1475 results about "Node count" patented technology

The invention relates to the technical field of power network resource management, and discloses a power network resource intelligent management method and system based on 6G and deep learning. The method comprises the following steps: acquiring node data through a 6G technology to obtain a topological mapping table, preprocessing the data to obtain a feature tensor, training a model cluster by adopting a space-time diagram convolution and long-short-term memory network hybrid architecture, analyzing a network state based on a model, generating a scheduling scheme, and issuing a control instruction through 6G network fragmentation to realize intelligent management of a power network. According to the invention, through the combination of the 6G communication positioning integration technology and the space-time diagram convolutional network and long-short term memory network mixed architecture, the real-time data acquisition precision and intelligent analysis capability of the power network are improved. Based on a collaborative optimization mechanism of multi-source data fusion and a deep learning model cluster, the comprehensiveness of power grid state evaluation and the accuracy of resource scheduling are enhanced.

Apparatus and methods are provided for data collection with RAN awareness. In one novel aspect, the UE performs data collection with RAN awareness. In one embodiment, the UE receives from the RAN node data collection configuration, which configures AI-ML model related parameters for the UE, performs data collection and delivers the collected AI-ML model related data through the RAN node destined to a UE server. In one embodiment, the UE further receives a data collection request from the RAN node, a core network entity or the UE server. In another novel aspect, the RAN node performs data collection with RAN awareness. In one embodiment, the RAN node accumulates one or more sets of AI-ML model related data collected by one or more other UEs and delivers the one or more sets of AI-ML model related data together with AI-ML model related data collected by the UE.

The invention discloses an active power distribution network fault positioning and identification method and system based on a space-time diagram network, and relates to the technical field of active power distribution network fault positioning and identification, and the method comprises the steps: obtaining data of each node of an active power distribution network after a fault occurs, building structured diagram data, marking fault nodes and types, and constructing a sample data set; pre-training the fault diagnosis model based on the space-time diagram network by taking the sample data set as source domain data; in each iteration process, a multi-scale adversarial disturbance addition method based on gradient optimization is adopted to generate adversarial sample data, through a data category dynamic balance mechanism and a confidence evaluation mechanism, the data are screened and added to a data set, and the updated data set is utilized to train a model; introducing target domain data, and finely tuning the pre-training model by adopting a transfer learning strategy based on dynamic coring maximum mean difference; and inputting actually acquired node data into the model to realize accurate positioning of fault nodes and accurate identification of fault types.

The invention discloses a node classification method and system based on a hybrid expert model.The node classification method comprises the steps that node data are input into a pre-trained hybrid expert model to obtain a classification result of the node data, and the hybrid expert model comprises a node mode extractor, a gating model and a plurality of classification models; the node mode extractor is used for extracting node mode information from given node data, and the gating model is used for receiving the node mode information and generating a model combination weight corresponding to each node; and dynamically selecting and combining the classification results of the node data by the plurality of different types of classification models according to the model combination weights corresponding to the nodes so as to obtain the final classification result of the nodes. The invention aims to solve the problem of node classification under a complex graph structure, accurately define a node mode under the condition that the node mode is complex, and properly combine different node predictors for different nodes.

The invention relates to the field of Web application, in particular to a tree structure data dynamic management method based on node hierarchical attributes and recursive updating. Comprising the following steps: S1, constructing a tree structure data system, and endowing each node with a hierarchical attribute which is used for identifying position information of the node in a tree structure; s2, receiving an operation instruction of a user for the tree structure data, wherein the operation instruction comprises node adding, node batch deleting, node moving, data exporting and importing, node querying and node quantity counting; and S3, according to different operation instructions, executing corresponding processing logic: based on node hierarchy attributes, applying a recursive update mechanism to realize dynamic management of tree structure data, and ensuring data consistency and accuracy.

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

The invention relates to the technical field of fault diagnosis, in particular to a big data-based bus duct fault diagnosis system, which comprises a data synchronization module, an anomaly identification module, an amplitude judgment module, a traceability positioning module and a risk classification module. According to the invention, automatic synchronization of multi-node data is realized through a time correlation mode, layered discrimination is carried out on an abnormal working condition state by means of continuous comparison of data streams, dynamic identification of node parameter fluctuation is realized through interval adjustment and trend aggregation, and abnormal signals are positioned and traced in a link topology. The node operation risk is comprehensively researched and judged in combination with historical and real-time parameters, linkage analysis and active classification of multi-dimensional information are formed, efficient connection of links such as monitoring, recognition, tracing and classification is promoted, operation and maintenance decision and data full-process response are supported, node difference judgment and risk prompt in a complex scene are facilitated, and the method is suitable for popularization and application. And intelligent operation and maintenance of power supply system data driving are improved.

