181results about "Data mining" patented technology

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 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.

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 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 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 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 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 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 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.

The application discloses a JSON data processing system implementation method based on FastAPI, which comprises the following steps: establishing a working directory based on FastAPI, creating a request processor in the working directory, and receiving original JSON data through the request processor; constructing a database object model in the working directory, generating a database table, and storing the original JSON data into the database table; constructing a business verification model in the working directory, preprocessing the original JSON data through the business verification model, and converting the processed JSON data into a Python object; creating a business processing tool in the working directory, processing the business logic of the Python object through the business processing tool, converting the processed Python object into a JSON data format; and creating a request agent and a middleware client agent of an external system in the working directory, and interacting the converted JSON data with the external system and the middleware. The application realizes an asynchronous processing kernel, supports high-concurrency tasks, improves the execution efficiency, and reduces the response time.

The application provides an object association recognition method and device and a storage medium, relates to the field of artificial intelligence, and the method comprises the following steps: obtaining target attribute features and target operation sequence data of a to-be-recognized object group; performing sequence pattern matching on the target operation sequence data based on a preset sequence pattern library to obtain a target operation sequence pattern of the to-be-recognized object group; performing sequence correlation processing on target operation features corresponding to the target operation sequence data by using a sequence correlation mining network of a preset relationship recognition model to obtain target sequence correlation features of the to-be-recognized object group; performing association relationship recognition on the to-be-recognized object group based on the target attribute features, the target operation features, the target operation sequence pattern and the target sequence correlation features by using an object group classification network of the preset relationship recognition model to obtain an association relationship category of the to-be-recognized object group. The application can effectively improve the generalization ability and timeliness of object association recognition.

The application claims a time-oriented RDF quad model for MongoDB storage and a redundant attribute elimination method, which comprises the following steps: step 1, constructing a time-oriented RDF quad data model, taking time information as a new tuple of RDF triple extension, expanding the triple into a quad model containing time information, and defining the expanded time-oriented RDF quad data model; step 2, storing time-oriented RDF quad instance data in a document-oriented non-relational MongoDB database; step 3, using a weighted-based design algorithm to find out the instance attributes with higher redundancy from the time-oriented RDF quad data in the form of text stored in the MongoDB database, and eliminating these redundant instance attributes in the ontology library that has been constructed. The RDF quad-based query model designed in the application has a more significant effect on resources with time characteristics, and at the same time solves the problem of ontology ambiguity caused by repetitive instance attributes.

The application provides a business flow conversion data analysis method and system based on artificial intelligence, relates to the technical field of data analysis, and performs initial retrieval on business flow conversion records in metadata to obtain process features, and then extracts retrieval deviation of data retrieval; the metadata is reconstructed according to the correlation matrix between each process feature to obtain a reconstruction sequence of business flow conversion; the task granularity between different task nodes in the business flow conversion process is determined based on the reconstruction sequence and the retrieval deviation; the business logic relationship between each business module in a preset business form is obtained, the data consistency loss in the initial retrieval of the metadata is determined through the business logic relationship and the retrieval deviation; the metadata is secondarily retrieved according to the task granularity and the data consistency loss to obtain second process features, and then a business form of business flow conversion is generated based on the second process features; and the application can realize hierarchical retrieval of business flow conversion data features, thereby improving the stability of the business flow conversion process.

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 provides a data processing method and a related device. The method comprises the steps of generating a data blood relationship map of first data based on a first database statement of the first data; in the data consanguinity map, determining a first object downstream of first data, the first data being a calculation dependency of the first object; determining a second object causing the upstream of the first data in the data consanguinity map, the first data being obtained in dependence on the second object; and performing task processing on the first data, the first object and the second object to obtain a processing result. According to the invention, the task processing efficiency of the data can be improved.

Embodiments of the present application provide a data processing method and device, electronic equipment and computer storage medium. The data processing method is used to generate a description information file related to a target object, comprising: obtaining a description framework and a plurality of materials of a plurality of types related to the target object, the description framework comprising attribute selection information corresponding to the target object; performing at least one processing on each material to obtain attribute information of the material, the attribute information comprising an attribute level and attribute content; selecting a target material from the material, the attribute content and the attribute level of which match the attribute selection information of the description framework; and generating the description information file according to the description framework and the target material. The data processing method can automatically generate the description information file.

