7990 results about "Cluster algorithm" patented technology

The invention relates to the technical field of prediction algorithms, in particular to an oil and gas well digital twinborn modeling and prediction analysis system which comprises an underground dynamic data analysis module for extracting variables based on pressure, flow velocity and temperature data, performing data association calculation, analyzing the pressure change rate and the flow velocity trend, calculating the temperature fluctuation range and establishing time period variable analysis; and generating a dynamic parameter mode. According to the invention, through an autoregressive integral moving average model, the dynamic data of each time period is integrated and smoothed, the long-term trend of the data is captured, the subtle change in the production fluctuation is identified, the accurate future yield fluctuation trend is generated, a density clustering algorithm is adopted, key variables are analyzed, and the change mode of abnormal fluctuation is extracted. According to the method, potential fault types are screened, similar data points are classified through the density relation between data, dependence on parameter setting is reduced, abnormity is recognized more accurately under the unsupervised condition, and the fault recognition capability is improved.

The invention provides a Python monitoring task resource use method and system, and relates to the technical field of intelligent monitoring and resource optimization, and the method comprises the steps: extracting process call stack information through a multilayer convolutional neural network, constructing a process dependency graph, calculating a process criticality score, and recognizing a core monitoring process. And obtaining resource use data of the core monitoring process through a resource monitoring coroutine, determining an optimal sampling period by adopting a self-adaptive feedback control algorithm, and writing the data into a time sequence database. And performing data aggregation analysis by using a sliding time window algorithm to obtain a performance index, and constructing an anomaly detection model. Dimensionality reduction is carried out based on a principal component analysis method to construct feature vectors, and load types are divided by adopting a clustering algorithm; and generating a differentiated process optimization strategy according to the feature vector and the load type. When performance fluctuation is detected, an optimization strategy is executed, the anomaly detection model and the deep reinforcement learning model are updated according to the performance improvement index, and finally an optimization diagnosis report is generated.

The invention discloses a point cloud differential defect detection system and detection method based on multi-feature fusion, which are mainly used for high-precision detection of surface defects of a side wallboard of a railway passenger car, and the method comprises the following steps: obtaining datum point cloud data through a designed standard side wallboard model of the railway passenger car, and collecting real-time point cloud data through a laser scanner; preprocessing the obtained point cloud data; calculating the difference between the real-time point cloud and the reference point cloud by adopting a difference method for fusing geometric features such as Euclidean distance, curvature and normal vector, namely calculating the Euclidean distance difference, curvature difference and normal vector difference between the reference point cloud and the real-time point cloud; and a comprehensive abnormal score is generated by further combining a dynamic weight fusion mechanism, and different region characteristics (such as a plane, a curved surface and an edge region) are adapted through dynamic threshold adjustment. According to the method, the defect region is segmented through the clustering algorithm, and the defect area and depth are quantified. The method has the advantages of high detection precision, strong adaptability and good real-time performance, and is suitable for complex surface defect detection in industrial production.

The invention discloses an object labeling method and system based on laser point cloud, particularly relates to the technical field of laser point cloud processing, and is used for solving the problem of inaccurate labeling result caused by a complex reflection phenomenon caused by a non-Lambertian surface in the prior art. The method comprises the steps of obtaining original point cloud data collected by a laser radar and performing preprocessing; identifying a non-Lambertian reflection region based on the reflection intensity information and the geometric topology feature, and carrying out filtering correction; correcting false mirror image points through multi-layer iteration correction, and reconstructing boundaries to generate corrected point cloud data; grouping and classifying target objects in the corrected point cloud data based on a clustering algorithm combining global features and local features; finely dividing the boundary of the preliminary segmentation result by adopting an attention mechanism network, and evaluating the uncertainty of a boundary region through a local mutual information entropy; and adjusting a boundary division strategy according to an uncertainty evaluation result, and generating an optimized segmentation result.

A database system generates a prompt for an LLM or other machine learning (ML) model to narrow the search space to highly relevant information about a database. A distinct instance of a classifier, a clustering algorithm, or a topic modeling model can be trained based on information from ML automation within the database system, respectively for each column or table in the database. Model instances can then be used during generative LLM inferencing to identify relevant sources of data to answer the user's question. Thus, the prompt generation combines ML automation and other ML models or an LLM for topic modeling and schema description.

