65 results about "Data mining algorithm" patented technology

The invention provides a cross-border e-commerce big data analysis system and method, and the method comprises the steps: obtaining multi-dimensional original business data of a cross-border e-commerce platform, carrying out the real-time collection through a distributed data collection system, and obtaining a structured business data set; data collected in real time is cleaned, converted and structured, a unified data model is constructed, and integration of heterogeneous data sources is achieved; storing the processed structured data in a time sequence database to provide data support for real-time analysis; performing real-time aggregation and statistical analysis on the newly added data to generate business index and key performance index data; a Lambda framework is adopted, batch processing and stream processing are combined, the historical data and the real-time data are subjected to full-amount analysis, and an analysis result is obtained; based on an analysis result, a data mining algorithm is adopted to identify a service mode and predict a future trend; a multi-dimensional data cube is constructed, multi-dimensional and multi-granularity data exploration and interactive analysis are supported, and management decision making is assisted.

The invention relates to a risk analysis method, and particularly provides a dynamic risk analysis method based on a Bayesian network. Converting the risk influence factors into a first group of network nodes based on a historical risk knowledge model, and constructing an initial topological structure according to a knowledge model logic relationship; secondly, acquiring multi-source real-time monitoring data, learning correlation among variables through a data mining algorithm, and generating a second group of nodes and a data-driven topological structure; then, connecting the historical information Bayesian network with the data-driven Bayesian network through a shared risk node, and constructing a comprehensive Bayesian network; meanwhile, a dynamic probability updating mechanism is established, and comprehensive network probability parameters are dynamically adjusted by adopting a weighted fusion algorithm in combination with a historical prior probability and a real-time posterior probability; and finally, performing risk reasoning based on the dynamically updated integrated network, and outputting risk quantitative indexes.

The invention discloses an electric vehicle charging compatible fault diagnosis method based on a knowledge graph, and the method comprises the following steps: constructing the knowledge graph, carrying out the fault diagnosis reasoning, and updating the knowledge graph, and the construction of the knowledge graph comprises the following steps: collecting multi-source data in the charging process of an electric vehicle, knowledge extraction is performed by applying a natural language processing technology and a data mining algorithm, knowledge from different sources is fused, a knowledge graph is constructed in a graph form, nodes represent entities, edges represent relationships between the entities, and attributes are attached to the nodes or the edges. According to the method, the knowledge graph is constructed, the electric vehicle charging multi-source data is comprehensively collected, knowledge is extracted and fused by using natural language processing and a data mining algorithm, and during fault diagnosis reasoning, input data is preprocessed based on combination of rules and machine learning, for example, abnormal value detection by using Z-score, missing value filling by using linear interpolation and the like.

The invention discloses an advertisement putting method and system based on big data analysis, and belongs to the technical field of advertisement putting, and the method comprises the steps: obtaining multi-source data of a user, carrying out the preprocessing of the multi-source data of the user through a big data processing technology, guaranteeing the quality and consistency of the data, integrating the data of different sources, and carrying out the processing of the data. Establishing a unified user data warehouse; a machine learning and / or data mining algorithm is used for deeply analyzing the user behavior data, user preference characteristics are mined, and the user preference characteristics comprise interest preference, consumption habits and advertisement acceptability; according to the preference characteristics of the user, constructing a precise portrait of the user; obtaining an advertisement putting target and platform characteristic data; and according to the accurate portrait of the user and an advertisement putting target, combining platform characteristic data, and applying collaborative filtering and a content recommendation algorithm to recommend personalized advertisement content to the user. According to the invention, the advertising effect of the advertisement on different platforms is ensured.

The embodiment of the invention discloses a data analysis method and device, electronic equipment and a storage medium, which are used for analyzing and aligning different field names in a plurality of historical data tables by using at least one of a large language model and a data mining algorithm, so that the data analysis efficiency is improved. The method comprises the steps that historical data is obtained, the historical data comprises a plurality of historical data tables, historical natural language query statements and historical structured query language SQL query statements corresponding to the historical natural language query statements, and each historical data table comprises at least one field name; the historical data are input into at least one of a large language model and a data mining algorithm, a target field alignment table is generated, and the target field alignment table comprises the mapping relation of different field names with semantic equivalence in multiple historical data tables; and according to the target field alignment table, updating the structured description information of the plurality of historical data tables, and generating updated structured description information of the plurality of historical data tables.

