433results 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 relates to the technical field of smart communities, and discloses a smart community resident health management method based on machine learning, and the method comprises the steps: continuously collecting the health feedback data of residents, such as the physical condition change after outdoor activities, carrying out the correlation analysis of the health feedback data with the previous health data and environment data, dynamically updating and optimizing a correlation model, and obtaining the health feedback data of the residents; the prediction precision of the model and the effectiveness of suggestions are improved, the relationship between the use condition of the community public space and the resident health indexes is analyzed, key public facilities and areas influencing resident health are identified, data support and decision basis are provided for community planning and facility reconstruction, and the method is suitable for popularization and application. Community resident health and environment condition analysis reports are generated regularly, and resident health index change trends are visually displayed for reference of community managers and residents. According to the invention, intelligent analysis and application of environment and health data are realized, and technical support is provided for building a healthy community.

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 provides a data mining method and device, equipment and a storage medium, and the method comprises the steps: carrying out the data preprocessing of multi-source heterogeneous scientific research data, generating a data set in a unified format, and mining a multi-dimensional scientific research portrait feature set from the data set; and based on the feature sets, performing pattern mining on academic output data of the researchers to obtain a research interest pattern set and a research trend index. Through predictive mining, a research interest prediction model and a project matching degree score are generated for historical research data, graph structure mining is performed on a researcher cooperation network by using the model, and a potential cooperation relationship is predicted. And finally, performing knowledge mining on the scientific research project data according to the project matching degree score, the network structure feature and the cooperation relationship prediction result, and generating a personalized project recommendation list and an interpretable recommendation basis thereof. According to the method, interest evolution, project characteristics and cooperation potential of researchers can be comprehensively considered, and recommendation accuracy and interpretability are improved.

The embodiment of the invention provides a document processing method and device and an information retrieval method and device. According to the document processing method, the text content of a target document can be subjected to fragmentation processing to obtain a plurality of text fragments, then each text fragment is input into a first large language model for model processing to obtain a processing result, the processing result comprises a plurality of knowledge fragments, and then the target document is processed according to the knowledge fragments. And generating first tag data corresponding to a plurality of preset tag categories, and correspondingly storing each knowledge fragment and the first tag data in a first database, so that the first tag data is used for knowledge question and answer retrieval. In the information retrieval method, after a user question in a text form is determined according to an information retrieval request of a user, second label data corresponding to the plurality of label categories are generated according to the user question, and then the user question is retrieved based on first correlation between at least part of first label data in a first database and the second label data. Knowledge fragments related to the user question are retrieved.

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 smart communities, and discloses a smart community intelligent security monitoring management method based on data interconnection, which comprises the following steps: acquiring real-time monitoring data, reading a preprocessed monitoring data set from a database, extracting feature parameters related to community security patrol, and determining the real-time monitoring data; the characteristic parameters comprise road length, road width, building height and building density, and constructing a community security patrol environment model based on the characteristic parameters; and aiming at the community security patrol route optimization model, solving by adopting an ant colony algorithm to obtain a plurality of candidate optimal patrol routes, evaluating the candidate optimal patrol routes according to historical patrol data, and selecting the patrol route with the optimal comprehensive evaluation index as the optimal patrol route. According to the invention, intelligent and fine management of community security patrol is realized, the patrol efficiency and the safety prevention and control level are improved, and powerful decision support is provided for community security management.

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 a coal mine hidden disaster-causing multi-source factor superposition disaster-causing risk prediction method, and relates to the technical field of coal mine construction risk prediction. The method comprises the following steps of S01, information acquisition and processing, S02, risk assessment model construction, S03, risk early warning result display, S04, accident risk assessment, S05, risk information three-dimensional visualization, S06, emergency rescue resource management and S07, monitoring and early warning technology application. According to the method, through utilization of a GIS information acquisition platform, multi-source data of a coal mine can be effectively integrated, and the risk of the coal mine can be effectively analyzed; comprising production monitoring, geographic information, safety management and control and the like; the method overcomes the defect of incomplete data acquisition in the prior art, and ensures that the risk assessment can be performed based on comprehensive and accurate data, thereby improving the monitoring capability of the hidden disaster-causing factors of the coal mine.

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.

