73 results about "Reasoning algorithm" patented technology

The invention discloses a knowledge graph question-answer interaction method and system based on artificial intelligence, and the method comprises the steps: receiving a natural language question input by a user, carrying out the hierarchical analysis of entity words and intention words in the question through a dynamic weight distribution mechanism, and generating an enhanced semantic vector containing entity association strength and intention probability distribution; traversing the knowledge graph based on the enhanced semantic vector, and constructing a hierarchical query sub-graph comprising a core entity, an associated entity and an implicit relationship; performing reasoning calculation on the hierarchical query subgraph by adopting a multi-path collaborative reasoning algorithm to generate a candidate answer set; and performing semantic consistency verification and redundancy elimination on the candidate answer set, generating an adaptive answer and an association explanation path in combination with historical interaction preference of the user, and synchronously feeding back a newly discovered entity association relationship to the knowledge graph for incremental updating to form a question and answer interaction closed loop. By utilizing the embodiment of the invention, the accuracy and interpretability of questions and answers can be improved, and the user interaction experience and the information acquisition efficiency are improved.

The invention discloses an SVG valve hall cooling efficiency evaluation method and system based on a probabilistic graph model, and the method comprises the steps: obtaining original time sequence data which is obtained through the collection of a cooling system multi-parameter monitoring sensor group disposed in an SVG valve hall in continuous T sampling periods; preprocessing the original time series data to obtain a credible time series data set; constructing a Bayesian network topological structure comprising three-level nodes of an environment layer, a component layer and an efficiency layer and causal dependence edges, and optimizing parameters of the Bayesian network topological structure by adopting a maximum likelihood estimation method to form a dynamic Bayesian network model after parameter calibration; and the credible time sequence data set is used as an evidence variable to be input into the Bayesian network model after parameter calibration, calculation is carried out through a belief propagation reasoning algorithm, a final control instruction set is generated through probability weighted scoring processing, the final control instruction set is fed back to a valve group monitoring system, and early warning and automatic load reduction are achieved. The problems of large evaluation deviation and early warning lag in the prior art are solved.

The invention relates to the technical field of data processing and risk monitoring, and discloses a performance risk intelligent monitoring and collaborative response method and system, and the method comprises the steps: an edge sensing node calculates the compression characteristics of performance flow data through employing a mean aggregation algorithm according to a current sliding window parameter, and transmits the compression characteristics to a cloud end; the cloud decision-making platform executes data standardization management, calculates performance risk probability by using a model reasoning algorithm, and calculates feature uncertainty indexes by using a game theory interpretation method and a variance statistical method; a feedback control strategy module generates a self-adaptive sampling control instruction according to the index, drives an edge node to dynamically update a sliding window parameter, and constructs an acquisition feedback closed loop; and the collaborative response module calculates a response priority by utilizing multi-dimensional weighted logic, calculates desensitized data by utilizing a differential privacy algorithm and then executes response. According to the invention, adaptive adjustment of data acquisition granularity and safe cooperative processing of risk events are realized.

The invention discloses a traditional Chinese medicine whole industry chain big data construction method and system, and relates to the field of data links, and the method comprises the steps: generating a standardized metadata template for seven links of a traditional Chinese medicine industry chain; block chain nodes are deployed in each link of an industrial chain, timestamp fingerprints are generated for key process data, and the key process data are automatically chained for evidence storage; constructing a data fusion model based on a graph neural network, and generating cross-link associated data; constructing a traditional Chinese medicine ontology knowledge graph, and mapping the association relationship between the cross-link associated data in the graph; full-chain digital tracing from implantation to clinic is realized through a path reasoning algorithm; based on the hybrid collaborative storage architecture, access management is carried out through dynamic fragmentation strategy data points; and constructing a process prediction model, detecting process parameter deviation, and carrying out risk early warning. The method has the advantages that through block chain evidence storage and combination of the graph neural network and the knowledge graph, full-chain digital tracing and risk early warning from implantation to clinic are realized.

