1265 results about "Data node" patented technology

The invention relates to the technical field of data processing, and discloses an industrial one-stop multi-source data acquisition and monitoring system and equipment, and the system comprises a data acquisition module which is used for obtaining multi-source industrial monitoring data and generating a data stream input node; the data preprocessing module is used for processing the input data in the data flow model to generate a standardized data flow; the data conversion module is used for dynamically converting data nodes in the standardized data stream according to different data protocols to generate a compatible data stream; the task scheduling module is used for optimizing the data flow and distributing the data flow to a target node according to the compatible data flow in combination with a node priority and a dynamic scheduling strategy in the data flow model; and the exception detection module is used for carrying out exception state analysis on the optimized data stream and judging whether an exception exists or not according to a preset rule. According to the technical scheme, the compatibility and reliability of the monitoring system can be improved.

The invention provides a distributed real-time database intelligent management method and system based on big data, and relates to the technical field of database management, and the method comprises the steps: predicting a resource demand through a neural network model, selecting a target data node, generating a data fragment mapping table by adopting an annular data structure, and constructing a fragment routing table to realize writing and migration of the data fragments. After a data processing task is received, data distribution is determined according to the fragment routing table, sub-tasks are split and distributed to target data nodes based on priorities, the resource occupancy rate is monitored, the tasks are redistributed when necessary, and finally a data processing result is generated. The method improves the database management efficiency and the resource utilization rate.

The invention provides a self-adaptive adjustment Internet operation and maintenance strategy generation method and system, and relates to the technical field of Internet, and the method comprises the steps: 1, dynamically collecting the operation state data of a target operation and maintenance environment through a distributed sensor and an edge node, and generating a multi-dimensional time series data set; 2, performing space-time correlation analysis on the multi-dimensional time sequence data set, determining a reference data node, constructing a two-dimensional correlation structure, and generating a dynamic judgment interval; and step 3, respectively selecting monitoring sample sets in the inner domain and the outer domain of the dynamic judgment interval, generating a trajectory feature sequence according to the time evolution relationship of the sample sets, and calculating a dynamic correction coefficient based on the trajectory feature sequence. According to the method, the self-adaptive circulation control is formed by dynamically adjusting the threshold baseline, the judgment interval and the strategy generation rule, the accuracy and effectiveness of the internet operation and maintenance strategy are improved, and the stability of internet operation is enhanced.

The invention provides a big data aggregation analysis method and system based on a typical service scene. The method comprises the following steps: receiving multi-dimensional service data streams from a plurality of heterogeneous data sources; carrying out deep mining processing on time characteristics and causal relationships of data by utilizing the multi-dimensional service data flow and combining time sequence analysis and causal inference technologies to obtain a cross-domain data association network; based on the cross-domain data association network, performing deep learning processing on features of nodes and edges in the network by adopting a graph neural network algorithm to obtain key data nodes and a combination mode thereof; according to the key data nodes and the combination mode thereof, dynamically adjusting a selection strategy of the key data nodes through a Q-learning algorithm to obtain an optimized data aggregation analysis model; and performing comprehensive analysis processing on the service data based on the optimized data aggregation analysis model to generate a comprehensive service insight report. According to the technical scheme provided by the invention, the efficiency and accuracy of big data aggregation analysis can be remarkably improved.

The invention provides a multi-branch network and cross attention multi-source data fusion slope displacement prediction method, which comprises the steps of collecting meteorological data, GNSS node data and radar three-dimensional data, and performing preprocessing and standardization processing on multi-source data; constructing a multi-branch network to perform feature extraction on each type of data; fusing the feature vectors of the multi-source data through a cross attention mechanism; a recent trend is captured by combining a multi-scale memory network with LSTM, periodic features are extracted by a one-dimensional expansion convolutional neural network, and a displacement predicted value is obtained through adaptive fusion; and training the model by adopting a course learning mechanism, and outputting a displacement predicted value. According to the method, the multi-branch network is constructed to carry out targeted feature extraction on different modal data, deep fusion is carried out on multi-source data by using a cross attention mechanism, different modal specific feature extraction, cross-modal association modeling and multi-scale prediction of the multi-source data are realized, potential information in the multi-source data is fully mined, and the multi-source data extraction efficiency is improved. And the accuracy and reliability of slope displacement prediction are improved.

