655 results about "Relationship analysis" patented technology

The invention belongs to the technical field of bridge diagnosis, and discloses a bridge crack intelligent diagnosis system based on multi-modal data fusion. By fusing multi-source data such as visual images, sound wave detection and vibration signals, comprehensive perception and characterization of crack features are realized; constructing a bridge crack characteristic spectrum diagram by adopting a cross-modal feature extraction and heterogeneous feature coding technology; generating a crack evolution situation map based on space-time correlation analysis and knowledge graph construction; the robustness of the system in a complex environment is improved through environmental adaptability feature enhancement and multi-scale characterization; constructing a bridge safety risk hypergraph in combination with multi-dimensional risk analysis and multi-agent collaborative diagnosis; analyzing and revealing a crack evolution mechanism by applying a causal relationship; and finally, through dynamic fusion and uncertainty quantification, a crack intelligent diagnosis comprehensive report is generated. According to the system, the limitation of traditional single-mode diagnosis is broken through, and dynamic prediction and accurate risk assessment of fracture evolution are realized.

The invention discloses a pharmaceutical quality traceability decision-making method and system based on a dynamic knowledge graph, and relates to the technical field of pharmaceutical quality control. The method comprises the following steps: defining a dynamic knowledge graph containing quality-related entities, wherein the dynamic knowledge graph at least comprises a quality event entity, a production element entity and a time sequence causal relationship between the quality event entity and the production element entity; capturing production data related to quality in real time, and generating event data with a quality rule identifier based on a preset triggering condition; and carrying out dynamic state modeling on the production element entity to generate a state vector containing a time sequence attenuation characteristic and a quality influence weight. By constructing the dynamic knowledge graph and designing the time sequence causal reasoning algorithm, real-time capture, dynamic modeling and causal relationship analysis of production data are achieved, the defects of the traditional technology in real-time performance and relevance are overcome, and an efficient and accurate intelligent solution is provided for pharmaceutical quality traceability.

The invention relates to a membrane pool optimization control method, system and equipment based on multi-agent collaborative decision-making and a medium, and the method comprises the steps: abstracting each membrane pool into an agent, building a state space and an action space, and creating a global reward function; applying hard constraint and dynamic soft constraint to the action space to obtain a limited action space; an agent relation analysis layer is embedded in the evaluation network, behavior characteristics and cluster cooperation intensity distribution of all agents are obtained through high-dimensional characteristic mapping and a multi-head attention interaction mechanism, and global value evaluation is generated by combining rewards of a global reward function and through cooperation intensity distribution correction; and synchronously optimizing strategy parameters of each agent based on global value evaluation by adopting a double-stage collaborative training framework, and injecting controllable exploration noise to drive collaborative convergence of the membrane pool cluster. The purposes of saving energy, reducing emission, reducing cost, improving efficiency, reasonably distributing productivity and prolonging the average service life of the membrane module are achieved, and water plant operation is obviously promoted to develop towards the intelligent and sustainable direction.

The invention provides a production line process scheduling optimization method and system based on an agent cluster. The method comprises the steps that the agent cluster comprising a plurality of distributed decision agent nodes is configured according to a production line process scheduling instruction; based on the production line architecture feature graph, a communication protocol between agent nodes is generated through node association relationship analysis, and a cooperation rule set is constructed in combination with a process priority rule and a resource constraint condition; the intelligent agent cluster is instructed to perform scheduling according to a communication protocol and a cooperation rule set, process tasks are dynamically allocated and adjusted through real-time state interaction among nodes, scheduling behavior logs are obtained, a reinforcement learning training sample set is constructed in combination with expected labels, decision parameters of intelligent agent nodes are optimized by using a multi-intelligent-agent reinforcement learning algorithm, and a reinforcement learning training result is obtained. According to the method, the dynamic response capability and the cooperation efficiency of automatic production line scheduling can be improved, and the method is suitable for efficient process scheduling in a complex production environment.

The invention relates to the technical field of AGV scheduling control, and discloses an AGV scheduling control method and system for automobile carrying, and the method comprises the steps: carrying out the data collection of a plurality of AGVs in a stereo garage, and obtaining a state information data set; performing causal relationship analysis on each AGV motion track to obtain a dynamic space-time causal reasoning graph; performing trajectory feature extraction and operation state prediction through a time convolutional neural network to obtain an operation trajectory prediction sequence of each AGV; carrying out deadlock loop identification in a local adjacent AGV and global garage range to obtain a deadlock risk state information matrix; according to the multi-AGV cooperative scheduling optimization method, the multi-AGV vehicle carrying task allocation and the driving path selection are subjected to anti-deadlock optimization to obtain a multi-AGV cooperative scheduling optimization strategy, the efficient cooperative execution of the multi-AGV vehicle carrying task in the stereo garage is realized, and compared with the prior art, the conflict and waiting time between the AGVs can be obviously reduced, and the operation efficiency and the service quality of the stereo garage are improved.

