412 results about "Data node" patented technology

The invention discloses a multi-dimensional time series data dynamic evolution analysis method and device, a medium and electronic equipment. The method comprises the steps that time series data are obtained and subjected to structured preprocessing; performing multi-dimensional quantization and attribute endowing on each piece of time series data, and constructing a three-dimensional data node containing a space coordinate, an initial weight and a timestamp; based on the set of the three-dimensional data nodes in a specific time window, constructing a three-dimensional data density field capable of representing data hotspot distribution and intensity in the time window; in the three-dimensional data density field, identifying a density peak region as a core data cluster, calculating the cluster mass according to the density and the coverage range of the core data cluster, and calculating the association strength among different core data clusters; and serializing a plurality of continuous time window data field state snapshots to form a dynamic evolution view capable of showing data hot spot generation and extinguishing and data cluster fusion and splitting processes. According to the invention, abstract multi-dimensional data is converted into a visual three-dimensional density field.

The invention discloses a carbon emission abnormity traceability method and device, a medium and equipment, and belongs to the field of carbon emission, and the method comprises the steps: receiving a traceability request containing a to-be-verified data identifier, positioning target link data from a block chain storage, and executing signature verification and Hash consistency verification, thereby guaranteeing the authenticity and consistency of the data. And if the verification of the target link data is passed, verifying the inclusion relation of Merkle tree root hash of each link upwards layer by layer to confirm the integrity of the full-chain data. And if verification failure of any level is found, the feature data of the level is combined with an anomaly detection model based on an isolated forest algorithm, and an abnormal data node is positioned. And then, according to the signature information of the abnormal data node and the preset tracking secret key, a group signature verification and private key derivation process is applied, the responsibility participant is traced, and the tracing process of the carbon emission abnormity is completed, so that the problem that the carbon emission abnormal point cannot be accurately and efficiently traced in the prior art is effectively solved.

The invention relates to the field of distributed storage, and discloses a distributed memory system based on erasure codes and a dynamic repair mechanism. Comprising a data node cluster, a metadata management cluster, a bandwidth token management module, a dynamic repair composer and a repair agent node. And the data node cluster divides the object into strip generation data fragments and verification fragments and dispersedly stores the data fragments and the verification fragments according to a placement rule. And the metadata management cluster maintains object stripe mapping, a fragment position set, erasure code protection configuration and a version epoch, and records a repair intention log. And the bandwidth token management module grants repair execution permission according to the fault domain budget. The dynamic repair composer generates availability repair and durability repair tasks, the temporary check fragments are written in when the number of the available check fragments is lower than a threshold value in the availability stage, and the configuration fragments are recovered and the temporary fragments are recovered in the durability stage. And the consistency of version epochs is verified before the agent node is repaired and written back, and if not, recalculation is carried out.

The invention relates to the technical field of electric data processing, in particular to a data blood relationship automatic tracking method and system based on a table in AI data, and the method comprises the steps: obtaining metadata in the table in the AI data, and analyzing a task log of a calculation engine to construct an initial directed acyclic graph; the initial directed acyclic graph comprises data nodes and explicit blood edges connected with the data nodes; and calculating a flow entropy coupling index between any two data nodes. According to the method, a flow entropy coupling index based on update time correlation and logic evolution divergence based on key entity distribution similarity are constructed, a graph comparison learning model is combined, under the condition that an explicit SQL grammatical structure is lacked, the implicit dependency of upstream and downstream nodes is automatically identified, and the flow entropy coupling index and the logic evolution divergence are combined. And end-to-end full-link data consanguinity complete tracking in a complex AI feature engineering scene is successfully realized.

The invention relates to a smart power grid space-time scheduling method based on a self-adaptive dynamic graph neural network, which comprises the following steps of: collecting time sequence data from distributed data nodes of a smart power grid in real time, and preprocessing the data; self-generating an adaptive adjacency matrix, and adjusting an edge weight in the adjacency matrix according to the change of real-time data by a self-adaptive algorithm; performing spatio-temporal feature extraction on the generated adaptive adjacency matrix through a spatio-temporal convolutional network STCN, capturing a complex spatio-temporal dependency relationship between power grid nodes, obtaining a feature graph containing a spatial and temporal dependency relationship, and representing the operating states and mutual dependency conditions of different time nodes of the power grid by the features; real-time scheduling optimization: inputting the extracted spatial-temporal characteristics into a scheduling optimization module, and calculating the optimal matching between the load demand and the power generation capacity; the node state is continuously monitored in the scheduling process, and the scheduling strategy is dynamically adjusted. Distribution of electric power resources is dynamically adjusted, and it is ensured that electric power supply of the load center meets requirements.

