85 results about "Apriori algorithm" patented technology

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 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 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 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 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 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 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 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.

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 relates to the technical field of tool management and control, and discloses a tool management and control method based on the Internet of Things and an enhanced Apriori algorithm, and the method comprises the steps: collecting tool data through combining the RFID technology of the Internet of Things and a wireless sensor; performing dynamic clustering analysis by using an improved DBSCAN algorithm, screening out a frequent item set by using space-time semantic enhanced Apriori, and generating an association rule; establishing a cost prediction model by using a multivariate adaptive regression spline, constructing a health state prediction model based on an attention mechanism neural network model, and constructing a residual life prediction model by using support vector machine regression; according to the method, the data is deeply mined based on the intelligent algorithm, the association rules are mined in combination with the improved DBSCAN algorithm and the Apriori algorithm fused with the space-time semantics, and decision support is provided for tool management and control. The tool management and control method has the advantages that the data is deeply mined based on the intelligent algorithm, and the association rules are mined in combination with the improved DBSCAN algorithm and the Apriori algorithm fused with the space-time semantics.

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 discloses a traditional Chinese medicine prescription recommendation and visualization method and device based on combination of an Apriori algorithm and a Jaccard similarity algorithm, a medium and equipment, which are applied to a doctor terminal, and the method comprises the steps of selecting a patient symptom list and a syndrome element list, generating a traditional Chinese medicine prescription recommendation set and performing scoring, and performing traditional Chinese medicine prescription visualization. According to the technical scheme disclosed by the embodiment of the invention, the patient symptom list and the syndrome element list are selected and are combined with the preset traditional Chinese medicine prescription set, the symptom list is analyzed by using the Apriori algorithm, and then the syndrome element list is analyzed by using the Jaccard similarity algorithm, so that the traditional Chinese medicine prescription recommendation set is obtained and visually displayed; the defects that in the prior art, traditional Chinese medicine prescription application only depends on doctor experience, objective standards are difficult to have, and traditional Chinese medicine syndromes are not combined for analysis are overcome, and objective, accurate and efficient traditional Chinese medicine prescription recommendation and visual display are achieved.

This invention relates to the field of cold chain warehousing inventory management technology, and more particularly to a cold chain warehousing inventory management system based on big data analysis. The system includes: a correlation factor determination module, used to classify the set of energy consumption-related parameters of cold storage into uncorrelated parameter sets and correlated parameter sets using a correlation degree model, wherein the correlation degree model is constructed based on the Apriori algorithm; a data imputation module, used to generate imputation parameters from the uncorrelated parameter sets using Lagrange interpolation; an energy consumption prediction module, used to generate predicted cold storage energy consumption from the imputation parameters and correlated parameter sets using an energy consumption prediction model, wherein the energy consumption prediction model is constructed based on an architecture that fuses a temporal convolutional network with gated recurrent units and an attention mechanism; and a cold chain warehousing inventory management module, used to manage the cold chain warehousing inventory according to the predicted cold storage energy consumption. This invention achieves intelligent cold chain inventory management based on predicted cold storage energy consumption.

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 discloses a distributed data management system applied to urban pipeline detection, which relates to the field of data management and comprises a data acquisition module, a slice making module, a database module, an association rule module and a query module. The data acquisition module collects urban pipeline operation data and historical query data; the slice making module obtains a slice data set according to historical query data, and then slices operation data; the association rule module utilizes an Apriori algorithm to mine a value association rule set; the query module outputs query and recommendation results according to the user real-time query and value association rule set; according to the method, the fragmentation rule can be reasonably designed, the high-frequency query data can be centrally stored, and the data query efficiency is improved.