The invention discloses a power grid network attack detection method and system based on deep learning, and the method comprises the steps: collecting power grid node data in real time, dividing node groups according to regions, and carrying out the hierarchical storage of the data according to the node groups; performing segmentation processing on the hierarchical data by using a fixed time window to generate a standardized hierarchical data set; extracting feature information for different network attack types; capturing features of false data injection, denial of service and distributed denial of service attacks by analyzing abnormal changes, node access frequency changes and frequency domain features in the time sequence, and generating a comprehensive feature set; a distributed deep learning model composed of power grid node group models is constructed, the power grid node group models are combined with a long-short-term memory network and a graph convolution network, a local objective function of the power grid node group models is optimized, and parameters of all the power grid node group models are synchronously optimized on the global level; and generating a classification report containing the affected nodes and the propagation paths, and planning a load redistribution and isolation strategy according to the real-time load and the transmission state.

The invention belongs to the field of fault monitoring, and particularly relates to a fault self-healing method and system under an intelligent operation and maintenance global P2P architecture, and the method comprises the steps: constructing a global operation and maintenance monitoring double-layer node network which comprises a basic monitoring subnet and a collaborative management and control subnet; the basic monitoring subnet collects platform operation data in real time according to a preset inspection strategy, determines an abnormal root node and an association node through causal association analysis, generates an early warning signal and uploads the early warning signal to the collaborative management and control subnet; after the collaborative management and control subnet receives the early warning, a fault self-recovery strategy is started, resources are dynamically allocated, abnormal node data batch migration and recovery are executed, and a self-recovery result is fed back to the basic monitoring subnet to trigger loop check until abnormity is eliminated; according to the method, real-time monitoring and intelligent analysis of operation and maintenance data are realized through cooperative work of the double-layer node network, and disaster tolerance and load adaptive adjustment capability of the system are effectively improved in combination with an automatic fault self-healing mechanism and data difference migration.

The invention provides a real estate full life cycle intelligent management method and system based on Internet of Things perception, and the method comprises the steps: distributing a unique hardware identifier of equipment, combining a lightweight digital certificate with a pre-shared key to achieve two-factor authentication, and storing a root certificate in a cloud end; an access authentication process is optimized, an identity label and a certificate signature request need to be submitted when a node accesses for the first time, a temporary session key is generated after the gateway verifies, and abnormal access is immediately isolated and reported; data integrity verification is implemented, and after a sensor collects data, an abstract is generated and data encryption transmission is carried out; after decryption, the receiving end recalculates the abstract for comparison, and if not, the abstract is discarded and nodes are marked to be abnormal; a data anomaly detection model is constructed, energy consumption fluctuation is determined based on historical data, a frequency baseline is collected, data reasonability is monitored in real time, secondary authentication is triggered when the data is abnormal, and node data access is suspended if the data fails; a hierarchical key system is established, hardware is solidified by a root key, session keys are alternated for 24 hours, and data encryption keys are dynamically derived and distributed through encryption channels.

The invention discloses a data transmission method and device, equipment and a medium, and relates to the technical field of computers. By adopting a redundancy architecture of multiple direct memory access channels and combining a channel transmission mode based on a trigger condition and a target screening strategy, the data transmission efficiency is improved. The appropriate target direct memory access channel is selected from the multiple direct memory access channels to execute the data transmission task, so that when a certain direct memory access channel has an error, the system can be automatically switched to other direct memory access channels to continue to execute the data transmission task; overall interruption of the non-transparent bridge link caused by a single direct memory access channel fault is avoided, meanwhile, for the direct memory access fault, the fault channel can be accurately repaired by adopting a fault-level-based hierarchical repair strategy, the whole non-transparent bridge link and all devices do not need to be reinitialized, and the fault channel repair efficiency is improved. The fault processing time is greatly shortened, the fault recovery efficiency is effectively improved, and the continuity of cross-storage node data transmission is guaranteed.

