134 results about "Rule mining" patented technology

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 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 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 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 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 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 invention discloses a civil aviation risk cause network association analysis method based on a semantic enhancement model, and the method comprises the steps: building an EnhancedBertLdaModel model fusing BERT semantic representation and quality adaptive adjustment, and achieving the high-precision topic mining of a civil aviation risk event text; designing an improved Apriori algorithm which introduces a factor number adaptive threshold mechanism, and dynamically capturing a strong correlation cause rule; and constructing an optimized Logistic regression model and cause association network, quantifying risk contribution through multi-source feature fusion, and identifying a core risk path. According to the method, deep semantic analysis and dynamic rule mining are fused, civil aviation risk causes can be comprehensively identified, multi-source data features are fused, and contribution of the multi-source data features to the risk is quantified; by improving the algorithm, the analysis complexity is reduced, and the accuracy and stability of the result are improved, so that the purposes of precisely mining and quantifying civil aviation risk causes are achieved, and a scientific decision basis is provided for civil aviation safety management.

The embodiment of the invention provides a reservoir group multi-target scheduling method and system fusing an SHAP interpretability analysis mechanism. The method comprises the following steps: carrying out training data integration based on obtained historical operation data of a target basin reservoir group, carrying out reservoir group scheduling model training based on a DNN network structure, and carrying out multi-target iterative optimization on model structure parameters to obtain an optimal parameter set meeting multi-decision preference; based on the optimal parameter set, through an SHAP interpretability analysis mechanism, calculating to obtain a factor contribution matrix of each model input factor vector relative to model prediction output; performing statistical feature extraction based on the factor contribution matrix of each model input factor vector to obtain a corresponding factor feature vector; and performing rule mining based on the factor feature vector, and performing rule optimization based on weight and explanatory evaluation to obtain a structured scheduling rule set for real-time scheduling aided decision making. The implementation of the method can provide more explanatory decision support for multi-target scheduling of the reservoir group.

The invention relates to the technical field of power system automation and big data mining, and discloses a power distribution network area grid power failure intelligent early warning method, system and device based on Apriori association rule mining and a medium. The method comprises the following steps: collecting multi-modal heterogeneous data and carrying out standardization processing on the multi-modal heterogeneous data; constructing a multi-layer regional network model to form a physical topology tree structure; establishing a space-time weighted transaction database fusing time attenuation and space weight based on the structure; an improved Apriori algorithm is introduced, and a strong association rule meeting the minimum weighted support degree and confidence degree is mined through bitmap matrix compression and weight vector operation; and finally, the power failure risk probability is calculated through matching of real-time monitoring data and rule base front components, graded early warning is triggered, and an operation and maintenance strategy is generated. According to the invention, the accuracy, timeliness and interpretability of power failure early warning are improved, and intelligent operation and maintenance decisions are supported.

The invention relates to the technical field of railway maintenance education, in particular to a high-speed railway four-electricity system intelligent teaching system and method based on AI, and the method comprises the steps: a teaching data obtaining module collects teaching process data, student learning tracks and industry fault data; the virtual simulation modeling module is fused with a BIM model and a technical specification text to construct a four-electric system twinborn body; the AI intelligent analysis engine constructs student ability portraits through a machine learning algorithm, generates a personalized learning path and intelligently answers questions; the virtual-real teaching interaction module builds a digital-intelligent practical training scene, carries out three-dimensional visual error correction and remote guidance through real-time motion recognition, and outputs practical training data. The cross-professional fault simulation module carries out cross-professional fault linkage simulation deduction through an association rule mining algorithm and outputs a deduction result; and the teaching effect evaluation module establishes a binary evaluation system combining process and practical operation skill assessment, and outputs teaching effect data. Therefore, the problems of fixed teaching strategy, low precision and the like in the prior art are solved.

The invention provides an electric power project data logic exception correction method and device, equipment and a storage medium, and the method comprises the steps: constructing a data logic verification network based on an association rule mining algorithm, so as to express a constraint relation between fields; non-zero dependence of the data record is verified through a constraint propagation mechanism, and a logic anomaly result is generated when constraint violation is detected; triggering automatic correction: obtaining similar normal cases from the historical knowledge base to generate a correction scheme, and applying the correction scheme to the abnormal field to obtain a modified record; and performing secondary verification to ensure that the correction is effective. According to the method and the device, through construction of the verification network, verification dependence, automatic correction and secondary verification, rapid, accurate and dynamic correction of logic anomalies is realized, and the defects of low manual correction efficiency and lack of verification are overcome.

