175 results about "Central node" patented technology

The invention relates to the technical field of micro-grid energy optimization, in particular to a distributed federal reinforcement learning-based hydrogen-containing micro-grid energy scheduling method, which comprises the steps of constructing a multi-region hydrogen-containing micro-grid system model, designing a state space, an action space and a reward function of an intelligent agent, constructing an Actor-Critic network and an experience pool, and completing environment initialization. The intelligent agent inputs the operation state of the equipment into the Actor network, updates the state of the equipment according to the output action, verifies the constraint and outputs a reward value; tuples are extracted from the experience pool to update local network parameters, and the exploration rate is updated regularly; when a federation interaction period is reached, exchanging Critic network parameters and updating federation parameters; and when the training round arrives, outputting an equipment operation plan, deploying the model to the local hydrogen-containing micro-grid in the island mode, and outputting an equipment output value. According to the scheme, strategy sharing and learning collaboration are realized through neighborhood collaboration and local communication among the regional intelligent agents, so that dependence on a central node is avoided.

The invention is suitable for the technical field of unmanned aerial vehicle cluster cooperative control, and provides an edge calculation distribution system and method for unmanned aerial vehicle cluster cooperative tasks, and the system comprises a multi-heterogeneous neural network module, a local fusion module, a global fusion module, a conflict resolution module, a task distribution module, and a self-adaptive optimization module. According to the invention, by constructing a multi-level semantic feature extraction and fusion architecture and combining an intelligent conflict resolution mechanism and a dynamic task allocation algorithm, efficient intelligent collaborative operation of an unmanned aerial vehicle cluster in a complex and changeable environment is realized, all core algorithms can be executed in a distributed manner in the local of the unmanned aerial vehicle through a distributed edge computing architecture, and the distributed edge computing architecture can be applied to the unmanned aerial vehicle cluster. Dependence on a central node is remarkably reduced, communication overhead and delay are greatly reduced, cooperative work can still be maintained even if part of nodes fail, and perfect balance between robustness and performance is achieved.

The invention discloses a power distribution network short-circuit parameter real-time monitoring method, system and equipment based on edge calculation and a medium, and belongs to the technical field of power system monitoring and protection, and the method comprises the steps: collecting data information in real time at each monitoring node of a power distribution network, generating a state vector reflecting a node operation state, and inputting the state vector to an edge calculation unit, processing through an internal first calculation model to obtain a power grid fault evaluation result of a corresponding node, and compressing and uploading the power grid fault evaluation result to a cloud; based on the uploaded data and the system operation state, a system performance evaluation result is generated in real time, and an adaptive optimization algorithm is used for dynamically adjusting internal model parameters of the edge calculation unit. According to the method, the composite algorithm model fusing feature extraction and parameter calculation is deployed at the edge node, the problems that a traditional centralized processing mode is large in data transmission delay and central node calculation pressure is concentrated are solved, and the technical effect of reducing bandwidth occupation of a backbone communication network is achieved.

The invention discloses a hybrid federal learning modeling method and device for a neurocognitive impairment evolution trajectory, and the method comprises the steps that a child node receives an expert global model distributed by a central node, and the expert global model is determined by the central node from an expert global model group according to metadata pre-registered by the child node; taking the expert global model as an initial model, and performing localization training by using the local multi-modal non-invasive physiological signal data to obtain a trained local model; extracting global sharing parameters in the trained local model, and uploading the global sharing parameters to a central node; and for the corresponding expert global model, the central node aggregates global shared parameters uploaded by all the child nodes training the expert global model to obtain an updated corresponding expert global model, issues the updated expert global model to the child nodes, and circulates until a termination condition is met. According to the method, the heterogeneous data of the child nodes are adapted, privacy is protected, and the adaptability and accuracy of trajectory prediction are improved.