A layout optimization method of water quality monitoring points based on a RF-C-SOM clustering algorithm includes: preprocessing collected water quality data to obtain preprocessed water quality data that are used as data, and using water quality categories as labels to train a random forest model to determine feature importance of water quality indicators; selecting important features based on the feature importance and model training accuracy, performing dimensionality reduction on the preprocessed water quality data to obtain dimension-reduced data; performing a fuzzy clustering on the dimension-reduced data to obtain a water quality section classification result, and based on it, determining initial weight values of a self-organizing mapping algorithm; initializing neurons and training a self-organizing mapping network model with the initial weight values; obtaining a point clustering result through the self-organizing mapping network model; and conducting a water quality index evaluation for the point clustering result before and after screening.

The invention relates to the technical field of cluster scheduling, and provides a GPU heterogeneous cluster scheduling method and system oriented to large model training and reasoning, which constructs a set of complete cluster scheduling system by integrating multi-source information such as hardware features, running states and historical task data and applying technologies such as a clustering algorithm, a fuzzy comprehensive evaluation method and reinforcement learning. Comprehensive, intelligent and dynamic management and scheduling of GPU cluster resources are realized, the cluster scheduling system can significantly improve the execution efficiency of GPU heterogeneous clusters in large model training and reasoning tasks, the resource utilization rate is improved, the energy consumption is reduced, and the stability and adaptability of the system are enhanced. And an efficient and reliable solution is provided for large-scale deep learning application.

The invention provides a power distribution network carbon emission analysis method, which comprises the steps of constructing a user power consumption behavior portrait based on user electric quantity data, learning an influence weight of a user power consumption habit on carbon emission by utilizing an attention mechanism, mining carbon emission modes under different power consumption behaviors through a clustering algorithm, and forming a user-oriented carbon emission interpretation model; an Apriori association rule mining algorithm is adopted, a frequent pattern in carbon emission data is extracted from three dimensions of a time period, equipment and a user, a key factor combination which obviously influences the carbon emission is screened out, and a rule is embedded into a block chain through an intelligent contract to execute carbon emission traceability; the diversity of carbon emission data is expanded by utilizing the generative adversarial network, carbon emission conditions in different extreme scenes are simulated, visual tracing of carbon emission whole-process data is realized through a block chain browser, and a decision basis is provided for accurately checking carbon emission and formulating an emission reduction policy.

A reactor partial discharge diagnosis method based on multi-source feature fusion and a deep network relates to the technical field of partial discharge detection, and comprises the following steps: S1, data preprocessing and feature extraction; s2, extracting multi-dimensional feature information; s3, processing the high-dimensional feature set through an SWFK-DPC clustering algorithm; s4, constructing a deep multi-source adaptive convolutional neural network (EDMSACNN); s5, introducing cross-domain feature compression and a double-path attention mechanism; s6, introducing a confrontation enhancement and classification module; and S7, model optimization and iterative training. According to the method, non-linear interaction of multi-source features is realized through tensor shrinkage and an attention mechanism, the limitation of traditional linear fusion is broken through, data distribution differences under different working conditions are effectively relieved based on an optimal transmission feature alignment technology, and the adaptive cross-domain capability is remarkably improved.

The invention relates to the technical field of document retrieval and information processing in the data processing technology, in particular to a document retrieval method based on multistage indexing and feature clustering, which comprises the following steps: performing high-dimensional space mapping on multi-modal features such as texts and images through a quantum embedding layer to generate cross-modal joint feature representation; a first-level index of a multi-level index architecture is dynamically initialized based on a meta-clustering algorithm, and semantic blocks of a second-level index are divided in combination with a multi-head self-attention mechanism. And an optimal transmission matrix is generated by using a Sinkhorn algorithm to align cross-node feature distribution. The multi-target mixed retrieval strategy is fused with vector retrieval, keyword retrieval and graph retrieval results, and weight distribution is dynamically adjusted. Through collaborative optimization of quantum calculation, federated learning and causal reasoning, a closed-loop technical architecture from feature analysis to dynamic index construction is formed, the problems of insufficient cross-modal fusion, static clustering deviation and semantic association deficiency are solved, and the precision, efficiency and dynamic adaptability of heterogeneous document retrieval are improved.