The embodiment of the invention provides a wafer defect detection method and device based on a data mining algorithm. The wafer defect detection method based on the data mining algorithm comprises the following steps: acquiring image data of a wafer; preprocessing the image data to obtain determined preprocessing data; feature extraction is carried out through clustering analysis based on the preprocessed data, and defect features are determined; and judging the defect features based on an integrated learning classification model, and determining defect information. A data preprocessing process is adopted, the data quality and the accuracy of subsequent analysis are improved, feature vectors are constructed in combination with multi-dimensional features such as gray scale, texture and edge subsequently, the similarity of image blocks is calculated through Euclidean distance or Manhattan distance, the feature vectors are clustered by using a K-means algorithm, and the similarity of the image blocks is calculated. The features capable of effectively representing the small defects of the wafer are automatically extracted, complicated feature extraction rules do not need to be manually designed, and the efficiency and adaptability of feature extraction are improved.

According to the equipment inspection data analysis method based on the Internet of Things provided by the invention, the data interface and the conversion standard are set, and the intelligent middleware, the general data acquisition terminal and the protocol conversion module are used for adapting to access of different equipment to the inspection system, so that uniform format transmission of data is ensured. And a visual low-code function customization module is created, a local cache and synchronization mechanism is constructed, and offline work and data synchronization are realized. A state evaluation model is established in combination with a sensor data calibration algorithm, and the operation state and potential faults of the equipment are accurately judged. And an intelligent decision support module is constructed, a fault prediction model is established by using a data mining algorithm, and personalized decision suggestions are generated. And an intelligent inspection path guiding module is set, and an inspection path is optimized according to decision suggestions and fault prediction. According to the scheme, the compatibility of equipment access is improved, the offline working capability is ensured, the equipment state is accurately evaluated, personalized suggestions are provided, the inspection path is optimized, and the inspection efficiency and accuracy are improved.

The invention discloses a safety intelligent diagnosis system for offshore facilities. The safety intelligent diagnosis system comprises a safety overview function module, a safety monthly report function module, an early warning center function module, an element setting function module and a weight setting function module. The invention further discloses an intelligent diagnosis method for safety of the offshore facilities, management elements, human elements, environmental elements and other elements are organized by applying intelligent technical means such as a big data mining algorithm, the Internet of Things and AI, and the method has three functions of overall analysis, monitoring and early warning and auxiliary decision making. Informatization and intelligentization of more than 220 safety management functions such as basic information, major hazard sources, dual prevention mechanisms, intelligent diagnosis, personnel positioning, operation process monitoring and the like are achieved, and the problems that traditional facility safety management and control means cannot be seen, management is incomplete and control is not good are successfully solved.

This invention belongs to the field of data processing technology and discloses a unified encapsulation method and system for data mining algorithms. The method includes encapsulating data mining algorithms into algorithm components that match a common interface template, building an evaluation model based on the algorithm components, constructing a pipeline using the algorithm components through a graphical interface to obtain the data mining model, automatically adjusting the data mining model through machine learning and optimization algorithms, and finally encapsulating and deploying the data mining model to a target area. This invention enables users to call different algorithms through a unified interface. Calling the algorithms as a service makes the algorithm components more adaptable, facilitates the maintenance and expansion of data mining software, lowers the barrier to entry for data mining, improves the interactive operation and user experience of machine learning, enhances the flexibility and maintainability of data mining, reduces reliance on business and domain knowledge, saves time and resources, and improves model performance.

One or more embodiments of the invention provide a financial data analysis method and system, electronic equipment and a storage medium. The method comprises the following steps: acquiring business data and financial data; according to the business data and the financial data, a business and financial relationship network is constructed through a data mining algorithm, and the business and financial relationship network reflects association relationships between businesses and between the businesses and finance; according to the financial data, the business data and the business and financial relationship network, quantifying the influence of the business on the finance through a causal inference algorithm, and constructing a business and financial dynamic mapping model; and performing data analysis on the business data and the financial data according to the business dynamic mapping model, and displaying a data analysis result. According to the technical scheme, the analysis depth of enterprise financial data can be effectively improved, the enterprise operation effect is improved, and powerful technical support is provided for scientific decision-making of enterprise operation and maintenance.

