243 results about "Rule mining" patented technology

The invention discloses an auditing decision support system and method based on a dynamic knowledge graph, relates to the technical field of computers, and aims to solve the problems that auditing data are heterogeneous and complex, risk identification is not timely and causal interpretation is lacked. According to the system, multi-modal audit data is collected in real time through a streaming event processing framework, and a dynamic audit knowledge graph with timeliness weight is constructed. Based on a graph calculation engine and cross-domain rule mining, identifying a high-frequency risk mode, and generating a risk conduction path graph; further fusing a multi-modal graph attention network, identifying and positioning abnormal entities, and outputting abnormal nodes and risk links thereof; and finally, the abnormal node embedding representation is dynamically updated through the time sequence diagram attention network, an interpretable audit causal map is generated in combination with a structural causal model, and closed-loop support from data acquisition and risk identification to interpretive audit decision is realized. The intellectualization and transparency of audit decision making are improved, and an efficient and traceable decision making basis is provided for a complex audit scene.

The invention relates to a part surface defect detection and process optimization method and system, and solves the problems that defect detection has defects, missing detection and erroneous judgment are easy to occur, and subsequent process improvement faces huge challenges even if defects are detected, and the method comprises the following steps: inputting a feature set into a double-branch fusion deep learning model, the first branch identifies defect types and quantization parameters by fusing three-dimensional features and two-dimensional features, and the second branch calculates the correlation degree between the defect features and each process through association rule mining and a random forest algorithm; when the three-dimensional features and the two-dimensional features both meet a preset defect threshold value and the association degree of a certain process exceeds a preset value, determining that the process is a root process; and analyzing a deviation value between the key parameter of the source process and the defect quantization parameter, and correcting the parameter through a dynamic adjustment mechanism according to the deviation degree. The method has the advantages that the defects of the part are accurately detected, the procedure is traced, parameters are dynamically adjusted, closed-loop optimization is formed, and the quality of the part is improved.

The invention discloses a report generation system and method based on a legal knowledge graph, and relates to the technical field of artificial intelligence and law, and the method comprises the steps: obtaining a legal appeal of a user through a non-standardized domain corpus collection engine, carrying out the multi-dimensional intention deconstruction through a semantic slicing model, and obtaining a judicial element topology network; inputting the judicial element topology network into a law deduction engine of dynamic value perception ability, calculating a decision vector weight through a dynamic value evaluation network, and generating a judicial decision path by using a time sequence feedback mechanism; based on a judicial decision path, through an association rule mining engine of a multi-modal knowledge graph, a dynamic weighting law retrieval confidence matrix is generated in combination with semantic coupling degree analysis and logic path influence evaluation. According to the method, qualitative change of legal consultation from information retrieval to intelligent decision is realized through collaborative innovation of a legal deduction engine with dynamic value perception capability and a legal semantic constraint decoder.

The invention provides an anti-fraud method and system based on a complex relation network, and the method comprises the steps: carrying out the preprocessing and association rule mining of multi-source data, constructing a heterogeneous relation network model, carrying out the abnormal behavior detection of real-time data, updating the heterogeneous relation network model based on an abnormal behavior detection result and historical data, and carrying out the recognition of the abnormal behavior. And performing risk quantification, community discovery, abnormal path analysis and related result visualization on the updated heterogeneous relation network model, calculating a fraud risk score of each node through a pre-trained machine learning model based on a related result, and finally generating a service instruction of each node based on the fraud risk score and a preset score interval. By adopting the method, the problems of poor adaptability, high false alarm rate, low gang crime recognition rate and the like of the existing anti-fraud technology can be relieved.

The invention discloses a mapping prediction method and system based on a hardware resource load and system operation relationship. The method comprises the following steps: collecting hardware index data in real time when an operating system operates; the method comprises the following steps: preprocessing hardware index data, performing association analysis on each index data by adopting an association rule mining algorithm, generating association rules, evaluating the association rules, screening out the association rules meeting conditions, and constructing a multivariable association model; based on a correlation analysis result, a load prediction model is constructed and trained, and the trained model is deployed in the system for load prediction; and dynamically adjusting a system hardware resource allocation strategy according to a prediction result, carrying out task migration and load balancing according to a predicted load condition, and automatically adjusting hardware resources through an automatic script or a scheduling tool. Through real-time monitoring and deep correlation analysis of indexes such as the CPU, the memory, the storage I / O and the network bandwidth, the resource utilization rate and the operation efficiency of the system are improved, and the stability and the reliability of the system are improved.