The invention relates to the technical field of equipment monitoring and management, and particularly discloses an AI oT-based distributed industrial equipment monitoring and management system and method, and the system comprises a distributed equipment monitoring module which collects operation parameters, performance indexes and environment data in real time through a plurality of types of sensors disposed on industrial equipment, a dynamic hierarchical edge architecture is utilized to process data and evaluate a static health state, and task intelligent layering and cloud collaboration are supported. And the multi-modal perception analysis module generates an equipment association graph based on the collaborative task, extracts multi-modal features by using a cross-node collaborative algorithm, and realizes dynamic health assessment by combining a CNN-LSTM fusion model and equipment implicit dependency relationship analysis. And the distributed equipment management module fuses equipment dynamic and static health data, cooperates with a task association graph and a fault tracing model, and realizes efficient resource configuration and fault prevention. The system guarantees intelligent monitoring and management of the whole life cycle of industrial equipment through a cooperation mechanism of edge intelligence and cloud deep analysis.

The invention relates to the technical field of distributed agent collaboration and graph database query optimization, in particular to a graph query processing method and system based on multi-agent collaboration, and the method comprises the following steps: reasoning a coordination agent to analyze the semantics and intention of user query by using a large language model; a structured multi-agent execution plan is output, and an entity search agent or a relation analysis agent needing to be called is explicitly called; the agent coordinator receives the multi-agent execution plan and selects agents according to task types and task allocation; obtaining a distributed intermediate result set; starting a result aggregation mechanism, and performing typed processing, priority ranking and deduplication merging on the distributed intermediate result set to obtain an arrangement result; and outputting an answer for answering the initial question of the user. The system comprises an agent execution plan output module, a multi-task execution module and an arrangement result reasoning module. According to the method, efficient, extensible and fault-tolerant graph query processing is realized.

The invention relates to the technical field of water level and water surface relation analysis, in particular to a lake water level and water surface relation analysis method based on a DEM and a remote sensing image, which comprises the following steps of: based on DEM and remote sensing image data, analyzing a space relation between measured data and the DEM, correcting abnormal elevation and adjusting original data, screening boundary points with small elevation difference, and calculating the water level and water surface relation of a lake; and performing statistics on water surface areas under different water levels to obtain a water level area change sequence. According to the method, through cooperative adjustment and automatic fusion based on multi-source spatial data, through elevation correction, spatial anomaly screening and time sequence change tracking, the consistency of spatial data is improved, elevation anomaly information is dynamically corrected, the discrimination capability of a water body spatial distribution boundary is enhanced, and dense water surface change information extraction is realized based on spatial overlapping data; the space node and area change trend under the water level grading condition can be automatically concluded, and the dynamic response characteristics of the lake space structure can be systematically reflected.

The invention provides a bank anti-money laundering data model construction method based on privacy computing, and the method comprises the steps: recognizing node role diversity and topology mutation characteristics through an encryption topology framework, extracting transaction paths and a multi-layer nesting relation between nodes, analyzing the role distribution of a transaction main body, and obtaining a dynamic path mutation set; according to the dynamic path mutation set, accounts with short use cycles and accounts with frequently changed transaction subjects are screened out and marked as potential suspicious transaction subjects, and a preliminary suspicious transaction subject list is obtained; identifying transaction records of the preliminary suspicious transaction subject list, screening out high-risk transaction subjects with false registration information or key identity information missing, and obtaining a high-risk transaction subject subset; and counting a plurality of small transactions of the high-risk transaction subject subset, identifying distribution characteristics and multi-layer transfer paths of the small transactions, and extracting fund splitting modes and hidden flow direction characteristics to obtain a high-risk transaction path characteristic graph.

