92results about "Data mining" patented technology

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 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 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.

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 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.

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 application discloses a data mining method and system for a social robot. The method comprises the following steps: determining an interactive object currently communicating with the social robot and collecting social media data; performing word segmentation and vectorization processing on the text through a natural language processing algorithm, and extracting an emotional tendency value through an emotional analysis model, and dividing the data into multiple emotional grades according to a preset critical value; extracting keywords and topic fields based on word frequency statistics and topic clustering, and deleting the data if the number of keywords is lower than a threshold value; constructing time series data according to social behaviors and time stamps, predicting an active period and a behavior trend by using an LSTM model, and adjusting the model through a sliding window mechanism; and combining multi-dimensional feature vectors, and assigning personalized labels to the interactive object through clustering results. The application realizes accurate analysis of behavior and emotional characteristics of the interactive object, and provides an effective personalized interaction strategy for the social robot.

The embodiment of the invention provides a traffic light element manufacturing position mining method and device, a computer readable medium and electronic equipment, and the method comprises the steps: obtaining the track point string data of a vehicle, and enabling the track point string data to comprise the track point data; performing time dimension aggregation on the data of each track point to obtain parking point data; in the unit time period, counting the number of unit durations with the parking point data on the predetermined road to obtain a first number; in a unit time period, counting the number of unit durations of the target traffic light controlled road with the parking point data, and obtaining a second number according to a counting result; if a comparison result of the first number and the second number meets a predetermined condition, taking the predetermined road as a candidate target road; and comparing the candidate target road with the difference of the map data to obtain a position where traffic light elements need to be made for the map data. According to the scheme of the embodiment of the invention, the false alarm probability of traffic light element manufacturing positions is reduced, and the labor cost is saved.

The invention discloses a man-machine collaborative geographical science popularization narrative generation and interactive teaching system and method, and relates to the technical field of wisdom education technology and geographical information science. The system comprises a user interaction module, an AIGIS analysis engine, a narrative generation engine, a story state manager and a multimedia presentation module. The user interaction module receives user input and interaction instructions, identifies user requirements and selects a corresponding interaction mode. And the AIGIS analysis engine executes teaching adaptive spatial analysis according to user input and outputs a structured result. And the narrative generation engine generates a scenario type narrative script and a guiding problem based on the structured analysis result in combination with a preset narrative generation logic. And the story state manager monitors events through rules, maintains task states and dynamically drives narrative flow adjustment. And the multimedia presentation module integrates the scenario type narrative script, the guiding problem and the visual content and multimedia resources generated based on the structured analysis result to perform immersive presentation.

To provide a method and a system for efficient zero-knowledge verification of compound statements that simultaneously require both arithmetic circuit satisfiability and dependent statements about the validity of a public key.SOLUTION: In a method, a prover sends, to a verifier, a set of data including a statement that is about an elliptic curve point and the output of a given function circuit whose input is equal to the corresponding elliptic curve point multiplier. The data includes individual wire commitments and batched commitments for the circuit, inputs and outputs of the statement. The prover sends an opening in response to a challenge from the verifier. Using the data received from the prover, the verifier determines that the circuit is filled, computes the elliptic curve point to verify the statement, and determines that it holds evidence for the statement. Upon receiving the data, the verifier determines through computation that the data fits the statement.SELECTED DRAWING: None

The invention relates to a report data tracing method and device, computer equipment, a computer readable storage medium and a computer program product. The method comprises the following steps: responding to a data traceability request for a target cell in a currently displayed target report; obtaining a target data blood relationship map corresponding to the target report; nodes in the data consanguinity map represent data entities, and edges among the nodes represent a data flow relationship; the data entity comprises at least one of a table cell, a data query statement and a database table field; traversing an upstream node quoted by a target node corresponding to the target cell according to the reverse direction of the edge in the target data consanguinity map by taking the target cell as a starting point until a node corresponding to the database table field is searched, and obtaining a target data flow path corresponding to the target cell; and displaying the target data flow path. By adopting the method, the report data tracing efficiency and accuracy can be improved.

Methods and devices for propagating transactions in a network of nodes, each node having one or more connections to other nodes. The method includes receiving a plurality of incoming transactions over a time period; combining the plurality of incoming transactions using network coding to generate a composite message; sending the composite message to one or more nodes in the network; and determining an adjusted time period based on an equilibrium constant parameter and a count of transactions in the plurality of incoming transactions received over the time period.

The present disclosure provides a currency settlement method and apparatus. The currency settlement method comprises: settling with a mining node using a virtual currency according to a workload performed by the mining node and an income type of the mining node; transacting with an exchange institution to exchange an amount of the virtual currency into an amount of a fiat currency; identifying a settlement account associated with the mining node, the settlement account being associated with a user card number, an account name, and user account information; and transferring the amount of the fiat currency to the settlement account.