The invention relates to the technical field of unmanned aerial vehicles, and provides an unmanned aerial vehicle target identification and threat assessment method and application, and the method comprises the steps: obtaining an original data stream of an unmanned aerial vehicle target from a multi-source sensor, and carrying out the processing, and obtaining a unified multi-source data set; generating a static feature set by adopting a feature extraction algorithm; generating a dynamic feature set by adopting a time sequence analysis algorithm; performing feature fusion by adopting a neural network model to obtain a fused feature set; generating an unmanned aerial vehicle target knowledge graph by adopting a relation extraction algorithm; extracting association strength from the unmanned aerial vehicle target knowledge graph, and mining threat modes by adopting a graph neural network algorithm to obtain a threat mode set; generating a threat evaluation vector set by adopting a probabilistic reasoning algorithm; generating a countering strategy set by adopting a classification algorithm; and updating the unmanned aerial vehicle target knowledge graph by adopting a feedback learning algorithm to obtain an updated knowledge graph. By adopting the method provided by the invention, the unmanned aerial vehicle target can be quickly and accurately identified, a targeted countering strategy is formulated, and accurate countering is realized.

The invention discloses an automatic testing method, system and equipment based on a protocol configuration table and a medium. The method specifically comprises the following steps: capturing a communication data packet between a game client and a game server; analyzing the communication data packet by using a protocol format reasoning algorithm based on a hidden Markov model to generate a protocol configuration table; constructing an automatic test robot, and simulating player behaviors based on the protocol configuration table to execute a protocol-level test; based on the protocol configuration table and the execution state of the test robot, preferentially exploring a high-risk branch path, and dynamically adjusting the test sequence; and constructing a defect root cause analysis engine according to a protocol level test result and a multi-modal detection result, and automatically positioning and outputting a problem root in combination with protocol data, game state data and a system log. According to the method, the whole process of game automatic testing is realized, the game problem is accurately and efficiently detected, the source is positioned, the manual testing cost and error are reduced, and the game testing quality and efficiency are improved.

The invention discloses a multi-modal reasoning algorithm based on bidirectional chain type thinking enhancement. The method comprises the following steps: firstly, performing bidirectional thinking chain generation and quality verification on original multi-modal question and answer data, constructing a multi-modal thinking chain data set (MCD), and providing standardized reasoning process data for a model; secondly, three types of annotation labels of knowledge types, question types and reasoning types are added on the basis of the MCD, and a knowledge-enhanced multi-modal thinking chain dataset (KMD) is obtained and used for model supervision fine tuning (SFT) to constrain model reasoning; and finally, generating multiple candidate outputs by using KMCD in combination with the structured cue word, and obtaining a strong model with complex reasoning ability through reinforcement learning training of two-dimensional adaptive reward strategy optimization (Di-GRPO). According to the method, the problems of low data quality and insufficient training focusing of the existing multi-modal reasoning task thinking chain are effectively relieved, and the reliability and the accuracy of multi-modal reasoning are improved.

The invention relates to the technical field of computers, and discloses a health degree monitoring method for a server storage system, and the method comprises the steps: collecting a multi-source heterogeneous operation index; constructing a dynamic causal inference graph based on system topology and driving specifications; an anti-factual reasoning algorithm is adopted to trace an abnormal propagation path so as to identify a root cause; performing mode matching in combination with a historical fault case library, and calculating a comprehensive health degree score of the storage unit; and triggering grading early warning according to the score. According to the invention, through explicit modeling of the physical and logic dependency relationship and fusion of causal reasoning and historical experience, the fault positioning accuracy and early warning effectiveness are significantly improved, and the operation and maintenance noise is reduced.

The invention relates to the technical field of agricultural remote sensing and ecological monitoring, in particular to a dendrocalamus brandisii biomass region estimation method combining remote sensing data and a heterosexual model. The method mainly solves the problem of insufficient estimation precision of dendrocalamus brandisii biomass caused by terrain interference and phenological period change in the prior art. According to the method, a multi-scale feature data set is generated through collaborative preprocessing of multi-source remote sensing data; training a random forest biomass prediction model based on ground measured data; performing phenological phase identification by using a time sequence satellite image, and establishing a phenological period self-adaptive parameter lookup table; performing terrain invariant feature conversion by adopting a conditional generative adversarial network to eliminate terrain interference; the phenological period parameters and the standardized features are fused, and a high-precision biomass spatial distribution diagram is output through a dynamic weighted fusion reasoning algorithm; and finally, automatic conversion and visual monitoring from biomass to carbon reserves are realized through a carbon sink accounting module.

