311results about "Data mining" patented technology

The invention discloses an enterprise big data mining method and system based on artificial intelligence, and the method comprises the steps: carrying out the dynamic mode alignment through employing a multi-mode hypergraph neural network according to enterprise multi-source heterogeneous data, and generating a time-space consistent multi-mode joint embedded tensor; inputting the multi-modal joint embedding tensor into an orthogonal adversarial manifold learning module, and generating a low-dimensional compact semantic embedding vector with enhanced category separability; performing space-time causal association mining on the semantic embedding vector, and outputting a space-time causal meta-path map containing the recessive commercial logic; and inputting the space-time causal element path map into a dynamic game adversarial interpretation framework, and finally outputting an enterprise-level intelligent decision map with anti-factual robustness. By utilizing the embodiment of the invention, the multi-modal data can be efficiently integrated and intelligently analyzed, and the accuracy and effectiveness of the mining result are improved.

The invention provides a learning data accurate mining method based on cognitive calculation driving. The learning data accurate mining method comprises the following steps of S1, performing multi-modal learning behavior data acquisition and heterogeneous integration; s2, a dynamic feature weight optimization step based on calculus; s3, performing cognitive state differential equation modeling; s4, a cognitive diagnosis hybrid model based on statistics; s5, incremental construction of the dynamic knowledge graph is carried out; s6, constructing a federated learning framework for privacy protection; s7, a cognitive intervention strategy is generated; s8, constructing a multi-granularity effect evaluation system; s9, a step of constructing an interpretability enhancement module; s10, a step of carrying out adaptive iterative optimization; the learning data accurate mining method based on cognitive calculation driving has the following advantages that the data utilization rate breaks through the limitation of a traditional method through federated learning and heterogeneous graph fusion; the attention prediction error is reduced through differential equation modeling, and the method is superior to all existing ARIMA / LSTM baseline models.

The invention relates to the technical field of intelligent environment monitoring and early warning, and discloses an intelligent environment monitoring and early warning system based on multi-sensor fusion, which comprises an intelligent environment monitoring and early warning system. The intelligent environment monitoring and early warning system comprises an intelligent sensing layer, an edge computing layer, a network transmission layer, a data storage layer, an intelligent analysis layer, an application service layer and a system support layer. According to the intelligent environment monitoring and early warning system based on multi-sensor fusion, multi-source monitoring means such as a fixed monitoring station, mobile monitoring equipment, a wearable terminal and satellite remote sensing are integrated through the intelligent sensing layer, and a multi-sensor fusion technology is matched, so that the dimension limitation of a traditional single monitoring mode is broken through; environmental parameters (pollutant concentration, meteorological factors, ecological indexes and the like) can be captured in all directions, and multi-dimensional and high-precision data acquisition in a complex environment is realized.

The invention discloses a high-value information mining method based on an expert thinking chain large model agent, which comprises the following steps of: inputting a name and a summary of a task, extracting entity and event information keywords in the task through a large language model, and performing text vectorization processing on the keywords and an expert thinking chain template; the cosine distance similarity between the keyword and the expert thinking chain template vector is calculated, and the expert thinking chain template with the highest similarity is obtained through matching; calling a search engine to search entity and event information keywords extracted from the task name and the summary, associating the keywords and searching related contents by utilizing a large model and the search engine aiming at each information keyword, and obtaining and recording a title, a publishing mechanism, publishing time, contents and a website of the searched webpage; extracting recorded titles, publishing structures and contents to obtain an entity, relationship and event triple, and organizing into a public security domain knowledge system for storage; performing intelligent analysis on the constructed public security domain knowledge system according to the expert thinking chain template to obtain a visual statistical chart and a structured analysis conclusion; and according to the visual statistical chart and the structured analysis conclusion, an analysis report is written and generated, and a final information mining analysis report is obtained. According to the method, a large number of complex data sets can be quickly identified, classified and analyzed, so that the process of information extraction and knowledge discovery is accelerated.