The invention discloses a distributed cooperative processing method, system and equipment for mass spectrum data analysis, and relates to the field of biological information distributed processing, and the method comprises the steps: receiving original mass spectrum data submitted by a user, and analyzing flow configuration and global parameter configuration; analyzing the analysis process configuration into a directed acyclic graph taking the mass spectrum data processing step as a node; splitting the directed acyclic graph into a plurality of parallel atomic tasks according to the quantity of the original mass spectrum data and the space occupation amount; each atomic task is allocated to a Worker node storing corresponding associated data, and the atomic tasks are executed in parallel; when it is detected that the atomic tasks fail to be executed, the Worker nodes are distributed to the atomic tasks which fail to be executed again; different data storage systems are adopted to store original mass spectrum data, intermediate results and final analysis results. According to the invention, efficient parallel processing of mass spectrum data is realized, and continuity and reliability of an analysis process are guaranteed.

The invention discloses an underwater sensor data collection and energy supplement method, system and device and a medium. The method comprises the following steps: acquiring initial residual energy information and initial operation mode information of an underwater sensor and sending the initial residual energy information and the initial operation mode information to an autonomous underwater vehicle; according to the initial residual energy information, the autonomous underwater vehicle judges the emergency degree of the energy supplement demand of the underwater sensor, and divides the underwater sensor into a charging node and a data node; according to the fault risk index of the charging node, the cruise energy consumption index of the underwater autonomous vehicle and the loss risk index, dynamically calculating the charging cost of the charging node, and controlling the underwater autonomous vehicle to go to the charging node with the lowest charging cost to execute wireless charging operation; in the wireless charging process, data collection of the target charging node is completed at the same time; and within the remaining charging time, performing a data collection only operation on the data node. According to the invention, data collection and energy supplementation of the sensor are realized through the autonomous underwater vehicle.

The invention provides a self-adaptive data consistency maintenance method for an electric power information system based on incremental updating, and relates to the technical field of big data processing, and the method comprises an initialization step, a detection step and a feedback step. In the initialization step, a network including regional power data nodes and a distributed account book is constructed, and full power data is stored; in the detection step, power data changes are confirmed, key attributes are used for marking data, automatic conflict detection is carried out through a distributed consensus algorithm, solutions for coping with conflicts are dynamically selected, and then an incremental updating mechanism is adopted for synchronously changing the data; in the feedback step, a monitoring system with a closed-loop feedback mechanism is established, the data consistency state is automatically tracked when the electric power data is changed, and abnormity is recognized. Compared with a traditional data synchronization method which needs to synchronize full data, the method provided by the invention only synchronizes the changed data fragments through an incremental updating mechanism, thereby ensuring the consistency of the data, remarkably improving the data processing efficiency and saving the network bandwidth.

The application is suitable for the technical field of data storage, and provides a data disaster recovery method and device, comprising the following steps: determining a fault type in a distributed cluster system and a range of data to be recovered; acquiring state parameters of multiple normal nodes in the distributed cluster system, and service sensitivity features and data block access features of the data to be recovered; performing data preprocessing operations; respectively adopting a service sensitivity evaluation model and a node state evaluation model to determine a data recovery priority list and a node priority list; determining a fault processing strategy according to the data recovery priority list and the node priority list; and executing the fault processing strategy. Through the above method, the real-time evaluation algorithm of the heterogeneous big data node is increased on the basis of dynamically updating the data recovery priority based on the service sensitivity evaluation model, and compared with the traditional technology, the fault perception of the business system can be reduced while the data disaster recovery efficiency is improved.

The invention discloses a medical data security sharing method and device and a medium, and relates to the technical field of data security sharing, and the method comprises the steps: collecting the related information of medical data nodes, generating a node grouping scheme, determining a list of network nodes at all levels, electing a second-level network aggregation node and a first-level network main aggregation node, the task publisher carries out federal learning task distribution; the method comprises the following steps: training a local model in a local data set by medical data nodes, performing intra-group aggregation on local model parameters uploaded by other medical data nodes in a group by each group of aggregation nodes after training is completed, and performing inter-group aggregation by a main aggregation node to complete global model aggregation; after node information is updated, the global aggregated in the current round is used as a starting point of training in the next round, and redistribution is carried out until the model converges; and deploying the final model to each medical data node service scene to realize medical data value sharing. Therefore, decentralization can be realized, the credibility of the whole process is guaranteed through multiple verifications, and cross-mechanism data security sharing is realized.