The invention provides an intelligent question answering method and system based on a traditional Chinese medicine classical meta-term engine. The method comprises the following steps: identifying and extracting core traditional Chinese medicine terms and a user query intention in a user question; the core traditional Chinese medicine terms are input into a pre-constructed traditional Chinese medicine classical meta-term engine to be processed, normalized mapping and semantic association extension of the terms are completed, and the engine is constructed based on a classification-term-word list-semantic CTVS association framework. Comprising a traditional Chinese medicine classical book term ancient and modern double-classification system C layer, a meta-term extraction and standardization T layer, a dynamic word list V layer and a semantic association network S layer. Carrying out graph pattern matching and reasoning in the S layer, traversing and integrating a plurality of deep association paths by taking classical element terms and corresponding modern clinical terms as double center nodes, and retrieving answer information associated with the query intention; and carrying out integration sorting and structured assembly on the answer information to generate a structured answer. Intelligent questions and answers can be performed on traditional Chinese medicine related questions of the user.

The invention discloses a hybrid cloud-oriented multi-cluster scheduling method and device and a medium, and relates to the technical field of distributed system resource management. The method comprises the following steps: continuously collecting multi-dimensional state data of a cluster to be scheduled, and encrypting and transmitting the multi-dimensional state data to a central node to obtain a global state view; if the multi-dimensional state data meets a preset triggering condition, analyzing a service strategy associated with the global state view to obtain a structured constraint condition and weight configuration; removing clusters contained in the global state view based on a structured constraint condition to obtain a candidate cluster list; calculating a cost factor, a performance factor, a utilization rate factor and a stability factor of each candidate cluster in the candidate cluster list, and performing weighted calculation with the weight configuration to obtain a comprehensive score of each candidate cluster; and extracting the cluster with the highest comprehensive score as a target cluster, and calling the integrated scheduling executor to perform flow scheduling and resource scheduling on the target cluster.

The invention is suitable for the field of power systems, and provides a power grid attack defense method, terminal equipment and a storage medium. The power grid attack defense method comprises the following steps: an edge node determines local abnormal information and a first response strategy according to local communication data, wherein the local abnormal information represents power grid attack information suffered by the edge node; determining local association information according to the local exception information and shared exception information of the adjacent nodes, wherein the local association information represents an association relationship between the edge node and the plurality of adjacent nodes; sending the local association information to the central node; obtaining traceability information obtained by the central node according to the local association information, wherein the traceability information represents attack source information and attack path information of the power grid attack; and determining a second response strategy according to the traceability information and the local abnormal information. Through the synergistic effect of the edge nodes and the center node, the full-chain identification and multi-point protection capabilities of the power system in a complex attack environment are improved.

The invention relates to the technical field of data processing, and particularly discloses a discrete data collection method and system based on hierarchical aggregation, by constructing a hierarchical aggregation architecture of computing nodes, intermediate aggregation nodes and a central node, the computing nodes cache discrete data to a local pre-write log queue, and the central node stores the discrete data in the local pre-write log queue; when a time window or a data volume threshold value is met, the data are subjected to batch processing compression and then sent to the corresponding intermediate aggregation nodes, the intermediate aggregation nodes decompress the data, aggregate and deduplicate the data and send the aggregated data to the center node through a pre-write log and a check point persistence state, and the center node can achieve non-repeated write-in of the aggregated data; and a back pressure signal can be sent to adjust the transmission rhythm of the intermediate aggregation node, and data collection is ensured to meet accurate primary semantics through cooperation of a transaction ID, a check point and an idempotent interface. The method can eliminate the I / O bottleneck of the central node, improves the collection efficiency and data reliability, enhances the fault-tolerant capability, and is suitable for discrete data collection of a large-scale distributed system.

The invention relates to the technical field of knowledge graph vectorization, and discloses a medical field-oriented knowledge graph vectorization representation method, which comprises the following steps of: firstly, constructing a multiple heterogeneous topological view based on a heterogeneous knowledge graph, and generating a detection parameter set covering a global fusion space so as to construct joint observation data corresponding to different distribution modes; then establishing a joint reconstruction task containing a public potential semantic base, and promoting a public base matrix to aggregate global structure information of all observation data by using an iterative algorithm; and finally, analyzing the converged model to extract a center node vector and generate an index.

