124 results about "Apriori algorithm" patented technology

The invention discloses an accurate dosing control system for water treatment, relates to the technical field of dosing control, and is used for solving the problem of poor dosing control accuracy in the water treatment process. Comprising a data acquisition module, a condition library construction module, a multi-target processing module and an optimization decision module. According to the method, a sensor is arranged in a water body to collect pollutant parameters, data are preprocessed, the types and the concentrations of pollutants are recognized through a support vector machine model, after recognition, an optimal processing parameter combination is called from a removal condition library, the condition library is dynamically updated by using an Apriori algorithm, and when multiple pollutants coexist, the optimal processing parameter combination is called from the removal condition library. Removal conditions are automatically adjusted according to interaction rules among pollutants, collaborative removal is realized, an optimal decision is solved by constructing a multi-objective optimization model with removal efficiency, medicament cost, processing time and environmental adaptability as targets and combining a genetic algorithm and a particle swarm optimization algorithm, intelligent dosing control is realized, and the system is suitable for large-scale popularization and application. And the accuracy and efficiency of water treatment are improved.

The invention discloses an abnormal behavior detection method and system based on a multi-source log and electronic equipment, and the method comprises the steps: collecting log data, carrying out alignment processing and data preprocessing, and generating normalized log data; carrying out attribute screening on the standardized log data set, and packaging the screened logs into a JSON format; extracting features of the log data, converting the features into data points, clustering the data points, and retaining a log cluster according to a clustering result; mining a hidden association mode between log attribute items in the log cluster by using an improved Apriori algorithm, generating an association rule, and constructing a knowledge graph based on the association rule; and constructing a time sequence of the log data by using a self-adaptive time window, and inputting the time sequence and the tag sequence of the log data into the LSTM neural network by combining context information in the knowledge graph and a tag sequence generated by an association rule to obtain an abnormal behavior detection result. According to the method, the log data format is unified, correlation analysis is enhanced, and the accuracy of an anomaly detection result is obviously improved.

The invention discloses a community fire-fighting toughness evaluation method and system based on an association rule and a complex network, and the method comprises the steps: analyzing the disaster cause of an accident case based on a community fire accident case, and determining a community fire-fighting toughness evaluation index system from four dimensions: defending, rescuing, recovering and adapting; an Apriori algorithm is adopted to mine a coupling relationship between indexes, an association rule is generated, and a community fire-fighting toughness index complex network is constructed; key factors influencing the community fire-fighting toughness are determined by calculating the degree of a community fire-fighting toughness node, the intermediary centrality, the clustering coefficient and the like; and finally, making a fire-fighting toughness improvement scheme according to the key factors. According to the method, the invisible relation between the toughness indexes can be mined, the key factors influencing the community fire-fighting toughness can be determined, the community fire-fighting toughness improvement scheme can be formulated, the problem that community fire-fighting toughness evaluation mainly depends on subjective experience at present is effectively solved, and improvement of the overall fire-fighting toughness of the community is facilitated.

The invention discloses a fault diagnosis method and system based on a multi-source data association rule and a graph neural network. The method comprises the steps of extracting high-frequency operation data and low-frequency time sequence state data based on historical data, and establishing an equipment operation feature set; an Apriori algorithm is utilized to screen correlation characteristics to calculate a correlation relation, and a fault symptom set is constructed; and taking the association relationship of the features as an adjacent matrix embedded graph neural network, and training the constructed fuzzy graph neural network based on historical data to obtain a fault diagnosis model. The system comprises a data acquisition module, a preprocessing module, a feature extraction module, a feature screening module, a feature association relationship analysis module, a fault diagnosis model training module, a fault diagnosis module and a database storage module, and can perform multi-source data fusion analysis and training and updating of a fault diagnosis model. According to the method, the fault diagnosis model is constructed by combining multi-source data fusion, feature extraction, association relationship mining and the fuzzy graph neural network, so that more accurate and efficient fault diagnosis is realized.