The invention discloses an ecological system total production value checking and evaluation method and system based on a large model, and relates to the technical field of ecological value evaluation.The method comprises the steps that an ecological total value checking and evaluation problem input by a user is received, and an LLM-EcoValue model is input for analysis and decision making; generating a decision thinking chain according to the prompt parameters; generating a structured workflow based on a preset accounting process of the total production value of the ecological system; according to the data node and the operation node of the structured workflow, generating a node prompt word of each evaluation operation step; inputting the node cue word into an LLM-EcoValue model, and generating an operation code of an ecological total value evaluation process; the operation codes are assembled to generate an executable program, and the executable program generates a total production value checking and evaluating result. By means of the technical scheme, full-process automation from data input to result display is achieved, and the technical threshold and the implementation cost are greatly reduced.

The invention provides an associated data acquisition method, a medium and electronic equipment, and the acquisition method comprises the steps: building a business-data circulation dual-domain topology model, and endowing each data node in the business-data circulation dual-domain topology model with a dynamic initial weight; identifying data nodes with strong business semantic association, establishing data association groups, and labeling business semantic tags for data belonging to the same data association group; constructing a cross-service data interaction chain, and generating a basic acquisition baseline according to the data interaction chain; identifying high cross nodes in the data interaction chain; classifying and screening the data nodes; and collecting the data content of each data node. According to the associated data acquisition method, a dynamic data topological associated acquisition mechanism driven by service logic is provided, and accurate acquisition of the service logs is realized by constructing a mapping model of the core service logic and the data flow of the signal system and combining a multi-dimensional weight fusion judgment algorithm and an adaptive content screening engine.

The invention provides a skip list supporting dynamic real-time data range query, a maintenance method and system and a data range query method and system.The query method comprises the steps that from a high-level index, backward traversal is conducted layer by layer through index nodes, and if the key of a data node corresponding to the current index node is smaller than start, backward traversal continues; if the key of the data node corresponding to the current index node is greater than end, descending to the next layer of index, and if the data link table is the bottommost layer of data link table, traversing the bottom layer of data nodes by utilizing a basic range query process; and if the unique identifier of the data node corresponding to the current index node is between start and end, processing by using a recursive divide-and-conquer process, and finally accumulating query results obtained by recursive divide-and-conquer. The method has the advantages that the statistical information is maintained in the index node, so that the statistical information can be directly used during query, and the query efficiency is greatly improved.

The invention provides a distributed power transaction abnormal electricity price identification method and device, and relates to the technical field of abnormal electricity price identification, and the method comprises the steps: obtaining distributed power transaction data, the distributed power transaction data comprises power transaction data of a plurality of nodes, and the power transaction data of the nodes comprises real-time electricity prices, real-time generated power and timestamps; preprocessing the distributed power transaction data; partitioning the preprocessed distributed power transaction data by using a preset clustering algorithm to obtain a plurality of electricity price partitions; and carrying out abnormal electricity price identification on all the electricity price partitions. The accuracy and the recognition efficiency of the abnormal electricity price recognition result can be improved.

The invention provides a data processing method which is applied to a distributed database, base tables of the distributed database are stored in different data nodes DN in a distributed mode, a coordination node CN can generate an XC table return plan of a global secondary index GSI according to non-batch query of the base tables, and the CN executes the table return plan. According to the method, a table return plan is executed once through two remote queries, and target data are obtained from a DN where the target data to be queried is located. In this way, the data query speed is increased, and the performance of the distributed database is also improved.

A method performed by a node for predicting a behavior of users of a communications network is described. The node manages an artificial neural network. The node merges a first pre-existing predictive model of the behavior in a first group of users with a second model of the behavior in a second group of users. The merging comprises establishing connections between the first model and the second model. Each of the connections has a respective weight. The respective weights of the connections are learned by respective connections of neurons in the artificial neural network based on data from a third group of users. The node also obtains a third model for predicting the behavior in the third group of users, based on the merged models and the data from the third group of users.