The application discloses a shale gas well flowback system optimization method based on distributed optical fiber data mining, and combines advanced distributed optical fiber (DTA / DAS) monitoring technology to "perceive" downhole temperature and "listen" to downhole sound; optical fiber monitoring results can quantitatively evaluate the fracturing reconstruction effect of a shale gas well and judge the gas production and liquid production profile distribution effect of the whole well section of the gas well. Through optical fiber data mining in the gas well flowback stage, the fracturing cluster efficiency and the artificial fracture conductivity are comprehensively considered to optimize the maximum flowback choke of the gas well, so that the gas well flowback system optimization work is realized.

The embodiment of the application discloses a learning resource recommendation method based on a knowledge graph, relates to the technical field of intelligent teaching, and comprises the following steps: acquiring a learner portrait of a learner and various types of learning resources used by the learner, and generating a multi-modal learning resource interaction graph; calculating a contrast loss function based on the multi-modal learning resource interaction graph; acquiring a matching coefficient of the learner and the learning resources based on the interaction relationship between the learner and the learning resources; calculating the interest degree of the learner to various types of learning resources based on the numerical value of the matching coefficient; sorting various types of learning resources based on the interest degree, and recommending learning resources to the corresponding learner in sequence based on the sorting result. By adopting the method, multi-modal information fusion of learning resources and historical learning data of learners are considered, and the two are jointly integrated into a learning resource interaction network, so that the expression meaning is enriched, and the precision of learning resource adaptation is improved.

The application discloses a mining evaluation method based on big data vulnerability mining, relates to the technical field of mining evaluation, extracts features related to vulnerabilities from collected data, analyzes and mines vulnerability patterns in the data through a data mining algorithm, identifies potential vulnerabilities, evaluates and analyzes the mining results to determine the severity and potential impact of the vulnerabilities, visualizes the mining results to help security teams or managers better understand and analyze the vulnerability situation, repairs the discovered vulnerabilities according to the suggestions in the report, verifies and tests the repaired vulnerabilities to ensure that the vulnerabilities are successfully repaired, and performs security evaluation and verification on the system again, so that the evaluation method not only effectively identifies potential vulnerabilities in the system, but also performs security evaluation and verification on the system again after repairing the vulnerabilities, thereby greatly improving the stability and security of system operation.

The invention discloses a civil engineering cost evaluation optimization system, and relates to the technical field of civil engineering cost management. The system comprises a PLM full-life-cycle cost data closed-loop management module, a PDM cross-organization collaborative management and control module, a PLM supply chain real-time data coupling module and a PDM cost process dynamic optimization module. The PLM full life cycle module realizes standardized acquisition, storage, analysis and continuous optimization of multi-source heterogeneous data, the PDM cross-organization module ensures efficient and safe cooperation of multiple participants through a unified data standard and a three-dimensional dynamic permission model, and the PLM supply chain module completes deep fusion and dynamic correction of supply chain real-time data and cost data. And the PDM cost optimization module realizes automatic identification and accurate quantification of changes and intelligent generation and rapid examination and approval of multiple sets of optimization schemes. According to the method, cost evaluation is promoted to be transformed from static lag to dynamic real-time and from dispersed independence to collaborative closed loop, the management and control precision and efficiency are improved, and technical support is provided for efficient landing of large EPC projects.

The invention discloses a lubricating oil on-line monitoring and calibrating method based on oil analysis. The method comprises the steps that real-time monitoring is conducted through a sensor; analyzing oil in a laboratory to obtain oil analysis data; associating the corresponding sensor real-time data with the oil analysis data, storing to form an actual historical association database, and predicting to obtain a virtual historical association database; according to a timestamp, combining the actual historical association database with the virtual historical association database to obtain a historical association data total database; and performing relation mining by taking the historical association data general library as a sample set to obtain an effective association rule, and performing calibration operation according to the effective association rule. According to the lubricating oil on-line monitoring and calibrating method based on oil liquid analysis, the data blank period of laboratory analysis is filled through the virtual data model; and meanwhile, by utilizing an association rule mining technology, internal association between real-time data and laboratory data is deeply mined, and calibration is carried out based on the internal association, so that the reliability and the accuracy of online monitoring data are greatly improved.

Embodiments of the present disclosure relate to a user interest mining method and system, an electronic device, and a medium. The method comprises: splitting a behavior sequence of a user; extracting a post sequence of an object user in a social relationship sequence of the user; extracting four sequences of post ID, post category, post time, and post author ID for each of the split sequence and the extracted post sequence; inputting the four sequences of post ID, post category, post time, and post author ID extracted for each sequence into a first deep neural network to obtain a group of vectors A for each sequence; performing an attention mechanism operation on the group of vectors A for each sequence by taking the current training post as input to obtain an attentioned vector A'; inputting the vector A' into a training model to obtain an estimated score for the current training post; and adjusting the training model according to the estimated score, and taking the adjusted training model as a user interest mining model.