The invention relates to the technical field of pipe gallery fire analysis, in particular to an underground pipe gallery fire feature correlation analysis system based on an improved Apriori algorithm, and aims to comprehensively collect underground pipe gallery data through multi-modal sensing equipment and pre-process the underground pipe gallery data into a standardized three-dimensional time sequence data set to provide a high-quality data basis for subsequent analysis; a multi-dimensional feature association matrix is constructed based on the three-dimensional time sequence data set, feature association strength is quantified, and a multi-dimensional feature association basis is provided for fire risk mining; a fire risk rule knowledge base is constructed through a space-time weighted support degree optimization Apriori algorithm, effective association rules are accurately extracted, and the accuracy and reliability of fire risk early warning are improved; based on the fire risk rule knowledge base, a decision tree model is established, association rule weights of branches of the decision tree are analyzed, fire risk grades are divided, a composite early warning instruction set is generated, different coping strategies can be adopted for risks of different grades, and the pertinence and effectiveness of emergency response are improved.

The invention relates to the technical field of nuclear power equipment quality management and control, in particular to a nuclear power unit quality defect source analysis and total value chain tracing method, which comprises the following steps: S01, collecting total value chain multi-modal data including structured data, logs, texts and sensor time sequence data, and carrying out BIO labeling, BERT coding, BiGRU-Attention feature extraction and CRF sequence labeling; constructing a mixed storage data resource pool; s02, mining association rules by adopting a dynamic pruning improved Apriori algorithm, processing sample imbalance in combination with chi-square test and an NSGAII algorithm, and analyzing defect causes; s03, a dynamic knowledge graph containing multiple types of nodes, relations and threshold attributes is constructed, and rapid defect tracing is achieved through entity linking and probability path searching; the objective of the invention is to solve the problems of difficulty in defect source calibration, low tracing efficiency and the like in nuclear power equipment quality control.

The invention discloses a drug intelligent management and prediction analysis method based on deep learning, and the method comprises the following steps: S1, collecting and normalizing drug related data, and generating unified drug data; s2, constructing an ETL process, setting a time granularity dynamic weight, and generating time sequence input; s3, constructing a drug knowledge graph, and establishing a semantic mapping relationship; s4, fusing the time sequence and the knowledge graph to extract periodic change and spatial difference features, and outputting a trend prediction result and a residual sequence; s5, mining a high-frequency drug combination rule based on an Apriori algorithm; s6, carrying out anomaly detection by using a module which is fused with an Isolation Forest and an auto-encoder; s7, integrating the results to generate a feature attribution; and S8, outputting all analysis results. According to the invention, integrated modeling of drug trend prediction, behavior mining and anomaly recognition is realized.

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 discloses a data association analysis method and system for network security, and relates to the technical field of data association, and the method comprises the steps: collecting log and event information, extracting features, forming an event feature database, enabling events of the event feature database to pass through an improved data association Apriori algorithm to obtain a frequent event set, generating an association rule according to the frequent event set, generating an effective security event association rule by using a support degree, a confidence degree and a lifting degree, and detecting a security threat or attack behavior; according to the improved Apriori algorithm, the number of scanning times of a database and generated redundant intermediate sets and candidate items are reduced, so that the efficiency of the Apriori algorithm is improved, the improved algorithm can directly perform self-connection to generate a next-level frequent item set by skipping generation of the candidate items, and the efficiency of the algorithm is improved. The time generated by scanning the target database is shortened, so that the algorithm efficiency is improved, and a data association system with network security is obtained more quickly.

The invention discloses a software supply chain security analysis method and system based on knowledge graph and GNN model fusion, a medium and a processor. The method comprises the steps of collecting power grid software supply chain safety related data, preprocessing and storing the data, constructing a knowledge graph, mining effective association rules by applying an Apriori algorithm, constructing a GNN model, introducing an attention mechanism for training and testing, and carrying out visualization and interactive design on the model. The system comprises an acquisition module, an atlas module, a mining module, a model module and an analysis module. A computer readable storage medium and a processor may execute the method. Compared with the prior art, the method has the advantages that scattered data can be integrated, association rules can be accurately mined, threats can be accurately identified by means of a GNN model, threat information can be conveniently checked and analyzed through a visual interaction interface, the capability and efficiency of power grid software supply chain safety analysis are comprehensively improved, and powerful support is provided for power grid safety protection.