This invention provides a method for identifying the importance of higher-order structures in complex networks. The specific steps are as follows: Step (1): Calculate the attribute values ​​of higher-order structures in the network, specifically including: Step 1.1 Calculate the size of the higher-order structure. Step 1.2 Calculate the degree deviation of the higher-order structure. Step 1.3 Calculate the circle-to-circle ratio deviation of the higher-order structure. Step (2): Construct an evaluation model for the importance of higher-order structures in the network. Step (3): Use the particle swarm optimization algorithm to solve for the optimized weight coefficients and obtain the importance ranking of higher-order structures in the complex network, specifically including: Step 3.1 Construct and initialize the particle swarm. Step 3.2 Optimize the particle swarm to obtain the optimization results. Step 3.3 Obtain the importance ranking of higher-order structures in the complex network. This invention comprehensively considers the mutual influence between the attributes of nodes in the network and the higher-order structures they form. It uses indicators such as the size of the higher-order structure, the degree deviation, and the circle-to-circle ratio deviation as importance evaluation indicators, breaking through the current limitation of only considering the attributes of nodes themselves. This effectively identifies important higher-order structures in the network and is of great significance for the cascading failure propagation and control of complex networks.

The invention discloses a risk prediction method and system based on QAR data and a convolutional neural network, and the method comprises the following steps: obtaining to-be-analyzed QAR data, and carrying out the preprocessing of the QAR data; carrying out clustering analysis on normal data and overrun data in the preprocessed QAR data by utilizing a K-means clustering algorithm, carrying out data mining by utilizing an FP-Growth algorithm, and extracting an association rule of an unstable approach event and an incentive of the aircraft; using rule samples consistent with the association rules as a sample set to train a convolutional neural network; and converting the QAR data into picture data, and predicting the unstable approach risk of the aircraft by adopting the trained convolutional neural network. According to the method, a K-means clustering algorithm is fused to improve an FP-Growth algorithm, multi-level association rule mining of QAR data and unstable approach risk inducements is carried out, and analysis and risk prediction of high-complexity unsafe events such as an unstable approach stage are realized by adopting a convolutional neural network.

A data mining method includes extracting a main character column of a data table of a data sheet; determining an evaluation measurement value of the data table according to a variance and an outlier ratio of the main character column of the data table; and determining a data mining value of the data table according to the evaluation measurement value of the data table.

The application discloses a kind of electric vehicle charging pile order exception clearing settlement method and device, comprising: obtaining the real-time order of vehicle networking platform;Based on the inference engine of preset detection real-time order whether it is abnormal order;Response to real-time order is normal order, then directly upload real-time order to vehicle networking platform;Response to real-time order is abnormal order, then based on correction algorithm or preset prediction model carries out numerical correction or data prediction to real-time order, obtains revised abnormal order and uploads to vehicle networking platform.One aspect, according to the mass historical abnormal data obtained by vehicle networking platform, mining clearing screening rule, combined with the experience rule of power grid expert custom clearing screening rule, the inference engine obtained can accurately detect abnormal order, on the other hand, it can be to electric vehicle charging pile abnormal order numerical correction or data prediction, reduce the economic loss caused by order exception.

The invention provides a motor starting frequency statistical system and method for a GIS (Gas Insulated Switchgear) circuit breaker, relates to the technical field of automatic control of a power system, and solves the limitation problem that the existing statistical mode is low in precision, efficiency and intelligent level. The method comprises the following steps: firstly, collecting a motor starting current signal and an opening and closing position signal, and carrying out digital preprocessing; based on the change time sequence of the motor starting current signal and the opening and closing position signal, a motor starting event is identified, and the starting time point and the duration time are recorded; after the starting information data corresponding to the motor starting event is stored in the database, a starting frequency report is analyzed and generated according to the motor starting historical record data, the incidence relation between the motor starting frequency and the health state of the GIS circuit breaker equipment is analyzed through the data mining technology, and maintainability suggestions are output. Accurate statistics of the starting times of the circuit breaker motor can be smoothly realized, and a reliable basis is provided for operation and maintenance of equipment.