The invention provides an unsupported rudder wing additive manufacturing method and device based on compensation, and belongs to the field of metal additive manufacturing. The method comprises the following steps: performing selective laser melting (SLM) simulation on a to-be-manufactured rudder wing by using a simulator to obtain simulation deformation data; based on a penalty function, adding and fusing the forming error and the geometric constraint to obtain a fusion objective function; the simulation deformation data, the node coordinates and the predicted node coordinates corresponding to the discrete node set of the rudder wing to be manufactured are substituted into the fusion target function, and target reversible deformation data are obtained through iterative optimization; determining a discrete node set based on a rudder wing three-dimensional manufacturing model; and performing reverse deformation compensation on the rudder wing three-dimensional manufacturing model by using the target reverse deformation data until a target rudder wing manufactured by the compensated rudder wing three-dimensional manufacturing model meets a preset condition, and outputting the target rudder wing. In conclusion, the method and the device can get rid of dependence on subjective experience, improve compensation precision and adapt to various application scenes.

The invention relates to a multi-area data migration method based on association perception and a storage system, and the method comprises the following steps: each storage node records data access information, and periodically uploads the data access information to a central control node, the data access information comprises a source area, time and target data of each access request, the heat of the nodes and the data volume of the nodes; hotness data processing is carried out at the central processing node to obtain hotspot data, and correlation analysis is carried out to obtain the correlation between the hotspot data and the area and the correlation between the hotspot data; and performing a data migration decision based on the data access information and the relevance, the data migration decision including determining nodes migrating data and migrated data in the nodes, and determining an area receiving the data and nodes receiving the data in the area. Compared with the prior art, the method has the advantages of reducing cross-regional access times, relieving generation of hotspots and the like.

The application discloses a data synchronization method and device, electronic equipment and a storage medium. The method comprises the following steps: obtaining a transaction identifier corresponding to each transaction, and a number corresponding to each sub-statement after each transaction is divided into sub-statements according to an execution order; and synchronizing the sub-statements located on different data nodes to the outside in sequence according to the transaction identifier and the number. The scheme provided by the application can keep the distributed data synchronized to the outside in sequence, and avoid synchronization failure of the distributed data due to the execution order.

The invention discloses a message processing method and device, equipment and a readable storage medium, relates to the technical field of communication, and aims to improve data service efficiency while ensuring data service security. The method comprises: receiving a first message of a data node, the first message being used for requesting a data service; according to the first message, a second message is sent to a first target data management function, and the second message is used for representing the data service requirement of the data node. According to the embodiment of the invention, the data service efficiency can be improved while the data service security is guaranteed.

This invention relates to the field of computer system technology based on specific computational models, and discloses a method and system for parsing and evaluating multi-source heterogeneous data streams based on semantic understanding of large language models. The method includes: extracting a logical dependency matrix representing the logical subordination relationship between data nodes in the multi-source heterogeneous data stream; generating a mask constraint tensor based on the logical dependency matrix to guide the feature fusion path of the large language model; injecting the mask constraint tensor into the self-attention computation layer of the large language model, using the tensor to suppress the distribution of attention weights, forcing feature extraction to converge within the logical constraint subspace, and generating a semantic parsing vector; and outputting the evaluation result based on the semantic parsing vector. This invention eliminates the feature dilution of core nodes by unstructured noise through numerical truncation of irrelevant weights in the computational path, avoids the illusion of association generated by semantic fusion, and improves the logical determinism of heterogeneous data parsing.

A data query request processing method is provided. In the method, a node device receives a data query request transmitted by another node device for querying data related to a first vertex in a graph database through a first processing model. The data query request carries an identifier of a start vertex. The node device transmits the data query request to a second processing model through the first processing model. The second processing model is configured to process data of the start vertex. The node device determines a third processing model based on the data query request through the second processing model, the third processing model being configured to process data of the first vertex or an intermediate vertex between the start vertex and the first vertex. The node device transmits the data query request to the third processing model through the second processing model.

The invention discloses a data consanguinity and hotspot awareness driven global data table analysis method and system, and relates to the technical field of data processing, and the method comprises the steps: reading a data flow log, ETL job metadata and a query dependency relationship of a global data table, and carrying out the analysis to form a multi-level data consanguinity map; log features are extracted to construct a popularity time sequence and a popularity spectrum; fusing the sensitivity spectrum and the popularity spectrum to construct a double-spectrum hotspot sensing model; and inputting real-time data characteristics into the model to obtain a node comprehensive influence score, and performing node strategy correction management. According to the method, the technical problems that the data dependency relationship of a global data platform is difficult to accurately describe, data hotspots cannot be comprehensively sensed, data node influence evaluation is inaccurate and resource allocation and data value are not matched are solved, and the purposes of clear data dependency, comprehensive hotspot sensing, accurate node influence evaluation and reasonable resource allocation are achieved. Therefore, the technical effects of accurate evaluation and efficient management and control of the middle table are achieved.