The invention relates to the technical field of computer network management, and discloses an intelligent alarm preprocessing method of an adaptive rule engine, which comprises the following steps: firstly, acquiring real-time operation data of network equipment and a preset alarm baseline, and analyzing a historical alarm sequence through an association rule mining model to obtain the preset baseline; then inputting the data into an alarm decision model based on an event atlas analysis algorithm, processing the data by the model by using a multi-dimensional time window algorithm, calculating adaptive weight correction processing parameters in combination with node resource load parameters, and outputting strategy parameters; and finally, adjusting rule engine judgment logic according to parameters to realize alarm intelligent filtering and aggregation. In addition, a link emergency optimization step is provided. The method improves the accuracy and adaptive capability of alarm processing, and is suitable for a complex network environment.

The invention relates to a standardized project management and intelligent professional scheduling system, and belongs to the technical field of project management and human resource intelligent scheduling. The project standardization module is disassembled into standardization task steps through a business process association rule mining algorithm, and project complexity and resource tensity are adapted by using a task attribute dynamic weight algorithm; the archive matching module constructs professional archives, extracts feature vectors through a multi-criterion decision-weighted bipartite graph matching algorithm, and generates optimal matching pairs in combination with adaptive weight adjustment and an integer linear programming model; the dynamic scheduling module plans a task execution scheme according to a resource constraint scheduling mechanism, and realizes efficient scheduling in combination with a skill supply and demand prediction algorithm and calendar integration; and the quality optimization module adopts a deliverable anomaly detection algorithm to monitor compliance, feeds back an iterative matching and scheduling strategy through a time sequence prediction optimization algorithm, and perfects a skill map based on a map increment updating algorithm. The system realizes a project full-process closed loop.

The invention relates to a building engineering quality monitoring method, system and device based on big data and a medium, and the method comprises the steps: fusing multi-source heterogeneous data of a current detected building, carrying out the time-space alignment and temperature compensation processing of a beam-column connection point, and generating a standard fulcrum stress data set; quantifying a nonlinear influence rule of a span length, an inclination angle and a fulcrum topological relation on stress based on an association rule mining technology, and generating a fulcrum-span association matrix; constructing a dynamic prediction model in combination with construction process parameters, deducing fulcrum stress distribution and generating a three-dimensional thermodynamic map; high-risk nodes are positioned through clustering analysis, a reinforcement scheme is matched, and a'detection-analysis-decision 'closed loop is formed; according to the method, the three defects of data splitting, difficulty in quantification of association rules and disjunction of decisions in the traditional technology are overcome, the recognition accuracy of high-risk nodes is greatly improved, and the problem of precise management and control of hidden quality hazards of large buildings is essentially solved.

The invention discloses an automatic driving test scene library construction method based on real traffic data, and relates to the technical field of automatic driving, and the method comprises the following steps: based on a selective sensor fusion framework, dynamically adjusting a fusion strategy of a multi-modal sensor according to a current driving environment, and obtaining corresponding scene elements; performing hierarchical classification on scene elements, constructing a risk assessment model, calculating a comprehensive risk score, and preliminarily dividing risk levels; constructing a rule-based classifier by adopting an association rule mining technology on the basis of results of hierarchical classification and preliminary risk grading, and carrying out risk grading on the scene to be evaluated; and the scene elements and the risk levels are stored in a structured manner, and an automatic driving scene library supporting multi-dimensional query is constructed. According to the method, the characteristics of the traffic scene can be captured more comprehensively, scene elements can be identified more accurately, the risk levels of the scene can be divided scientifically, and the scene library is constructed by combining the scene elements and the risk levels, so that the diversity and pertinence of the test scene are improved.