The invention relates to a federated learning-based privacy protection personalized recommendation system, which comprises a user side, an edge side and a cloud side, and is characterized in that the user side is a basic unit for data generation and local training, each user maintains a personalized recommendation model on own local equipment, the edge side is used as an intermediate aggregation layer, and the cloud side is used as a cloud side; the cloud end is used for preliminarily aggregating local models of users in a certain geographic area or logic area, the edge end is generally deployed in an edge server or an area data center, and the cloud end is a central node for updating and distributing a global model and is used for secondarily aggregating area aggregation models uploaded by the edge ends to form a global recommendation model; according to the scheme, user privacy is strictly protected, and collaborative recommendation is realized under the condition of not transmitting original user data through a differential privacy and security aggregation dual protection mechanism.

The invention discloses a distributed training method for a new energy cluster power prediction model. The method comprises the following steps: collecting time sequence data of each power station in a target range, and carrying out abnormal data detection, deletion and missing data filling operation; constructing a basic structure of a power prediction model corresponding to the edge nodes, the regional coordination nodes and the global center node in the target range, and initializing model parameters; performing regional adaptation fine tuning on training data of the edge nodes, performing iterative training on a power prediction model of the edge nodes to obtain local model parameters of each edge node, performing regional aggregation and global aggregation on the local model parameters of each edge node to obtain a power prediction model of each edge node; obtaining model parameters of power prediction models corresponding to the regional coordination nodes and the global center node; and carrying out convergence judgment on the model parameters corresponding to the global center node, if the model parameters converge, stopping iterative training, and if the model parameters do not converge, executing next iterative training, so that engineering practicability and expandability are considered.

The invention discloses an intelligent factory data monitoring method and system based on distributed collaborative sampling, belongs to the field of intelligent factory data monitoring, and aims to solve the problems of real-time monitoring, quality control, equipment maintenance and production optimization in the production process. The system is composed of a data acquisition module, a production control module, a local processing module, a data center module and a user interaction module. And efficient data processing and storage are realized through the distributed collaborative sampling network, and the expandability and flexibility of the system are optimized. The production management and control module is responsible for distribution and sampling detection of production tasks, ensures product quality and realizes production continuity when equipment is abnormal. The local processing module analyzes equipment data, establishes a prediction model, and timely finds and early warns deviation in equipment and production. The data center module analyzes early warning production data in real time, transmits an early warning signal through a central node, and drives a production line to operate. And the user interaction module provides dynamic information updating, so that a user can obtain and feed back data in real time.

The application discloses a sample data labeling system, a labeling method applied to the sample data labeling system and related devices. The sample data labeling system comprises an edge node and a center node. The edge node acquires key features of sample data, judges whether the sample data is unknown sample data according to the key features, performs labeling processing on the sample data when the sample data is unknown sample data, obtains a first labeling result, and sends the first labeling result to the center node. The center node receives the first labeling result, performs consistency processing on the first labeling result when the first labeling result indicates that the unknown sample data is successfully labeled, obtains a second labeling result, and performs labeling processing on the unknown sample data when the first labeling result indicates that the unknown sample data fails to be labeled, and obtains a third labeling result.

The invention belongs to the technical field of ocean monitoring, and particularly relates to a wide-area target monitoring intelligent buoy system which comprises a water surface part, a floating body part and an underwater part. The water surface part is integrated with a visible light vision acquisition module with a rotating mechanism, a sea surface environment monitoring module, an antenna module, a positioning module, a remote sensing receiving module, a communication module and the like; the floating body part comprises a floating body block, and an embedded cluster intelligent processing module, a data storage module and a power supply module which are arranged in the floating body block and communicate with one another; the underwater part comprises an underwater acoustic sensing module and an underwater magnetic sensing module. According to the invention, through integration of a plurality of sensors, water surface and underwater integrated cooperative detection is realized; the embedded cluster intelligent processing module adopts a cluster architecture without a central node, has the capabilities of task dynamic allocation and fault seamless switching, and greatly improves the comprehensive detection performance, the intelligent decision-making level and the long-term stability in a marine environment of the system.

