calculating a target cache value of the current node to the target information resource according to the target cache value, generating a local cache decision parameter after correction of a dynamic adjustment factor, if the local cache decision parameter exceeds a node cache trigger threshold value, storing the target information resource into a local cache of the current node and generating a cache resource distribution update instruction, and updating the cache resource distribution update instruction. And adjusting the adjacent node cache synchronization strategy according to the cache resource distribution updating instruction, thereby realizing the cooperative operation of the cache resources, and improving the resource utilization rate and access efficiency of the distributed network.

The invention relates to the technical field of intelligent power distribution system state monitoring, in particular to a cable connection state online monitoring method for a low-voltage cable branch box, which comprises the following steps of: applying a disturbance excitation signal to a connection node of the cable branch box, and acquiring transient voltage, current and phase response parameters of the node in different excitation periods; establishing a mapping relation between the node electrical characteristics and the connection state; performing dynamic reference calibration on the acquired signals based on a multi-node space-time coupling matrix to form an adaptive judgment reference of electrical coupling between nodes; a connection degradation sensitive index is extracted through feature sequence differential calculation, a connection state quantitative evaluation result is generated, and real-time updating and anomaly recognition are carried out according to the state change trend; according to the method, the problems of misjudgment and delay caused by a monitoring mode depending on indirect parameters such as temperature and current are solved, high environmental adaptability and degradation identification precision are achieved, and the operation safety and monitoring reliability of the low-voltage power distribution system are greatly improved.

The invention discloses a prestressed beam simulation method, system and device based on a vector type finite element, and relates to the technical field of finite element simulation, and the method comprises the steps: building a prestressed beam numerical model through a vector type finite element method; dispersing the prestressed beam numerical model into a plurality of nodes and a plurality of prestressed beam units; calculating a stiffness matrix of each prestressed beam unit; determining a control equation of each node based on the stiffness matrix; and solving the control equation of each node by adopting a central difference method, and updating the state of each node according to the solving result to finish the simulation of the prestressed beam. The method can achieve the simulation of the complex behaviors of the prestressed beam, and is high in calculation efficiency and good in calculation stability.

The invention discloses an intelligent network data security protection method, system and device and a medium thereof, and the method comprises the following steps: obtaining network flow data, monitoring the network flow of the network flow data through a flow collection device and a network monitoring tool, extracting the feature information of a data packet, carrying out the preprocessing based on the feature information of the data packet, and obtaining the network flow data; according to the convolutional neural network and the LSTM, time sequence features are captured, modeling is carried out according to the convolutional neural network, and a relation between devices in the network and a topological structure of a data stream are determined; on the basis of privacy protection and calculation sharing of homomorphic encryption, encryption calculation is carried out through a Paillier encryption algorithm, summation and multiplication operations are carried out on encrypted data, the encrypted data are distributed on multiple nodes, local training is carried out on the basis of a federal learning technology, and the data are obtained. According to the method, deep adaptive learning is combined with the graph neural network, so that the detection precision of an attack mode is improved, particularly modeling of complex attack behaviors is realized, data privacy protection is ensured, and meanwhile, the calculation efficiency is improved.

The invention provides a charging station access node selection method and system considering the vulnerability of a power distribution network, and belongs to the field of charging station site selection. The method comprises the steps that a power distribution network topological structure of a power distribution network is acquired, and the power distribution network topological structure comprises a plurality of power distribution nodes; calculating a plurality of node vulnerability parameter sets of the plurality of power distribution network nodes according to the power distribution node vulnerability index set; based on a Jaya-AHP method and an entropy weight method, performing index weight fusion distribution on the power distribution node vulnerability index set to obtain a vulnerability index weight set; and according to the vulnerability index weight set and the multiple node vulnerability parameter sets, calculating and screening to obtain an optimal access node, and performing charging station access. By accurately identifying the vulnerability difference of each node of the power distribution network, the centralized layout of the charging station in a node area with relatively high vulnerability is effectively avoided, and the access node with low vulnerability and proper geographic position is preferably selected, so that the impact of the charging load on the power grid is reduced.

The invention discloses a management system of a server and the server, relates to the technical field of computers, and realizes efficient interconnection and centralized management between a plurality of computing nodes and a management module by introducing an interface module between the computing module and the management module. Specifically, each computing node comprises a computing unit and a programmable logic device, a plurality of computing nodes are connected through the programmable logic device in the interface module, and the first management module obtains the operation data of each computing node through the interface module, so that the design that one management module manages multiple nodes is realized, the management units can be reduced, and the management efficiency is improved. And the cost is reduced, so that the expandability and the management efficiency of the system are improved.