The invention discloses an emergency response decision optimization system and method for an intelligent refrigerant system, and the method comprises the steps: carrying out the automatic data collection, crawling and collecting the text data of an emergency plan, a historical emergency event and a risk factor related to the refrigerant system, carrying out the intelligent text preprocessing, and carrying out the decision optimization. Potential association relationships in the data are mined through an association rule mining algorithm, and a multi-layer complex network model is constructed. The multi-layer complex network model is input to a convolutional neural network through an embedded layer, key features are automatically extracted, and the model is trained, so that optimal emergency response strategies in different risk scenes are learned. The system receives risk input data in real time through an application program interface, and rapidly outputs optimized emergency resource configuration and coping measure combination to assist a decision maker to make an optimal decision in an emergency.

The invention provides a production line product quality tendency defect determination method, medium and system, and belongs to the technical field of production digital data processing.The production line product quality tendency defect determination method comprises the steps that firstly, quality detection data is collected to construct a feature matrix, and a time sequence feature vector is obtained through singular value decomposition; and calculating a defect tendency index, and establishing association mapping in combination with a defect category vector. Then calculating the parameter utility by adopting a multi-level analysis method, and weighting the defect tendency index; a bidirectional long-short-term memory neural network is constructed as a discrimination model, a special defect screening layer is included, and dynamic identification of defect types is realized. According to the method, a defect feature database is established to store historical data, real-time monitoring and early warning of production line products are realized through model training and association rule mining, and finally early warning information is output, so that the technical problem that the quality defect tendency of the production line products cannot be accurately identified and predicted in the prior art is solved.

The invention relates to the technical field of maintenance work order management, in particular to an intelligent management system for after-sales maintenance work orders, which realizes equipment clustering based on a K-means algorithm, considers different user habits and use environment differences, and improves the fault diagnosis precision. A fault propagation network is constructed in combination with association rule mining and a graph convolutional network, and a fault influence path between hardware is described, so that accurate identification of target fault hardware is realized, alternative hardware is intelligently determined in combination with recent maintenance records, and misjudgment and repeated maintenance are avoided. And the intelligent order dispatching module is used for preferentially dispatching the nearby maintenance personnel with the required spare parts by matching the position information of the maintenance personnel with the real-time spare part inventory, so that the maintenance response efficiency is improved. And the model error correction module continuously monitors the operation state of the equipment after maintenance is completed, automatically adjusts node features and edge weight parameters if a prediction result does not accord with actual maintenance, updates a graph convolution model through an incremental learning mode, and improves the self-adaption and evolution capability of the model.

The invention provides an intelligent prediction method and system applied to system log security audit, and the method comprises the steps: firstly obtaining a historical log data set of a power monitoring system, carrying out the event correlation modeling, generating a log event correlation model, generating a security event prediction rule library based on the log event correlation model through a rule mining algorithm, and carrying out the prediction of the security event. And then obtaining a real-time log data stream, inputting the log event association model to obtain a real-time event association result, matching the real-time event association result with the rule base to generate an abnormal event prediction result, finally generating a security audit report according to the abnormal event prediction result, sending the security audit report to the power monitoring terminal, and updating rule base parameters according to feedback information. Therefore, the abnormal event in the power monitoring system can be predicted in advance, and the intelligent level and the safety guarantee capability of system safety auditing are improved.

The invention relates to a photovoltaic electric field output prediction method and device based on association rule mining, and a medium. The method comprises the following steps: preprocessing historical data of a photovoltaic electric field; according to the preprocessed historical data, establishing an association relationship between the photovoltaic output parameters and environmental factors in the historical data, and screening out key influence factors; the environmental factors comprise a shielding factor, a surface temperature rise factor and a corrosion factor; performing association rule mining on the key influence factors through an association rule mining algorithm, establishing an association rule function, and dynamically detecting and correcting parameters of the association rule function; and predicting the output of the photovoltaic electric field by using the association rule function. Compared with the prior art, the method has the advantages of high precision, high stability, high robustness and the like.

The invention relates to the technical field of big data analysis, in particular to a big data intelligent association analysis decision-making method based on deep learning, and the method comprises the steps: obtaining and aligning multi-source heterogeneous big data according to a preset rule, inputting an end-to-end training model, and then generating a high-order association feature vector representing an element association relationship through an association feature generation layer; information density indexes are obtained through internal state auditing; the decision output layer outputs a decision result based on the vector, and dynamically adjusts a weighting coefficient for balancing decision precision and feature efficiency in loss calculation according to an audit result; then combining a decision result, an adjustment coefficient and a true value to calculate a composite loss value, and finally synchronously updating two-layer model parameters; according to the method, dynamic collaborative optimization of rule mining and decision targets is realized; and a closed-loop mechanism is constructed through composite loss value feedback, weighting coefficient dynamic adjustment and parameter synchronous updating, so that a decision result reversely optimizes a correlation feature generation strategy.