The invention relates to the technical field of food and beverage, in particular to a food and beverage network sales trend prediction model construction system and method based on multi-source data fusion and deep learning. Comprising a data acquisition unit; a data processing unit; the model construction unit is used for constructing a deep learning prediction model, and an improved LSTM-Transform fusion algorithm is adopted to realize nonlinear mapping modeling of the food and beverage sales trend by integrating time sequence feature modeling and a global dependency relationship analysis technology; a model training verification unit; and a prediction output unit. According to the method, multi-source data such as network sales platform data, social media emotion texts, weather information and industry information are integrated, cross-correlation features such as time dimension features, text emotion features and price elasticity-weather influence are extracted in combination with a feature engineering technology, influence factors of food and beverage sales are comprehensively covered, and the sales quality is improved. The problem that a traditional scheme is single in data dimension is solved.

The invention discloses a multi-source field-level blood relationship tracking method and system. The method comprises the following steps: extracting features from a multi-source heterogeneous system, matching templates, triggering a corresponding acquisition strategy, and outputting an original blood relationship log; reading an original blood relationship log to generate an abstract syntax tree, extracting a source / target field set to output a field-level mapping relation, further writing the field-level mapping relation into a graph database to form a blood relationship edge, writing metadata into a relational database, establishing a two-level index, and outputting a blood relationship graph capable of being quickly positioned; and taking the blood relationship map as input, returning a target path with the maximum sum of confidence coefficients by adopting a weighted shortest path algorithm, outputting a link-level traceability result, and displaying the link-level traceability result. According to the method, complete tracking of the field-level blood relationship is realized, the query response time is shortened to a millisecond level, rapid access to multiple types of data sources is supported, and the problems of insufficient blood relationship analysis precision, low query efficiency and weak multi-source adaptability in the prior art are solved.

The invention provides a call center dialogue sentiment analysis method and system fusing voice and text, and relates to the technical field of voice signal processing, and the method comprises the steps: obtaining a voice signal of a call center and a corresponding transliteration text, extracting intonation, speed, sound intensity and pause sentiment features from the voice signal, meanwhile, deep language analysis is carried out on the transliterated text, and semantic emotion features related to context are extracted; and performing cross-modal correlation analysis on the voice emotion features and the semantic emotion features, and generating a time sequence correction coefficient for feature alignment by constructing a corresponding relation analysis framework between feature sequences. According to the method, language emotion information in voice and text is integrated, more accurate and comprehensive recognition of conversation emotion of the call center is realized, customer satisfaction is improved, and reliable emotion analysis basis is provided for efficiently processing customer appeals.

The invention provides a code quality adaptive evaluation and optimization method, system and device and a medium, and belongs to the technical field of computers. The method comprises the following steps: performing redundant information cleaning and standardization processing on an original code; selecting an adaptive parser to convert the processed code into an abstract syntax tree, and optimizing the abstract syntax tree; performing embedding processing on each node of the abstract syntax tree by using a pre-trained deep learning model to generate a node semantic vector, and performing aggregation, feature extraction and vector normalization operation to obtain a semantic vector representing code semantic features; extracting a dependency relationship from the abstract syntax tree to construct a program dependency graph, extracting node features by using a neural network model, and generating a dependency relationship analysis result; based on the semantic vector and the dependency analysis result, identifying a code potential problem by using an optimization algorithm and generating an optimization suggestion; codes are automatically modified according to optimization suggestions, the optimization effect is re-evaluated, and related models and algorithms are iteratively optimized according to feedback information.

The invention discloses a tea tree liquid phase chip and application thereof, and relates to the technical field of molecular detection. The invention discloses a tea tree liquid phase chip and application thereof, according to site screening requirements and probe design principles, the tea tree liquid phase chip comprises 5781 SNP sites, and tea tree resource genetic typing can be realized based on a target interval genome sequence liquid phase capture accurate positioning sequencing typing technology. The tea tree liquid chip can realize low-cost genetic typing, is mainly specific to tea trees, can realize variety identification and genetic relationship analysis of the tea trees, scientifically guides hybridization improvement work of the tea trees and assists protection and development of germplasm resources of the tea trees, and has relatively high application values in multiple fields of tea tree breeding. The tea tree liquid phase chip can be used for tea tree genetic diversity evaluation, germplasm resource and genetic relationship identification, genetic map construction and gene localization, whole genome association analysis and tea tree molecular marker assisted breeding.