The invention relates to the technical field of PVB production, and particularly discloses a PVB production process energy efficiency optimization system based on Apriori algorithm and edge cloud collaboration, which is used for solving the problem of insufficient process parameter control precision of a heating kettle and a condensation kettle, and comprises a sensing layer and an edge calculation layer, the sensing layer is connected with the edge calculation layer, the edge calculation layer is connected with a data middle table layer, and the data middle table layer is connected with a data storage layer. The data middle table layer is connected with an application display layer and a rule execution and processing device, and the edge calculation layer is used for mining a preliminary frequent item set based on an improved dynamic Hash-Apriori algorithm, generating a preliminary association rule, processing and analyzing local data of a condensation kettle and a heating kettle within a short time, transmitting the processed data to the cloud analysis and storage layer, and storing the processed data in the cloud analysis and storage layer. Executing an asymptotic identification method and a multi-target heuristic evolutionary algorithm by a table layer in the data; through the dynamic Hash-Apriori algorithm, intelligent optimization of technological parameters of the heating kettle and the condensation kettle in the PVB production process is achieved.

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 belongs to the technical field of intelligent food manufacturing, and particularly discloses a multi-modal data fused marinated product flavor directional design method and system. S1, constructing an auxiliary material database; s2, mining internal associations of components in the high-quality auxiliary materials by adopting an Apriori algorithm; s3, evaluating the sensory flavor of the marinated product to obtain a product index data set; s4, establishing a multivariate mapping model between the auxiliary material parameters and the sensory quality on the basis of a Python library of Statsmodels; s5, establishing a user score matrix in combination with consumption preference; and S6, optimizing the auxiliary material component model by using a genetic algorithm, and solving by combining the multivariate mapping model obtained in the S4 and the sensory weight obtained in the S5 to obtain the auxiliary material component with the optimal quality. By adopting the multi-modal data fused marinated product flavor directional design method and system disclosed by the invention, the problems of sensory flavor quality consistency control limitation and auxiliary material optimization intelligent management design management of traditional sauce marinated poultry meat are solved, and intelligent control between auxiliary material and flavor sensory is realized.

The invention discloses an intelligent analysis method and system for gas station operation data, and relates to the technical field of operation data analysis, and the method comprises the steps: collecting gas station data for preprocessing, generating symbolized sequence data for the preprocessed data through employing a symbolized approximate aggregation SAX algorithm, and calculating the similarity of the symbolized sequence data by using a dynamic time warping (DTW) algorithm, constructing an accumulative error matrix, and backtracking an optimal alignment path to obtain high-similarity sequence data. According to the method, a continuous time sequence is converted into a discrete symbol sequence through the application of a symbolic approximate aggregation algorithm, the data complexity is remarkably reduced, the trend characteristics of original data are reserved, a symbol quantile interval is generated through a cumulative distribution function of Gaussian distribution and an inverse cumulative distribution function of standard normal distribution, and the probability that the symbol quantile interval is larger than the discrete symbol sequence is reduced. The symbolization process has a strict statistical basis, and the frequent pattern is extracted by using an Apriori algorithm through a high-similarity sequence data set.

The invention belongs to the technical field of recommendation algorithms, and discloses a personalized learning path recommendation method based on knowledge relation mining and graph embedding driving, and the method comprises the following specific steps: 1, mining a knowledge point dependency relation, constructing a weighted directed graph, carrying out the data analysis on public data sets ASSISTments and Junyi, and carrying out the data analysis on the public data sets ASSISTments and Junyi; the implicit dependency relationship among knowledge points is mined through an improved Apriori algorithm, time dynamics and sequential dependency characteristics in the learning process are fully considered, and a weighted directed graph among the knowledge points is constructed according to the mined dependency relationship and the weight of the mined dependency relationship. According to the method, the implicit dependency relationship between the knowledge points is mined through an innovative method, the weighted directed graph is constructed, and a graph embedding technology is integrated into a recommendation model in combination with a unique self-defined embedding layer, so that compared with an existing recommendation method based on a simple association rule or a shallow network, the method has the advantages that the recommendation efficiency is improved; the mining and expression ability of the complex logic relation between the knowledge points is remarkably improved, and the recommendation omission rate of the sparse knowledge points can be effectively reduced.