The application provides a charging pile remote regulation and control method and system based on a cloud platform, and belongs to the technical field of new energy infrastructure and smart grid technology.The method comprises the following steps: collecting historical charging data of charging pile users and historical operation data of power grids in the regions where the users are located, generating user charging original data sets and power grid original operation data sets; and using a data mining algorithm to construct a user behavior and power grid state double-dimensional model and generate double-dimensional model data; through the construction of the user behavior and power grid state double-dimensional model, the user charging habits and the real-time operation characteristics of the power grid can be accurately captured, detailed and comprehensive data support is provided for the regulation and control of the charging pile, the problem of inaccurate regulation and control caused by single data dimension in the traditional mode is effectively avoided, and the scientificity and rationality of the charging regulation and control decision are improved.

The present invention discloses a database alarm suppression method and related devices, which can obtain the current fault event sheet; determine the event category corresponding to the fault event sheet through a pre-established category recognition model, wherein one fault event sheet corresponds to one event category; send corresponding alarm information according to the event category and the alarm suppression rules pre-established through a data mining algorithm, wherein the alarm suppression rules record the correspondence between different event categories and the alarm information. It can be seen from this that the present invention will send corresponding alarm information according to the different event categories of the fault event sheet through the pre-established alarm suppression rules, which can reduce the alarm information to a certain extent, avoid the formation of an alarm storm, and thus improve the effectiveness of handling alarms.

The invention discloses a power grid fault-oriented power grid operation regulation and control method, device and equipment and a medium, and the method comprises the steps: carrying out the classification of historical business data of a target power grid through a preset classification algorithm, and obtaining a plurality of core feature modes; through a preset data mining algorithm, according to pre-acquired power grid topology data, analyzing correlation coupling between the core characteristic modes to obtain a correlation coupling analysis result; according to the core characteristic mode and the correlation coupling analysis result, respectively constructing a resource allocation model and an overall planning model to respectively obtain a resource allocation result and an overall planning result, and generating a power grid operation decision result; and according to the power grid operation decision result, generating a corresponding operation regulation and control instruction so as to carry out operation regulation and control on the target power grid. According to the invention, reliable, comprehensive and automatic power grid operation regulation and control can be carried out for a power grid fault scene, so that stable operation of the power grid can be quickly recovered.

A system and method for analyzing data transfers using pattern mining, including: categorizing sequences of events into categories based on an order of the events in the sequences; identifying, for one or more sequences in a given category, subsequences of events in a dataset of event data using one or more data mining algorithms; and accepting or denying a data transfer based on applying logical rules to the data transfer, where the rules may be determined using the identified subsequences. In some embodiments, event sequence categories may include an order sensitive category and an order insensitive category, and identifying one or more subsequences of events may include applying a first data mining algorithm to order sensitive sequences, and applying a second data mining algorithm to order insensitive sequences; rules may be determined based on calculating metrics for identified subsequences, describing occurrences of the subsequences in the dataset of event data.

The invention relates to the technical field of intelligent production data monitoring, in particular to a sulfuric acid material balancing system and method. The system comprises a data acquisition module used for acquiring data in a sulfuric acid production process and verifying the data; the balance calculation module is used for triggering material balance calculation in time according to the operation instruction and the updating condition of the production data; the model building module is used for building a sulfuric acid production material balance data model by adopting a mathematical modeling method based on the results of the process flow analysis and the parameter determination; the analysis mining module is used for carrying out deep analysis on the production data by applying a data mining algorithm, and presenting a data analysis and mining result in a visual mode; and the data management module is used for storing various data in the sulfuric acid production process, quickly querying required data according to conditions, and carrying out maintenance and safety management on a database. The technical scheme can improve the sulfuric acid production efficiency and ensure the product quality.