The invention relates to a data quality checking method and device based on graph embedding, and belongs to the technical field of computers.The method comprises the steps that service data nodes and service domain nodes are input into a graph embedding module to obtain service data node vectors and service domain node vectors respectively; inputting the business data node vector and the business domain node vector into a feature conversion module, performing feature extraction, and then outputting an updated business data node vector and an updated business domain node vector; and constructing an edge vector of the updated service data node vector and the service domain node vector through a node aggregation module, performing feature extraction and prediction based on the edge vector, outputting a prediction score of an edge existing in the service data node and the service domain node, and determining a check result of the service data according to the prediction score. According to the method and the device, the automatic quality check of the irregularly input data is realized, the labor cost is saved, and the accuracy and the credibility of the data quality check are improved.

This application provides a data scheduling method, a distributed storage system, a computing device, a storage medium, and a program product. The data scheduling method includes: responding to a data scheduling request, determining at least one piece of data to be scheduled corresponding to a target object; determining at least two media types corresponding to the target object from a plurality of media types; determining a target storage medium corresponding to each of the at least one piece of data to be scheduled and a target data node providing the target storage medium according to the at least two media types; and storing the at least one piece of data to be scheduled in the target storage medium of its corresponding target data node. The technical solution provided by this application can improve the service availability of a distributed storage system.

The present disclosure provides a data lineage generation method and device, electronic equipment and storage medium, relates to the technical field of computers, in particular to the technical field of data mining, data production processing and information flow. The specific implementation scheme is: in response to receiving a data lineage generation request, determining a target data node of a data lineage to be generated in a target directed acyclic graph; determining a target path in the target directed acyclic graph; obtaining the assignment data of the specified data node in the target path; according to the assignment data, executing a data generation process according to the target path to obtain target data; and generating a data lineage about the target data node according to the target data, the assignment data and the target path. Through the present scheme, a more fine-grained data lineage can be generated.

The invention provides a fault root cause determination method and device, electronic equipment, a storage medium and a product, and belongs to the technical field of cloud computing. The method comprises the steps that after a target service requested by a target service request fails, tracking data, log data and index data corresponding to each node are acquired based on node information of at least one node on a service path corresponding to the target service request, and the nodes provide a service interface called in the process for the target service; performing feature extraction on the tracking data, the log data and the index data corresponding to each node to obtain a tracking feature vector, a log feature vector and an index feature vector corresponding to each node; fusing the tracking feature vector, the log feature vector and the index feature vector corresponding to at least one node to obtain a total fusion feature vector; and determining a fault root cause of the target service based on the total fusion feature vector. The fault root cause determination efficiency can be improved, and the determination cost can be reduced.

The invention discloses a sub-graph matching method and device, and the method comprises the steps: obtaining a query graph containing N query nodes and a data graph containing M data nodes, and determining a candidate set, containing candidate data nodes corresponding to the query node in the data graph, of each query node; according to a first sequence used for traversing the N query nodes, the candidate sets of the N query nodes are filtered in sequence, and the step of filtering the first candidate set of the first query node comprises the substeps that a first set of all second query nodes which are contained in the query graph and are adjacent to the first query node is determined; for each candidate data node in the first candidate set, determining whether the candidate data node has at least one connection with the candidate set of each second query node in the first set through a plurality of threads; if the check result is no, removing the candidate data nodes from the first candidate set to update the first candidate set; the updated first candidate set is used for determining sub-graphs isomorphic to the query graph in the data graph.

The invention provides a data access method and device and related equipment, and the data access device comprises the steps: receiving and forwarding an operation request for target data through an access node; the data node receives a corresponding operation request for the target data forwarded by the access node, and obtains a lock type corresponding to the target data from the lock types corresponding to the stored multiple pieces of data; and the data node verifies the lock type corresponding to the target data, and performs corresponding operation on the target data when the verification result is passed. In the data access device, the data node not only stores the target data, but also stores the lock type corresponding to the target data, so that the data node can independently complete the verification of the lock permission of the target data, and the access node only participates in the forwarding of the message; the processing efficiency of the operation request is improved by eliminating the time delay overhead caused by message forwarding between the access node and the lock affiliation node which is used by other access nodes.