The invention relates to the technical field of edge computing, and discloses a network data efficient caching method and system based on edge computing, which realize dynamic identification of hotspot data and optimal caching position decision by analyzing edge node data flow characteristics and network topology states in real time. Firstly, data packet meta-information is extracted from an edge node, a content feature vector is generated, and data popularity is evaluated; secondly, analyzing a network topology structure and a link state, and screening candidate cache nodes; then, factors such as service priority, network reliability and topology stability are fused, and an optimal cache position is determined by adopting a reinforcement learning algorithm; and finally, the cache content is updated, the cache state is synchronized between the edge nodes, and the cache distribution is optimized. The data access efficiency and the resource utilization rate of the edge network can be effectively improved, and the method adapts to dynamically changing network environments and user requirements.

The invention provides a database knowledge body construction method and a structured query language generation method, a relational database knowledge body comprises a plurality of knowledge body units, and the plurality of knowledge body units are mutually connected through associated fields to form a knowledge network; each knowledge body unit corresponds to one data table, each knowledge body unit comprises multi-layer node data, and the multi-layer node data comprises table information, field information, field vectors, field enumeration value information and field enumeration value vector representation of the data tables. The construction method comprises the steps of obtaining first data table information based on data table metadata; reasoning and extracting the text code pairing data set through a large language model to obtain second data table information; and fusing the first data table information and the second data table information to construct a relational database knowledge body. Refined, low-redundancy, accurate, comprehensive and rich dependency data is provided for the TEXT2SQL task of the large language model, and the TEXT2SQL generation capacity of the large language model is improved.

The invention discloses an electronic lead seal data management method based on a block chain, relates to the technical field of internet data services, and aims to solve the problems of node data asynchronization, state updating failure and potential safety hazards of an intelligent contract in the prior art. The data consistency is ensured through a distributed clock synchronization algorithm and a version number verification mechanism; the reliability and fault tolerance of state updating are improved by adopting an improved Byzantine fault-tolerant algorithm and an asynchronous two-stage transaction submission mechanism; data integrity and traceability are guaranteed through a state machine detection mechanism and transaction log records; the security and maintainability of the smart contract are improved based on an automatic code auditing algorithm and a proxy contract mode of a graph neural network; real-time data monitoring and exception repair are realized by using an under-chain auditing mechanism and a cross-chain data consistency verification protocol; according to the invention, the data consistency, the system safety and the reliability of electronic lead seal data management are obviously improved.

The invention relates to the technical field of execution traceability, in particular to a block chain-based flood storage and detention area ecological compensation execution traceability processing method and system, and the method comprises the following steps: extracting remote sensing data to generate a delay factor list, recognizing ecological trigger nodes to generate a calibration node data set, constructing a reconciliation path structure, and extracting boundary nodes; and generating a permission mapping and responsibility signature block, and writing the permission mapping and responsibility signature block into a responsibility tracking chain structure area. According to the method, logic reconciliation is carried out on compensation and payment behaviors through timestamps and hash records, node tracking and continuity inspection of fund payment paths are realized, operation authority matching is carried out in combination with a contract authority table after time jump is identified and boundary nodes are calibrated, and a responsibility tracking structure chain is constructed step by step; according to the method, responsibility binding of fund behavior nodes and whole-process signature traceability and chain traceability of a transfer behavior are effectively realized, the authenticity discrimination capability of compensation execution and the operation responsibility traceability capability are enhanced, and the verification precision and process integrity of a fund execution path are improved.

The invention relates to the technical field of automatic operation and maintenance, in particular to a visual operation and maintenance data analysis system which comprises a node data collection module, an index sorting and screening module, a multi-parameter linkage recognition module, a resource node screening module and an event track tracing module. And analyzing operation data of each node, and grouping node types according to time labels. According to the method, the operation data of different nodes are subjected to hierarchical grouping and archiving, dynamic linkage merging of various operation and maintenance parameters is realized in combination with time labels, operation and maintenance concerned objects are automatically screened under a multi-index change trend, and accurate screening of abnormal nodes and resource redundancy nodes is completed according to a comprehensive feature track. The operation and maintenance data is promoted to be converted from single-point monitoring to full-link linkage analysis, and the intuition and the globality of operation and maintenance event response are improved in the visual archiving and tracing process, so that systematic closed-loop monitoring of node states, resource allocation and operation and maintenance risks is promoted.