The invention relates to the field of sentiment analysis, in particular to an aspect sentiment triple extraction method based on double-feature enhancement of a graph neural network, which comprises the following steps of: inputting a text X for encoding and splicing, and outputting features through dimension conversion; inputting the features into a graph convolution decorrelation module to perform feature enhancement of a graph structure, and expanding the features of nodes by using an orthogonalization method; a syntactic structure and topological distance information of the graph are fused into a Transform module to serve as a graph neighbor enhancement module, and enhanced term features are obtained; and constructing mark information of text words, and decoding term features by using a span reasoning algorithm to obtain a final aspect emotion triple. The method has the advantages that a double-feature enhancement mechanism is introduced into a graph neural network framework, the problem of balance between insufficient syntactic information utilization and weak semantic modeling ability of a traditional aspect emotion triple extraction method is effectively solved, the over-smoothing problem is relieved, and the information extraction ability is remarkably enhanced.

An automated functional fault localization method for software product lines integrates techniques such as Spectrum-Based Fault Localization (SBFL), machine learning algorithms, data mining, and information theory. By utilizing code blocks as the detection granularity, it performs probability allocation from three perspectives: prediction, actual execution, and correlation. The suspiciousness values of code blocks are then computed using uncertainty reasoning algorithms, enabling the rapid identification of suspicious statements. Building on this, a more precise assessment of these statements is achieved by evaluating at three levels of granularity: global, local, and code block. This ultimately results in more efficient and accurate fault localization. The code blocks directly correspond to internal feature interactions, significantly enhancing the efficiency of searching for these interactions. For program statements with a higher likelihood of causing software errors, this method allows for quick identification and localization without manual intervention, thereby facilitating the maintenance of software product line systems.

The application belongs to the field of report generation, and particularly relates to a report intelligent generation method and system, which constructs a business demand space, fuses business entities, industry knowledge, data sources and accounting rules to construct a four-dimensional correlation graph model, adopts a cause-effect reasoning algorithm to establish a dynamic cause-effect chain of entities-industry-data-rules, combines a dynamic game attribute graph to realize real-time sensing of data flow changes, adjusts and resolves conflicts through a state machine model to drive attribute weight game, and generates a precise business correlation accounting result space; based on a multi-modal generation algorithm and a context sensing template library, intelligent adaptive generation from structured data to multi-form reports is realized; through deep coupling of a dynamic game engine and the four-dimensional graph, the application significantly improves the automatic generation accuracy and timeliness of reports in complex business scenarios, and supports real-time correlation and compliance accounting of cross-industry multi-source heterogeneous data.

The invention relates to the technical field of operation and inspection of wind turbine generator equipment, and discloses a knowledge graph and deep learning-based fan anomaly monitoring and operation and inspection optimization method, which comprises the following steps of: acquiring operation data of a wind turbine generator through an SCADA (Supervisory Control and Data Acquisition) system, and acquiring state data of the wind turbine generator through a CMS (Content Management System) system; obtaining operation and maintenance data of the wind turbine generator; constructing a TCN-BiGRU-Attention network model to analyze the multi-dimensional data, and outputting an anomaly type and confidence coefficient predicted by the model; constructing a double-layer knowledge graph; taking the exception type as input, extracting a feasible scheme according to a graph reasoning algorithm, and outputting an operation inspection strategy triple; and inputting the operation and maintenance strategy triple and the multi-dimensional data into the large language model, performing multi-round semantic reasoning and strategy optimization, and outputting an optimal operation and maintenance strategy. The method has the advantages that the high precision of anomaly identification and the high reliability of operation and maintenance decision are realized, the expandability and the practical value are good, and the method is suitable for various scenes such as intelligent wind power plant operation and maintenance, remote state monitoring and intelligent scheduling.

The application discloses a version knowledge graph reasoning method and system based on large language model enhancement, relates to the technical field of dynamic knowledge graph, and comprises the following steps: adopting a semantic drift detection and compensation mechanism, comparing the context coding differences of the same entities in different versions in an initial knowledge graph, identifying drift, dynamically adjusting entity embedding vectors, and outputting a compensation update graph; adopting a multi-hop reasoning algorithm enhanced by an LLM, performing multi-hop reasoning on the compensation update graph, performing symbolic reasoning, vector reasoning and context reasoning in parallel in each hop, and obtaining entity relationship reasoning results through dynamic weight fusion; and superimposing the entity relationship reasoning results on the compensation update graph through a cloud collaborative node, adding entity edges and automatically maintaining version history logs, and obtaining a reasoning fusion version knowledge graph. Through the multi-hop reasoning algorithm enhanced by the large language model, the deep semantic mining capability and reasoning accuracy of the cross-version entity relationship are effectively improved.