The invention relates to the technical field of low-altitude Internet of Things, and particularly provides a target situation awareness method and system based on the low-altitude Internet of Things, and the method comprises the steps: firstly obtaining a target awareness data set which is cooperatively collected by multiple nodes in the low-altitude Internet of Things and comprises three-dimensional position data and environment disturbance data, and then carrying out the feature extraction of the target awareness data set, the method comprises the following steps: generating spatial distribution characteristics and continuous change characteristics of a target object, inputting the spatial distribution characteristics and the continuous change characteristics into a pre-trained situation awareness model to generate situation association characteristics, and obtaining current situation attributes and situation development tendency information of the target object through a situation classification layer; and finally, according to the current situation attribute and the situation development tendency information, generating a collaborative awareness instruction containing a target positioning identifier, and sending the collaborative awareness instruction to a low-altitude Internet of Things management terminal, thereby comprehensively and accurately sensing the low-altitude target situation, providing an effective collaborative awareness instruction for the low-altitude Internet of Things, and improving the safety and management efficiency of the low-altitude field.

The invention discloses a cross-currency financial transaction real-time settlement and reconciliation system and method. The system is composed of a transaction matching module, a settlement engine module, an intelligent contract module, a reconciliation checking module and a distributed account book module. The transaction matching module completes multi-currency order matching according to a price-time priority rule; the clearing engine module is based on an intelligent contract adjustment algorithm and a plurality of parallel channels; the reconciliation checking module generates a bill abstract for each liquidation result, and constructs a tamper-proof abstract chain; the distributed account book module synchronizes a multi-node balance state through an adjacent weighted aggregation algorithm and verifies abstract consistency; meanwhile, a load balancing and feedback control mechanism is built in, and a clearing path and channel task distribution are dynamically adjusted. According to the invention, second-level matching, high-frequency reconciliation and auditing and tamper-proof accounting management in a multi-institution and multi-currency environment are realized, and an efficient, safe and extensible technical solution is provided for cross-border payment and settlement.

In various examples, data storage is managed using a distributed storage management system that is resilient. Data blocks of a logical block device may be distributed across multiple nodes in a cluster. The logical block device may correspond to a file system volume associated with a file system instance deployed on a selected node within a distributed block layer of a distributed file system. Each data block may have a location in the cluster identified by a block identifier associated with each data block. Each data block may be replicated on at least one other node in the cluster. A metadata object corresponding to a logical block device that maps to the file system volume may be replicated on at least another node in the cluster. Each data block and the metadata object may be hosted on virtualized storage that is protected using redundant array independent disks (RAID).

Methods and apparatus are described for enhanced node communication within a wireless node network having nodes and a server. The method begins with a first node associating with a second node, and the first node capturing relevant node information. The first node transmits the relevant node information to the server via the second node operating as an intermediary for indirect communication with the server. A third node captures relevant node information and transmits the relevant node information to the server without using the second node as an intermediary or bridge to the server. Each of the first node, the second node, and the third node may be wireless transceiver-based nodes and respectively implemented as integrated circuits.

The invention belongs to the technical field of computers, and particularly discloses a data migration method based on a heterogeneous network memory pool and a data center, the method is applied to the data center, the data center comprises a management node and a plurality of cabinets, each cabinet comprises a plurality of nodes, CXL is adopted between the nodes in the same cabinet, RDMA is adopted between the nodes of different cabinets, and the management node is connected with the management node. The method comprises the following steps: monitoring access popularity of a node to a memory page; under the condition that the access popularity of the first node to the target memory page is greater than a popularity threshold value, migrating the target memory page from a second node to which the target memory page belongs to the first node through the management node, and initializing the access popularity of the first node to the target memory page; according to the method and the device, the access times and the access frequency of each cross-cabinet page are tracked, so that the high-heat target memory page with relatively high access times and access frequency can be migrated from the node to which the target memory page belongs to the CXL memory of the node of the cabinet to accelerate access.

The invention discloses a metadata prefetching method and device, a storage medium, electronic equipment and a program, and relates to the field of metadata management of a distributed storage system.The metadata prefetching method comprises the steps that a first information block corresponding to a metadata operation event in a target storage area of each node is obtained; updating the three-dimensional correlation degree between any two metadata in each node according to the first information group; determining a prefetching priority corresponding to any two pieces of metadata through the updated three-dimensional correlation degree; and generating a heat association list of the metadata in each node according to the prefetching priority, and respectively sending the heat association lists corresponding to the plurality of nodes to the client so as to indicate the client to prefetch the target metadata in the plurality of nodes according to the plurality of heat association lists. Through the method and the device, the problem of low pre-fetching accuracy caused by dependence on static path association of the metadata and poor cross-node adaptability of a pre-fetching method for the metadata in a distributed storage system is solved, and the pre-fetching accuracy is improved.

The embodiment of the invention provides an action sequence execution method, electronic equipment, a storage medium and a program product. The method comprises the steps that a global action sequence is generated based on semantic features representing user instructions, and the global action sequence comprises a plurality of node actions with an execution sequence; after executing each node action, performing anomaly identification on each node action to obtain an identification result corresponding to each node action; generating a target action sequence taking the target node action as a starting point under the condition that the identification result indicates that the target node action is abnormal; the target action sequence is used for updating the global action sequence; according to the updated global action sequence, interface elements in the operation interface are processed until the execution target of the user instruction is completed. According to the technical scheme of the embodiment of the invention, the technical problem that the success rate of the automatic execution process is not ideal in the prior art is solved.