The present application discloses a distributed database-oriented compensating transaction implementation method. The method specifically comprises: a client initiates a transaction request, and determines, by means of an execution directory, whether a transaction SQL exceeds a limit value (S1); a coordination node allocates a timestamp to a transaction initiated by the client in step S1, and simultaneously sends a related operation of the transaction to a data node (S2); upon receiving the transaction request, the data node determines whether to commit or roll back the transaction, and returns an execution result to the coordination node for confirmation and verification, and if the verification fails, the coordination node notifies the data node and performs a compensation operation (S3); and when the operation is completed, the coordination node commits the transaction, performs regular cleaning, and returns the execution result to the client (S4).

The invention relates to the technical field of data processing, and discloses a multi-source heterogeneous data semantic alignment method and device and computer equipment.The method comprises the steps that multi-source heterogeneous spatio-temporal data are collected through a center data node and a plurality of edge nodes, spatial attribute feature extraction and inter-unit spatial correlation calculation are carried out, and multi-source heterogeneous spatial data are obtained; obtaining a city data semantic association degree feature matrix; inputting the urban data semantic association degree feature matrix into a multilayer graph attention network for feature extraction to obtain a node feature vector matrix and a task association degree adjacency matrix; generating a distributed scheduling scheme of the data processing tasks according to the node feature vector matrix and the task association degree adjacency matrix; according to the method, data processing tasks are distributed to computing nodes according to a distributed scheduling scheme, alternate iterative optimization is carried out based on a city data semantic association degree feature matrix, a semantic mapping relation set is obtained, and intelligent scheduling of the data processing tasks and efficient semantic alignment in a distributed environment are achieved.

The invention relates to the field of data management and knowledge maps, and provides a civil administration data consanguinity knowledge map construction method and device based on a large model, and the method comprises the steps: constructing a basic entity network based on the metadata feature extraction of civil administration data; based on the basic entity network, analyzing the data table structure to generate a data table structure relation model; analyzing the complex structured query language statement, identifying field-level blood relationship, and generating a preliminary blood relationship graph; all physical source tables are traversed, virtual table redundancy and data node repetition are eliminated, and an optimized blood relationship graph is generated; and based on the optimized blood relationship graph, identifying public data nodes and combining field-level data blood relationships to generate a final civil administration data blood relationship graph. According to the method, the problems of difficulty in accurately analyzing complex SQL statements, processing virtual table redundancy and data node repetition in the prior art are solved, and high-precision and automatic civil administration data consanguinity knowledge graph construction is realized.

The invention belongs to the technical field of project management, and discloses a project management big data dynamic optimization method, which comprises the following steps: acquiring project optimization information; constructing a project knowledge graph, and performing graph traversal on the project knowledge graph based on the real-time task set of the project needing to be managed to obtain a corresponding dynamic sub-graph; image reasoning is carried out based on the obtained dynamic subgraph, corresponding project expected information is obtained, whether project risks exist in the project needing to be managed or not is judged based on the project expected information, and if the risks exist, corresponding risk early warning information is generated; and constructing a standby coping strategy set based on the risk early warning information, performing strategy selection optimization based on the dependency relationship among the data nodes in the dynamic subgraph to obtain a corresponding optimal coping strategy, performing real-time monitoring on the execution process of the corresponding optimal coping strategy, and performing information feedback based on the monitoring result. According to the invention, the overall level and efficiency of project management are significantly improved.

The invention discloses a case evidence logic deduction method based on a graph neural network. The method comprises the following steps: S1, forming a standardized case evidence data set; S2, forming a multi-level case evidence graph; s3, performing vectorization representation on case evidence data nodes and edges of the multi-level case evidence graph, and calculating weights of the case evidence data nodes; s4, inputting the multi-level case evidence graph into a graph neural network, and calculating a logic relation weight between case evidence data nodes; s5, identifying case evidence data nodes with low credibility; and S6, based on the logical relationship weight of the case evidence data nodes and a causal reasoning result, calculating an optimal case evidence deduction path by adopting a path search algorithm, identifying a case core fact chain, and generating a case deduction conclusion. According to the invention, different types of case evidence data can be reasoned in respective hierarchies, information mixing is effectively avoided, and the accuracy and interpretability of case fact deduction are improved.