The invention discloses a psychological counseling management system based on artificial intelligence, and relates to the technical field of psychological counseling. Historical human body physiological index data, historical human face data and historical language data are collected to construct a sample set; meanwhile, the historical human body physiological index data, the historical human face data and the historical language data in the sample set are processed through a data processing method, and analysis is conducted through a data analysis method based on the processed historical human body physiological index data, historical human face data and historical language data; and after the analysis is completed, summarizing the analyzed data, constructing a psychological counseling analysis model through a reasoning algorithm, finally collecting human body physiological index data, human face data and language data in real time, and analyzing and managing the psychology of the user by constructing the psychological counseling analysis model, so that the accuracy of psychological counseling management is improved.

The invention provides an R-KGCN emergency disposal method for the silt danger of a floating wing suspension door in the storm surge period of a super-huge tide gate. According to the method, a system framework covering knowledge modeling, reasoning definition and reasoning optimization is constructed on the basis of a relationship-enhanced knowledge graph convolutional network, and structured expression and intelligent reasoning of sediment disaster emergency disposal knowledge are realized. The method comprises the following main technical links: (1) constructing a knowledge graph mode layer and a data layer oriented to a sediment disaster scene, and establishing a'feature-event-disposal 'ternary model and a hierarchical relationship; (2) proposing a structured reasoning problem definition framework of an emergency processing task, and supporting modeling requirements of multi-source, multi-scale and multi-relation semantics; (3) designing an R-KGCN inference algorithm integrated with a semantic relationship enhancement module, and improving the accuracy and interpretability of inference; and (4) forming a knowledge reasoning and intelligent recommendation method oriented to sediment dangerous cases, and providing decision support and emergency response guarantee for operation scheduling of the oversize tide gate.

The invention relates to an exciting transformer winding and clamp defect detection method fusing a global-local attention mechanism and slice reasoning, and belongs to the technical field of intelligent operation and maintenance of power equipment and computer vision. According to the method, for the problems of strong background interference, high missing detection rate of tiny defects, insufficient positioning precision and the like in the prior art, an improved YOLOv8 model is constructed, and an auxiliary bounding box is introduced to optimize a loss function to enhance the positioning capability of the irregular defects; a receptive field convolution block attention module and a reverse residual attention module are adopted to fuse local features and global dependence so as to suppress complex background interference, space-to-depth convolution is utilized to realize lossless downsampling and reserve fine texture features, and a slice-assisted super reasoning algorithm is combined to improve the detection performance of small targets in a high-resolution image. According to the method, the accuracy and the positioning precision of defect identification are remarkably improved, the omission ratio is effectively reduced, and reliable technical support is provided for intelligent operation and maintenance of the exciting transformer.

The application relates to the technical field of artificial intelligence, and discloses a defect determination method and device, computer equipment and a storage medium, which comprise the following steps: collecting software data of target software, inputting the software data into a pre-trained defect prediction model for defect risk prediction processing to obtain target risk defects with risk evaluation indexes higher than a preset index threshold; taking the target risk defects as target nodes, extracting feature data from the software data to generate associated nodes, forming a cause-effect dependency graph according to the cause-effect correlation between the target nodes and the associated nodes; based on a graph model cause-effect reasoning algorithm and the cause-effect dependency graph, screening candidate root causes of the target risk defects from the associated nodes; and determining root cause results of the target risk defects according to the associated data of the candidate root causes. The application can be specifically used in the fields of financial technology and health care, improves the identification accuracy of defects and root causes of software, and thus improves the software management efficiency.

The invention provides a vehicle fault diagnosis method, device and system, and relates to the technical field of vehicle fault diagnosis. The method comprises the following steps: acquiring multi-source observation data of a vehicle in a current time slice; determining a target reasoning algorithm from a plurality of candidate reasoning algorithms; taking the multi-source observation data as input of the trained dynamic Bayesian network model, and performing current reasoning by using a target reasoning algorithm and the dynamic Bayesian network model to obtain a current reasoning result; and according to the current reasoning result, determining a potential fault component from the plurality of components. By means of the method, fault evolution can be dynamically tracked, so that accurate recognition and quick response to faults of vehicles (such as automatic driving mine cars) are achieved, and therefore stable operation of the system and personnel safety are guaranteed.

The present application relates to the technical field of true and false identification of South Red Agate, and particularly relates to a true and false intelligent identification method of South Red Agate based on a knowledge graph. The method comprises the following steps: collecting original data of South Red Agate samples, and preprocessing the original data to obtain preprocessed data; processing the preprocessed data through a multi-dimensional feature convolutional neural network to obtain comprehensive feature data of the South Red Agate samples; introducing a graph reasoning layer into the multi-dimensional feature convolutional neural network, and reasoning through a dynamic topology reasoning algorithm based on the comprehensive feature data of the South Red Agate samples to obtain an optimal path; and based on the optimal path, intelligently identifying the true and false of the South Red Agate samples. The method solves the technical problems of insufficient accuracy and low efficiency of traditional true and false identification methods of South Red Agate.