The application provides a kind of mining method of data fluctuation relationship of silk making workshop based on association rule, and belongs to the field of data mining.For the problem that fluctuation relationship between silk making process parameters is difficult to describe, the application fully mines fluctuation relationship between different variables with the help of association rule.Relying on data discretization, a data preprocessing method for association rule mining of silk making data is designed, and silk making data is converted into fluctuation data containing original data information;Further, the fluctuation rule formula of the data to be mined is designed by using the principle of association rule.The application fully mines the fluctuation rule between different process parameters, improves the interpretability of the rule and the accuracy of the association result, can accurately judge whether there is a fluctuation relationship between each process parameter of the silk making workshop, is conducive to the statistics and management of the process parameters of the silk making workshop, and is convenient for adjusting process parameters to optimize the process when the quality of silk making products is problematic.

The invention belongs to the field of knowledge base intelligent retrieval, and provides a government affair department knowledge base retrieval method based on semantic enhancement, which comprises the following steps of: dividing various types of files into different processing units, extracting analysis data according to corresponding processing modes of the files, forming various types of ontology trees, calculating similarity according to comprehensive weight, and performing mapping replacement; user query content is preprocessed, a comparison learning model is input to obtain a semantic vector, the semantic vector is normalized into a query vector, and similar documents are retrieved by calculating a dot product; calling a deep learning association model, extracting entity names and attribute contents, obtaining pooling vectors, inputting the pooling vectors into a graph convolutional network model, and performing mapping unification on entities and relationships from different knowledge bases to form a cross-base government affair knowledge graph; and calculating node access degrees on the knowledge graph, marking isolated nodes by comparing a preset threshold value, obtaining entities and relation modes by using an association rule mining algorithm, merging the entities and the relation modes to the knowledge graph, and carrying out iteration through an evaluation result.

A voltage sag homologous identification method and system based on association rule mining, relating to the technical field of power quality mitigation. Transactions are formed by means of screened candidate homologous sag events, a transaction table is formed, a plurality of frequent itemsets are screened on the basis of the support of each itemset in the transaction table, so as to construct a canonical-order tree corresponding to the transaction table, thereby reducing the number of misleading transactions and improving the effectiveness of voltage sag homologous identification. In addition, association rules for candidate homologous sag events corresponding to every two frequent itemsets are determined by means of the canonical-order tree, waveform similarity comparison is performed on voltage waveforms corresponding to the candidate homologous sag events, and whether the two candidate homologous sag events are homologous sag events is determined, thereby improving the accuracy of voltage sag event homologous identification.

The invention discloses an operation and maintenance data management system and method based on a large model, and relates to the technical field of data management. The system comprises a data acquisition module, a data processing module, a rule mining module, a dynamic adaptation module and a feedback optimization module. The data acquisition module acquires environment sensitive parameters and operation threshold data and performs calibration verification, the data processing module constructs an environment-threshold association data set, the rule mining module generates a dynamic anchoring rule base through a pre-training large model, and the dynamic adaptation module matches rules to generate an adjustment instruction and an operation and maintenance suggestion. And the feedback optimization module records the evaluation adjustment effect and supports the updating of the rule base. According to the method, the dynamic anchoring adaptation of the operation and maintenance threshold value and the environment change is realized, and the operation stability and the operation and maintenance accuracy of equipment are improved.

The invention discloses a large language model-based battery material field data processing method and related equipment. The method comprises the following steps of: obtaining battery thermal management material field heterogeneous data; performing information structuring and semantic analysis processing on the heterogeneous data in the battery thermal management material field through a preset large language model to obtain text data; performing context analysis and association rule mining processing on the text data through the large language model to obtain a parameter association rule; and performing standardization processing on the text data according to the parameter association rule to obtain a battery data set. The embodiment of the invention can improve the processing efficiency of heterogeneous data in the field of battery thermal management materials, and can be widely applied to the technical field of artificial intelligence.