This invention discloses a method for automatic switching and resource coordination of a system in daily and extreme scenarios, relating to the field of intelligent Internet of Things (IoT) communication technology. Specifically, it includes setting up a probe program at an edge node and initializing its first state to "off," setting up a central node and initializing its first state to "off," collecting data, loading rules to process the data, generating first data and transmitting it to the central node, the central node judging the first data and issuing a state switching command to each edge node, switching the central node's first state to "on," the edge nodes receiving the state switching command switching their first state to "on," loading new preset rules to process the data, generating second data and transmitting it to the central node, the central node obtaining the first and second data from the edge nodes and judging them; if the judgment result meets preset conditions, it issues a device operation command to the edge nodes.

The application provides a multi-terminal data privacy collaborative processing method and system based on a central networking node, relates to the technical field of data processing, and comprises the following steps: deploying a plurality of heterogeneous privacy computing nodes designed by different privacy computing protocols, network communication protocols and upper-layer applications; setting a central coordination node for the demand of interconnection and intercommunication of the heterogeneous privacy computing nodes, wherein the central coordination node has protocol compatibility; enabling each heterogeneous privacy computing node to be connected with the central node once through the protocol compatibility of the central node, and establishing indirect interconnection and intercommunication channels between all the privacy computing nodes through the central node. The application realizes the collaborative processing of calculation localization and management centralization.

An enterprise ESG rating task-oriented double-path graph representation learning method is provided in the embodiment of the application, and belongs to the technical field of data processing, and specifically comprises the following steps: step 1, constructing an ESG heterogeneous graph atlas with a target enterprise as a center node, and extracting a target subgraph of multiple types of nodes and their corresponding relationships from the k-hop neighborhood thereof; step 2, dynamically selecting a structure enhancement mode for the target subgraph based on a learnable enhancement strategy, and generating multiple enhanced views with different structures but consistent semantics; step 3, performing semantic smoothing and structure denoising processing on each enhanced view to improve semantic consistency and structure purity; step 4, aggregating multiple denoised enhanced views to form a unified enterprise graph representation vector; and step 5, inputting the enterprise graph representation vector into a classification model to predict the enterprise ESG rating or risk category. Through the scheme of the application, the rating accuracy and adaptability are improved.

A canopy with a detachable top rib structure includes support columns and a first canopy positioned atop the support columns. The first canopy includes a frame and several first support rods, one end of which is fixed to the frame, and the other ends converge at the central node at the top of the first canopy. Each first support rod includes a first tube, a second tube, and a connecting sleeve. The top of the first tube is inserted into the connecting sleeve, and the bottom of the second tube is inserted into the connecting sleeve. This detachable top rib structure allows the shorter first and second tubes to be fixedly joined together to form a longer first support rod via the connecting sleeve. The connecting sleeve ensures strong stability of the connection between the first and second tubes, simplifies the connection method, and facilitates installation and disassembly. The disassembled shorter tubes are easy to store and transport. Furthermore, the first support rod has an aesthetically pleasing design.

The application relates to the technical field of computing power allocation, and provides a computing power allocation method and system supported by an edge collaboration of a cloud computing platform, which comprises an architecture of deploying a complete dialogue model by a center node, carrying a light node model by an edge node, and configuring a semantic recognition module by a local node, the local node generates a conversation constraint set through a semantic recognition and semantic compression algorithm, long conversation model omission of user early demands is avoided, and dialogue accuracy is improved. In addition, a semantic complexity coefficient is obtained by analyzing the constraint set, the edge node collects self-computing power related parameters to evaluate node states, divides computing power rich and tight nodes, the computing power tight node processes low complexity tasks according to the self-computing power margin, the rest of the tasks are scheduled to the center node or adjacent computing power rich nodes, the tasks are distributed according to the complexity coefficient, the edge node computing power is fully utilized, and the overall computing power utilization efficiency is improved.

A kind of data chain intelligent anti-interference decision method of intention situation double drive includes the following steps;Step one: the message requirement of task intention translation is received by each node of data chain, and the multi-dimensional resource situation information related to task node is called from situation database by the center node of subtask execution area;Step two: establish multi-objective optimization decision model, and set weight for multiple sub-objective functions according to intention requirement;Step three: based on intention requirement and multi-dimensional resource situation information, existing anti-interference strategy in strategy library is quickly matched, if it does not exist, then call intelligent algorithm to solve multi-objective optimization decision model as needed, and obtain each parameter decision result;Step four: each parameter decision result is verified, when strategy is feasible, anti-interference decision result is updated to strategy library, and is issued to each task node, and the anti-interference strategy is executed by task node.The present application improves transmission capacity in harsh environment, and intelligently resists complex unknown interference threat.