The invention belongs to the technical field of data processing, discloses a statistical data association mining platform and method based on a knowledge graph, and aims to provide a comprehensive platform for extracting, integrating, analyzing and explaining complex economic data from various heterogeneous data sources so as to support more effective decision making. Collecting original data from various heterogeneous data sources, and preprocessing the original data to generate a comprehensive data set; secondly, mapping the comprehensive data set into a predefined knowledge graph to form a network structure of nodes and edges, and updating the knowledge graph in real time; then, feature extraction is carried out on the updated knowledge graph, and a comprehensive feature vector is generated; then, the comprehensive feature vector is used as input, a potential mode in the data is obtained, and statistical association is mined through the potential mode; and finally, generating an interpretation report, displaying the potential mode in the knowledge graph and the mined statistical association, and increasing the transparency of mode interpretation.

The invention relates to the technical field of digital public services, in particular to a digital public service integration platform and device based on intelligent recommendation and a medium. The method comprises the following steps: firstly, collecting and analyzing basic information and behavior data of a user in real time through a user portrait construction module, and constructing a multi-dimensional user feature vector; secondly, performing structured processing on the government affair service data by using a service feature extraction module, and extracting a service association relationship and scene features; then realizing accurate matching of the user feature vector and the service feature matrix based on an intelligent recommendation engine; and finally, continuously collecting user feedback and dynamically adjusting a recommendation strategy through a real-time feedback optimization module. According to the method, scene-based precise pushing of the government affair service is realized, a continuous optimization mechanism of the recommendation effect is established, the recommendation strategy can be dynamically adjusted according to the user characteristics and behaviors, and the intelligent level and user experience of the government affair service are remarkably improved.

The invention relates to the technical field of AC / DC module control, and discloses an intelligent control method and system for multi-mode switching of an AC / DC module. According to the method, a dynamic topology mapping model is constructed according to an impedance spectrum matrix, and a physical connection relation is converted into a node admittance relation network; generating an energy distribution state sequence based on the power fluctuation vector, and analyzing migration constraint conditions among different operation modes by adopting an association rule mining algorithm; when an operation mode switching instruction is detected, activating a node admittance relation network in the dynamic topology mapping model, and inputting a current energy distribution state sequence to an association rule mining algorithm; according to a real-time matching result of the node admittance relation network and the migration constraint condition, generating a switching control instruction containing an impedance adjustment gradient and a power distribution weight; and the power semiconductor execution unit transmits the switching control instruction to the AC / DC module. According to the method, multi-mode intelligent switching control of the alternating-current and direct-current module can be realized.

This application provides a cloud service test method, including: constructing an application programming interface API knowledge graph of a cloud service, where the API knowledge graph includes a reference relationship between an API parameter and a resource object; then identifying an API dependency relationship based on the reference relationship between an API parameter and a resource object; and then testing the cloud service based on the API dependency relationship, to obtain a test result. In the method, the API dependency relationship is identified based on the reference relationship between an API parameter and a resource object. Even if API parameter names do not match, the API dependency relationship can be accurately identified based on a same pointed resource object, thereby improving accuracy of a cloud service test. The method is universal and can meet a service requirement.

The invention discloses an electric power technology contribution degree evaluation method and system based on a knowledge graph, and relates to the technical field of electric power technology innovation contribution degree evaluation, and the method comprises the following steps: classifying electric power technology data based on a triple, and obtaining first triple data; obtaining local credibility data of the first triple data, constructing a triple reliability scoring model based on a BP neural network, and screening the first triple data to obtain second triple data; obtaining contribution degree evaluation features of the power patent knowledge graph constructed based on the second triple data; and constructing a contribution degree evaluation model based on a BP neural network according to the contribution degree evaluation features, sorting model outputs, and generating an innovation contribution degree evaluation table, thereby solving the problem of one-sided evaluation results of the innovation contribution degree of the power technology, and comprehensively quantifying the innovation contribution degree of the power technology patent.

