23 results about "Data mining algorithm" patented technology

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.

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 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.

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.

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 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 invention discloses a power grid dispatching instruction and potential risk association analysis method, which comprises the following steps of: obtaining multi-source data related to a power grid dispatching instruction and a potential risk, and preprocessing the multi-source data; based on the preprocessed multi-source data, a scheduling instruction and potential risk association knowledge base is constructed, the basic information layer serves as a bottom layer support of the association knowledge base, and the association rule layer extracts a hidden association rule of the scheduling instruction and the potential risk from the multi-source data through a data mining algorithm to form an association rule base; the case library layer collects historical scheduling instructions and potential risk associated cases, establishes a structured case file and an efficient retrieval mechanism, and provides referenced historical experience for risk pre-judgment of the current scheduling instructions; and inputting to-be-analyzed data into the deep learning-based correlation analysis model for analysis, and outputting a correlation analysis result. By constructing the association knowledge base and combining with the deep learning model, the accuracy and reliability of the risk analysis result are improved, and the model accuracy is high.

The invention relates to the technical field of animal epidemic disease epidemiological survey data analysis and processing systems, and discloses an animal epidemic disease epidemiological survey data analysis and processing system. The exploration data warehouse is used for reading survey data of animal epidemic situation epidemiology, the model library is used for storing various calculation models and algorithm formulas, and the component library is used for storing customer division data, customer loss data and customer product data of different industries. The mining algorithm library is used for performing mining calculation on data according to information of the model library, the component library and the exploration data warehouse and importing a calculation result into the survey data client, the knowledge storage end is used for receiving and storing the data imported by the data mining algorithm library, and the survey data client is used for reading the data of the knowledge storage end for query. The problems that a traditional data analysis and processing system is complex in architecture design structure and low in efficiency are solved.

The present application belongs to the technical field of coal washing and sorting modeling and intelligent control, and particularly relates to a dense medium separation dynamic simulation method and system. The method covers the dynamic mechanism model of the whole process of dense medium separation, and on the fast time scale, the key equipment parameters in the density control loop that cannot be directly measured are estimated online. In combination with the Monte Carlo data mining algorithm, the error between the simulated value and the actual value of the medium density is used to generate the tutor signal to supervise the learning and online correction of the online estimation. On the slow time scale, based on the error between the simulated value and the actual sampling value of the clean coal ash, the Bayesian optimization algorithm is used to optimize and update the key separation parameters of the cyclone separation process. The present application effectively solves the problems of insufficient dynamic tracking precision and adaptive ability of the traditional simulation model by using the hybrid intelligent driving and double time scale parameter updating mechanism, and provides a high-precision simulation platform and technical support for the intelligent control and operation optimization of the dense medium separation process.

The application discloses a kind of drill-and-blast method karst tunnel construction surrounding rock overbreak and underbreak control method and system, first construct geologic twin three-dimensional digital fine model, after fracture rock mass macroscopic and microscopic mechanics parameter calibration and blasting excavation numerical simulation, the influence area of karst to blasting overbreak and underbreak is divided into strong, weak, micro, no influence area and is classified;Again, the contribution degree of overbreak and underbreak influencing factor is calculated and sorted by artificial intelligence data mining algorithm, and the blasting parameter optimization index is obtained;Then get the surrounding rock parameters and karst structure information of working face;Finally, the classification results, optimization index, surrounding rock and karst information are input, and the blasting and charging parameters are optimized by self-evolution bidirectional long and short memory neural network algorithm, and the blasting test is verified in three-dimensional model, until the overbreak and underbreak meet the specification requirements.The present application can accurately identify key influencing factors, intelligently optimize blasting parameters, and effectively solve the overbreak and underbreak control problem of karst stratum tunnel construction.

The invention relates to a nozzle throat liner ablation prediction system based on data mining, which belongs to the technical field of aerospace and comprises a data import and loading module, a full model training module, a selective verification module, a prediction module and a result export module. The data importing and loading module is used for importing historical data and inputting the historical data into the model training module; the full model training module processes the data through a plurality of data mining algorithms to generate a prediction model; the selective verification module is used for selecting different types of models for training and verification according to requirements; the prediction model performs ablation prediction through real-time data input; the result export module stores the prediction result; the method has the beneficial effects that the ablation trend of the nozzle throat liner can be predicted in advance, replacement is carried out in time, nozzle faults caused by serious ablation are avoided, assistance is provided for designers to accurately estimate an inner trajectory and provide accurate ablation data, and the safety and reliability of a system are improved.