The invention discloses an unmanned aerial vehicle mine resource monitoring system based on multi-modal data fusion, and relates to the field of mine resource monitoring, and the unmanned aerial vehicle mine resource monitoring system comprises a model construction module, an adjustment control module, an atlas construction module, a data fusion module and a monitoring module. A function is introduced for analysis, an unmanned aerial vehicle motion control strategy is obtained; a model is used for entity recognition; a relation triple is obtained through dependency syntactic analysis; coordinate atlas space attributes are fused, a topological relation is constructed, a conflict relation is verified, and implicit relation analysis is completed; the method comprises the following steps: obtaining a mine modular geological map architecture, generating node embedding features by using map attention network coding, associating sensor data with map nodes by using a cross-modal attention mechanism, outputting physical enhancement features, executing monitoring analysis on mine resources, keeping stable unmanned aerial vehicle group control, and ensuring that fused data is accurate.

The invention relates to the technical field of pattern classification, in particular to a pattern classification method and system based on a regional label, and the method comprises the following steps: extracting a local label threshold based on a pixel gray range, analyzing the regional difference of a pattern image, screening a change optimal group as a reference, analyzing a gradient direction and amplitude mutation, and screening layered breakpoints. And integrating the regional features, and judging category attribution to obtain a discriminant quantity. According to the method, detailed pixel relation analysis and regional structure feature extraction are executed step by step, label affiliation accurate adjustment is achieved based on local statistics and spatial attribute association synchronization, boundary anomaly is judged through gradient and label change multiple parameters, layering is carried out, nodes are automatically positioned according to attribute mutation, and category affiliation is decided by multi-dimensional attribute interaction. Layered and partitioned analysis of high-detail and complex patterns is realized, hierarchical attribution requirements under multiple scenes are adapted, the region classification accuracy is enhanced, and the response capability to the internal differential and hierarchical relationship of the pattern structure is improved.

The invention relates to the technical field of communication network security, in particular to a communication network security auditing method and system under multi-source heterogeneous data association, and the method comprises the steps: collecting network flow data and log data in a communication network in real time; distinguishing non-service flow data and signaling data (also called service flow data) from the collected network flow data; performing deep analysis and classification processing on non-service flow data, performing unified analysis and association analysis on log data, and performing protocol decoding and content analysis on signaling data; constructing a multi-source heterogeneous data association relationship analysis model, integrating non-service flow data, signaling data and log data, mining space-time association and causal relationship among the data by applying a machine learning algorithm, and constructing a network security situation map; and based on a multi-dimensional analysis result, automatically generating a security audit strategy matched with the multi-dimensional analysis result. According to the invention, accurate detection, real-time blocking and risk early warning of communication network security threats are realized, and the network security protection capability and the operation and maintenance efficiency are improved.

The invention belongs to the field of big data, and relates to a data consanguinity analysis visualization method, which comprises the following steps of: obtaining metadata of target data from a plurality of data sources, and determining a metadata type and a metadata attribute of the metadata; obtaining a blood relationship model of the target data; based on the blood relationship model, the metadata type and the metadata attribute, performing data blood relationship analysis on the target data by adopting a preset graph algorithm to obtain a blood relationship analysis result of the target data; and based on the blood relationship analysis result, establishing a blood relationship graph of the target data, and displaying the blood relationship graph in a preset interactive interface. The invention further provides a device, equipment and a medium. In addition, the invention also relates to a block chain technology, and the metadata can be stored in a block chain. The method can be applied to the fields of financial insurance, medical health management and the like, and the blood relationship graph of the data can be efficiently and accurately established and displayed.

The invention provides a large model retrieval enhancement generation method fused with a multi-source knowledge base, and relates to the technical field of large model retrieval enhancement generation. The method comprises the steps that firstly, user query is analyzed, multiple implied independent retrieval intentions are recognized and separated, and a knowledge source identifier is distributed to each retrieval intention subtask based on a mapping rule; according to the method, related evidence fragments are acquired in parallel from a heterogeneous knowledge source library in a targeted manner, multi-level relation analysis is further performed on the original evidence fragments by introducing a cross validation engine, so that a structured evidence conflict graph is constructed, and then the evidence conflict graph is intelligently processed by applying a game theory excitation algorithm. The method comprises the following steps: obtaining an evidence set with internal consistency, forming a guide signal through structured analysis, and finally, inputting original user query, the synthesized evidence set and the guide signal into a large language model in combination with a pre-constructed cue word template to generate a final answer under constraint, so as to realize retrieval enhancement generation fused with multi-source knowledge base retrieval.

