112 results about "Data exploration" patented technology

The invention discloses a data analysis method and device based on multi-agent cooperation and a storage medium. The method comprises the following steps: receiving a natural language request by a workflow arrangement agent, and generating an execution plan comprising a plurality of subtasks and corresponding task processing units by consulting a data exploration agent and a data engine understanding agent; in the execution stage, the professional agent matched with the subtask converts the subtask into an executable instruction, and the data engine dispatch agent calls and coordinates the corresponding task processing unit to execute the instruction. According to the method, through a layered collaborative architecture, the separation of planning, translation and execution is realized, and the automation level, modularization and expandability of data analysis are remarkably improved.

The invention relates to the technical field of geophysical electromagnetic data exploration, in particular to a fast Bayesian inversion method based on machine learning. Acquiring aviation electromagnetic model data for electromagnetic forward modeling; establishing a candidate forward modeling model for training according to the aviation electromagnetic model information, and obtaining electromagnetic response data obtained by forward modeling; preprocessing the aviation electromagnetic model data and the electromagnetic response data; constructing an optimized neural network, taking the preprocessed aviation electromagnetic model data as input, taking the electromagnetic response data as output, and training the neural network; according to the trained neural network, obtaining a weight and a bias coefficient used for fitting forward modeling calculation, and obtaining an optimized forward modeling fitting matrix; and obtaining forward modeling data according to the optimized forward modeling fitting matrix, establishing a Bayesian inversion model, and performing Bayesian inversion according to the forward modeling data. According to the method, the fitting matrix after machine learning is used for replacing a forward modeling calculation process, and the efficiency of variable-dimension Bayesian inversion can be effectively improved.

The invention provides a cross-border e-commerce big data analysis system and method, and the method comprises the steps: obtaining multi-dimensional original business data of a cross-border e-commerce platform, carrying out the real-time collection through a distributed data collection system, and obtaining a structured business data set; data collected in real time is cleaned, converted and structured, a unified data model is constructed, and integration of heterogeneous data sources is achieved; storing the processed structured data in a time sequence database to provide data support for real-time analysis; performing real-time aggregation and statistical analysis on the newly added data to generate business index and key performance index data; a Lambda framework is adopted, batch processing and stream processing are combined, the historical data and the real-time data are subjected to full-amount analysis, and an analysis result is obtained; based on an analysis result, a data mining algorithm is adopted to identify a service mode and predict a future trend; a multi-dimensional data cube is constructed, multi-dimensional and multi-granularity data exploration and interactive analysis are supported, and management decision making is assisted.

In one aspect, a hardware risk information system for implementing a local risk information agent system for assessing a risk score from a hardware risk information comprising: a local risk information agent that is installed in and running on a hardware system of an enterprise asset, wherein the local risk information agent manages a collection of the hardware risk information used to calculate a risk score of the hardware system of the enterprise asset by tracking a specified set of parameters about the hardware system, wherein the local risk information agent pushes the collection of the hardware risk information to a risk management hardware device; a risk management hardware device comprising a repository for all the risk parameters of the hardware system of the enterprise asset, wherein the risk management hardware device generates the risk score for the hardware system using the collection of the hardware risk information, and wherein the risk management hardware device comprises a neural network processing unit (NNPU) used for local machine-learning processing and summarization operations used to generate the risk score; an analytics and dashboarding component that provides the risk score information via a set of graphical components viewable by a user, wherein the set of graphical components displays a set of insights about the plurality of enterprise assets based on the risk score data obtained by the plurality of local risk information agents; and a neuroscience and cognitive based risk-management dashboard component that uses a set of user interface (UI) analytics to score and plot data exploration along a decision continuum.

The invention discloses a table data exploration type visual analysis system and method based on a large language model. The method comprises the steps that original table data are analyzed, and a data model is established; automatically excavating insights and generating three different forms of the insights; user dominant exploratory data analysis and data story generation are carried out in the interaction module; an insights engine in the calculation module searches insights with structural association through an insights graph; and the big language model in the reasoning module performs insight recommendation through semantic association recommendation. According to the method, the structural table data can be modeled, so that data analysis and insight mining are performed. Potential insights in a table are automatically mined, so that a user gets rid of repeated and dense analysis operations, and interactivity, transparency and interpretability of an exploratory data analysis process are enhanced by fusing human intuition and insights, computing power of an external actuator and intelligence of a large language model, thereby promoting efficient and intuitive data analysis.