The embodiment of the invention discloses an automatic code standardizing method and device, a storage medium and a program product. The method comprises the following steps: obtaining a to-be-standardized code; performing grammar analysis on the to-be-standardized code to generate an abstract syntax tree; traversing the abstract syntax tree to obtain the type of each node; collecting node data according to the type; a plurality of mapping tables are constructed, standard rules in a preset standard rule base and optimization rules in a preset optimization rule base are stored in the multiple mapping tables, and the mapping relation between the standard rules and the optimization rules is established; judging whether the node data is abnormal or not based on the type, a standard rule base and a plurality of mapping tables; if yes, optimizing the abstract syntax tree based on the multiple mapping tables and the abnormal node data; and generating a code meeting the specification based on the optimized abstract syntax tree. According to the method, the code quality, consistency and maintainability are improved by automatically standardizing the code, and meanwhile, the development and maintenance cost is reduced.

The invention relates to the technical field of information processing, in particular to an Internet of Things data processing method and system based on a cloud platform, and the method comprises the following steps: based on multi-modal data uploaded by Internet of Things equipment, analyzing equipment node data synchronization delay and timestamp matching degree, carrying out the statistics of node communication frequency and equipment relevance, screening a communication optimal node pair, and carrying out the communication optimal node pair; and carrying out identifier distribution, calculating a data packet transmission success rate, and forming a node identifier distribution result. According to the method, the multi-modal data uploaded by the Internet of Things equipment is analyzed, the node communication efficiency is optimized, accurate allocation of communication resources is realized, the transmission delay and failure rate are reduced, the data transmission efficiency is improved, the data access popularity is identified, the utilization rate of storage resources is improved, the service life of the equipment is prolonged, and a high-frequency data retrieval path is dynamically optimized; the retrieval speed and accuracy are improved, the response time is shortened, and the data processing efficiency and the resource utilization rate are comprehensively improved from collection to storage, transmission and retrieval.

The invention discloses a multi-split cross-platform distributed monitoring system state data acquisition method, which belongs to the field of system operation and maintenance and data monitoring, and comprises the following steps: acquiring node data through a data acquisition module arranged on a distributed monitoring system node, and introducing a self-adaptive acquisition strategy into the data acquisition module, adaptively adjusting the node data acquisition frequency according to the resource utilization condition; a node data monitoring strategy is formulated according to user requirements, and the monitoring strategy is optimized by using a hybrid monitoring algorithm; a Delta compression technology is utilized to compress node data to obtain a compressed data packet, the compressed data packet is transmitted, and a multi-level transmission optimization mechanism is introduced in the transmission process of the compressed data packet. According to the method, the technologies such as message bus publishing and subscribing, a customized data acquisition strategy and data flow optimization are integrated, so that consistent state monitoring can be realized among different platforms and hardware architectures, and the efficiency and reliability of system operation and maintenance are greatly improved.

The invention provides a mask-masked transverse federal heterogeneous graph learning method and device. The method comprises two stages of client local training and global aggregation. The client deploys a local model based on the initial parameters distributed by the global server, shades random shading input features by using dynamically generated masks to mine a high-order topological structure, and optimizes the topology perception capability through edge reconstruction loss; meanwhile, node attributes are randomly masked, the masking proportion is dynamically adjusted, and semantic robustness is enhanced in combination with consistency semantic loss; and jointly updating local parameters by combining the node classification loss. In the global aggregation stage, the server calculates a clustering coefficient to measure the compactness of a graph structure according to an edge set under an appointed meta path of each client, dynamically distributes aggregation weights in combination with the number of nodes, fuses client parameters rich in topological information, and iteratively optimizes a global model. According to the method, the problem of semantic inconsistency caused by dynamic change of client data can be solved, the local graph structure value is captured, and the global generalization ability and convergence efficiency are improved.

