48 results about "Apriori algorithm" patented technology

The invention discloses a mineral resource metallogenic law simulation and target prediction system based on big data, and relates to the technical field of geological exploration. The multi-source data acquisition and standardization module supports acquisition of five types of data, and the data are preprocessed and then transmitted to the distributed database; mining key elements by using an improved Apriori algorithm, and constructing a correlation graph; building a model based on Unity 3D, and integrating three types of dynamic simulation; performing three-level prediction by using a CNN-LSTM model; three-dimensional rendering and multifunctional display are supported; and automatically updating data, models and rules to form an optimized closed loop. According to the method, multi-source data is integrated to improve ore-forming element recognition comprehensiveness, dynamic simulation fits geological reality, AI prediction is accurate, uncertainty analysis is included, visual interaction is practical, iterative optimization continuously improves efficiency, invalid exploration cost is reduced, and mineral exploration is efficiently guided.

The invention provides an MES-based defect detection and quality control optimization method and system. Defect prevention and process dynamic adjustment are realized through a full-process data closed loop. The method comprises the following steps: collecting multi-process data, extracting features such as material batches, process parameters and equipment numbers to construct defect tags, cleaning historical data, constructing time window features, performing multi-class defect classification prediction by adopting an LSTM + attention mechanism model, and analyzing feature contribution degrees in combination with an SHAP value. And setting a multi-stage early warning mechanism, triggering equipment pause and maintenance notification based on a yield threshold, performing graded response according to defect severity, dynamically adjusting equipment parameters, and linking process optimization suggestions. And iterating the model and the rule, returning rework data to generate an optimized sample, mining a defect-parameter association rule in combination with an Apriori algorithm, and updating the equipment health degree evaluation model. The problems of hysteresis quality and staticization of traditional quality control are solved, and an intelligent closed-loop system from defect prediction to process optimization is constructed.

The invention relates to the technical field of information security, in particular to a network security event association detection method based on big data analysis. Comprising the following steps: data acquisition; feature extraction and fusion; correlation detection is carried out, wherein an improved Apriori-Bayesian fusion algorithm is adopted, and discretization processing is carried out on the event feature vectors; mining a frequent item set by using an improved Apriori algorithm; and risk assessment and result output. According to the method, an improved Apriori-Bayesian fusion algorithm is adopted, discretization processing is carried out on event feature vectors according to types, meanwhile, a security event weight factor is introduced to calculate the item set weighted support degree, and a minimum support degree threshold value is dynamically adjusted to mine a frequent item set; the association confidence coefficient is calculated in combination with the Bayesian network, and the confidence coefficient is corrected through the space-time association coefficient, so that the association relationship between the network security events can be scientifically judged, the problems of limited association judgment accuracy and lack of quantitative correction in the traditional technology are solved, and the association false alarm and missing report probability is reduced.

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 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 discloses a needle report data analysis system based on an artificial intelligence algorithm, and particularly relates to the technical field of industrial production intelligent management and data processing. The needle report data analysis system comprises a data acquisition module, an edge preprocessing module, a cloud analysis module and a decision feedback module; obtaining standardized original annotation data; features are purified through grading preprocessing and an attention gating algorithm, and a lightweight brief report is generated; the cloud integrates physical prior and an AI algorithm to realize life prediction and root cause positioning, generates a multi-dimensional report and mines association rules by improving KNN and Apriori algorithms; and a process adjustment instruction and a maintenance work order are generated based on scene adaptive federal incremental learning, and model iteration is realized through data backflow. According to the method, the problems of cross-fabric adaptation, fuzzy abnormal recognition and the like are solved, and the stitch quality and the production efficiency are improved.

The invention belongs to the technical field of data processing, and provides a supplier evaluation method, system and equipment based on business data in order to solve the problems of inaccurate risk prediction, business interruption and the like. And cross features describing association risks of supplier multi-dimensional data are used as input of the enhanced LightGBM model, so that full-dimensional data coverage of dominant indexes and hidden risks is realized, and one-sided evaluation caused by a single data type is avoided. And for the suppliers with high risk levels, the risk behavior combination-risk level association rule mined by the Apriori algorithm is adopted to clarify the association relationship of multiple risk behaviors and recommend alternative suppliers, so that purchase project interruption caused by supplier risks is effectively avoided, and the purchase business continuity is ensured.