A method and apparatus for cache warming in a distributed storage system is described. The method can include detecting a destructive change to one or more nodes of an existing cluster of cache data nodes. The method can also include allocating a new cluster of cache data nodes in a write-only mode, and streaming data from each cache data node of the existing cluster to cache data nodes of the new cluster. The method can further include servicing a data access request from a selected cache data node of the existing cluster while writing data from the data access request to a selected cache data node of the new cluster. Furthermore, the method can include in response to a determination that data from the cache data nodes of the existing cluster has been successfully streamed to the new cluster, servicing new data access requests with the new cluster.

The invention relates to a brain inspired data service system and a brain inspired data service method, which are characterized in that a layered architecture is constructed, the layered architecture comprises a data node cluster deployed in a mixed manner, the data node cluster specifically comprises a core node, an acquisition node and a gateway node, and the core node, the acquisition node and the gateway node are respectively used for processing core service data, real-time edge data and cross-domain connection; the small-world connection layer realizes same-domain efficient communication through high-clustering local connection, and breaks a data island through dynamic long-range cross-domain connection; the intelligent control layer integrates a DMN control center, a collaborative state monitoring engine, a prediction analysis module and a reinforcement learning driven resource scheduler. The system initializes a topological structure through a small-world network manager, and when the communication frequency of a cross-domain node exceeds a threshold value, long-range connection is dynamically inserted to ensure that the hop count of a cross-domain path is stably not higher than a preset value. The DMN control center realizes dual-mode switching based on a system load threshold; in an idle period, data pre-cleaning, knowledge graph construction, predictive pre-fetching and resource pre-allocation are executed; and optimizing routing in combination with a graph traversal algorithm in a task period.

The invention relates to the technical field of electronic detection, and discloses an electronic component dispensing detection method. The method comprises the following steps: acquiring dispensing image data of the surface of the electronic component, and generating a standardized dispensing data matrix containing glue position, thickness and uniformity characteristics through standardized preprocessing; constructing a dispensing correction matrix based on an adaptive window frame, and performing spatial reference dynamic correction on the standardized data matrix to obtain a spatial correction dispensing data matrix; inputting the data into a multi-layer sensor fusion network for feature fusion, and outputting a multi-source fusion dispensing data set; constructing a multi-dimensional abnormal feature incidence matrix based on the data set, and identifying abnormal dispensing data nodes by using a dynamic threshold detection algorithm; performing parameter optimization iteration on the multi-source fusion data set by using a gradient descent optimization algorithm to generate an optimized dispensing parameter set; and finally, constructing a three-dimensional visual dispensing quality model, and establishing a dynamic mapping relationship between model parameters and glue physical characteristics. The method can more comprehensively detect the glue quality.

The invention discloses an energy-saving control method and system of a dehumidifier, a medium and equipment, and relates to the field of dehumidification equipment. The method comprises the following steps: acquiring node position data, node air velocity data, node humidity data and node temperature data in a space by arranging sensor nodes, and establishing an air flow model by utilizing a fluid dynamic model in combination with three-dimensional space data. Generating a moisture distribution diagram based on the model, determining a target area with the moisture concentration exceeding a preset threshold value, then determining an optimal target airflow channel according to the position of the target area, and controlling the rotation direction of a fan to enable the suction direction of the fan to be consistent with the airflow direction of the target airflow channel. The target air speed of the draught fan is set according to the channel airflow speed, the operation frequency of the compressor and the opening degree of the electronic expansion valve are cooperatively adjusted by combining the humidity value of the target area, the target air speed and the operation parameters of the dehumidifier, precise dehumidification control based on space moisture distribution is achieved, the dehumidification efficiency of the dehumidifier is improved, and therefore the energy-saving capacity of the dehumidifier is improved.