The invention relates to the technical field of data processing, and particularly discloses an intelligent cache replacement method and system based on application awareness, and the method comprises the steps: sensing a cache state of an application scene, and determining a candidate cache replacement strategy according to a cache replacement strategy matching rule; the method comprises the following steps: collecting data of a storage node data access mode, a cache access state, a capacity state and a power consumption state of an application scene, and preprocessing the data to generate historical data; according to historical data, training a GRU model to predict future cache hit information, and determining a cache hit rate of the application scene; calculating strategy benefits according to the cache hit rate and the evaluation index of the cache hit rate, and comparing the candidate cache replacement strategy with the current cache replacement strategy; and when the cache hit rate is lower than a preset threshold value and a strategy benefit comparison result exceeds a set value, configuring the candidate cache replacement strategy as a current cache replacement strategy.

The invention discloses a communication fault diagnosis method and device based on a power system, equipment and a medium. The method comprises the following steps: S1, collecting and outputting multi-source data; s2, preprocessing the multi-source data; s3, performing topological constraint attention mechanism weighting processing on the data matrix; s4, performing dynamic threshold calculation on the fusion feature matrix; s5, positioning the abnormal node list; s6, calculating to obtain a posterior probability; s7, matching the rule base to obtain a standardized disposal instruction; according to the method, the communication traffic is collected through the compressed sensing collection algorithm, the line impedance parameters are integrated into the observation matrix in combination with the topological parameters, key node data are collected preferentially, and the effective data acquisition rate is increased; the accuracy of switch false alarm identification is improved by outputting a time-space unified fusion feature matrix; the computing resources are focused to the abnormal nodes and neighborhoods thereof through the abnormal node mask matrix, so that the positioning precision of the aged equipment is improved, and the judgment omission rate caused by switch aging is prevented from being improved.

The invention discloses a method for designing a tree data storage mode in a relational database by using preorder traversal, which comprises the following steps: S1, basic node data of a tree structure is constructed in the relational database, each node comprises id, name, lvalue and rvalue attributes, and the lvalue and the rvalue are used for identifying the position relationship of the nodes in a tree through an interval representation method; s2, setting a query condition according to a node ID needing to be queried, and determining a descendant node range of the node through lvalue and rvalue attributes of the node; s3, the query results are subjected to ascending sort of the left value lvalue, and a sort list (sort List) is constructed; and S4, by traversing the sortedList, in combination with the temporary set level List and the cache level NodeCache, calculating and setting a father node ID (pid) of each node and whether the father node ID (pid) is a leaf node isLeaf attribute or not according to the logic of pre-order traversal, thereby completing the rapid construction of the tree structure.

The invention discloses a method and device for detecting code quality, and relates to the technical field of computers. A specific embodiment of the method comprises the following steps: acquiring a code file of which the quality is to be detected, and matching an analyzer according to attribute information of the code file; processing the code file by using a parser to obtain an abstract syntax tree; traversing the abstract syntax tree, and determining the circle complexity of the code file through a strategy of counting and judging the number of nodes; and counting the code line number of the code file, determining a quality analysis result of the code file according to one or more of the circle complexity and the code line number, and triggering an alarm in response to the condition that the quality analysis result is greater than or equal to a preset threshold value. According to the implementation mode, the abstract syntax tree aiming at the code characteristics can be generated, the code quality is systematically evaluated from multiple dimensions, and developers are helped to quickly identify complexity and potential problems in the codes, so that subsequent continuous improvement and optimization of the codes are facilitated.

The invention relates to the technical field of intelligent manufacturing and block chains, in particular to a production process traceability method of a hub type transmission device, which comprises the following steps: acquiring three types of operation data and aggregating to generate a synchronous value, calculating a parameter rate to judge whether the parameter rate exceeds a limit and extract a sudden change section, comparing a trend with a threshold value and reconstructing into a clamping state node, and packaging the signature and broadcasting to generate a verification node, and analyzing the node to supplement a link to generate right confirmation data. According to the method, parameter synchronism is calculated and recognized through the offset rate, the linkage analysis capability among multi-dimensional data is improved, accurate recognition of a sudden change section is achieved through comparison of continuous period parameter variation difference and double threshold values, and abnormal fragments are effectively extracted and an independent structure is formed by combining trend consistency and pressure difference value judgment; a signature and broadcast mechanism ensures the consistency of node data, link comparison and breakpoint reconstruction maintain the integrity of a data link, and the anomaly recognition precision, the node credibility and the production data closed-loop management capability are enhanced.