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 relates to a data analysis processing method for a PMS homologous system, and the method specifically comprises the steps: carrying out the preprocessing, normalization and standardization processing of obtained data parameters, generating a PMS equipment ledger, building a corresponding association rule through an association rule Apriori algorithm, carrying out the verification and detection of data recorded in the PMS equipment ledger, and carrying out the verification and detection of the PMS equipment ledger. The method comprises the following steps of: obtaining a verification result, generating a verification detection result, finally, analyzing and processing the verification detection result in a multi-dimensional comprehensive analysis mode so as to determine abnormal record data, finally, comparing the abnormal record data with a preset white list database, screening out matched record data, integrating residual abnormal records, and after the operation is completed, obtaining a verification result. Based on the maintenance knowledge base, analyzing and processing the abnormal record data, and generating maintenance decision suggestions corresponding to the abnormal record data one by one; the method has the advantages of accurate data analysis, high efficiency and convenience.

This application relates to the fields of architectural engineering technology and artificial intelligence technology. It discloses a method, apparatus, equipment, and medium for obtaining the causes of tower crane accidents based on the Apriori algorithm. The method includes: determining the word vector, position encoding vector, and segment vector of the current tower crane accident text; fusing the word vector, position encoding vector, and segment vector of the current tower crane accident text to obtain a fused vector; determining multiple causal relationship triples corresponding to the current tower crane accident text based on the fused vector; using the Apriori algorithm to obtain multiple preferred triples from the multiple causal relationship triples corresponding to the current tower crane accident text; and determining the tower crane accident cause of the current tower crane accident text based on a directional interest model. This application is beneficial for improving the efficiency of obtaining the causes of tower crane accidents.

The present application relates to the technical field of data processing, and especially relates to a log data management method for terminal access authentication, which comprises the following steps: in the log data of the access authentication of a target terminal, according to all authentication success transactions of any target user, the Apriori algorithm is used to obtain the behavior association rule of any target user; according to the time law of each authentication success of any target user, the habit association rule of any target user is obtained; each habit association rule and each behavior association rule are associated to obtain the behavior habit association rule of any target user; according to the support degree and the confidence degree of the corresponding behavior association rule and habit association rule of each behavior habit association rule, the frequent association rule of any target user is obtained; and according to the frequent association rule of each target user, the target terminal is detected abnormally, so that the accuracy of the abnormal behavior detection of the log data of the target terminal authentication is improved.

The invention discloses a credit overdue intelligent early warning and intervention system based on real-time data analysis. The system comprises six core modules including a data acquisition module, a data cleaning and preprocessing module, a real-time data analysis module, an intelligent early warning module, an intervention measure execution module and a model optimization module. The data acquisition module acquires multi-dimensional data of a borrower in real time through an API (Application Program Interface); the data preprocessing module processes data by adopting methods such as Z-Score standardization and the like; the real-time analysis module performs comprehensive analysis by using an ARIMA (Autoregressive Integrated Moving Average) model, an Apriori algorithm and a K-Means algorithm on the basis of an Apache Flink framework; the intelligent early warning module adopts a random forest algorithm to construct an early warning model; the intervention module automatically executes corresponding measures according to the risk level; the optimization module continuously improves various models. According to the invention, the risk early warning response time is shortened to a minute level, the early warning accuracy is improved by more than 30%, the labor cost is reduced by 40%, the credit default risk is effectively reduced by about 25%, and the intelligence and automation of credit risk management are realized.

The invention discloses a travel itinerary chain planning method fusing multi-modal social network information, and the method comprises the following steps: constructing a travel information knowledge graph database through social network travel information mining, feature analysis and extraction, classification and integration; based on a clustering algorithm and an Apriori algorithm, generating different user group labels, and extracting a time-place-behavior combination of different user groups; carrying out travel destination category division, destination prediction and destination recommendation by fusing social network travel information; and determining a travel destination extension set, generating a personalized travel chain, and dynamically adjusting the travel. According to the method, the multi-modal social network information can be effectively fused, the journey chain meeting the personalized requirements of the user can be generated, journey optimization is realized based on real-time dynamic factors, the convenience and satisfaction of travel are remarkably improved, data support is provided for the tourism industry, and the method has good practical application value and popularization prospect.

The invention discloses a firewall communication log anomaly detection method based on association rule and sequence pattern fusion, which comprises the following steps: 1, acquiring and cleaning formatted firewall historical logs, and extracting a source IP, a destination IP, a port, a protocol and an action field; 2, mining a static association rule by adopting an Apriori algorithm; 3, mining a sequential communication mode by adopting an improved PrefixSpan algorithm, and fusing the sequential communication mode with a static rule to construct a comprehensive behavior model; and 4, matching a real-time communication log with the model, and if the real-time communication log meets the front part but does not meet the back part or deviates from the sequence mode, judging that the real-time communication log is abnormal and triggering alarm and visual display. According to the method, the normal communication behavior model can be constructed by using the existing firewall log on the premise that the existing production network architecture is not changed, so that the abnormal communication can be quickly detected and alarmed, and the safety and reliability of the production network can be improved.

