1046 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.

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 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 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 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 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.

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 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 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 cooperative control, in particular to a multi-compartment NVR (Network Video Recorder) state reporting and cooperative control system based on multicast communication, which comprises a state sensing aggregation module, a communication load evaluation module, a cache scheduling release module, a synchronous boundary correction module and a cooperative control configuration module. According to the invention, the node group in the frequently activated state can be identified through the normalization quantization processing of the starting frequency of the video channel in the carriage, the reading of the temperature sensor and the writing rate of the hard disk, and the load evaluation is carried out by combining the message data volume and the channel transmission capability of the node in the historical multicast period. A conflict early warning mark can be formed before a state conflict occurs, correction of time migration among multiple nodes is completed based on comparison of a node receiving timestamp difference value and a clock synchronization tolerance, a control instruction set for time alignment is constructed based on a unified time correction result, a trigger control task is broadcast and executed in a time window packaging mode, and the time correction efficiency is improved. And the communication resource use efficiency is improved.

The invention relates to the technical field of communication, in particular to a satellite communication anti-interference transmission method based on a frequency hopping and spread spectrum fusion algorithm. According to the method, interference power is collected at multiple nodes, multiple sub-carrier frequencies and multiple moments to construct an interference observation tensor, interference features are extracted through low-rank tensor decomposition, and interference of each sub-carrier frequency in a future frequency hopping time slot is predicted; calculating a signal to interference plus noise ratio and capacity according to predicted interference in combination with transmitting power and noise, constructing a utility function containing a service weight and a balance coefficient, and distributing working sub-carrier frequency to each satellite communication node under concurrency constraint to generate a frequency hopping sequence; a transmitting end performs channel coding, modulation and spectrum spreading on service data and transmits the service data according to a frequency hopping sequence, and a receiving end completes synchronization, de-spreading and decoding and performs weighting processing by utilizing predicted interference. The method improves the spectrum utilization rate and the interference suppression capability, and enhances the satellite communication reliability.

Systems and apparatus are described for enhanced communication within a wireless node network. A system includes a server and multiple nodes, where each node can generate sensed data about or around the node and receive external data from another node according to a server tracked logical connection between those nodes. At least a subset of the nodes can connect to the server using a first connectivity mode or an intermediary connectivity mode. In the first connectivity mode, a first node of the subset can transmit a message directly to the server without using intermediary ones of the nodes (where the message includes sensed data about or around the first node). In the intermediary connectivity mode, the first node can receive the external data from the another node and transmit the external data to the server (where the external data is sensed data about or around the another node).

Methods and apparatus are described for enhanced node communication within a wireless node network having nodes and a server. A first subset of nodes transmits first data messages to the server without using an intermediary device where the first data messages include sensed data generated by the nodes of the first subset of the plurality of nodes about or around the nodes of the first subset of the plurality of nodes. A second subset of the plurality of nodes receives external data from the other of the nodes. The second subset of the plurality of nodes are intermediary devices that transmit the received external data to the server, wherein the received external data from the other of the nodes comprises sensed data about or around the other of the nodes.

The invention discloses a communication method and system of a wireless ad hoc network, and relates to the technical field of wireless ad hoc networks. The method is applied to a data link layer of the wireless ad hoc network, the wireless ad hoc network comprises a plurality of nodes, each node communicates according to a time frame period, each node comprises an interception mode and a sending mode, and the method comprises the following steps: in the current time frame period, each node executes the following steps: receiving data in the interception mode, and sending the received data in the sending mode; updating a node neighbor table based on the received data; judging whether data sending time is up or not according to the information of each neighbor node in the node neighbor table; and switching to the sending mode to send the data under the condition of determining that the data sending time is up, and switching to the interception mode after the data is successfully sent. The whole wireless ad hoc network does not need complex interactive protocols such as time slot distribution, time slot recovery and the like, so that the decentralized wireless ad hoc network in a real sense is realized.