The invention provides a data association analysis method and system based on weight improved Apriori, and the method comprises the steps: collecting original railway accident data, screening and numbering the information of railway accident causes, accident types and accident levels, and generating a multi-attribute railway accident data set; calculating the weight of the accident data based on an entropy method; and according to the weight of the accident data calculated based on an entropy method, setting improved support and confidence indexes, and mining association rules in the railway accident data. According to the method, the weight of each accident grade is calculated through an entropy method, the Apriori algorithm is improved, a new support degree and confidence index algorithm is designed, more strong association rules containing accident cause information are obtained, weighted association rule mining of railway accident data is achieved, the accident severity degree is considered when the accident data are processed, and the probability of accident occurrence is lowered. The effectiveness of accident data mining is improved, the efficiency and accuracy of accident and fault analysis and prevention are improved, and the traffic safety of a railway system is effectively guaranteed.

The invention provides a monitoring system and method applied to a carbon fiber production line, and the system comprises a data collection module which collects process related data and product image data; the data processing module is used for preprocessing the data and matching and marking the data according to production batches; the big data module is used for analyzing the processed process related data based on a big data technology; the data analysis module performs defect analysis based on a machine vision technology and a fuzzy clustering algorithm, and performs association rule mining based on the big data module through an Apriori algorithm; and the early warning module is used for receiving the abnormal data and respectively carrying out early warning prompt. Defect analysis is carried out based on the machine vision technology and the fuzzy clustering algorithm, association rule mining is carried out through the Apriori algorithm, the hidden association relationship between the product defects and the production process is accurately found, the carbon fiber production line can be integrally monitored, and the production quality of the carbon fiber production line is improved.

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 discloses an asphalt pavement disease correlation analysis method based on data mining. The method comprises the following steps: firstly, acquiring detection data of 15 diseases including cracking, block crack, longitudinal / transverse crack (light / heavy), pit slot, track, strip repair and the like, deleting a single disease or disease-free road section, and constructing a Boolean matrix; through an improved Apriori algorithm, frequent 1-item sets are scanned and screened for the first time, and all frequent item sets are generated through row-column compression optimization (row compression deletes non-frequent item set supersets based on anti-monotonicity and column compression eliminates transactions incapable of forming k-item sets) and secondary scanning. And calculating the support degree, the confidence coefficient and the lifting degree, screening a strong association rule meeting a threshold value, identifying key diseases and high-frequency induced diseases, and formulating combined maintenance measures. According to the method, the efficiency is improved through row and column compression and binary operation, the disease symbiosis mode is accurately mined, a data-driven scientific basis is provided for preventive maintenance, the service life of the pavement is prolonged, and the whole-cycle cost is reduced.

The invention discloses an enterprise hidden danger-accident association early warning method based on text mining, and relates to the technical field of association early warning and oil gas safety. According to the method, the high relevance between hidden dangers and accidents can be better revealed, the incidence relation between'accident cause segmented words' and'hidden dangers' is used as a data source of the model, and an association rule mining algorithm (Apriori algorithm) is used for finding out the hidden dangers; a potential association mode between the hidden danger and the accident is mined through a chain of hidden danger-keyword-accident. The method not only comprehensively reflects the relationship between the hidden danger and the accident from the perspective of data, but also reveals that the hidden danger is the key cause of the occurrence of some accidents, and the analysis method is obviously superior to the traditional hidden danger analysis means depending on subjective judgment or single dimension.