The invention relates to the technical field of big data processing, and particularly discloses a computer data management system based on big data. The system comprises a distributed data acquisition module which captures multi-source data through an API gateway and a crawler and embeds a quality label; the intelligent storage scheduling engine is used for realizing automatic migration of cold and hot data by adopting a column-type and distributed hybrid architecture; fusing a computing framework, and dynamically coordinating flow processing and batch processing resource execution feature extraction; the self-adaptive strategy center is used for generating data partitions and encryption strategies based on reinforcement learning; and a multi-level security protection system is adopted, and attribute base decryption and dynamic desensitization are deployed. The problems of resource scheduling rigidity and security protection lag in the prior art are solved, the storage cost is reduced by more than 40%, the query delay is compressed to be within 200ms, and the method is suitable for real-time decision-making scenes in the fields of e-commerce and finance.

A photovoltaic power generation associated physical quantity mining method based on historical time series data analysis, and a related apparatus. The method comprises: acquiring historical time series data of multiple dimensions during photovoltaic power generation; calculating degrees of mutual information between the historical time series data of the multiple dimensions of photovoltaic power and the photovoltaic power; selecting the historical time series data of which the degree of mutual information satisfies a set requirement to serve as data related to photovoltaic data; and using a linear discriminant analysis (LDA) method to perform feature dimension reduction on the selected historical time series data to obtain data having undergone dimension reduction processing. In the present invention, by using a maximal information coefficient (MIC) feature selection method, data most related to photovoltaic power generation is selected from original feature variables, and then by using an LDA-based feature dimension reduction method, high-dimensional data is mapped to a lower-dimensional space. By means of the MIC feature selection method and the LDA-based feature dimension reduction method, the accuracy of photovoltaic power generation prediction is effectively improved.

The invention discloses a role playing model fusion system and method based on retrieval and reflection, and belongs to the technical field of natural language processing. The implementation method comprises the following steps: 1, performing data cleaning on original role historical dialogue data, and performing semantic screening by using semantic similarity to form historical dialogue context data; 2, event knowledge associated with the roles is constructed through association degree matching; 3, constructing a role reflection strategy mode by using historical dialogue data, and generating role reflection data; 4, when a user inputs a question, replying by adopting a multi-source fusion mode based on role thinking; compared with the prior art, in a multi-scene task, a dynamic retrieval role knowledge base is utilized to screen task related background knowledge, and a thinking mode modeling framework based on a role reflection mechanism and a context fusion strategy are constructed to generate high-quality answers conforming to the context; and thus, the fusion ability of the role playing model is improved, and the consistency of role words and language styles is realized.

The present application discloses a sensitive data identification method and apparatus, a device, and a computer storage medium. A text mining technology is used to mine a plurality of sensitive data rules from a data security specification file of a target industry to form a sensitive data rule base, the rule base is continuously augmented by using technologies such as NLP and NER, and after data to be identified of the target industry is obtained, a sensitivity class and a sensitivity level of the data to be identified can be identified by matching the sensitive data rules in the sensitive data rule base corresponding to the target industry with the data to be identified.

The invention discloses an intelligent team forming system and method, relates to the technical field of artificial intelligence and big data technology cross application, and aims to solve the problems of low team forming efficiency and poor team forming effect of a traditional team forming system. The system is provided with a user input module, a large language model support layer, a dialogue type information acquisition module, a multi-dimensional user portrait construction module, a self-adaptive intelligent matching recommendation module and an interpretable matching decision module to form a complete intelligent interaction closed-loop system. When a team formation demand exists, the system performs deep semantic understanding, multi-strategy fusion and graph network analysis to calculate an optimal team combination, and provides clear matching decision interpretation; team forming result feedback is collected, matching strategies and user portraits are continuously optimized, and the accuracy of future recommendation is continuously improved; the team forming quality and the user experience are continuously improved; the problems that a traditional team forming system is low in team forming efficiency and poor in team forming effect are solved.