The invention belongs to the field of intelligent operation and maintenance of an electric power system, and provides an electric power intelligent question and answer multi-modal retrieval method and system. Converting the multi-modal data of the power industry into structured representation of nodes and edges of the knowledge graph; performing optimization and de-duplication processing on the edges between the data nodes to obtain optimized edges between the nodes, and constructing an initial knowledge graph; incremental change information is determined based on the newly added multi-modal data, and after neighborhood optimization and consistency check are adjusted according to the incremental change information and the edge weight, an optimized knowledge graph is generated; extracting multi-modal features, and aligning the multi-modal features in the same feature space to obtain aligned multi-modal features; and matching the query features with the knowledge graph, retrieving to obtain a node most related to the query, dynamically analyzing the content and associated information of the selected node, and generating corresponding multi-modal response data. And the complex query requirement of the user is met.

The invention relates to the technical field of power grid data analysis, in particular to a power consumer anomaly detection and risk assessment method, system and device based on spatial-temporal feature fusion and a storage medium. The method comprises the following steps: acquiring multi-source power consumption data of various power consumers, constructing a space-time correlation feature extraction model, and establishing a user behavior space-time coupling relationship through time sequence analysis and spatial distribution characteristic analysis; constructing a multi-dimensional risk mapping model based on a long short term memory network and a convolutional neural network, establishing a nonlinear mapping relation from feature information to a risk assessment result, and identifying an abnormal power consumption behavior; establishing a distributed node credibility evaluation system, and performing credibility quantitative analysis on the data nodes in combination with a fuzzy comprehensive evaluation method; and based on an abnormal detection result and credibility evaluation, intelligent analysis and dynamic early warning of the electric charge management and control risk are realized. The time-space coupling characteristic of power consumption data can be fully mined, and high-precision identification of abnormal power consumption behaviors and accurate detection of non-technical power loss are realized.

System and method for context prediction via application-specific process-to-product causal networks for generating manufacturing insights. A process graph is obtained that comprises a set of data nodes, including operation specific process data nodes and product data nodes. A probabilistic graph model (PGM) is learned for the industrial process based on the process graph and historic process and product data collected for the industrial process in respect of the data nodes The PGM comprises a computed structure and a set of relationship parameters.

The present application relates to a method, device, storage medium and electronic device for the financial audit of a database. The method includes: obtaining a financial audit rule base through a management node, and loading the financial audit rule base to all data nodes in real time; receiving a first SQL statement sent from a client through a computing node of the database, and sending the first SQL statement to a target data node of the database; determining whether the first SQL statement is a high-risk operation according to the financial audit rule base through the target data node, and reviewing the first SQL statement if it is determined that the first SQL statement is a high-risk operation and the corresponding processing method is a review process; terminating the first SQL statement if the review result of the first SQL statement is that execution is not allowed, and returning the processing result of the first SQL statement to the client. The present application solves the technical problems of insufficient security of financial audit implemented by database proxy services and insufficient flexibility of built-in interception based on databases.

The invention discloses a GoldenDB database change risk management and control method and system. The method comprises the steps that change scheme data is obtained, a coding format is recognized, effective change content is extracted, the change content is disassembled into single transactions, semantic analysis is executed, and change types are classified based on keyword matching and a rule base; wherein the change types comprise data change, structure change, parameter change and permission change; generating a distributed execution plan according to the change types, analyzing the distributed execution plan, executing information in parallel, and determining data nodes and computing nodes corresponding to the four change types respectively; respectively constructing risk assessment models in the execution domains corresponding to the four change types; and converting the risk assessment result of each node into a standardized data format, aggregating and analyzing the cross-node common risk, generating a father-child tree management and control report, and executing blocking, rollback or alarm operation according to the risk level.

The invention provides a compression storage method and device for large-scale census data and a medium, and relates to the technical field of data processing.The method comprises the steps that data structure analysis is conducted on a census data storage waiting set, and a data hierarchy node tree is constructed and obtained; performing node value feature analysis on the data level node tree to obtain a data node value topology; according to a data node value topology configuration compression strategy, adaptive compression processing is carried out on the census data storage waiting set, and a compressed data storage waiting set is obtained; performing hierarchical mapping construction of the graph database by taking the data hierarchical node tree as a hierarchical connection constraint to obtain a distributed graph database; and taking the data hierarchical node tree as a data storage guide, and performing storage node filling of the distributed graph database by adopting a compressed data storage waiting set. The technical problem that the storage resources cannot be fully utilized due to the fact that the data relation and the data value are difficult to analyze and evaluate in the existing general survey data storage process is solved, and the storage resource utilization rate and the storage efficiency are optimized.