The application discloses a talent digital representation method and device based on semantic enhancement, and the method comprises the following steps: acquiring comprehensive environment information of neighbor nodes from a talent information database, and inputting the comprehensive environment information and triplets into a semantic enhancement intelligent agent to generate a semantic enhancement text with a fusion organization background, and then encoding the semantic enhancement text into an enhanced embedding vector by using a large language model, and inputting an initial embedding vector of a center node and the enhanced embedding vector of the neighbor nodes into a potential energy aggregation algorithm to obtain talent digital representation; and the application can obtain talent representation information which accurately reflects the real potential energy and situational value of talents in a business organization.

The invention discloses a multi-mode starting automatic control method and system based on a PC Farm array server, and relates to the technical field of server cluster management.The method comprises the steps that a central node creates a fine-grained strategy instruction and a corresponding mirror image instruction according to deployment requirements, and the fine-grained strategy instruction and the corresponding mirror image instruction are compressed into a mirror image compressed package through LZMA; the edge node extracts the verification label from the mirror image compressed package, analyzes the verification label through quantum error correction control, repairs a bit flipping error, and generates an anti-interference standardized instruction stream; based on the anti-interference standardized instruction stream, generating a quantum code stream data block through Hamming code coding, writing the quantum code stream data block into a configuration interface address, and correcting signal deviation to trigger a reset command; and executing the reset command, collecting a BIOS startup log, and comparing the BIOS startup log with pre-stored features to generate a startup state verification report. According to the method, accurate calibration and sequential control of hardware configuration are realized, the accuracy of starting parameters is ensured, and the high efficiency and stability of automatic deployment are improved.

A method is provided for training a machine learning ML model for monitoring people based on a data feed capable of describing people. The method is performed by a training data provider (i). The method includes: obtaining (40) a data feed capable of describing people; selecting (42) anonymization levels from a plurality of anonymization levels; anonymizing (44) the data feed according to the selected anonymization level to obtain a processed data feed; and sending (47) the processed data feed as training data for training a central ML model in a central node. Different anonymization levels are available and changes to the levels can be requested by the central node.

The invention relates to an intelligent home control method and system, intelligent equipment and a medium, and belongs to the technical field of artificial intelligence, and the method comprises the steps: generating a unique identifier based on own hardware behavior characteristics, carrying out the cross verification of environment data of peripheral intelligent equipment which is accessed to the network, autonomously establishing a trust relationship, and building a dynamic network without a central node; collecting user behavior data, environment characteristics and equipment capability data; processing the user behavior data, the environment characteristics and the equipment capability data to obtain scene data fragments; autonomous evolution of scene data is realized through distributed collaborative learning, and intelligent control logic adaptive to a current scene is formed; and responding to an instruction sent by a user, cooperatively verifying the intention of the user with other intelligent equipment, and matching the corresponding scene data fragment to execute a control operation. The stability of the smart home is improved.

The application relates to the technical field of metamaterials, and proposes a metamaterial unit based on a fourteen-direction radiation symmetrical branch and an array structure of the metamaterial unit. The unit comprises a center node and fourteen branches extending outward from the center node; the fourteen branches are in a spatial full-symmetrical distribution, wherein six axial branches extend along the positive direction of an X-axis, the negative direction of the X-axis, the positive direction of a Y-axis, the negative direction of the Y-axis, the positive direction of a Z-axis and the negative direction of the Z-axis of a spatial rectangular coordinate system respectively, eight diagonal branches extend along directions which are equal acute angles with the X-axis, the Y-axis and the Z-axis and point to the body diagonal direction of eight spatial octants respectively, the lengths of all the branches extending outward from the center node are equal, and all the branches have the same cross-sectional size. A plurality of metamaterial units are arranged periodically in two dimensions or three dimensions to form a metamaterial array, which is used for constructing a lens, a focusing panel or a radome. The metamaterial unit has excellent wide-band stability and consistency in performance under any polarization direction.