The invention provides a user interest intelligent recommendation method and system based on the Internet, and relates to the technical field of the Internet, the system collects and integrates behavior data of a user on different platforms in real time to form a multi-source data set; by utilizing natural language processing and time sequence analysis technologies, the system deeply digs user interests and interest change trends, and identifies potential interest sudden increase trends; through a clustering algorithm, the system divides users with similar interest characteristics into dynamic user interest clusters, and dynamically adjusts parameters of the user interest clusters according to instantaneous data; the system predicts future demands of the user through a deep learning model, generates personalized recommendation content in combination with a time sequence analysis model and user interest cluster features, and pushes the personalized recommendation content to the user through multiple channels. According to the method and the device, the related content is accurately pushed under the condition that the user suddenly changed interest characteristics are improved.

The invention provides a large-scale network node scene construction method and system based on a network target range, and relates to the technical field of network security, and the method comprises the steps: dividing network nodes into a plurality of attack and defense levels through calculating the vulnerability association degree between the nodes, and screening a target network node template based on a vulnerability chain-type propagation coefficient. Secondly, optimizing a network topology structure by adopting a graph theory clustering algorithm, and constructing a connection relation between nodes based on service affinity; and finally, according to the resource utilization rate index, constructing a node operation environment, deploying network services, implanting a vulnerability program and carrying out anomaly detection and repair, and finally generating a large-scale network node scene meeting resource optimization scheme constraints. According to the method, the large-scale network target range with a complex topological structure and a real vulnerability environment can be automatically constructed, and the efficiency and authenticity of network security attack and defense drilling are improved.

The invention relates to the technical field of software engineering, in particular to a source code detection method, which comprises the steps of source code preprocessing, code dependency analysis, semantic understanding and analysis, code quality evaluation, dynamic behavior simulation, intelligent vulnerability detection and the like to realize comprehensive detection of potential vulnerabilities and quality problems in source codes. A support vector machine and a K-means clustering algorithm are combined, known and unknown vulnerabilities can be effectively recognized, in addition, a continuous optimization feedback mechanism is introduced, a mixed vulnerability detection model can be continuously optimized according to repair operation and feedback information of a developer, the detection accuracy is improved, and the coverage range is expanded. The generated comprehensive detection report covers code structures, vulnerability positioning, quality problems and optimization suggestions, supports integration of various output formats and development tool chains, helps developers to efficiently repair problems, and improves code quality and project progress; according to the method, the comprehensiveness, the self-adaptability and the user experience of code detection are effectively improved.

The invention discloses an aircraft flow field prediction method and system of a multi-region physical drive neural network, and the method comprises the steps: constructing a continuous region mask and high-dimensional physical parameter sampling system, carrying out the global sampling of high-dimensional physical parameters through employing a Latin hypercube sampling method, and carrying out the space division through combining with a KMeans clustering algorithm; inputting the space coordinates, the continuous area mask, the wall surface distance and the physical condition parameters into an AMPD model, and generating a boundary layer mask, an eddy current mask and a physical residual error; inputting the boundary layer mask and the eddy current mask into a physical constraint driven loss function system, and establishing a multi-target residual minimization loss function for training an AMPD model; based on the multi-target residual error minimization loss function and the physical residual error, training an AMPD model by adopting a course learning training strategy; wing surface flow field reconstruction is carried out through the trained AMPD model, aircraft flow field prediction is completed, and high-precision and high-efficiency intelligent prediction of wing streaming is achieved.

The invention discloses a real-time surrounding rock deformation monitoring and data acquisition method and system, which is applied to long-distance weak surrounding rock tunnel construction, and comprises the following steps: determining the dynamic change trend of underground water seepage rate and ground stress distribution gradient by adopting a time sequence analysis method; based on the trend, carrying out risk partitioning on the tunnel construction section by adopting a K-means clustering algorithm, determining a deformation sensitive area, and optimizing the spatial distribution of the monitoring points according to the deformation sensitive area; monitoring data are acquired in real time, and when the data fluctuation period exceeds a threshold value, the data acquisition frequency of the corresponding monitoring point is automatically improved; processing high-frequency acquired data by adopting a long-short-term memory network to obtain a real-time surrounding rock deformation prediction result; the prediction result and the multi-source real-time geological parameters are fused, a Bayesian updating method is adopted for processing, a quantitative surrounding rock stability evaluation result is obtained, closed-loop self-adaptive optimization of a monitoring scheme and accurate risk prediction are achieved, and the safety early warning capacity of tunnel construction and the utilization efficiency of monitoring resources are remarkably improved.