The invention provides a blood relationship analysis method based on a data dependency relationship, electronic equipment and a medium, and relates to the technical field of data blood relationship analys.The method comprises the steps that an SQL statement vector corresponding to each SQL statement is generated according to all table names and field names corresponding to each SQL statement; obtaining an SQL statement vector list A; all SQL statement vectors in A are clustered to obtain a cluster list B; analyzing each SQL statement in the Bj to extract a corresponding table name, a field name and an operation type, and establishing a field-level blood relationship graph Xj between the table names and the field names of the SQL statements in the Bj; establishing a cross-cluster blood relationship mapping table QR corresponding to the B; fusing the clusters in the B to obtain a global blood relationship analysis graph QW; displaying the QW in a layered interactive view; according to the method, the bottlenecks of the traditional blood relationship analysis method in accuracy, efficiency and availability are solved, and efficient and reliable technical support is provided for large-scale data management.

The invention provides a hot continuous rolling process fault diagnosis method and device based on a multilayer dynamic causal diagram, and relates to the technical field of engineering process monitoring. The method comprises the following steps: according to a historical key variable data set, performing causal relationship analysis by using a dynamic causal entropy algorithm to obtain a multi-layer dynamic causal graph, a causal entropy data set and a quality-related causal entropy data set; training a causal entropy prediction model to be trained by using the causal entropy data set and the quality-related causal entropy data set to obtain a causal entropy prediction model, a fault detection threshold and a quality-related fault detection threshold; according to the actual data, performing fault prediction by using a causal entropy prediction model to obtain fault prediction data; and performing fault analysis based on the fault detection threshold and the quality-related fault detection threshold to obtain a fault detection result and a fault tracing path. The hot continuous rolling process fault diagnosis method is based on the multi-layer dynamic causal diagram and is high in judgment precision and high in interpretability.

The invention discloses a two-dimensional construction drawing exporting method and system based on a three-dimensional BIM model, and the method comprises the steps: analyzing the geometric information and attribute information of a component from obtained three-dimensional BIM model data, and the geometric information comprises pipeline geometric information, equipment geometric information and structure geometric information; on the basis of the analyzed geometric information, automatically identifying a labeling object; carrying out labeling processing on the identified labeling object, calculating an optimal angle and a character avoidance position of a labeling lead in a labeling process, and generating optimized labeling information; on the basis of the analyzed attribute information, automatic layer mapping is conducted on the component, and standardized layer information is generated; on the basis of the analyzed pipeline geometric information, performing axonometric projection transformation on topological relation data obtained through pipeline topological relation analysis, and generating axonometric map information including a pipeline space connection relation and flow direction information; and integrating the labeling information, the layer information and the axonometric drawing information, and outputting a standardized two-dimensional construction drawing.

The invention relates to the field of data analysis, in particular to an internet-based convergence media data relation analysis system. The fusion media data relation analysis system based on the Internet comprises a fusion media data acquisition module, a fusion media data processing module and a hot topic analysis module. According to the method, the keywords of the convergence media data are extracted, the corresponding clustering clusters are constructed, then the correlation degree of the two pieces of convergence media data is calculated on the basis of the clustering clusters, and the convergence media data higher than the correlation threshold value are combined to be used for analyzing the hot topic condition of the current time period; and the correlation degree is calculated based on the similarity of the clustering clusters and the TF-IDF value, and the distribution condition of keywords corresponding to the clustering clusters is also considered in the calculation process, so that the accuracy of fusion media data matching can be improved.

The invention discloses an internal and external sentiment causal relationship analysis method, system, device and medium, and relates to the technical field of sentiment analysis, the method comprises the following steps: obtaining internal text sentiment data and external text sentiment data of a to-be-tested field; respectively extracting internal emotion features and external emotion features of the internal text emotion data and the external text emotion data, respectively clustering text emotions according to the internal emotion features and the external emotion features, and respectively converting the extracted emotion features into feature vectors; determining nodes in a directed graph model according to the internal emotion features, the external emotion features and the emotion classification result; determining an initial directed edge between the nodes according to the co-occurrence frequency of the emotional characteristics, and screening the directed edges of which the co-occurrence frequency is greater than a co-occurrence frequency threshold; constructing a directed graph model according to the determined nodes and the initial directed edge between the screened nodes; and analyzing the causal relationship between the internal emotion and the external emotion of the to-be-tested field through the directed graph model.

The invention provides an AI-based security risk analysis method, and relates to the technical field of data processing, and the method comprises the steps: dynamically determining three spatial reference positions through a spatial topological relation analysis algorithm based on spatial coordinates in a business data result; generating a dynamic risk partition based on the spatial reference position; executing a space grid division operation on the dynamic risk partition to form a discretized space analysis unit; calculating a grid deformation gradient tensor as a risk distribution adjustment value based on the geometric deformation parameters of the spatial analysis unit; and generating spatial risk thermodynamic data points describing spatial risk distribution according to the structured semantic information, the knowledge enhancement context, the business data result and the risk distribution adjustment value. According to the method, the risk analysis target, the key entity and the spatial constraint condition can be accurately extracted, and the convenience of man-machine interaction and the accuracy of semantic understanding are improved.