The invention discloses a visual interaction artificial intelligence algorithm automatic arrangement and deployment method, and belongs to the technical field of artificial intelligence development and deployment. The method comprises the steps that after a training data set is uploaded, a system automatically analyzes a format and generates a visual analysis report; importing an algorithm operation environment and codes, and configuring parameters and a required data set through a visual interface; after a user inputs a training task, a system dynamically schedules an algorithm and a data node automatically arranges a process and generates a directed acyclic graph; the intelligent resource scheduler allocates computing resources and monitors a training state; and after the training is completed, deploying a pipeline publishing model API through servitization. According to the method, the training process of the algorithm can be automatically arranged, the training process of the algorithm is greatly shortened, and the problems that in the prior art, an automatic arrangement function is lacked, automatic parameter mapping is not supported, the resource scheduling strategy is single, and the multi-mechanism joint training requirement cannot be met are solved.

The invention relates to the technical field of transformers, and particularly discloses a transformer monitoring data processing method and system, and the method comprises the steps: obtaining the monitoring data of each part in a target transformer and a corresponding transformer state correlation graph; preprocessing the monitoring data; associating the preprocessed monitoring data to data nodes in a transformer state association graph; when abnormal data nodes occur, associated data nodes are tracked and acquired along causal influence edges based on the abnormal data nodes, and the abnormal data nodes are data nodes with monitoring data deviating from a preset normal range; analyzing the change characteristics of the monitoring data of the abnormal data nodes and the associated data nodes, and obtaining an abnormal mode; generating early warning information according to the abnormal mode, component nodes in the transformer state association graph and physical connection edges; according to the method, timely early warning is realized, the equipment damage risk is reduced, and the safe and stable operation level of a power system is improved.

The invention provides a cloud police service analysis method and system based on big data analysis, and the method comprises the steps: obtaining a real-time police service data flow and a historical police service database of a target region, and carrying out the fusion to obtain a dynamic police service feature set; screening out a key police service feature subset according to a preset police service analysis model training rule to generate a multi-level police service analysis model; inputting the real-time police service data flow into a first police service data node of a real-time analysis layer, and carrying out initial classification on the real-time police service data flow based on a first type of analysis model to obtain an initial classification result set; triggering a second police service data node of a deep verification layer according to the initial classification result set, and performing data verification on the initial classification result set through a second type of analysis model to generate multi-level police service early warning signals; and adjusting analysis model parameters of at least one police service data node in the real-time analysis layer and the deep verification layer, and outputting an optimized police service strategy. According to the invention, the timeliness and accuracy of police service strategy optimization can be improved.

The invention discloses a power plant data node backup method and backup system based on big data, and belongs to the technical field of power plant data backup. In the process of executing file encryption, version creation and Hash calculation, an AES-256 strong encryption algorithm is selected to encrypt the content of a power plant data file; through the step, the safety and reliability of the whole power plant data backup method are remarkably improved. Firstly, the application of the AES-256 encryption algorithm ensures the confidentiality of the power plant data in the transmission and storage process, and the power plant data is not easy to leak even in the face of advanced network attack or physical storage medium loss, thereby greatly protecting the sensitive information and operation safety of the power system. The generation of the hash value not only ensures the integrity of the power plant data before and after encryption, but also provides a non-tampering proof for data verification, so that the backup and recovery process of the power plant data is more reliable and efficient.

The invention provides an electric power communication network joint optimization method and electronic equipment, and belongs to the technical field of resource regulation and control. The method comprises the following steps: determining communication cost information of a power network according to the data volume of each data node in a communication network and a pre-constructed communication network equivalent model; determining generator scheduling cost information and load aggregator scheduling cost information of the power network according to the operation standby adjusting power of each generator in the power network, the operation standby adjusting power of each load aggregator and a pre-constructed power network model; and determining target total scheduling cost information of the power network according to the communication cost information, the generator scheduling cost information, the load aggregator scheduling cost information and a pre-constructed power communication network joint optimization model. According to the invention, the method can achieve the effects of reducing the power resource scheduling cost, improving the response speed and accuracy of power resource scheduling, and improving the implementability of power resource scheduling.