The invention discloses a multi-center hierarchical management architecture unmanned cluster management method and system based on a minimum diameter spanning tree, and the method comprises the steps: collecting communication delay information among all nodes, and constructing a completely undirected graph which takes the nodes in an unmanned aerial vehicle group as vertexes and the communication delay among the nodes as edge weights; sorting the edges of the completely undirected graph from small to large according to the edge weights, and determining an optimal tree generation interval meeting the constraint of the multi-center hierarchical management architecture based on a bipartite enumeration method; solving a minimum diameter spanning tree of the sub-graph according to the sub-graph corresponding to the optimal spanning tree interval, and taking the minimum diameter spanning tree as the optimal spanning tree meeting the multi-center hierarchical management architecture; according to the degree of each node in the optimal generation tree and a central node of a diameter link, roles are distributed to each node; and when the inter-node communication index does not meet the preset threshold value, triggering regrouping by the master node. The method provided by the invention can still keep low delay and expandability in a complex scene.

The present specification provides a drilling risk determination method, device and electronic equipment based on federal learning, belonging to the technical field of drilling construction. Before implementation, multiple node terminals and a central node server deployed in multiple different oil and gas regions cooperate to perform a preset federal learning based on oil and gas region data privacy protection according to a preset joint training rule, on the premise that sample data does not leave the local oil and gas region, and obtain a global risk prediction model by performing a preset asynchronous update every interval preset time period combined with information freshness. In the specific implementation, the reference data of the drilled well in the first oil and gas region, the associated data of the target well, and the current global risk prediction model in the current time period are obtained; and the current global risk prediction model is matched and adjusted by using the reference data of the drilled well; and whether the target well currently exists a drilling risk is determined by processing the associated data of the target well by using the adjusted current first risk prediction model.

The invention relates to a cooperative communication method between nodes of an automatic driving system, electronic equipment and a storage medium. The method comprises the following steps: a first automatic driving system node broadcasts a service declaration message to an automatic driving communication network; the second automatic driving system node determines whether a communication intention exists according to the service identifier; a third automatic driving system node which determines that the communication intention exists in the second automatic driving system nodes sends a service connection establishment request to the first automatic driving system node; the first automatic driving system node determines whether the first automatic driving system node and the third automatic driving system node are compatible or not; and if so, carrying out resource allocation and establishing communication connection. According to the scheme provided by the invention, the communication relationship between the nodes of the automatic driving system can be established as required, and discovery of services and establishment of communication connection do not depend on a central node any more, so that the single-point fault risk of the central node is eliminated, and normal operation of the automatic driving system is guaranteed through reliable communication.

This disclosure relates to a task scheduling method and apparatus, and pertains to the field of computer task scheduling technology. The method includes: when selecting a target central node for a task to be scheduled, considering the load values ​​of at least two candidate central nodes, where the load values ​​reflect the resource load of the candidate central nodes, thereby enabling the target central node to execute the task to be scheduled in a timely manner and shortening the queuing time of the task; further considering the matching value between the task to be scheduled and at least two candidate central nodes, where the matching value reflects whether there are sufficient remaining resources on the candidate central nodes to execute the task to be scheduled, thereby improving the execution efficiency of the task to be scheduled; additionally, the running status of each task and candidate central node can be monitored in real time, and tasks can be migrated in a timely manner when task execution progress is abnormal, candidate central nodes are overloaded, or are in a faulty state.

The invention belongs to the technical field of data processing, and relates to a service management method and device, computer equipment and a storage medium, and the method comprises the steps: judging whether a target service instance is successfully started or not when a starting signal of the target service instance is monitored based on a target registration tool; if yes, generating a service online message based on a message ID corresponding to the target service instance, the broadcast state information and the authentication information; sending the service online message to other service instances in the cluster; judging whether the target service instance meets a formal online condition or not based on the monitored response data of other service instances to the service online message; and if yes, generating a formal online message of the target service instance, and sending the formal online message to other service instances in the cluster. In addition, service online messages may be stored in the blockchain. The method and the device can be applied to a service management scene in the field of financial science and technology, the problem of system paralysis caused by a fault of a central node in an existing centralized architecture is avoided, and the system stability is improved.