The invention provides a multi-source heterogeneous data fusion method and device. Equipment state data of a computer monitoring system, equipment ledger and historical operation and maintenance data of a production management system and environmental parameter data of a meteorological system are accessed in real time through a distributed data bus; performing standardization processing on the three types of heterogeneous data based on a unified spatio-temporal index model, and establishing a dynamic association model including a time dimension, a space dimension, an equipment dimension and a working condition dimension; performing causal relationship analysis on the cross-system data by using the dynamic association model to generate a multi-dimensional data association result related to the current accident; and according to a data association result, dynamically generating an emergency disposal scheme and pushing the emergency disposal scheme to a remote centralized control center so as to support real-time decision and disposal operation. According to the method, second-level fusion and intelligent correlation analysis of cross-system heterogeneous data can be realized, the diagnosis efficiency and emergency disposal accuracy of hydroelectric accidents are remarkably improved, the response time is effectively shortened, and the risk of repeated accidents is reduced.

The invention discloses an LCD display screen production whole-process collaborative management method and system based on the Internet of Things, particularly relates to the technical field of production process collaborative management, and is used for solving the problems of circular waiting and resource stiffness caused by lack of global resource allocation dependency relationship analysis in a complex production scene in an existing collaborative management method. Equipment state data and material position data of each process are acquired in real time, a resource allocation relation graph with production equipment as resource nodes and production tasks as task nodes is constructed, a resource access sequence of each production task is analyzed to identify a circular waiting path, and a process association network and a production flow network are combined to evaluate a blockage propagation risk. And finally, on the basis of the relief urgency degree, the production tasks are selected to carry out resource reallocation so as to realize the relief of the circular waiting path, and the overall efficiency of the production line and the resource utilization rate are improved.

The invention discloses an ontology-based data space dynamic access control method and device, and a medium. The method comprises the following steps: constructing a data dictionary connector and a basic connector; establishing a semantic mapping relationship between the global ontology and the local ontology to obtain first access mapping; establishing data field mapping between the data source and the local ontology to obtain second access mapping; performing policy relationship construction on the second access mapping to establish a policy relationship between the data source and the local ontology; obtaining a data request, and determining an accessible field corresponding to the identity of the requester through data access control based on the data request and the first access mapping; carrying out strategy relation analysis on the accessible field to obtain an access query statement; and according to the access query statement, obtaining fine-grained access data through virtual mapping knowledge domain mapping. According to the method, the technical problem that a centralized access control strategy is difficult to respond to the authority change demand in multi-organization cooperation in time is solved.

The invention relates to the technical field of data visualization, in particular to an industrial internet-based data comprehensive treatment visualization application system, which comprises a node process extraction module, a data link organization module, a communication relation analysis module, an abnormal node treatment module and a comprehensive treatment display module. According to the method, the node sequence is set, the progressive trend is judged based on the time difference, the reverse nodes are screened out, the time sequence consistency is guaranteed, the node time interval continuity is checked in sequence, the node dependency relationship is dynamically modeled, the connection distortion risk is reduced, and the connection weak sections are screened; the method is advantaged in that connectivity abnormity identification efficiency is improved, node timestamp offset and dependence missing frequency are extracted, abnormity classification management is completed according to a standard production takt and a threshold value, abnormal node attribution is refined, traceability and treatment precision is improved, and industrial process data treatment depth, transparency and decision support capability are enhanced.

The invention discloses a PLC variable association relationship analysis method and device based on weight calculation and electronic equipment. The method comprises the following steps: constructing a program dependency graph representing a PLC variable dependency relationship; according to the program dependency graph, quantifying association strength between PLC variables, and calculating to obtain a dependency edge weight; according to the dependency edge weight, calculating an association relationship score between the PLC variables, and identifying to obtain the association relationship score between the PLC variables; and determining potential attack point risk information of the industrial control system according to the identified association score, and guiding a defense system to discover an intrusion point or a fault source. According to the embodiment of the invention, the strong association relationship before the variable can be comprehensively and reliably identified; in addition, direct dependence between variables can be accurately recognized, indirect influence relations are captured, and the method can be widely applied to the technical field of computers.