The embodiment of the invention provides a data analysis method and device, electronic equipment, a storage medium and a program product. The method comprises the steps that data analysis chapters matched with received data analysis requirements are selected from a chapter library, chapter information of the data analysis chapters is obtained, and the chapter information comprises chapter analysis attribute information and a chapter generation strategy set; generating a data analysis content framework based on the chapter analysis attribute information of the data analysis chapter and the chapter generation strategy set; generating chapter content of the data analysis chapter according to the data analysis content framework, the chapter generation strategy set of the data analysis chapter and the data analysis requirement; and splicing the chapter content of the data analysis chapter to obtain data analysis content meeting the data analysis requirement. By means of the technical scheme, the generation efficiency and accuracy of the data analysis content can be improved.

The present invention discloses an intelligent system integration method based on multi-modal data fusion, comprising: S1, obtaining multiple data sources of different modalities; S2, preprocessing the original data in the multiple data sources of different modalities to generate preprocessed data; S3, based on the preprocessed data, mining the potential correlation relationships between the data of each modality to generate a set of inter-modal association rules; S4, dynamically adjusting the data fusion strategy according to the set of inter-modal association rules to generate an adjusted fusion strategy; S5, performing weighted fusion on the data of each modality according to the adjusted fusion strategy to generate fused comprehensive data; S6, processing the fused comprehensive data to generate integrated data; S7, during the operation of the intelligent system, based on the integrated data and new data source input, updating the association rules in real time. The present invention has the advantages of strong flexibility, high efficiency, strong adaptability and low data processing cost.

An embodiment of the present invention provides a data analysis method and system based on an intelligent management platform. By selecting a target user requirement mining model of the first target magnitude in the target platform user requirement mining task, then pushing platform big data to each target user requirement mining model based on the target platform big data collection task of the second target magnitude, and selecting target platform big data push data of the third target magnitude from the platform big data push data. Then, determine the relevance between the user requirement mining graph of the target platform user requirement mining task and the keyword features of the platform big data push keywords, and determine the platform big data push data with a relevance greater than the first target value as the platform big data push data matching the target platform user requirement mining task. In this way, it can provide an effective data basis for user requirement mining, and further improve the accuracy of subsequent user requirement mining.

The invention relates to the technical field of industrial internet, and discloses an industrial internet platform intelligent analysis system which comprises an industrial internet platform. The industrial internet platform is in communication connection with a data acquisition module, a data storage and processing module, a data analysis and mining module, a decision support module, a security module, a user interaction module, an application development service module and an intelligent edge computing module. According to the invention, through the application development service module, the capability of rapidly developing and deploying industrial internet applications can be provided for enterprises, the difficulty and cost of application development are reduced, and rapid innovation of the enterprises based on an industrial internet platform is supported; the intelligent edge computing module can be responsible for computing and processing at the edge of equipment or a network, the computing pressure of a far end is relieved, the data processing speed and efficiency are improved, real-time data processing and analysis are achieved, and the efficiency and intelligence of an industrial production system are improved.

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

The embodiment of the invention provides a behavior data processing method and device based on a graph database, a medium and a product, and relates to the technical field of data processing. Specifically, the method comprises the following steps: acquiring behavior data reported by Internet of Things equipment; on the basis of the behavior data, the incidence relation among a plurality of operations stored in a graph database is updated, a directed graph is obtained, and the directed graph corresponds to the same Internet of Things device and / or different Internet of Things devices; wherein each node in the directed graph corresponds to each operation in the behavior data, and the directed edge in the directed graph is determined based on the attribute information of each operation in the behavior data. According to the method, the behavior data is stored through the graph database, the incidence relation among the multiple operations is stored in the graph database in the form of the directed graph, the data and the relation among the data can be stored, deeper user behaviors can be analyzed, the processing efficiency is improved, and the user experience is improved. And a better basis is provided for matching user requirements for equipment optimization.