The application provides a review recommendation method based on a meta-learning heterogeneous graph attention neural network. The method constructs original data into required metadata modes through a meta-learning process and a graph neural network model. A designed local graph extraction algorithm constructs the relationship among three types of nodes of users, commodities and reviews, expands a graph attention network structure taking each user as a task, and is used for capturing the preference information of the corresponding reviews of the user.

A method for processing behavior data, a method for controlling an autonomous vehicle, apparatuses thereof, a device, a storage medium, a computer program product, and an autonomous vehicle are provided. The method includes: acquiring historical driving data, the historical driving data comprising lane-level navigation data; and performing data mining on the historical driving data to obtain driving feature information, the driving feature information comprising at least one of: a lane-change position feature, a traveling speed feature, or a traveling path feature.

The invention relates to the technical field of education evaluation, and particularly discloses a proposition examination question rating method and system based on big data deep mining, and the method comprises the steps: integrating historical examination, student answering and teaching resource data, and carrying out the cleaning and normalization to form a super-large-scale data set; generating a standard problem solving logic tree according to an examination question text and knowledge point tags, and generating an application difficulty matrix and a correlation application degree matrix based on knowledge point structure features and correlation to serve as proposition features; analyzing the answer record and score distribution by using a big data algorithm, and obtaining rating results such as a difficulty coefficient in combination with a mechanical power dissimilatory cardinal number; using a neural network model to learn proposition and rating characteristics to obtain an examination question rating model; obtaining a rating result based on the to-be-rated examination question logic tree and the rating model; and rapid and scientific rating of the new examination questions is realized. A powerful tool is provided for education workers in the aspects of question proposition, examination question screening, teaching effect evaluation and the like, teaching and examination arrangement optimization is facilitated, and the education quality is improved.

The invention relates to law enforcement integrated management hardware and a management process. The law enforcement integrated management hardware comprises a server, a local machine, a database, a display, a processor and input equipment, the platform comprises a screen capture module, a statistical column module, an analysis column module, an evaluation column module, a sunshine police service disclosure module, an airing column module, a business training module, a data mining module, a supervision and management module, a comprehensive query module, a video retrieval module, a role management module and a large-screen data analysis and display module.

This invention discloses a method for optimizing the allocation of epidemic prevention and control resources based on complex networks. The method includes: acquiring big data from online social platforms to measure participants' attitudes and opinions on epidemics and corresponding prevention and control measures; designing and executing specific decision-making experiments to measure participants' feedback to different types of incentives and information; analyzing experimental results and data to extract individual behavioral rules in different research scenarios and constructing a new generation of agent-based epidemic transmission models; conducting multi-layer community analysis based on deep learning to achieve accurate community discovery within a multi-layer framework; combining community discovery with impact maximization research to identify key user sets in the multi-layer system, allocate prevention and control resources, explore the evolutionary patterns of epidemics, and achieve optimized control of the epidemic transmission process under multi-dimensional uncertainties in parameters and models. This invention contributes to the rational allocation of epidemic prevention and control resources, providing effective protection for the lives and health of the people.

The present disclosure provides a distributed graph data processing system, method, apparatus and device, and a storage medium. The system includes: a computing node in a distributed architecture, wherein the computing node includes a computing unit and a persistent storage unit that are in a corresponding relationship, and a storage manager is deployed in each computing unit. The storage manager divides the graph data adjacency list into graph data blocks, determines a key value corresponding to each of the graph data blocks, and then records and stores a corresponding relationship between key values of the graph data blocks and storage location information.

The present invention discloses a method, device and storage medium for mining key nodes in a directed network, belonging to the field of network information mining technology. The mining method comprises: decomposing strong connectivity to obtain a strongly connected subgraph with zero in-degree, selecting a node with the smallest in-degree difference and the smallest in-degree from all strongly connected subgraphs with zero in-degree to meet the reachability condition; then using a greedy algorithm to select nodes that are farther away from the key node group and have a smaller in-degree minus out-degree and in-degree from the strongly connected subgraph with the smallest eigenvalue in each cycle and add them to the key node group; that is, using the graph theory of measurement indicators, the shortest path concept and the greedy algorithm to achieve the mining of key nodes in a large-scale directed network. Since key nodes can directly or indirectly affect all other nodes in the network, the stability of the existing complex network node importance evaluation method in the control synchronization and diffusion capabilities of the directed network is improved.