The present application provides a multi-terminal ecosystem fund allocation management system for an e-commerce enterprise, comprising: acquiring tax jurisdiction information of each business entity, comparing tax policies of different regions, and determining whether there is a difference between the tax policies; if there is a difference between the tax policies, acquiring from financial systems of each business entity fund distribution-related transaction data and tax information, and obtaining a unified-format tax information dataset by means of data cleaning and integration; using an association rule mining technology, finding an association mode between fund allocation and tax processing from the tax information dataset, so as to form a tax association rule library for fund allocation; on the basis of the tax association rule library for fund allocation, constructing a tax benefit evaluation model for fund allocation, and predicting a tax impact of a solution by means of inputting different fund allocation solution parameters.

The invention discloses a low-altitude economic unmanned aerial vehicle data processing method and system based on a large model, and relates to the technical field of low-altitude economic data processing, and the method comprises the steps: extracting low-altitude multi-target decision features of a low-altitude semantic decision map, carrying out the fuzzy reasoning of the low-altitude multi-target decision features, and generating an anti-interference control instruction set; based on the anti-interference control instruction set, fault logs and task execution data during operation of the unmanned aerial vehicle are collected, and an evolution strategy library is generated through association rule mining; and performing causal analysis according to the evolution strategy library, generating a cluster coordination rule upgrade package, performing dynamic verification on the cluster coordination rule upgrade package, and outputting a self-healing strategy. According to the method, the reliability and the safety of task execution of the unmanned aerial vehicle are improved by constructing the semantic association large model and generating the anti-interference control instruction set.

The invention relates to the field of artificial intelligence, and discloses a large language model illusion suppression method and system based on formalized proof, and the method comprises the steps: carrying out the logic mapping of the input and output of a language model through a formalized logic verification framework, and carrying out the logic verification of the output of the language model according to rule matching and recursive reasoning, identifying model illusion data; on the basis of an association rule mining algorithm, performing illusion mode mining on the model illusion data; clustering the illusion modes by utilizing clustering analysis to obtain a clustering result of the illusion modes; and based on a clustering result of the illusion mode, using a rule learning algorithm to formulate an inhibition rule, generating a branch rule of the decision tree, and performing illusion inhibition on an output result of the language model according to the inhibition rule and the branch rule. Through automatic rule learning and association rule mining, the occurrence of illusion modes is reduced, and the reasoning quality of the language model is improved.

The invention discloses an emergency pre-examination grading system and method based on collaborative decision of a large language model and a tree model. The method comprises the following steps: constructing a data set according to patient information and screening data; training an initial random forest model based on the data set, generating a basic decision rule, mining and extracting high-frequency features based on association rules, and combining to obtain a candidate rule pool; constructing a cue word structure adaptive to the field, driving LLM to complete rule correction, and forming a correction rule set; performing multiple rounds of rule random division and rule combination generation based on the correction rule set, and then screening out an optimal rule; and on the basis of a specific scene, the expert rule and the corrected optimal rule are fused, matching is carried out on patients, and emergency pre-examination grading is realized. According to the method, the interpretability is improved while the grading accuracy is improved, and the problem of poor cross-courtyard generalization of a machine learning model is effectively solved. The generalization ability of the rule is remarkably improved, and the method is adaptive to a multi-center combined diagnosis and treatment scene.

The invention discloses an industrial building business service management method and device and a medium, and belongs to the technical field of building service management, and the method specifically comprises the steps: constructing a multi-dimensional data fusion model; defining cost, progress, quality and security dimension data ranges and acquisition nodes, and storing the data ranges and the acquisition nodes in a model database after standardization; based on the standardized data of the multi-dimensional data fusion model, analyzing a dimension internal relationship by using an association rule mining algorithm, defining a dimension mapping relationship, and integrating to form an association rule base; establishing a multi-dimensional linkage analysis module; the module is established by taking a model database as a data source and a multi-dimensional data association rule base as a logic support, and a threshold triggering mechanism and a cross-link data calling function are set; constructing a business collaborative decision support module; business requirements of multiple departments are integrated; a multi-dimensional data visualization monitoring platform is built; and designing a linkage trend chart based on a result of the multi-dimensional linkage analysis module.