The invention relates to the technical field of knowledge graph vectorization, and discloses a medical field-oriented knowledge graph vectorization representation method, which comprises the following steps of: firstly, constructing a multiple heterogeneous topological view based on a heterogeneous knowledge graph, and generating a detection parameter set covering a global fusion space so as to construct joint observation data corresponding to different distribution modes; then establishing a joint reconstruction task containing a public potential semantic base, and promoting a public base matrix to aggregate global structure information of all observation data by using an iterative algorithm; and finally, analyzing the converged model to extract a center node vector and generate an index.

The invention belongs to the field of big data and the field of financial science and technology, and relates to a case allocation method, which comprises the steps of obtaining staff comprehensive information and historical case data of a collection staff, and constructing a collection staff database through preprocessing; and performing influence factor analysis on the historical cases in the database to obtain data analysis results of the collection personnel. Meanwhile, an initial weight factor representing the basic ability and experience of the collection person is obtained; and in combination with the initial weight and a data analysis result, calculating a weight factor of each collection person by using an analytic hierarchy process. The weight factor guides the distribution of new cases. And when a to-be-allocated case is received, the case is reasonably allocated to each collection person according to the case data and the weight factor. The invention further provides a device, equipment and a medium. In addition, the invention also relates to a block chain technology, and staff comprehensive information and historical case data can be stored in a block chain. Accurate case allocation can be realized, and then collection efficiency and success rate are improved.

The invention relates to the technical field of road engineering construction, in particular to a tamping degree deduction method suitable for soft foundation layer abutment back backfilling. The method comprises the following steps: S1, collecting and evaluating geological data in a project area, researching key parameters such as soil type, water content, bearing capacity and compressibility of a soft foundation layer, and analyzing and evaluating geological characteristics of the soft foundation layer; and S2, based on the analysis result of the soft foundation stratum characteristics, hydraulic tamping equipment is subjected to adaptive transformation and upgrading. By deeply analyzing the characteristics of a soft foundation layer and combining advanced hydraulic tamping equipment and a real-time monitoring system, the strength and frequency of a tamping point can be accurately controlled, it is ensured that abutment back filling soil achieves an ideal compaction effect, smooth transition between a bridge and a road surface is guaranteed, and the construction quality of the bridge is improved. The driving experience is fundamentally improved, the service life of road facilities is prolonged, the construction efficiency is improved by optimizing hydraulic tamping equipment and a construction process, and the labor cost and the time cost of construction are reduced.

The invention relates to the technical field of hydraulic engineering and computer application, and particularly provides an industrial water demand prediction method based on big data mining and physical mechanism dual-drive, and the method comprises the steps: carrying out the space-time alignment and feature anchoring of a multi-source database by taking an industrial water full-cycle element as an index; establishing an inter-index nonlinear semantic association network; multi-dimensional indexes in the nonlinear semantic association network are input into the dynamic entropy weight redundancy elimination model, redundant features are eliminated, and an industrial water sensitive factor set is generated; constructing an industrial water constitutive equation with explicit interpretability in combination with physical correlation characteristics of the core control chain; and performing bidirectional embedding with a social economic parameter prediction module to form a double-engine prediction architecture which takes a physical mechanism equation as a core driving force and takes a real-time data stream as a dynamic corrector, thereby realizing mechanism-data dual-drive prediction of industrial water demand evolution. According to the method, the problem that a prediction result of a traditional data-driven model in an industrial scene is not traceable is solved.