The invention discloses a data query method and device based on a database, electronic equipment and a storage medium. The method comprises the following steps: receiving a data query message sent by a client, and generating a target execution plan based on the data query message; wherein the data query message comprises an execution message; determining the type of the Portal object based on the execution message; and under the condition that the type of the Portal object is anonymous Portal, constructing a first message based on the first data volume and the target execution plan and sending the first message to the target data node, so that the target data node executes the target execution plan and returns query results, and query is stopped immediately until the number of the returned query results meets the first data volume. According to the scheme, the message is constructed according to the data volume and the execution plan under the condition that the type of the Portal object is the anonymous Portal, and the target data node is controlled to only query and return the data meeting the data volume according to the message, so that the waste of database communication and memory resources is avoided, and the database performance is improved.

A system including a media data optimization engine (MDOE) and a method for automatically creating structured data objects for media content rendered in one or more languages in an electronic document of a business entity are provided. The MDOE identifies non-textual objects including media content rendered in one or more languages in the electronic document and generates textual objects in the corresponding language(s) therefrom. The MDOE transforms the textual objects into structured data objects based on configurable criteria and generates a dynamic index-oriented object for the structured data objects specific to the business entity. The MDOE connects the structured data objects to the dynamic index-oriented object by creating linked data nodes therefrom with the dynamic index-oriented object as a core. The MDOE connects the dynamic index-oriented object with the linked data nodes to the electronic document, thereby facilitating dynamic changes to the electronic document and dynamically optimizing the electronic document.

The embodiment of the invention provides a concurrent access control and data processing method and device, a storage medium and a program product. In the embodiment of the invention, a concurrent access control scheme based on a snapshot isolation technology is provided for a database, and the concepts of two-level timestamps, namely a system-level timestamp and a user-level timestamp, are put forward in the concurrent access control scheme; the user-level timestamp performs concurrent access control in a memory state based on the user-level timestamp for data items in a database memory, and the system-level timestamp performs concurrent access control in a system state based on the system-level timestamp for data nodes in an external memory of the database. In the embodiment of the invention, the two layers of timestamps are fused with the snapshot isolation technology, while the advantages of the snapshot isolation technology are exerted, the implementation of concurrent access control can be simplified based on the two layers of timestamps, the concurrent access control can be realized in the memory state and the system state at the same time, and the concurrent performance of the database can be improved.

The embodiment of the invention provides a protocol converter and method for a network card and electronic equipment. The protocol converter comprises a protocol controller, a crossbar switch and a high-speed serial computer extension bus PCIe interface. The protocol controller is provided with a transmit-receive interface which is connected with the data node. The data node is not provided with a processor; the PCIe interface is connected with the RDMA network card and is used for transmitting data between the data node and the RDMA network card; the protocol controller further comprises a sending management unit and a receiving management unit, and the sending management unit is used for receiving data sent by the data node and sending the data to the PCIe interface through the crossbar switch in a message mechanism; and the receiving management unit is used for receiving data transmitted by the PCIe interface through the crossbar switch. By adopting the protocol converter, a data node without a processor can realize data transmission through the RDMA network card, and the data transmission rate of a common node is improved.

The invention relates to a progress tracking and early warning method and system for clinical test items. The method comprises the steps that subject recruitment progress data, follow-up visit completion data, test process data and project milestone completion data of a clinical test center are collected and subjected to quality scoring, and a data quality scoring matrix and a data node connection state are obtained; performing network mapping to obtain a project progress dependency graph; extracting spatial features and time sequence features to obtain risk feature vectors; performing double-target dynamic assessment to obtain a risk assessment report; dynamically adjusting the early warning index to obtain an early warning rule configuration file; and performing early warning trigger judgment, executing hierarchical pushing processing, and generating an early warning information pushing record. According to the implementation of the invention, the system resource consumption is reduced, and the processing capability of large-scale multi-center clinical test items is improved.