The invention discloses a traffic hub passing vehicle monitoring method and system, and relates to the technical field of vehicle monitoring, and the method comprises the steps: obtaining a traffic scene image; a target detection algorithm is adopted to identify vehicles in the traffic scene image in real time, and target information is output; storing and processing the target information and the traffic event data based on an open source framework to obtain historical traffic flow data, and predicting a future traffic flow trend based on the historical traffic flow data; performing level-to-level management on authorities of managers based on an access control framework, and allocating data access authorities; and performing correlation analysis on the related factors of the traffic accident by using a data mining algorithm to obtain an accident liability analysis report. Through a YOLOv8 target detection algorithm and a multi-scale feature fusion technology, high-precision vehicle identification is realized, the detection capability of a small target vehicle is improved, and real-time data processing and management in a complex traffic scene are ensured.

This application provides an optimized control system and decision-making method for peak shaving of thermal power units, relating to the field of machine learning technology. The method includes: collecting and analyzing the operating characteristic values ​​and peak shaving performance data of thermal power units; training a neural network model capable of predicting the peak shaving performance of thermal power units using machine learning; clustering thermal power units with similar operating characteristics using a clustering algorithm; and using a data mining algorithm to analyze the correlation reasons for poor peak shaving performance of thermal power units, uncovering the implicit patterns and regularities between peak shaving performance and operation of thermal power units, thereby achieving control and management of the thermal power units. The system integrates a working condition data acquisition module, a peak shaving performance prediction module, a working condition pattern clustering module, and a peak shaving decision mining module to collect thermal power unit data in real time, quickly predict peak shaving performance, and analyze correlation reasons. This application uncovers the performance correlation reasons of thermal power units, providing insights for optimization decision-making.

The invention relates to the technical field of marketing service, in particular to a big data-based digital marketing service system, which comprises the following components: a data collection and analysis module which is used for acquiring browsing records, purchasing behaviors and social media interaction data of users in real time through butt joint with various digital platforms, processing and analyzing the browsing records, the purchasing behaviors and the social media interaction data, and sending the data to a cloud server; and the quality and the integrity of the data are ensured. And the target market positioning module is used for dividing the users into different subdivided markets by utilizing a machine learning and data mining algorithm according to the user portraits and the behavior patterns, and determining a target user group with the most potential. And the personalized recommendation module is used for utilizing a personalized recommendation algorithm machine learning and data mining algorithm based on the target user group and the marketing target. By collecting, processing and analyzing massive user data and combining an artificial intelligence algorithm and a personalized recommendation technology, accurate target market positioning, a personalized marketing strategy and a whole-course tracking analysis function are provided for enterprises.

The embodiments of the present application belong to the field of data processing and relate to a business risk assessment method, apparatus, computer equipment, and storage medium. The method comprises the following steps: constructing a risk knowledge graph based on business data; obtaining risk transmission paths from the risk knowledge graph based on a graph search algorithm; obtaining associated risk event information from a database based on the risk transmission paths, and analyzing the associated risk event information using a data mining algorithm to obtain risk event impact information; quantifying the risk event impact information and training a risk event impact assessment model based on the quantified risk event impact indicator data; performing risk simulation evolution based on the risk transmission paths and the risk event impact assessment model to obtain risk event impact results; and performing risk analysis based on the risk event impact results to obtain a comprehensive risk value. The present application enables accurate risk assessment of an enterprise's business.

The application relates to a risk analysis method, in particular to a dynamic risk analysis method based on a Bayesian network. Based on a historical risk knowledge model, risk influencing factors are converted into a first group of network nodes, and an initial topological structure is constructed according to a logical relationship of the knowledge model; secondly, multi-source real-time monitoring data are acquired, a correlation between variables is learned through a data mining algorithm, a second group of nodes and a data-driven topological structure are generated; subsequently, a historical information Bayesian network and a data-driven Bayesian network are connected through shared risk nodes to construct a comprehensive Bayesian network; meanwhile, a dynamic probability updating mechanism is established, a weighted fusion algorithm is adopted to combine historical prior probability and real-time posterior probability, and the probability parameters of the comprehensive network are dynamically adjusted; finally, risk reasoning is carried out based on the dynamically updated comprehensive network, and risk quantitative indexes are output.