The application discloses a content-tracing-based enterprise culture hot word analysis method, and relates to the technical field of enterprise culture analysis.The method comprises the following steps: collecting culture-related original data from three dimensions, and constructing an enterprise-specific culture term library through preprocessing, density peak clustering, de-redundancy and matching with top-level culture elements; classifying enterprise internal texts to be analyzed according to departments and text types, automatically matching terms of corresponding categories in the enterprise-specific culture term library, and performing directional word segmentation based on preset rules; calculating the scene coverage of terms according to department scenarios and text type scenarios, calculating the culture correlation degree by using Jaccard similarity, and screening core hot words by combining the Apriori algorithm; locking target hot word origin texts based on the core hot words, counting the track data of the origin text transmission channels, identifying the transmission nodes and constructing the transmission track; and the method improves the accuracy of hot word analysis and provides support for targeted promotion of enterprise culture.

The invention relates to the technical field of bulk commodity operation, in particular to a data sharing and analysis method and system based on multi-dimensional association, and the method comprises the steps: collecting multi-source heterogeneous data through a mixed mode of interface synchronization and manual reporting, and quantifying an unstructured text into a standardized index through a bidirectional LSTM semantic analysis algorithm; a cosine similarity and grey relational degree fusion algorithm and an LSTM dynamic weight optimization model are adopted, association rules are mined in combination with an improved Apriori algorithm, and market pre-judgment is achieved through three sub-models of supply and demand, price and risk; and finally, establishing a closed-loop mechanism, and outputting an inventory adjustment scheme and a futures hedging strategy. The system correspondingly comprises four functional modules, full-process data processing and decision support are achieved, and scientificity and operability of operation decision are improved.

The invention belongs to the field of industrial safety and artificial intelligence, and discloses a chemical accident risk analysis method and system based on text mining and GNN-Apriori association rules. The method comprises the following steps: cleaning and structuring a chemical accident text, fusing TextRank, BM25 and BERT algorithms, and extracting a high-quality keyword set through a weighted model; constructing a graph structure based on a keyword co-occurrence relationship, and learning node semantic embedding by using GNN; inputting the embedded vector into an improved Apriori algorithm, and mining a semantic enhanced association rule in combination with semantic similarity, support degree, confidence and lifting degree; and finally, constructing a chemical accident causal complex network, and evaluating key nodes by using node centrality by taking the improvement degree as an edge weight to realize risk propagation path analysis and visual display. According to the method, chemical accident causes can be intelligently identified, semantic association is mined, a causal network is modeled, and interpretable decision support is provided for accident prevention and the like.

The application discloses a threat node perception method based on a deep learning graph neural network theory to improve network security performance, detection efficiency and accuracy of a threat node, comprising the following steps: performing data cleaning on original data traffic, generating information association rules by using an Apriori algorithm, and then performing feature conversion and data preprocessing to obtain preprocessed data; based on the association rules, constructing a public opinion key node topology structure of relationships between nodes involved; converting a time domain signal into a frequency domain by using Fourier transform of a graph, solving eigenvalues by using a Laplacian matrix, and constructing a spectral domain graph model of public opinion information; defining a structure of a graph neural network, and using the preprocessed data and the public opinion key node topology structure to train the spectral domain graph model; then using a cross-validation set to detect whether a prediction result of the spectral domain graph model is correct, and calculating accuracy, precision, recall and F1 score indicators of the spectral domain graph model.

This application relates to the field of data linkage and update technology, specifically to a big data-driven smart city data linkage and update method and system. The method includes: determining the attribute correlation degree between different attributes by analyzing the frequency differences between data items with different attributes, thereby determining the associated attributes of each attribute; determining the suppression factor of the result itemset in each association rule based on the frequency of data items in the associated attributes of the attribute in a transaction, combined with the attribute correlation degree; adjusting the confidence of association rules obtained by the traditional Apriori algorithm based on the attribute correlation degree and the suppression factor, and using the adjusted confidence to construct an association rule base, i.e., a data association model; significantly improving the accuracy and reliability of association rule mining, and effectively enhancing the real-time performance, automation level, and inter-system collaboration capabilities of smart city data updates.