The invention discloses a water environment pollution monitoring management method and system based on big data, and particularly relates to the technical field of water environment monitoring. A drainage basin is divided into a pollution influence main control area, a pollution diffusion boundary area and a pollution interaction transition area; extracting high-frequency pollution factors by using a big data clustering algorithm, constructing a high-frequency pollution factor incidence matrix, establishing a pollution diffusion path prediction model in combination with historical flow direction data and a time sequence analysis technology, and outputting a potential pollution propagation area and intensity; the current pollution diffusion state is analyzed through dynamic matching of real-time monitoring data and the prediction model, and a pollution diffusion real-time prediction result is generated; according to the method, the pollution risk level of each area in the drainage basin is evaluated based on the prediction result, the partition pollution early warning information is generated, the historical treatment case data is combined, pollution prevention and control and treatment suggestions are provided, precision and intelligence of water environment pollution monitoring are achieved, support is provided for water pollution treatment, and the method has remarkable environmental protection benefits and practical application value.

The invention relates to the technical field of computer processing, in particular to a software architecture code generation method and system based on a large language model, and provides the following scheme: obtaining demand information input by a user, analyzing demands by using a natural language processing technology, and extracting functional elements; performing classification processing on the functional elements through a clustering algorithm to generate a functional module group; mapping the function module group to a system architecture layer, and generating an architecture diagram represented by UML (Unified Modeling Language); and generating a corresponding source code according to the UML graph, and optimizing the source code. The optimization strategy comprises static analysis of format problems and potential vulnerabilities in identification codes and performance optimization of complex functions. Through combination of the large language model and the automatic code generation and optimization technology, the software development efficiency is improved, manual intervention is reduced, and the correctness and maintainability of the generated code are ensured.

The invention relates to the technical field of constructional engineering safety monitoring, and discloses a constructional engineering safety monitoring method and system based on machine learning, and the method comprises the steps: obtaining an original detection data set; according to the original detection data set, adopting a noise reduction algorithm to obtain a monitoring data set; according to the monitoring data set, a clustering algorithm is adopted to divide a building structure into a plurality of areas, and a high-risk area is obtained; according to the high-risk area, time sequence data are extracted, a time sequence analysis method is adopted to calculate a quality index change trend, and a trend analysis result is obtained; according to the trend analysis result, analyzing quality index relevance between different areas through a relevance mining algorithm to obtain a potential chain reaction risk assessment report; and inputting the potential chain reaction risk assessment report into a pre-trained building engineering safety monitoring model to obtain a building structure health trend report. According to the method, self-adaptive building quality safety dynamic global analysis can be realized.

The invention discloses a battery energy recycling method for an underwater robot, and the method comprises the steps: integrating the equipment state and environment data of the underwater robot, screening the data through a particle filtering algorithm, capturing the depth information of an underwater environment in real time, collecting the material attribute of a battery and an energy transmission efficiency parameter, and recognizing the parameter through a k-means clustering algorithm. The method comprises the following steps: constructing constraint conditions of an energy recovery network, constructing an energy recovery network topology model according to energy loss information and node layout, obtaining an optimal path by applying an Euleran path algorithm, determining an arc curvature by adopting a non-dominated sorting genetic algorithm, dynamically updating topology model parameters, obtaining an optimized energy recovery arc, and constructing an arc optimization mechanism. A logistic regression algorithm is used for scoring arcs and identifying efficient nodes, an Apriori association rule mining algorithm is used for simplifying a network topology structure, calculating structural differences and updating a model, a fitness function algorithm is used for monitoring the network state and energy loss in real time, and the transmission efficiency is evaluated.

The invention provides a cold and hot data object life cycle feature extraction method based on deep learning, and the method comprises the steps: carrying out the clustering of feature vectors through employing a clustering algorithm, dividing the data into different viscosity level data clusters according to a clustering result, and enabling the data in each viscosity level data cluster to have similar access modes and life cycle features; for each viscosity level data cluster, analyzing the access time distribution and life cycle stage of the data cluster, and judging the heat type to which the data cluster belongs, including continuous heat, periodic heat, temporary heat and cold; according to the current popularity type and the historical access mode of the viscosity level data cluster, predicting the popularity change trend of the viscosity level data cluster in a future period of time, and obtaining a popularity change curve of each cluster; by monitoring the access condition and the heat change of the data in real time, the migration and replication modes of the data between different storage layers are dynamically adjusted, and when the actual access mode of the data is deviated from the predicted mode, data re-clustering and storage optimization are triggered.