The invention discloses a steam turbine flow characteristic identification and optimization method based on big data mining. The method comprises the following steps that S1, influence factors of all valves are analyzed through downstream valve flow characteristics, and DEH valve set characteristics are obtained; s2, converting the original data into standardized data suitable for analysis and modeling through data preprocessing; s3, the power and the flow serve as entry points, valve set characteristic analysis is combined, and an identification method of the flow characteristics under the unit test working condition is studied; s4, performing big data mining clustering calculation by using a data mining algorithm in combination with historical data and stable working condition identification elements, and extracting target data; and S5, fitting the actual traffic characteristics by adopting a deep learning method. Through data collection and preparation, data analysis and modeling, model verification and adjustment, flow characteristic optimization and real-time monitoring and maintenance, the optimal flow characteristic of the regulating valve can be obtained, and the operation efficiency and reliability of a steam turbine are improved.

The invention discloses a spacecraft data transmission resource usage scheme generation method and device, and the method comprises the steps: obtaining historical data transmission segmental arc data, carrying out the feature extraction of the historical data transmission segmental arc data, and obtaining a feature parameter set; constructing an initial data transmission demand prediction model based on a dichotomy prediction model, and training the initial data transmission demand prediction model based on the feature parameter set and a preset data mining algorithm to obtain a target data transmission demand prediction model; obtaining a data transmission visual arc section in a prediction time period, inputting the data transmission visual arc section into the target data transmission demand prediction model for prediction, and obtaining a use prediction result of the data transmission visual arc section in the prediction time period; and carrying out conflict resolution on the data transmission resources based on the prediction result and a preset resolution rule to obtain a spacecraft data transmission resource use demand pre-allocation scheme.

A system and method for analyzing data transfers using pattern mining, including: categorizing sequences of events into categories based on an order of the events in the sequences; identifying, for one or more sequences in a given category, subsequences of events in a dataset of event data using one or more data mining algorithms; and accepting or denying a data transfer based on applying logical rules to the data transfer, where the rules may be determined using the identified subsequences. In some embodiments, event sequence categories may include an order sensitive category and an order insensitive category, and identifying one or more subsequences of events may include applying a first data mining algorithm to order sensitive sequences, and applying a second data mining algorithm to order insensitive sequences; rules may be determined based on calculating metrics for identified subsequences, describing occurrences of the subsequences in the dataset of event data.

The invention provides a heating smoking set, a heating control method thereof and a storage medium, and the heating control method comprises the following steps: S1, a control assembly obtains smoking associated data, and selects and records temperature change data of an aerosol forming substrate according to the smoking associated data; s2, the control assembly inputs the temperature change data into the puff number prediction model, and residual puff number prediction information is obtained; and S3, the control assembly adjusts the heating assembly to heat the aerosol-forming substrate according to the prediction information of the number of residual puff. According to the method, the suction frequency, the suction depth, the suction number and the heating time are associated by using a data mining algorithm of machine learning, and a personalized suction model is formed according to personal suction habits and training data of a user. The smoking set can dynamically change the mouth number prompt according to the smoking habit of a user, the smoking frequency of the smoke cartridge for the specific user can be determined, dry burning in the last minute is avoided, invalid heating time can be shortened, and energy consumption is reduced.

The invention discloses an industrial enterprise energy efficiency diagnosis method based on a big data analysis technology. According to the method, a fusion data set is constructed by integrating multi-source heterogeneous data of energy, production, environment, management and the like; further defining and calculating energy efficiency key performance indicators; a dynamic energy consumption reference model is established through machine learning, and real-time energy efficiency abnormity diagnosis is achieved; performing root cause analysis through a data mining algorithm, and positioning a problem source; and the energy-saving potential is quantitatively evaluated, and finally the diagnosis result and the optimization suggestion are visually output. According to the invention, closed-loop management from monitoring to diagnosis optimization is realized, the defects of strong subjectivity, data islanding and lack of intelligent analysis in the prior art are overcome, and the method has the advantages of being comprehensive, accurate, real-time and strong in operability.