The invention belongs to the technical field of data mining, and particularly relates to a multi-modal AI-based massive network data hierarchical mining system, which comprises a network data hierarchical mining end, a mining multi-dimensional evaluation end and a central supervision end, data access is performed through the multi-modal data adaptive access module, the multi-modal data preprocessing module executes special optimization for different modal characteristics and synchronously deletes repeated data and invalid data, the dynamic hierarchical semantic analysis module realizes three-layer analysis, and the multi-modal characteristic cross-domain fusion module effectively eliminates characteristic isomerism between modals. The hierarchical association rule mining module performs hierarchical mining according to surface, deep and cross-modal architectures, so that the accuracy, efficiency and rule coverage rate of mass multi-modal data mining are remarkably improved, and the technical defects that an existing multi-modal data mining system is poor in access compatibility and shallow in semantic analysis are effectively overcome.

The invention relates to a classification association rule mining-based identification method and device for transmission power constraints of working lines in a power grid safety unit combination, computer equipment, a computer readable storage medium and a computer program product. The method comprises the following steps: constructing a line blocking state recognition sample set according to historical operation data of a pre-constructed security constraint unit commitment model in a plurality of historical scheduling cycles; the line blocking state identification sample set comprises a first sample set under a ground state line transmission power constraint and a second sample set under a fault state line transmission power constraint; performing merging processing on items in the item set, and constructing rule items about a line blocking state; constructing a classification association rule set according to frequent rule items corresponding to the same line blocking state sample labels in the rule items; and according to the classification association rule set, detecting whether the line transmission power constraint in the new scheduling period plays a role or not. By adopting the method, the recognition precision of the transmission power constraint of the working line can be improved.

The invention belongs to the technical field of behavior regulation mining, and particularly relates to an Apriori algorithm-based action rule mining method, which comprises the following steps of: 1, performing data cleaning on situation data, finding out available data, and classifying action rule data into conditions and actions; in the second link, the action rules of each category are called item sets, if the number of times that the various rules appear together in the item sets is larger than the minimum support degree threshold value, the item sets are integrated into frequent item sets, and the evaluation criteria of frequent item set mining comprise the support degree and the confidence degree; according to the method, for data such as situations, a frequent item set library of action class rules is constructed based on an Apriori algorithm, and mining of the action class rules is achieved through frequent item sets.

The invention provides a three-proofing lacquer process optimization method and system based on big data analysis, and the method comprises the steps: firstly obtaining a historical process data set of a three-proofing lacquer coating process, including a process parameter sequence, a coating quality detection result and an environment influence factor record, and then carrying out the feature extraction of the historical process data set, the method comprises the following steps: obtaining coating process characteristics, quality index characteristics and environment characteristics, then carrying out association rule mining based on the coating process characteristics, the quality index characteristics and the environment characteristics, identifying key process parameters and environment influence conditions which influence the coating quality, generating a process parameter adjustment scheme set according to an identification result, and adjusting the coating quality according to the process parameter adjustment scheme set. And performing simulation verification on the process parameter adjustment scheme set, determining an optimal process parameter combination and environment control requirements, and generating a process optimization instruction, thereby improving the quality and stability of the three-proofing paint coating process, reducing the cost and improving the efficiency.

The application provides a three-proofing paint process optimization method and system based on big data analysis, first, a historical process data set of a three-proofing paint coating process is acquired, containing a process parameter sequence, coating quality detection results and environmental influence factor records, then feature extraction is performed on the historical process data set to obtain coating process features, quality index features and environmental features, then association rule mining is performed based on the coating process features, quality index features and environmental features to identify key process parameters and environmental influence conditions affecting coating quality, a process parameter adjustment scheme set is generated according to the identification results, the process parameter adjustment scheme set is simulated and verified to determine an optimal process parameter combination and environmental control requirement, and a process optimization instruction is generated, so that the quality and stability of the three-proofing paint coating process can be improved, the cost can be reduced, and the efficiency can be improved.

The invention discloses a multi-modal large model problem data tracing method based on semantic association rule mining, and the method comprises the steps: obtaining multi-modal input data, extracting an intermediate semantic feature, constructing a semantic chain structure representing a semantic dependency relationship between modals, and applying an association rule mining algorithm on a semantic chain, so as to achieve the multi-modal large model problem data tracing. And generating a semantic rule set with high confidence. The system comprises a semantic feature extraction module, a semantic chain construction module and a problem data positioning module, and can effectively improve the interpretability of a large model in an exception processing path and the automatic identification capability of problem data. According to the method, the technical problems that a traditional multi-modal model depends on manual analysis in the problem data tracing process, the visualization ability is weak, and the diagnosis efficiency is low are solved, and accurate positioning of problem fragments under complex semantic logic is achieved; the method is suitable for the field of question data analysis and model debugging in multi-mode intelligent systems such as image-text question answering, voice understanding and video analysis.