Artificial intelligence (AI) systems may manipulate search results about an entity to inject a predetermined bias into the search results. A bias reduction artificial intelligence (AI) system and method may perform the same automated search about the entity on open source and closed source AI systems. The AI-generated search results for the open and closed source AI systems may be compared to determine differences in results. The differences may be analyzed to determine attempts by the AI systems to manipulate search results about the entity to inject predetermined bias. If an attempt at predetermined bias is identified, the bias reduction AI system may reduce the predetermined bias by sharing data about the entity between the open source AI system and the closed source AI system to cause the system to use machine learning to update the algorithms and the first and second data sets. Incentives may also be provided to the open and closed source AI systems to reduce biases.

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.

A similarity search system includes a database of original vectors, a hierarchical database of bins and a similarity searcher. The hierarchical database of bins is stored in an associative memory array, each bin identified by an order vector representing at least one original vector and the dimension of the order vector is smaller than the dimension of the original vector. The similarity searcher searches in the database for at least one similar bin whose order vector resembles an order vector representing a query vector and provides at least one original vector represented by the bin resembling the query vector.

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 a financial comprehensive auditing method and system, and the method comprises the steps: deploying a financial smart contract on a block chain, carrying out the modular design of the smart contract, and enabling each sub-module to correspond to a department; configuring an auditing rule template for each sub-module, and customizing auditing rules by corresponding departments based on the auditing rule templates; analyzing auditing rules of all departments based on a decision tree algorithm, and generating auditing check points; based on an intelligent contract event monitoring mechanism, auditing check points of all departments are monitored in real time, when a detection event does not accord with a corresponding auditing rule, early warning is carried out, and an auditing report is generated; the audit report is analyzed regularly, audit rules of all departments are updated based on analysis results, and the audit rules are encrypted and deployed on the block chain; and comprehensively auditing the finance of each department based on the updated auditing rule. According to the invention, high efficiency, flexibility and safety of financial auditing are realized through technical means of modular design, version control, real-time monitoring, data analysis and the like.

The invention discloses an OLAM-based data mining analysis system, which belongs to the technical field of data mining analysis, and comprises a data integration preprocessing module, a multi-source data fusion module, an OLAM multi-dimensional analysis module, a data mining integration module, a visual analysis platform module and a visual portal push module, the OLAM multi-dimensional analysis module is used for a user to slice, block, rotate, roll up and drill down data from multiple angles and multiple dimensions, the OLAP tool is used for creating a multi-dimensional data set and defining the dimensions and measurement, the flexibility and depth of data analysis are improved, the data mining integration module is used for providing multiple programming languages and algorithm libraries, and the data mining integration module is used for providing the multi-dimensional data set and the multi-dimensional data set and the multi-dimensional data set and the multi-dimensional data set and the multi-dimensional data set and the multi-dimensional data set and the multi-dimensional data set. The method supports the user to carry out customized data analysis, reads the data through an API interface, imports the data into a user development environment, achieves the flexible processing and deep mining of the data, provides an interactive data exploration capability through a visual graphical interface, and reduces the data analysis threshold.

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.

This disclosure provides systems for generating customized datasets. An example dataset generator mines contacts from multiple members of an enterprise, applies a pattern recognition process to determine attributes of the contacts aligned with objectives of the enterprise, stores the contacts and associated attributes in a relational data structure, and through a machine learning process, generates a customized contact dataset of the contacts with attributes relevant to a user request, an event, or a notification received. The example dataset generator may also generate an action steps dataset for the user. The dataset generator may aggregate contacts from calls, chats, emails, SMS messages, and video feeds of an enterprise, apply a pattern recognition process to determine attributes of the contacts, and through a machine learning process, partition and distribute the contacts and associated action steps on respective dashboards of multiple customer service representatives of the enterprise.

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 object group identification method and related equipment. The method comprises the following steps: acquiring operation content information of a plurality of content publishing objects in at least one dimension; according to the operation content information, aggregating the content publishing objects to obtain a primary object group; for each primary object group, mining social relation data corresponding to each content publishing object in the primary object group so as to mine a target social association object having an influence on the primary object group; and combining the primary object groups according to the target social association object and the abnormity degree of the content publishing objects in the primary object groups to obtain a potential abnormal target object group. The method can be better suitable for an organized team discovering anomaly detection scene, the discovery efficiency of the abnormal object and the recall rate of the abnormal object are improved, and therefore the detection range of the abnormal object is widened.

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.