The invention discloses a one-time plot rule mining method and device for a process event log, and belongs to the field of plot mining. The distance between two events is limited by adopting a time interval constraint, and repeated use of the events is avoided by adopting a one-time condition so as to avoid mining excessive meaningless plots. Moreover, when a strong one-time plot rule (i.e., a one-time plot rule with confidence greater than or equal to a minimum confidence threshold) is mined, a triple mechanism of candidate plot generation, support degree calculation and rule generation is provided. In a candidate plot generation stage, eliminating redundant candidate plot extension by adopting a plot connection strategy; in the support degree calculation stage, the support degree is calculated based on the position index; in a rule generation stage, firstly, all frequent one-time plots are mined, and then strong one-time plot rules are screened according to a minimum support degree threshold value and a minimum confidence coefficient threshold value. The technical effect of efficiently and accurately mining the strong one-time plot rule is achieved.

The invention discloses a postoperative infection monitoring and early warning method and system, and relates to the technical field of clinical monitoring, and the method comprises the steps: collecting a postoperative infection multi-source alignment data set, building an individual reference model, dynamically extracting postoperative change features, building a behavioral physiological coupling model, recognizing abnormal interaction, and quantifying the infection risk. According to the method, multi-source data are fused, an individualized dynamic baseline model and a behavior-physiological coupling model are established, intelligent recognition and grading early warning of postoperative infection risks are achieved, and the method is high in practicability and easy to popularize. Model parameters are automatically corrected by utilizing a self-learning and feedback recharge mechanism, a closed-loop process of monitoring, prediction, intervention and optimization is formed, infection symptoms can be recognized in advance, manual dependence is reduced, the real-time performance and accuracy of postoperative infection prevention and control are improved, and intelligent decision support is provided for clinic.

The invention provides an enterprise employee demand recommendation method and device based on multi-dimensional data driving, and the method comprises the steps: collecting the associated data of enterprise employees, and generating a standardized data set; constructing a dynamic preference model based on the standardized data set; the method specifically comprises the following steps: mining strong association rules of employees-commodity categories by adopting an association rule mining algorithm to form a preference rule base; dynamically adjusting the weight of each rule in the preference rule base on the basis of a time attenuation coefficient; using the employee satisfaction score as a supervision signal, and iteratively adjusting the confidence threshold of each rule in the preference rule base to complete the iterative training of the model; and receiving welfare conditions input by an enterprise, screening commodities conforming to the preference rule base from the commodity pool based on the dynamic preference model, and generating multiple groups of personalized gift bag recommendation information containing the commodities. According to the invention, the defect that the diversified and personalized demands of enterprise customers on welfare demand schemes are difficult to meet at present is overcome.

The invention relates to the technical field of data processing, and discloses a hydrocephalus correlation analysis method and system based on multi-dimensional data. The method comprises the steps that a feature matrix is generated by obtaining and standardizing hydrocephalus multi-dimensional data, after the matrix is discretized, an association rule mining algorithm is adopted to extract an association mode, a rule set is obtained in combination with knowledge graph verification, data clusters are divided based on mahalanobis distance clustering, mixed effect time sequence model fitting parameters are established for all the clusters, and a mixed effect time sequence model is obtained. And calculating a target sample attribution cluster and predicting a symptom improvement trajectory and a confidence interval. According to the method, automatic integration, credible association rule extraction, precise subtype division and individualized symptom trajectory prediction of the multi-dimensional data of the hydrocephalus patient are realized.

The invention discloses a control method of an air tightness detector. The method comprises the following steps: acquiring air tightness characteristic data and historical detection data of a product; according to the historical detection data, performing model construction based on an association rule mining algorithm to obtain a parameter-defect mapping model; extracting similar product parameters in the airtightness feature data, and inputting the similar product parameters into the parameter-defect mapping model to obtain adjustment detection data; extracting air tightness characteristic parameters from the adjustment detection data, and inputting the air tightness characteristic parameters into a pre-trained air tightness prediction model to obtain an air tightness prediction value; and according to the historical detection data, setting an early warning threshold value of an air tightness change trend, and when the air tightness prediction value is greater than the early warning threshold value, generating an air tightness detection report. According to the method, dynamic adjustment and intelligent prediction of detection parameters can be realized, and the precision, the efficiency and the adaptive capacity of the air tightness detector are improved.