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 an architecture performance intelligent alarm and root analysis method based on big data analysis, and belongs to the technical field of artificial intelligence, and the method comprises the steps: obtaining monitoring data in a distributed architecture system, and carrying out the preprocessing; generating alarm event data through an alarm rule engine based on the preprocessed monitoring data, mining a frequent mode in the alarm event data by using an Apriori algorithm, and performing association rule analysis based on the frequent mode to obtain an alarm mode feature vector; splicing the alarm mode feature vector with the time sequence feature of the alarm event, inputting a splicing result into an LSTM model for prediction, obtaining an alarm event prediction trend feature, and splicing the alarm event prediction trend feature with the alarm mode fusion feature to obtain an alarm fusion feature; and classifying the alarm fusion features by using an alarm classification and root cause analysis model based on a random forest algorithm, and outputting an alarm category and a root cause. According to the invention, the accuracy and effectiveness of the alarm event are improved.

The invention provides a credit debt price fluctuation prediction method and prediction device based on public opinion analysis, and the method comprises the steps: obtaining first public opinion data and second public opinion data, determining a first event tag and a second event tag based on the first public opinion data and the second public opinion data, and determining a risk conduction coefficient according to correlation; integrating the second public opinion data and the first public opinion data according to the risk conduction coefficient to obtain a data wide table; processing the data wide table through an APRIORI algorithm and an LSTM algorithm to obtain an event combination set and an event chain; and predicting a price transaction probability through a logistic regression model or predicting a first preset risk event occurrence probability through an MLP model based on the event combination set and the event chain, and generating target alarm information under the condition that any probability exceeds a limit. The method solves the problem that in the prior art, public opinion data mining is insufficient in a credit debt evaluation method, so that the accuracy of an evaluation result is low, and enterprise financial loss is caused.

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 discloses an advertisement putting management method and system based on big data comprehensive analysis, and relates to the field of advertisement putting management.The method comprises the steps that feature items of purchase preference influence factors are extracted based on historical big data records fed back by advertisement putting, and a preference training sample list is established; on the basis of a machine learning algorithm model, purchase preferences are judged and predicted; historical records of different commodities purchased by each user are analyzed, and a purchased commodity flow set is established; on the basis of an Apriori algorithm, purchasing a preference judgment result according to a machine learning algorithm model, and recommending other advertisements associated with the preference advertisement; and updating and optimizing machine learning algorithm model parameters and a purchased commodity flow set according to an actual result fed back by advertisement putting. The system and the method have the advantages that intelligent delivery and effective delivery of the advertisement are realized, so that the advertisement delivery is more in line with the requirements of the public, and the acceptance of browsing the advertisement is increased.

The invention discloses a method, a device and a computer program product for mining an environment association rule of an electricity supplier, and the method comprises the steps: S1, collecting multi-source environment data of the electricity supplier, and carrying out the screening of a Delphi method to form a minimum data set of the association rule; s2, converting the minimum data set into a transactional database, and optimizing an Apriori algorithm by adopting a complete connection strategy and matrix vector space mapping to generate a frequent item set; s3, on the basis of the generated frequent item set, candidate association rules are generated by setting the threshold values of the minimum support degree, the confidence degree and the weight test coefficient, and the candidate association rules are filtered by applying a consequent constraint condition; and S4, constructing an analysis system integrated with the optimized Apriori algorithm, sorting the filtered association rules according to the priority order of the weight test coefficient, the confidence coefficient and the support degree, and visually outputting the sorted association rules. According to the method, the Apriori algorithm is improved, the multi-dimensional association rule in the electric power field is deeply mined, and electric power resource allocation is optimized.

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 present invention discloses a method for analyzing and troubleshooting defects in power communication equipment. The method comprises: collecting historical power communication equipment defect data from the power communication equipment transmission network to generate a primary database of power communication equipment defects; quantifying the primary database of power communication equipment defects according to data discretization rules pre-determined based on the characteristics of power communication equipment defects to generate a database of power communication equipment defect-influencing factors; and importing a pre-built improved Apriori algorithm model for analysis to obtain frequent item sets with varying support and confidence levels and two types of strong rules. Advantages: Based on the physical model of power communication equipment and based on the characteristics of the equipment defect database, the method improves the traditional Apriori algorithm and performs correlation analysis on power communication equipment defects based on the improved Apriori algorithm, effectively identifying and detecting the causes of power communication equipment failures and locating the fault points.

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.