The invention provides a trusted execution environment dynamic construction method and system for localized application operation, and the method comprises the steps: collecting mechanical data in real time during the operation of localized industrial equipment, carrying out the irreversible binding of the mechanical data with an equipment unique identifier and a timestamp to form a data set, and storing the data set in a database; after block chain network segmentation, cross validation is carried out among multiple nodes to generate verification information; performing multi-dimensional coupling on the verification information and the real-time operation parameter stream, and extracting associated characteristics of the equipment state and the data credibility; and dynamically constructing a trusted execution environment adaptive to the current security requirement in combination with domestic application operation data. According to the method, the dynamic collaborative guarantee capability of data credibility and execution environment security of the domestic industrial equipment in a complex operation environment is improved.

The invention discloses a supply chain full-process traceability system and method based on a block chain and the Internet of Things, and the system comprises an Internet of Things data collection module which is used for collecting the article state data of each node of a supply chain in real time; the block chain network is composed of a plurality of nodes and is used for storing supply chain data which cannot be tampered and supporting intelligent contract verification; the data preprocessing unit is used for cleaning, encrypting and standardizing the acquired Internet of Things data; the traceability query interface is used for a user to retrieve the full life cycle data of the article through the unique identifier; the consensus mechanism module is used for ensuring data consistency by adopting an improved Byzantine fault-tolerant algorithm; according to the method, the problems of data islands, trust loss and response delay in a traditional supply chain tracing scheme can be solved, the stability, reliability and accuracy of data tracing are improved, the defects in the prior art are overcome, and the method has high application value.

The invention discloses an optimal scheduling method for large model reasoning service and a related device. A plurality of reasoning service instances and a plurality of nodes used for running the reasoning service instances are deployed in the reasoning service of the target large model, firstly, hardware utilization indexes and service utilization indexes of all the reasoning service instances are collected, and collection time and instance IDs are recorded. Then, the indexes are preprocessed, and hardware preprocessing indexes and service preprocessing indexes are obtained; thirdly, multiple model parameters of the target large model and hardware computing power parameters of a target computer are obtained, and the ID, the hardware preprocessing index and the service preprocessing index of each reasoning instance are associated and integrated with the model and the computing power parameters to form an associated data set; and based on the data set, respectively calculating an HRUI, an SPHM and an HRVE of each reasoning service instance, so as to generate a capacity adjustment strategy and a request routing strategy of the target large model based on the HRUI, the SPHM and the HRVE.

A system includes a network node device operable to dynamically connect to different networks and different types of networks based on factors including latency and noise levels in each network. The system allows for low power transmission of data packets as well as obfuscation of transferred data packets by splitting packets for a single file across multiple nodes in a mesh network, where the mesh network then recombines the signals to externally convey the data. The system leverages the use of LoRa communication across one or more networks to communicate critical data, such as location data for particular devices, at long ranges with minimized risk of detection.

According to the embodiment of the invention, the invention provides a workflow creation method and device based on a model, equipment, a storage medium and a program product. The method comprises the following steps: in response to a received workflow editing request sent in a data object, determining a workflow framework corresponding to a workflow by utilizing at least one machine learning model based on the workflow editing request, a current draft of the workflow and information of the data object, the workflow framework indicates corresponding node types of a plurality of nodes included in the workflow and a topological structure of the plurality of nodes, and the current draft comprises a structured representation of a current state of the workflow; determining corresponding configuration information of the plurality of nodes according to the workflow framework; and determining the workflow based on the workflow framework and the corresponding configuration information of the plurality of nodes. The workflow creation process can be implemented using a large model. For example, a large model may be utilized to create a workflow for a database table.

The invention relates to the technical field of information, and discloses a digital right near-field real-time cancel-after-verification system based on edge computing, which comprises a space-time coding module used for generating a unique cancel-after-verification identifier according to the geographic position and time of a user, and transmitting the identifier to a dynamic weight module; a dynamic weight module which is connected with the space-time coding module, generates a right and interest distribution strategy based on a federated learning model and an optimization algorithm, and outputs the optimized strategy to a block chain fragmentation module; the block chain fragment module is connected with the dynamic weight module, dynamically divides a consensus network according to the real-time cancel-after-verification density, executes distributed verification in fragments and generates a cancel-after-verification authorization instruction; and the cancel-after-verification execution module is used for distributing cancel-after-verification tasks according to the authorization instruction and synchronously updating the multi-node inventory state. By adopting the technical scheme of gridding coordinate mapping, time window division and nonlinear hash of the space-time coding module, the technical effect that the verification identifier is unpredictable and uniquely bound is achieved.