The invention discloses an alarm association rule generation method and device based on clustering, equipment and a medium. The method comprises the following steps: collecting alarm logs in response to an alarm log information collection instruction, and sorting the alarm logs according to a timestamp sequence; performing clustering processing on the sorted alarm logs based on an elbow rule to obtain a plurality of alarm clustering groups; dividing each alarm cluster group into at least one transaction according to a time window, and generating an item set; and performing frequent item set mining on each item set by adopting an association rule learning algorithm, generating an alarm association rule for describing an attribution relationship among the fault description information, and storing the alarm association rule in the graph database. Different from the prior art which only depends on timestamp sorting, the embodiment of the invention creatively puts forward that alarm logs in a time window are grouped by utilizing an elbow rule to form a high-cohesion alarm clustering group, so that subsequent association rule mining focuses on an alarm combination with strong space-time association, and the rule confidence is remarkably improved.

The invention is suitable for the technical field of graph data, and relates to a graph association rule mining method and device, equipment and a medium. The method comprises the following steps: acquiring graph data of a service system, and determining a target attribute corresponding to a target task and a target node tag corresponding to the target attribute; obtaining a reference graph mode according to the target attribute and the target node label; determining target sub-graph data matched with the reference graph mode from the graph data; obtaining graph features of the target sub-graph data, and performing decision tree training according to the graph features by taking the target attributes as training tags to obtain a reference decision tree; and performing association rule extraction on the reference decision tree to obtain a target association rule. The mining efficiency of the graph association rule can be improved.

The invention discloses an aircraft general assembly information association rule mining method and device based on a fine tuning large language model and a medium, and relates to the field of electronic data digital processing, the method comprises the following steps: S1, collecting multi-source production element data in an aircraft general assembly process file; s2, performing field adaptation by a large language model fine tuning method based on low-rank adaptation; s3, extracting structured element information, and constructing a semantically optimized item set; s4, mining association rules of the production elements and the final assembly links based on an association rule mining algorithm; and S5, optimizing and outputting a high-confidence association rule set. According to the method, the accuracy of information extraction and the depth of production element association analysis are effectively improved by combining fine adjustment of a large language model with association rule mining, introduction of a low-rank matrix and deep combination of a mining algorithm, so that the recognition precision of the association relationship between production elements is greatly improved; and reliable data support and decision basis are provided for optimization of the final assembly process.

The embodiment of the invention provides a fault positioning method and device based on association rules, equipment and a program product. Relates to the field of energy storage system fault diagnosis. The method comprises the following steps: acquiring a state code, a fault code and an operation parameter of the energy storage thermal management equipment; based on the state code, the fault code and the operation parameter, generating a fault rule base by adopting an association rule mining algorithm; unsupervised clustering analysis is carried out on the operation parameters, and abnormal data deviating from the standard are screened out; fusing the abnormal data with the fault rule base to generate a fault map; and based on the fault map, a fault positioning report of the energy storage thermal management equipment is output, the fault positioning report at least comprises a fault propagation path and a maintenance scheme, and the fault propagation path is a path of a causal relationship between the fault code and the abnormal mode. The method is used for achieving the effect of improving the fault positioning efficiency and accuracy.

The embodiment of the invention provides a knowledge graph logic rule mining method based on a large language model, and the method comprises the steps: selecting a triple from a knowledge graph, and taking the triple as a sample triple; determining a sample logic rule through the sample triad; reasoning and expanding the sample logic rule through a large language model to obtain a final logic rule; and applying the final logic rule to the knowledge graph to complete the knowledge graph. According to the technical scheme, the large language model is combined with the knowledge graph, and a small number of rule examples in the knowledge graph are utilized to guide the large language model to generate a large number of high-quality logic rules, so that the efficiency and the accuracy of carrying out logic rule mining on the large-scale knowledge graph are improved.