The embodiment of the invention provides a method and device for generating bank training course content based on a large model and a processor. The method comprises the steps that structured data and unstructured data in a bank are acquired; preprocessing the structured data and the unstructured data to obtain preprocessed structured data and unstructured data; constructing a dynamic knowledge graph based on the preprocessed structured data and unstructured data, and dynamically updating the dynamic knowledge graph based on incremental updating and a graph reasoning algorithm; based on the dynamically updated dynamic knowledge graph, combining an RAG technology and a graph neural network, and constraining the output of a large model in the bank field in real time; and generating bank training course content based on the binding cue word and the real-time binding bank field large model. According to the scheme, the bank training course content can be dynamically generated.

The application discloses a heat release tracing method for new energy station debugging quality problems and a related device, comprising: preprocessing environmental data and equipment data of the new energy station to obtain preprocessed data; constructing a standardized event stream according to the preprocessed data; calculating the dynamic failure rate of each basic event in the standardized event stream; based on the dynamic failure rate of each basic event in the standardized event stream, hierarchically combining each basic event according to system function relationship and logical dependence to construct a dynamic fault tree model of the new energy station; based on the dynamic fault tree model of the new energy station, through a time progression logic reasoning algorithm, combining the dynamic failure rate of each basic event, calculating the delayed probability contribution degree of each basic event, identifying the key root cause event and the propagation path, and the method and the related device can improve the tracing accuracy and response efficiency of the new energy station debugging quality problems.

The invention discloses an aviation plug health assessment method based on evidence reasoning, and relates to the technical field of equipment health assessment, and the method comprises the steps: determining assessment indexes reflecting the health state of an aviation plug, and obtaining the data of the aviation plug under each assessment index; obtaining a reference value corresponding to each health level in each evaluation index, converting data of the aviation plug under each evaluation index into confidence distribution of the reference value based on the reference value, and forming evidence of each evaluation index; for each health level, fusing the evidence of each evaluation index by using an evidence reasoning algorithm to obtain the evidence of each input evaluation index, and mapping the evidence into the confidence of output relative to each health level; and calculating a final utility value for representing the health state of the aviation plug according to the utility value of each health level and the confidence coefficient. According to the method, information generated in the electrical performance testing process and the mechanical performance testing process is comprehensively utilized, the uncertainty in the evaluation process is reduced, and the credibility of the evaluation result is improved.

The invention discloses a medication auditing method and system based on multi-modal artificial intelligence reasoning, and relates to the technical field of medical information, and the method comprises the steps: obtaining an original specification of a commercially circulated drug and individual parameters of a user; constructing a multi-modal drug knowledge base according to an original factory specification in combination with the multi-modal pre-training model; based on the multi-modal drug knowledge base, according to a multi-source consensus fusion algorithm, constructing a dynamic auditing rule base; and on the basis of the individualized parameters and the dynamic auditing rule base, repeated medicine taking and medicine interaction judgment are carried out through an artificial intelligence reasoning algorithm, and an auditing result is obtained. According to the method, the problems of multi-modal data processing surface processing, weak individual parameter fusion, lack of scene adaptability and fuzzy risk output of a traditional method are solved, more accurate, comprehensive, dynamic and practical medication safety auditing is realized, and medication safety is ensured.

The invention relates to the technical field of artificial intelligence and engineering management, and discloses an artificial intelligence data analysis system and method for an engineering project, and the system comprises a data collection assembly, a data storage module, a knowledge graph construction unit and an AI analysis module. The knowledge graph construction unit converts multi-source heterogeneous original data into a unified engineering project knowledge graph. The AI analysis module comprises: a machine learning sub-module configured to call data and obtain context feature data of a knowledge graph, and generate a predicted value or a security risk score; and the intelligent decision-making sub-module is configured to execute a graph reasoning algorithm on the knowledge graph when the score meets a preset triggering condition, and generate a decision-making scheme. The method comprises the steps of data acquisition, data storage, engineering knowledge graph construction and AI intelligent analysis and decision making. According to the method, the engineering heterogeneous data fusion problem is solved, the prediction accuracy is improved by fusing the context features of the knowledge graph, and active intelligent decision is realized through graph reasoning.