The invention discloses a construction site safety helmet wearing detection system and method based on computer vision, and the method comprises the following steps: obtaining an aligned and enhanced multi-modal data set, and carrying out the processing through employing a multi-scale feature extraction and fusion method, and obtaining a fusion feature set; acquiring a fused feature set and an enhanced depth image, and processing by adopting a depth perception density clustering RPN algorithm to obtain a candidate region set; then, a multi-stage safety helmet detection and classification method is adopted for processing, preliminary safety helmet detection and classification results are obtained, and a classification result set is formed; and obtaining an obtained candidate region set, a classification result set and an aligned and enhanced multi-modal data set, and processing the obtained candidate region set, the obtained classification result set and the aligned and enhanced multi-modal data set by adopting a multi-stage post-processing and result optimization method to obtain a final safety helmet detection and classification result. The detection efficiency is greatly improved, and the use effect in an actual scene is good.

The invention provides an intelligent garden environment monitoring data analysis method and system. Sensors are deployed to collect temperature, humidity and other data in real time to form a data set. Analyzing an environment parameter change mode of each region by adopting a time sequence clustering algorithm, and determining characteristics and trends; and carrying out dimension reduction on the data by using a multi-dimensional scale transformation algorithm to a three-dimensional space for visualization, and generating an environment state map. And evaluating the plant fitness in combination with a plant physiological ecology knowledge base, and generating a scoring matrix to guide the preliminary layout scheme. And dynamically adjusting the layout by applying reinforcement learning, selecting an optimal scheme, and predicting a future environmental evolution path based on the optimal scheme and historical data. An automatic control system framework is constructed, a self-adaptive control algorithm is designed, intelligent regulation and control of plant layout are realized, garden management efficiency is improved, and ecological protection and sustainable development are promoted. Therefore, full-chain intelligent management from environment monitoring to plant layout optimization is realized, and the intelligent level of garden environment monitoring and management is remarkably improved.

The invention discloses an unmanned ship multi-agent collaborative obstacle avoidance method and system for complex sea conditions, and relates to the field of unmanned ship multi-agent collaborative obstacle avoidance, and the method comprises the steps: obtaining the real-time data of each unmanned ship and the surrounding environment; generating a candidate obstacle target point cloud cluster list based on a density clustering algorithm; on the basis of a Kalman filter, real-time absolute motion state estimation of the candidate obstacles is obtained, and an obstacle feature list is output; determining a safety radius compensation amount required by autonomous obstacle avoidance of each unmanned ship; obtaining the safe sailing space of each unmanned ship at the current moment; constructing a global synthetic potential field, and generating a group of optimal alternative paths of the unmanned ship from the current position to the target point; on the basis of adopting a consensus binding algorithm, an unmanned ship multi-agent collaborative obstacle avoidance path reaching a consensus is obtained. The method has the advantages that safe, efficient, cooperative and consistent intelligent obstacle avoidance of a multi-unmanned-ship cluster in a real complex marine environment is realized.

The invention relates to the technical field of instrument multi-task optimization, in particular to an intelligent instrument multi-task real-time optimization method and system based on dynamic resource scheduling. The optimization method comprises the following steps: acquiring a target item of each task in real time through a sensor array, constructing a multi-dimensional feature vector, dividing each task into task categories by using a fuzzy clustering algorithm, and presetting an initial priority for the task categories for multi-task feature parameter acquisition and classification modeling. According to the method, the multi-dimensional feature vectors including the task urgency degree, the calculation complexity and the data interaction frequency are constructed, and the fuzzy clustering algorithm of the task dependency constraint is introduced, so that the task categories are accurately divided, the cross-category interaction overhead of the dependency task is effectively reduced, and the compatibility of a scheduling strategy is improved from the source.

The invention relates to a text division method and device and electronic equipment. The method comprises the steps that sentences in a target text are recognized, the sentences are converted into sentence vectors, position codes for representing the positions of the sentences in the target text are constructed, the mapping relation among the sentence vectors, the sentences and the position codes is established, semantic clustering is conducted on the sentence vectors, multiple theme class clusters are generated, and a preset large language model is used for generating a plurality of theme class clusters based on the sentence vectors in the theme class clusters. The method comprises the following steps: generating a subject tag corresponding to each subject class cluster, converting the subject tag of the subject class cluster into a corresponding subject tag vector for each subject class cluster, calculating the similarity between the subject tag vector and a sentence vector in the subject class cluster, and dividing a target text based on the similarity and position codes. According to the scheme provided by the invention, the advantages of the semantic clustering algorithm and the large language model can be fully utilized, the recognition accuracy of each theme in the text is improved, and the accurate division of the text according to the theme is realized.