The present invention discloses an intelligent leg training and rehabilitation method, including constructing a leg training and rehabilitation system based on a deep learning model and a data mining and analysis algorithm; analyzing the healing degree of the patient's rehabilitation training according to the output of the leg training and rehabilitation system, and customizing the leg rehabilitation training content for the patient; monitoring the performance and progress of the patient during the rehabilitation training through the leg training and rehabilitation system, and adjusting the leg rehabilitation training content; based on the results monitored by the leg training and rehabilitation system, introducing an expression analysis module to monitor the patient's expression state and perform human intervention; evaluating and optimizing the training content of the leg training and rehabilitation system; to avoid over-reliance on deep learning and data mining algorithms, introducing an expression analysis module to provide real-time expression state analysis, and adjusting the training and rehabilitation strategy in real time according to the analysis results, providing leg rehabilitation support more in line with the patient's expression state for the patient, and improving the rehabilitation speed of the patient's legs.

The present application relates to a kind of 5G / B5G power communication network traffic analysis method based on multi-time series data mining, specific method steps include: 1) 5G / B5G power communication network traffic characteristic analysis 2) establish 5G / B5G power communication network traffic model, select multi-time series data mining method to establish traffic model, describe the real situation of 5G / B5G communication network service based on power service.Different from the traditional sense of power communication traffic analysis, new division is carried out to 5G / B5G power communication traffic level, using multi-time series data mining algorithm, 5G / B5G power communication traffic is reasonably statistically described, multiple traffic characteristic time series are analyzed as a whole, produce effective abnormal network traffic characteristic association rules, accurately describe the security situation of entire 5G / B5G power communication network, power communication fault prediction, network design, traffic control, resource management and network design have very important application value.

The present invention discloses an AI-based automatic coding method for BOM material information. First, the data structures of the material lists at different stages of the product are unified and stored as a single set of material list data. Single data with unique part numbers is screened, and some of the data is compared with the standard database. When they do not match, they are first cleaned and then compared. Knowledge graphs, vector conversion, cosine distance, etc. can also be used for further processing to convert the single set of material list data into standard data. For data that cannot be matched, multiple strategies can be used to select matching entries. It also involves the format recognition of the material list and the selection of processing tools to ensure data integrity. When denoising the single set of material list data, key parameters are extracted by combining multiple data sources, and association rules are mined through data mining algorithms and denoising operations are performed. The present invention can improve the accuracy and standardization of material data, greatly improve the coding efficiency, and realize intelligent and refined material management.

The invention discloses a blast furnace gas utilization rate time sequence judgment and self-adaptive decision making system and method, and the system comprises a blast furnace full ironmaking data processing module which is used for preparing a'material distribution-furnace feeding-operation-monitoring-test 'full-chain dynamic data set based on a computer technology and an ironmaking process theory; the blast furnace gas utilization rate prediction module is used for predicting the gas utilization rate by adopting a machine learning or data mining algorithm based on the historical operation data and the real-time data; the case library dynamic adjustment module is used for performing clustering analysis on historical data of key monitoring targets and matching fluctuation adjustment ranges of the monitoring targets under different coal gas utilization rate grades to form a case library; the self-adaptive feedback control module is used for setting a corresponding control strategy based on an output result of the prediction module; and the integration module is used for integrating the modules into a closed-loop control system to realize time sequence judgment and self-adaptive decision of the blast furnace gas utilization rate.

The invention discloses a social media data mining agent system based on multi-module collaboration, and the system comprises a target definition module which is used for determining a data mining direction through an agent according to a problem inputted by a user; the query generation module is used for generating a corresponding query statement by the intelligent agent according to the data mining direction, and obtaining information of a data mining object according to the query statement; the tool matching module is used for matching a data mining tool by an intelligent agent according to the information of the data mining object so as to execute a corresponding data mining algorithm to perform data mining and obtain a data mining result; the content screening module is used for screening one or more groups of valuable contents by an intelligent agent according to a data mining result, and generating corresponding visual chart configuration; and the chart analysis module is used for analyzing and generating a conclusion nodule by the intelligent agent according to the visual chart configuration. According to the method, the given social media data are automatically mined and analyzed by utilizing the intelligent agent, and the data mining efficiency can be improved.