The invention provides a maritime affair text entity and relation joint extraction method based on a depth reasoning algorithm, belongs to the technical field of maritime affair text entity and relation extraction, and aims to solve the technical problems that multilevel nesting, low reasoning efficiency, poor field adaptability and the like cannot be solved at the same time when maritime affair texts are processed in the prior art. The method comprises the following steps: S1, carrying out word segmentation on an input maritime affair official document to obtain a Token sequence, and mapping the Token sequence into a context vector sequence by utilizing a non-autoregressive Transform encoder subjected to maritime affair corpus fine adjustment; s2, constructing a depth reasoning model, and executing an entity starting point detection sub-task, a nested entity span recognition sub-task and an entity relationship reasoning sub-task in parallel; and S3, performing end-to-end training on all learnable parameters in the step S2 by adopting a joint loss function, so that the three sub-tasks of entity starting point detection, nested entity span recognition and entity relationship reasoning share the same encoder and are mutually calibrated, error propagation is eliminated, and the adaptability of the maritime affair field is improved.

This invention proposes a map data interaction method and system based on urban road networks, comprising: acquiring open-source geographic data; identifying topological relationships between road entities using a computational geometry-based topological relationship reasoning algorithm; constructing an urban road knowledge graph; anchoring semantic nodes through spatial indexing, extracting their neighborhood association subgraphs, and using viewport adaptive computation and force-directed layout algorithms to focus the association subgraphs on the knowledge graph view, establishing a mapping relationship from map to knowledge graph; parsing the geometric attributes of semantic nodes, using a viewport fitting algorithm to drive map positioning and zooming, and establishing a mapping relationship from knowledge graph to map; receiving the user's original query statement, performing intent recognition and structured query generation through a large language model, and combining the knowledge graph retrieval results with the current intent recognition results to generate predictive decision support information; this method improves the semantic understanding depth, human-computer interaction efficiency, and decision-making level of urban traffic.

The application provides a multi-view relationship network chart question and answer method based on an attention mechanism, and the method comprises the following steps: S1, acquiring a data set required to be processed; S2, inputting a chart image and a corresponding text question in the data set as input items respectively; S3, configuring a fusion reasoning algorithm model to output a final result. By improving the image encoder model of the traditional relationship network, an effective transformer attention model CoT module is introduced, and the problem that the extraction capability of RN for image feature information is limited is solved; a novel multi-view relationship module is proposed, the pairing process is improved based on pixel points and channel information, the problem that RN regards all feature vectors as equally important without highlighting more effective relationship image feature pairs is solved, and the problem that the overall information of each channel of the image is ignored in the traditional RN pairing process is solved.

The present application belongs to the technical field of sensor fault diagnosis of railway traffic system, and particularly relates to a traction transmission system grid-side current transformer sampling loop fault diagnosis method, which combines physical feature extraction based on power balance principle and regularization extreme learning machine algorithm to establish a multi-working-condition grid-side current estimation model, uses a sliding window double statistical test method to perform abnormal detection on the residual error of actual values and estimated values of two-way grid-side current signals, establishes a Bayesian diagnosis model integrating hidden Markov model idea, defines a four-state space containing normal and various fault states, simultaneously introduces a state transition inertia mechanism to suppress random noise, adopts a logic lockout mechanism to exclude transient power failure interference caused by train passing through a neutral section, and in a non-lockout state, iteratively calculates the posterior probability of each state through Bayesian inference algorithm, and realizes diagnosis and positioning of the grid-side current transformer sampling loop fault according to the maximum posterior probability principle.

The invention discloses a malicious behavior detection method and system based on data mining, and relates to the technical field of information security, and the method comprises the steps: mining malicious behavior modes of which the occurrence frequency exceeds a preset threshold value from a malicious software sample based on an attribute behavior map, and forming a malicious behavior mode library; performing graph structure matching operation on each attack sub-graph mode in the malicious behavior mode library and the attribute behavior graph of the to-be-tested software, counting the occurrence frequency of each attack sub-graph mode in the attribute behavior graph of the to-be-tested software, and generating a high-dimensional graph structure feature vector; and performing causal association analysis on the graph structure feature vector by adopting a causal reasoning algorithm, quantitatively evaluating the causal action intensity between each feature dimension and the malicious behavior, and selecting the feature dimensions with clear causal association to form an optimized feature subset. According to the method, feature screening mechanism innovation from statistical correlation to causal correlation is realized.