The invention discloses an unmanned aerial vehicle cargo distribution management method and system based on strategy optimization, and particularly relates to the technical field of cargo distribution management. Through an improved incremental K-means clustering algorithm, a KD-Tree index is combined to optimize nearest neighbor search, order area grouping efficiency is improved, based on an order cluster set O, mixed integer programming and a Hungary algorithm are adopted to carry out unmanned aerial vehicle task matching, a task distribution moment M is optimized, a distribution path is dynamically adjusted to deal with real-time weather and airspace limitations, and in the distribution process, the distribution efficiency is improved. Data such as order liquidity index, battery consumption rate and task balance degree are acquired through an unmanned aerial vehicle sensor, and the scheduling delay condition is judged by using fuzzy logic reasoning, so that the system can still realize efficient, balanced and intelligent unmanned aerial vehicle scheduling in a high-load scene, the distribution delay is greatly reduced, the no-load rate is reduced, and the overall logistics efficiency is improved.

A data processing system implements receiving a plurality of natural language prompts for a language model; storing the prompts in a privacy protecting datastore in which the prompts are inaccessible from outside of the privacy protecting datastore; analyzing the prompts stored to generate embedding vectors representing the plurality of natural language prompts; storing the embedding vectors in the privacy protecting datastore; analyzing the embedding vectors using a clustering algorithm to generate clusters of related embedding vectors in the privacy protecting datastore; generating, for each cluster, theme information summarizing the embedding vectors of the cluster; and storing the theme information associated with each cluster of the plurality of clusters in a second datastore outside of the privacy protecting datastore, the second datastore being accessible for querying the data stored therein.

The invention discloses an intelligent customer service method for adaptively adjusting an interaction strategy by using reinforcement learning, and the method comprises the following steps: S1, obtaining a natural language text inputted by a user, and generating a multi-dimensional state vector; s2, grouping the users by using a real-time dynamic clustering algorithm, and updating a clustering result; s3, generating an action space in a specific state, and enriching the action space in combination with a rule-based strategy and a learning-based strategy; s4, aggregating neighbor node features through a graph convolutional network, and extracting local features; s5, weighting adjacent node features by using a graph attention network, and dynamically allocating attention weights; s6, carrying out interaction strategy optimization by adopting a spiral ladder type deep exploration network, and optimizing action selection; and S7, combining online learning with an experience playback mechanism, continuously updating an interaction strategy, and making a quick response to dynamically changing user demands. According to the method, reinforcement learning, the graph neural network and the sentiment analysis technology are combined, and intelligent customer service interaction strategy self-adaptive adjustment and optimization are achieved.

The invention belongs to the technical field of e-commerce, and particularly relates to a big data-based recommendation algorithm design in an e-commerce platform, which comprises the following steps: completing data preprocessing based on data collection and preprocessing; constructing user portraits to generate personalized user portraits, and classifying the users by using a clustering algorithm to find group features of the users; analyzing the user similarity and the commodity similarity, and calculating the similarity between the users; carrying out user-based collaborative filtering and article-based collaborative filtering, and carrying out dimensionality reduction processing on user and commodity features by using an SVD or latent semantic model; modeling user behaviors by using a neural network, and performing data compression and feature extraction by using an auto-encoder; the user feedback data is collected for continuous optimization and iteration of the model, and online learning and A / B test are adopted to adjust the recommendation strategy, so that the method has the effects of realizing more accurate and personalized commodity recommendation, and improving the shopping experience of the user and the sales conversion rate of the platform.

The invention relates to a large-scale data mining method based on multi-tuple data optimization. The method comprises the following steps: firstly, carrying out importance analysis and sorting on each dimension feature in multi-tuple data by designing a feature evaluation mechanism based on information gain and mutual information; on a data set after feature selection and extraction, an improved clustering algorithm is applied, the advantages of density clustering and hierarchical clustering are combined, and a cluster of a structure is recognized in high-dimensional multi-tuple data; based on a cluster obtained through clustering analysis, a cross-dimension association rule mining algorithm is designed, and a cross-dimension association relationship is revealed in the multi-tuple data; constructing a hybrid prediction model fusing multiple regression and deep learning technologies; and the prediction accuracy of the target variable is improved by utilizing the multi-dimensional information in the multi-element group data and the mined association rule.