The invention belongs to the technical field of data processing, and provides a data management optimization method and system. The method comprises the following steps: based on an acquisition strategy, acquiring log and business data of each child node, and calculating a data acquisition success rate and a data acquisition delay evaluation value to measure an acquisition condition; transmitting the collected logs and service data of each child node to a central node, carrying out multi-class data processing, and calculating and determining a quantitative index of a data processing delay condition so as to optimize a data processing process; monitoring the data after processing the multiple types of data in real time, identifying a risk event by using a self-constructed risk event identification model, and calculating a risk score of the risk event, including analyzing and calculating a risk monitoring result so as to update a monitoring rule and a feature library of the risk event identification model; and when the calculated risk score exceeds a preset threshold value, an alarm mechanism is triggered, and alarm information is pushed to safety management personnel of the related child nodes. According to the invention, the multi-node data acquisition and data processing process is effectively optimized.

A system and method for data compaction and encryption of anonymized data records. A dataset may be pre-processed by dividing into sourceblocks at reasonable intervals and tallying each sourceblock's frequency, creating a tally record of tokens and count values. This tally record may then be anonymized and transmitted to a data deconstruction engine which combined with a library manager creates a codebook and performs optimization techniques on the codebook. The data deconstruction engine and library manager may be distributed across multiple nodes or devices. The received anonymized tally record may be parsed into individual tokens by identifying the tokens with the highest count value. The tokens may then be sent descending order of count value to the library manger where each token may be assigned a codeword. A half-backed codebook is then created using the tokens and each token's unique codeword, before sending the half-backed codebook to a system user.

The present application relates to the field of information retrieval, and in particular relates to a data processing method, an electronic device, a readable storage medium, and a program product. In the method, when a query result of a query vector needs to be searched in a constructed graph index, neighbor nodes of at least some nodes among multiple nodes included in the graph index satisfy at least one of the following: the included angle between two vectors obtained by subtracting a node from any two neighbor nodes of the node is greater than or equal to a first angle threshold value, the distance between any two neighbor nodes of the node is greater than or equal to a first distance threshold value, and the number of neighbor nodes of the node within a preset space range is smaller than a first number. Thus, the neighbor nodes of the nodes in the constructed graph index are relatively dispersed in space, and the problem that a query result falls into a local optimum due to being unable to find nodes at a certain portion of space during a node-based neighbor node search process, because the nodes are densely distributed at a certain position, is avoided.

The invention relates to the technical field of data security, and particularly discloses a copyright data security management method and management system based on a block chain, and the method comprises the following steps: constructing a block chain network comprising a plurality of sub-chains, each sub-chain comprising a plurality of nodes; when it is judged that the proposal i meets the change condition, the management mechanism node submits the proposal i to a public proposal pool, and after the content of the proposal i is subjected to cross validation through the author node and the publisher node, voting weights are distributed to the nodes according to historical behavior data and types of the nodes; and calculating voting scores in real time based on the voting weights and the voting conditions of all the nodes, obtaining a voting result according to the voting scores, and if the voting result is passing, triggering the intelligent contract by the management mechanism node to automatically execute the proposal i. According to the method, copyright confirmation, voting decision making and rule upgrading can be collaboratively completed under rapid and safe conditions.

The embodiment of the invention provides an emergency rescue wireless communication network topology prediction method based on deep learning. According to the method, an emergency rescue wireless communication network system is constructed, initial system parameters are generated, and the emergency rescue wireless communication network system comprises a plurality of nodes; acquiring node positions, environment information and inter-node signal strength in real time, and updating node states in the emergency rescue wireless communication network system according to the node positions, the environment information and the inter-node signal strength; the node state of the current time step is input into the trained prediction model to be analyzed and processed, a prediction result is output, the prediction model is composed of a graph convolutional network and a generative adversarial network, and the prediction result is used for indicating the topology connection condition of the emergency rescue wireless communication network in the next time step; and determining a corresponding emergency rescue strategy in the target rescue area according to the prediction result. According to the method, the reliability of a communication network is improved, and efficient and orderly development of rescue actions is guaranteed.