The invention provides a computing power service dynamic resource allocation method and system applied to AI model training. The method comprises the following steps: firstly, collecting real-time computing power resource use data (including computing node load, memory occupation and data transmission delay) and model training state data (including training task stage identification, model parameter updating frequency and training data batch processing progress) in AI model training; generating a computing power resource demand association feature set, constructing a computing power resource dynamic allocation decision model including resource allocation priority judgment, adjustment amplitude calculation and scheduling opportunity selection units based on the set, and outputting a computing power resource allocation scheme (including computing node number, memory capacity and data transmission bandwidth adjustment instructions) through the model. Resources are scheduled according to the scheme, and new data are collected to update the feature set, so that dynamic and accurate allocation of computing power resources is realized, and the resource utilization rate and the training efficiency are improved.

The invention provides a computing power center resource centralized self-adaptive control scheduling system and method, and the method comprises the steps: S1, a user submits a computing power training task through an API, and sends the task to a DAG task composer, and forms a task list to be scheduled; s2, the intelligent monitoring analysis module is used for continuously collecting node data and calculating real-time CS and HS scores of each node; s3, analyzing the dependency relationship type data by a task composer, and calling a dynamic programming algorithm in an adaptive scheduler to make a decision; s4, the adaptive scheduler performs dynamic task distribution according to the task list of the DAG task orchestrator and the intelligent monitoring analysis module, and S5, the distributed runtime and environment management module is used for uploading the prepared Docker mirror image to the distributed storage; and S6, through the unified communication adaptation layer, the mirror image is pulled from the distributed storage on the target node and the container is started, the user does not need to concern the difference of the operating system in the whole process, and efficient and intelligent adaptive scheduling is realized.

The invention relates to the technical field of identity verification, in particular to an enterprise registration self-service terminal and an identity verification method and system thereof, and the method comprises the following steps: comparing dynamic two-dimensional code parameters item by item based on the enterprise registration self-service terminal, a verification serial number, a card issuer identifier and a validity period, and judging the consistency of each field; and according to the matching of the result and the registration system information, identifying the offset condition of the handling process, and outputting a sequence offset index. According to the invention, through synchronous acquisition of multiple parameters of the identity card, in combination with two-dimensional code dynamic feature comparison and chip data analysis, real-time association of identity data in three aspects of time, source and content is realized, each node is bound with a unique identifier, an equipment source and time sequence relationship, and a node data abstract series full-process log chain is handled; interrupt, offset and risk handling nodes can be positioned in the whole process, a data chain structure supports afterward tracing and node-level traceability, and handling safety, data integrity and traceability are remarkably improved.

The present application relates to the technical field of middleware, and in particular to a cache data semi-synchronization method, comprising comprises the following steps: writing and running a distributed cache proxy layer service using a preset programming language, receiving a read / write command request from an application client, parsing a Key consistent hashing value and sending same to a rear-end Redis sharding node. A master instance parses a message packet, marks a write operation and records same in an AOF file. Upon write to disk, the master instance sends an AOF file transfer command to a slave instance node, and the slave instance acknowledges reception. When receiving a reception response, the master instance updates an acknowledgment offset of the slave node and responds to the client. When network delay is high, data of a master node and a slave node is marked as "asynchronous", and it is automatically downgraded to asynchronous replication. According to the present application, by means of a self-developed Redis kernel, an asynchronous ack mechanism is used to optimize redis to achieve master-slave semi-synchronization, thereby enhancing data reliability of a single point of failure, and eliminating the influence introduced by a native Redis asynchronous replication mechanism such as data delay and data consistency.

The invention discloses a group behavior recognition method based on dynamic and static characteristics and multiple interactive networks, and relates to the technical field of computer vision, comprising the following steps: preprocessing original video data and skeleton node data to obtain dynamic and static characteristics of participants; multi-level character features are obtained by extracting coarse-grained key character information and restoring fine-grained character information; the method comprises the following steps of: obtaining a complex space-time interaction feature by applying a Space-Transform and a Temporal-Transform, and obtaining the complex space-time interaction feature by applying the Space-Transform and the Temporal-Transform; predicting an identification result through data fusion; dynamic features are utilized to supplement static features, and group behaviors of participants are comprehensively and meticulously analyzed; in order to emphasize the key figure information, the key figure information is used as a consistency constraint to guide the focus of the space-time interaction network, and the recognition capability of the model on the space-time dynamic state of the key figure is enhanced.