The invention discloses a method and device for mining associated transactions among multiple objects based on graph risk control, and relates to the technical field of artificial intelligence and the financial field, and the method comprises the steps: determining a first object and at least one second object having a transaction relationship and / or association relationship with the first object; establishing an entity relationship network according to the first object, the at least one second object and the corresponding association relationship; establishing a transaction relationship network according to the first object, the at least one second object and the corresponding transaction relationship; and performing multi-source network superposition on the entity relationship network and the transaction relationship network to generate an associated transaction knowledge graph between the first object and the at least one second object so as to mine associated transactions. According to the method and the system, the associated transaction path and the risk point can be clearly displayed through atlas analysis for an enterprise which transacts through a complex equity structure and an association relationship, so that a hidden associated party is identified, and the aim of mining associated transactions among multiple objects is fulfilled.

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

The invention discloses an intelligent system integration method based on multi-modal data fusion. The method comprises the following steps: S1, acquiring a plurality of data sources of different modals; s2, preprocessing the original data in the plurality of data sources of different modalities to generate preprocessed data; s3, based on the preprocessed data, mining a potential association relationship between modal data, and generating an inter-modal association rule set; s4, dynamically adjusting a data fusion strategy according to the inter-modal association rule set, and generating an adjusted fusion strategy; s5, according to the adjusted fusion strategy, performing weighted fusion on the modal data to generate integrated data after fusion; s6, processing the integrated data after fusion, and generating integrated data; and S7, in the operation process of the intelligent system, updating the association rule in real time based on the integrated data and the new data source input. The method has the advantages of high flexibility, high efficiency, high adaptability and low data processing cost.

The invention discloses an airway data management method and system.The airway data management system comprises an endoscope and a server in communication connection with the endoscope, and the method comprises the steps that multi-modal basic data in the airway management process is obtained; respectively extracting a single-mode feature sequence for the basic data of each mode; extracting a cross-modal airway event node based on each single-modal feature sequence and the sequential relationship of each single-modal feature; mining a clinical airway management event in the current airway management process based on the cross-modal key event node; and mining and storing the structured airway management knowledge chain in a clinical airway management event. An anesthesia process depending on subjective experience is converted into quantifiable and analyzable multi-modal data, analysis and knowledge extraction of medical data are realized, a knowledge chain with clear clinical significance can be formed, and real scene support is provided for teaching training, clinical decision support and scientific research analysis.

The invention discloses a multi-dimensional data acquisition and analysis system and method based on network communication, and belongs to the technical field of data acquisition and analysis. According to the method, a distributed crawler architecture is designed, enterprise dynamic data and market dynamic data are collected, and a relation graph is constructed; calculating an industrial thermodynamic diagram index based on the relation graph to obtain an industrial thermodynamic diagram; defining an investment attracting target, constructing enterprise portraits based on the enterprise dynamic data, and screening the enterprise portraits meeting the investment attracting target to obtain a candidate enterprise set; calculating the matching degree of the investment attraction area and the enterprise features to obtain a matching degree set; using a gradient boosting tree to train a sorting model based on the matching degree set, and outputting recommendation scores of the enterprises to obtain a recommended enterprise set; calculating the geographical fitness and the industrial collaboration, and inputting the random forest model to obtain the entry probability of the enterprise; and when the entering probability of the enterprise does not accord with the threshold value, screening a better land parcel in the investment attracting area, and maximizing the entering probability of the enterprise.

The invention provides a knowledge mining method and device, an intelligent customer service system, equipment, a medium and a product, and relates to the technical field of computers, in particular to the technical fields of artificial intelligence, data processing, large models, intelligent customer service and the like. The method comprises the following steps: extracting candidate question and answer pairs and a first service label from an artificial customer service session log, and inputting questions in the candidate question and answer pairs into a question and answer model to determine a basic question and answer pair and a second service label; based on the first service label, the second service label and the basic question and answer pair, determining whether the candidate question and answer pair is a new question and answer pair; and in response to the candidate question and answer pair being a new question and answer pair, updating a knowledge base of the question and answer model based on the candidate question and answer pair. According to the invention, the knowledge base of the question and answer model of the existing intelligent customer service system can be automatically supplemented and optimized.