The invention discloses a radio detection sensitivity improving method and system based on AI operation. The method comprises the following steps: acquiring an original weak signal of a measurement node, and carrying out noise reduction, feature extraction and data encapsulation to generate a processed weak signal; processing signals synchronously uploaded by multiple nodes in the same time period are sorted into a signal feature set; constructing a map neural network node space based on the geographic position of the measurement node and the detection map, and inputting the signal feature set into the space to form a joint signal feature; performing signal reconstruction on the joint features through a signal reconstruction model constructed by a multi-scale memory network, wherein the model is trained by adopting a small sample-adversarial mode; when it is detected that the joint feature anomaly ratio exceeds a threshold value, the adversarial sample is updated, and model retraining is triggered. According to the method, high-precision reconstruction and abnormal self-adaptive optimization of weak signals are realized through multi-node spatial-temporal feature fusion and a dynamic adversarial training mechanism.

The invention discloses a multi-party supply chain sensing data encryption measurement and traceability method based on space-time coupling watermark embedding, which comprises the following steps of: constructing a double-domain coupling model of a time sequence and a space distribution characteristic, and embedding dynamic watermark information into a sensing data stream to realize data source identity identification and tamper-proof protection. A self-adaptive key generation and credible chain verification mechanism is introduced in an encryption metering stage, so that the data integrity and confidentiality can be guaranteed on the premise of not influencing the real-time transmission performance of the data; in the tracing stage, a data transmission path is reconstructed by using the watermark tracing matrix, and data responsibility definition and anomaly detection among multiple nodes are realized. The method is suitable for high-security data management and credible tracking in a supply chain multi-node collaborative environment.

The invention belongs to the technical field of order fulfillment process monitoring, and discloses an order fulfillment process delay early warning method and system based on multi-node state acquisition, and the method comprises the steps: collecting the dynamic state data of multiple nodes in a fulfillment process in real time; matching judgment is carried out through the node rule base, primary early warning is triggered, and an early warning context data set is constructed; inputting the data set into a pre-trained delay risk prediction model, and outputting an overall risk probability and a key influence node identifier; issuing a grading early warning signal according to the risk probability; carrying out interpretable root cause positioning through a root cause analysis rule chain based on directed acyclic graph representation; incremental learning is carried out on the prediction model based on early warning feedback data to form an optimized closed loop; the system correspondingly comprises a data acquisition module, a rule judgment module, a risk prediction module, an early warning release module, a root cause positioning module, a feedback learning module and the like. According to the invention, the early-stage, accurate and automatic early warning and root cause analysis of the full-process risk of order fulfillment are realized.

The invention relates to the technical field of bridge pier load monitoring, in particular to a bridge pier load monitoring method and system in bridge construction, and the method comprises the following steps: based on a bridge pier foundation structure, building the correlation among multiple nodes through calculating the distance among the nodes, the physical characteristics and the mechanical characteristics; according to the method, the bridge pier space structure diagram is established, node load characteristics are updated through the diagram convolution operation, the monitoring precision of bridge pier bearing load changes is remarkably improved, combined modeling of time sequence data and space characteristic data is combined, the space-time law of the load changes can be recognized, the construction scheme is optimized, and the construction efficiency is improved. The layout and configuration of construction equipment are dynamically adjusted, the dynamic adjustment of the adaptive convolution kernel optimizes feature aggregation according to the features of the support structure, the response capability to load change is improved, the safety and high efficiency in the construction process are ensured, the method provides accurate real-time monitoring and dynamic optimization, and the construction efficiency is improved. And monitoring delay and errors in a traditional method are avoided.

The invention relates to the technical field of network security, in particular to a network security situation awareness method based on artificial intelligence, which comprises the following steps: deploying probes on a plurality of key link nodes of a network, and acquiring a specific type of network basic maintenance message flowing through the node; based on the obtained message, microcosmic time sequence characteristics of the message are extracted, and a first time sequence signal is formed; comparing the first time sequence signal with a pre-stored node reference time sequence signal, executing collaborative deviation calculation, and generating a collaborative deviation coefficient; and based on the collaborative deviation coefficient, determining a local vibration intensity value of the node, converging local vibration intensity values of a plurality of nodes in the network, and executing spatial correlation fusion calculation. According to the method, anomaly detection is realized by analyzing the time sequence coordination rule of the network basic maintenance message, any abnormal condition damaging a normal coordination mode can be captured, and the recognition rate of unknown threats is improved.