The invention discloses a long-distance transmission line inspection path optimization algorithm, and relates to the technical field of long-distance transmission line inspection, and the method comprises the following components: a multi-source data fusion and preprocessing step, a space-time association rule mining step based on an improved Apriori algorithm, a fault occurrence probability prediction model construction step and a predictive inspection path planning step. Through multi-source data fusion and preprocessing and in combination with space-time association rule mining based on the improved Apriori algorithm, the fault occurrence probability of each section of the long-distance transmission line under different time, space and meteorological conditions can be accurately identified, which is helpful for polling personnel to preferentially pay attention to high-risk sections, dynamically adjust the polling coverage frequency and improve the polling efficiency. Therefore, while the inspection quality is ensured, the inspection efficiency is remarkably improved, and the unnecessary inspection workload is reduced.

The invention discloses a massive alarm log noise reduction and correlation analysis method based on a dynamic aggregation log codebook. According to the method, association analysis based on the Apriori algorithm is achieved for the alarm logs, an association log sequence is generated, a specific data structure-log codebook (Log Code-book) is generated through regularization processing, and codebook of the automatic association relation is achieved. In addition, noise reduction processing and multi-device association analysis of the massive alarm logs of the devices are achieved through the log codebook, and the alarm log association processing effect is remarkably improved. According to the invention, through a double-period codebook aggregation framework based on period broadcasting and timing uploading, a relatively high log association analysis effect is maintained in a dynamic network topology environment, and relatively high robustness is represented in various network environments.

The present invention provides an analysis method for defect rates caused by device paths, characterized by comprising the following steps: S1, acquiring a dataset; S2, establishing an algorithm input dataset by means of ETL processing; S3, by means of algorithm model operation iteration, using an Apriori algorithm to calculate confidence and lift metrics for all paths, so as to form path ranking metrics as an algorithm output; and S4, outputting an algorithm model result, and using lift as a ranking metric to obtain suspicious paths. By analyzing defect rates on device paths, problems in the device manufacturing process can be found, and targeted amelioration measures can be taken, improving product quality and reliability.

The invention relates to an aerospace component incidence relation mining method. The method comprises the following steps: preprocessing aerospace component selection list data; carrying out space navigation component selection list similarity analysis: constructing a selection list disorder tree and an electronic single machine selection component adjacent matrix, calculating selection list structure similarity and selection component specification similarity, and screening similar component lists; and for the screened similar component list, iteratively retrieving all component association selection frequent item sets which have the support degree greater than the minimum support degree threshold and possibly have association relationships, constructing a rule meeting the minimum confidence coefficient from the frequent item sets, and setting the support degree and the confidence coefficient value to obtain the component association selection frequent item sets. And adopting an Apriori algorithm to obtain a high-frequency association rule in the aerospace component selection process. According to the method, pre-analysis is performed from the two aspects of the component analysis range and the list similarity in a targeted manner, the problem of low mining efficiency is solved, manual subjective judgment is not needed, and the data mining accuracy is improved.

The present application relates to the field of rail transit and data analysis technology, aiming to solve the problem that the prior art lacks spatial and temporal multi-dimensional correlation analysis of defects of the contact net, track and tunnel in urban rail infrastructure, and provides a decision support for defect elimination, and provides a kind of urban rail infrastructure spatial and temporal multi-dimensional correlation analysis method based on Apriori algorithm and Pearson coefficient, through data collection and arrangement, respectively, multi-parameter space analysis and multi-parameter time analysis are carried out;Space analysis adopts Apriori algorithm to calculate the correlation degree between item points, and then obtains the final spatial correlation degree between item points through time dimension weighted aggregation;Time analysis adopts Pearson correlation coefficient algorithm to calculate the correlation coefficient between detection parameters, and then obtains the final time correlation coefficient through multi-dimensional aggregation;According to the final spatial correlation degree and the final time correlation coefficient, the correlation between item points is judged, and a decision basis for defect elimination is provided.

A machine learning based (ML-based) computing method and system for automatically identifying equivalent entities, is disclosed. The method involves obtaining transaction data, standardizing data fields, and generating unique transaction numbers. Entities with identical transaction numbers are grouped into initial groups. An ML model, utilizing an apriori algorithm, eliminates certain groups to form intermediate groups based on frequent item sets. An iterative unionization process then merges intermediate groups to form resultant groups of equivalent entities. The method further includes assessing model accuracy, with optional re-training if accuracy falls below a threshold. Standardization steps include column name alignment, datetime and numeric conversion, currency conversion, intra-company removal, and write-off removal. Resultant groups are provided to end users via interfaces, allowing accurate, automated identification of equivalent entities in financial transactions.