The application discloses a recommendation system explanation method and device based on weighted association rule mining, first, using the rating data of users to the items to train the latent factor recommendation model, based on the trained latent factor recommendation model, generating top-N recommended items for each user; then, the rating data of users to the items is preprocessed into weighted transaction data in the form of <T id ,{(I1,R1),…,(I x ,R x )}>; then, using the weighted transaction data as input, generating weighted association rules using the weighted association rule mining algorithm; finally, matching the recommended items of each user with the weighted association rules, generating personalized explanations for the users. The recommendation system explanation method based on the weighted association rule mining uses the weighted association rules to explain the recommended items, decouples the explanation mechanism from the recommendation model, generates explanations after the recommendation model provides the recommended items, can be applied to different latent factor recommendation models, and has the characteristics of flexibility and generality.

The application is suitable for the field of model explanation, and relates to a rule mining method and device for recommending model explanation, equipment and medium. For any user-item pair in graph data, a corresponding neighborhood graph and a connected subgraph are determined, a candidate subgraph is determined from the connected subgraph, a target subgraph is determined from the candidate subgraph according to a graph evaluation score, a target graph pattern is extracted from the target subgraph, for any variable in a pattern path of the target graph pattern, a target predicate is determined from all predicates corresponding to the variable, a candidate premise condition is formed by the variable and the target predicate, a candidate explanation rule is obtained according to the target graph pattern and a candidate premise condition set corresponding to all pattern paths in the target graph pattern, and a candidate explanation rule satisfying a preset condition is determined as a target explanation rule from all candidate explanation rules. The target explanation rule reflecting the prediction principle of the recommendation model is mined from the graph data as the global explanation of the recommendation model, and the effectiveness of the explanation is improved.

The invention discloses an Internet of Things data processing method and an Internet of Things system, and the Internet of Things data processing method comprises the following steps: S1, data collection; s2, data transmission; s3, data preprocessing; s4, data analysis; and S5, decision execution, and the Internet of Things data processing system comprises a sensing layer, a network layer, a processing layer and an application layer. According to the method, the adaptive sampling technology and the multi-link transmission technology are combined with a dynamic bandwidth allocation strategy, and the algorithm based on machine learning and deep learning is adopted, so that the system is endowed with powerful data processing capability, and a more comprehensive basis is provided for decision making by combining an association rule mining algorithm, and the accuracy and the real-time performance of data processing are improved; and the intelligent development of the application of the Internet of Things is powerfully promoted.

The invention discloses a multi-objective optimization method and system for a business expansion scheme of power supply load access driving, and relates to the related technical field of power distribution network optimization, and the method comprises the steps: constructing a large user load feature library through a historical power utilization data set; performing load mode rule mining to obtain a large user load mode identification rule base, and determining a new user load mode according to new user installation parameter traversal matching; performing power grid business expansion scheme analysis in combination with the target power grid characteristic data, and generating a baseline business expansion scheme parameter threshold; and constructing multiple optimization objectives of the business expansion scheme, carrying out global solution optimization, and determining a target power grid business expansion scheme. The technical problems that in the prior art, a business expansion scheme lacks a data-driven decision, user load characteristics are difficult to accurately match and multi-conflict targets cannot be collaboratively optimized are solved, and the technical effects that efficient configuration of power grid resources is achieved, scientificity and accuracy of the business expansion scheme are improved, and safety and stability of power grid operation are improved are achieved.

The invention provides a big data mining method and device applied to LPDDR performance detection, and the method comprises the steps: receiving an original performance record stream, carrying out the data reconstruction, and generating a time-aligned data reconstruction set; and performing association rule mining on the data reconstruction set, extracting a frequent co-occurrence relationship between performance states in a time dimension, and screening to generate a performance influence association mode set. And mapping the set to a state transition space, constructing a directed weighted performance state evolution graph, and calculating and identifying a key state transition path through graph density. And generating a detection adjustment instruction based on the path, and transmitting the detection adjustment instruction back to a control unit of the detection system so as to reconfigure the sampling frequency and the detection item execution sequence. According to the method, accurate self-adaptive adjustment of the LPDDR detection process is realized by mining the deep association mode of the performance data.