This application provides a transaction processing method, apparatus, electronic device, storage medium, and program product, relating to the field of artificial intelligence technology. The method includes: obtaining a data export request; obtaining data to be processed based on the data export request; wherein the data to be processed is business data between the current user and a target object. The data to be processed is then mined using a preset data mining algorithm to determine the correlation relationships between the data. Based on the correlation relationships, business prediction is performed on the data to be processed to obtain prediction result information; wherein the prediction result information represents the completion degree of the business. Based on the prediction result information, business strategy information is generated; wherein the business strategy information represents the strategy that the current user needs to execute at the current time. This method aims to improve the accuracy and efficiency of transaction decisions.

An event monitor engine receives incoming event information from a plurality of information sources. An event data mining engine executes a data-mining algorithm on the raw event information to produce a structured output of event information from the plurality of information sources in a common event-information format. An organization information structuring engine receives information about an organization from a plurality of different business systems of the organization, and creates a structured organization description that includes information about resources of the organization in a common format. A threat assessment engine applies threat assessment criteria to the structured out-put of the event information and to the structured organization description to produce an event-resource assessment of whether an event is likely to impact a resource of the organization. A response advisor engine determines a response recommendation for each event-resource assessment based on response criteria, and outputs the response recommendation to be displayed.

The invention discloses an unmanned cluster task evaluation method and system based on data mining, and belongs to the technical field of unmanned cluster task evaluation, and the method comprises the steps: designing a unified unmanned equipment state data format and a unified evaluation index system; establishing a relation model of the time sequence state data of the unmanned cluster and the task stage through a data mining algorithm; in the task execution process of the unmanned cluster, state data are collected in real time, the unmanned equipment is clustered, and the current task stage of each clustering group is determined based on the relation model and the voting mechanism; dynamically distributing and optimizing the weight of each stage according to the identified task stage and the completion condition thereof; and calculating the overall task completion efficiency of the unmanned cluster in combination with the task completion condition and the weight of each stage. According to the method, dynamic identification and weight adaptive distribution of the unmanned cluster task stage are realized, the real-time performance and accuracy of unmanned cluster task evaluation are improved, and powerful support is provided for efficient collaboration and task planning of the unmanned cluster.

This invention relates to the field of big data-driven software development management technology, and discloses a big data-based software development management method, including the following steps: S1, big data collection and preprocessing based on a distributed architecture; S2, requirements analysis and decomposition based on the GBDT machine learning model; S3, project planning and resource allocation based on intelligent scheduling algorithms; S4, development process control and quality inspection based on Flink real-time stream computing; S5, risk warning and handling based on the DNN deep learning model; S6, project acceptance and data review based on data mining algorithms. By adopting a distributed architecture-driven full-process data collection, standardized preprocessing, and dedicated storage management solution, this invention solves the problems of data disorder, poor reusability, and insufficient support, which lead to a lack of scientific data basis in subsequent requirements analysis, project planning, and other stages.

The invention discloses a power transmission line unmanned aerial vehicle inspection method and device based on a data mining algorithm. The method comprises the following steps that data acquisition equipment and an airborne edge calculation unit are installed on an unmanned aerial vehicle; the unmanned aerial vehicle inspects the power transmission line according to a predetermined route; carrying out the preprocessing of the related line data obtained in the inspection process through an airborne edge calculation unit; electrical equipment is rapidly recognized through a lightweight target detection model according to the preprocessed data, and the data are classified into useless data, conventional data and high-value data; the equipment state is evaluated according to the conventional data through a classification model, the normal data of the equipment are marked as normal data, and the abnormal data of the equipment are marked as high-value data; useless data are deleted, normal data are compressed and transmitted in a delayed mode, and high-value data are stored with the original resolution reserved and transmitted preferentially. According to the power transmission line unmanned aerial vehicle inspection method and device based on the data mining algorithm provided by the invention, the intelligent level and the operation efficiency of power transmission line inspection can be comprehensively improved.

The invention belongs to the technical field of safety production management and accident analysis, and particularly discloses a limited space poisoning suffocation accident risk evolution path deduction method and system. According to the method, multiple historical cases are concluded and analyzed, and a multi-level system accident cause model is constructed; performing quantitative analysis on the historical accident case data by applying a related data mining algorithm so as to establish a quantitative association relationship among the cause factors; receiving one or more initial risk factors input by a user, and deducing and identifying a risk path according to the established quantitative association relationship; and comprehensively evaluating the identified risk path, determining a key risk factor combination, and generating a decision analysis report containing preventive management and control suggestions. The problems that traditional accident analysis is insufficient in depth and the process is difficult to quantify are solved, a scientific, quantitative and highly targeted basis is provided for safety management decision, and scientificity and effectiveness of limited space operation safety management are remarkably improved.