The invention discloses an intelligent workflow guiding method and system oriented to a government affair process, and relates to the technical field of government affair informationization. The method comprises the steps that a government affair dictionary database, historical form data and process association data are integrated, after cleaning standardization is conducted through an ETL tool, an Apriori algorithm is adopted to mine a field high-frequency association relation, and then the field high-frequency association relation is obtained; constructing a tree form field filling chain and calculating the weight; when a user fills in a form, the system automatically identifies a high-priority trigger field, recall candidate filling chains by using an inverted index, and determine an optimal chain for automatic filling in combination with context similarity; and finally, performing format, required item and business rule multi-dimensional compliance verification on the form, and if the verification is not passed, generating a correction prompt and guiding correction. The government affair data normalization and filling accuracy are improved, and the government affair process processing efficiency is effectively improved.

The application discloses a logistics service evaluation method and system based on data mining, and relates to the technical field of logistics.The method overcomes the defects of the existing method, such as relying on manual subjectivity, single index, data fragmentation, static evaluation, etc., through a complete technical closed loop of multi-source data fusion, index system construction, weight calculation, comprehensive scoring, dynamic updating and correlation mining, and optimization verification.In S1, the multi-source data is associated with the transportation task ID and timestamp as the key to avoid manual collection bias;in S2, a multi-dimensional evaluation index system is constructed by grouping according to dimensions to achieve comprehensive coverage;in S3, the eigenvalue method is used to calculate the index weight vector;in S4, the evaluation grade and comprehensive score are determined by combining the triangular membership function and the fuzzy synthetic operator;in S5, the Apriori algorithm is used to mine the association rules and generate optimization strategy instructions to realize the conversion from evaluation to action;in S6, the optimization effect is verified and the weight is iteratively adjusted to form a closed loop mechanism of evaluation, optimization and improvement.

The application discloses a kind of maintenance strategy formulation method and system for improving the reliability of stability control system, comprising the following steps: first, collect system-level abnormal fault data in stability control system, establish fault tree model according to fault data, analyze the influence of potential failure of each component in stability control system on the reliability of overall stability control system;Second, based on the constructed fault tree model, the improved Apriori algorithm is used to analyze the influencing factors of potential failure in stability control system, and strong association rules between influencing factors and potential failure are constructed, to provide a theoretical basis for maintenance strategy to reduce failure rate;Finally, the maintenance strategy is optimized using Markov model method to improve the failure repair rate, and the stability control system failure repair time is reduced by finite time optimization control algorithm.The method of the present application aims to reduce the failure rate and failure repair time of stability control system to improve the reliability of stability control system.

The application discloses a kind of based on expert system's drive controller module construction method and system, belong to rudder machine design field, method mainly includes: S1.Establish controller module database;S2.Adopt improved Apriori algorithm and dig the association rule between controller module, and construct rule base;S3.Based on the rule base, construct controller module reasoning system, for automatically recommending controller module according to input information;S4.Construction evaluation function carries out comprehensive evaluation to the reasoning result of controller module reasoning system, optimizes the selection of controller module, obtains drive controller design scheme;S5.Based on the drive controller design scheme obtained in step S4 updates controller module database.The application realizes the construction of controller module application system by the rule that is dug, promotes the application of modularization, realizes controller autonomous design, and then improves the research and development efficiency of electric rudder, shortens development cycle, satisfies market demand.

This invention relates to a method, system, and equipment for establishing a pipeline risk analysis model based on fuzzy Bayesian networks, belonging to the field of pipeline risk analysis technology. The method for establishing the pipeline risk analysis model includes: constructing a pipeline failure analysis Bayesian network based on historical pipeline accident data and the Apriori algorithm; calculating the prior probabilities of basic risk factors based on expert opinions on basic pipeline risk factors and fuzzy comprehensive evaluation; obtaining the conditional probabilities of sub-nodes in the pipeline failure analysis Bayesian model based on expert pairwise evaluation of the relative importance of basic pipeline risk factors, as well as the analytic hierarchy process (AHP) and the ranking node method; and establishing the pipeline risk analysis model. This invention can calculate the failure probability of pipelines with complex multi-risk factors and identify key pipeline failure events. Based on the probabilities, it can assess the safety and reliability of pipeline systems, prevent accidents, reduce costs, and optimize resource allocation.