The invention relates to a medical health big data exploration system, and the system comprises a demand understanding module which is used for converting a data exploration demand into a structured exploration task; the compliance review module is used for performing compliance review on the probing task; the code generation module is used for generating a query code according to the probing task and the target database characteristics; the query execution module is used for querying query result data corresponding to the query code from a target database according to the query code; the query result analysis module is used for performing multi-dimensional analysis on the query result data to obtain a multi-dimensional analysis result; a report generation module; and the result presentation module is used for presenting the exploration report in an interactive mode in a user interface and supporting iterative improvement of the report by providing a feedback mechanism, so that the problems of data isomerism, difficulty in quality evaluation, low exploration efficiency, high professional threshold, insufficient privacy protection and the like in the existing medical data exploration process are solved.

A copilot platform for identity security governance and lifecycle management, used for capturing the complexity and relatedness of identity security data. The copilot platform integrates Knowledge Graphs and Large Language Model to enhance data exploration and understanding. The LLM converts natural language queries into Cypher queries, enabling interaction with graph databases. The copilot platform includes query annotation to facilitate LLM for recognized entities and for enduring necessary correctness to those entities if required and that increases overall accuracy of the Copilot. The LLMs and data metrics are used to summarize the data for the end user. The copilot platform uses an AI system for interacting with a user to learn about the state of user identity security, take action when required and, given the complexity of IGA data, including information on differentiated dashboards and custom reports, for allowing the user to visualize and manage the information effectively.

A method for providing a treatment recommendation to a physician for treating a patient is disclosed. The method comprises determining, from a processor in communication with a patient data repository, a first treatment recommendation based on a combination of selected patient demographics from the patient data repository applicable to the patient, and operational parameters of a plurality of ventricular assist devices (VADs) suitable for treating the patient, the first treatment recommendation having a first survival rate and comprising the use of a first VAD. The method then obtains a first signal from using the first VAD on the patient. The method then determines a second treatment recommendation based on the first signal and the first treatment recommendation, the second treatment recommendation having a second survival rate. The method then provides the second treatment recommendation to the physician if the second survival rate is higher than the first survival rate.

The invention relates to the technical field of data processing, in particular to a data cleaning and conversion method and system based on a large language model, and the method comprises the following steps: data input, data processing, data statistical analysis, manual auditing, rule adjustment and feedback, and data output. The method has the beneficial effects that data exploration and cleaning are carried out by integrating a large language model, and a dynamic and intelligent data cleaning process is realized by combining manual auditing and a rule feedback mechanism. The system supports the access of various data sources, has automatic processing capability, and ensures the accuracy and reliability of data processing at the same time. Besides, the system has good flexibility and expandability, can adapt to different application scenes, and improves the user experience and the transparency of data processing through a detailed processing report and a visual user interface.

A system and method for client-side geospatial data analysis are disclosed. An application bundle, including an embedded database, is sent to a client device, which then locally loads raw geospatial data files. Each coordinate point is associated with a grid index, and user queries—such as polygon-based filters, zoom operations, or attribute-based searches—are executed on the client device in near real time. This design removes the need for continuous server requests, thereby reducing latency and enabling immediate visualization of changes to map views or filters. The approach supports rapid interactions with large datasets, as filtering and aggregation tasks remain under the client's control. Furthermore, the embedded database can perform optimization or allocation routines (for example, delivery assignments) locally, instantly displaying results to the user. By eliminating heavy reliance on external servers, this invention offers a responsive, cost-effective platform for comprehensive geospatial data exploration and decision-making.

The invention discloses a dialogue type business intelligent analysis method based on a natural language, and relates to the technical field of business management systems, and the method mainly comprises the steps: recognizing the intention type of a user intention contained in a natural language request through a general large language model; according to the identified specific intention type, calling a corresponding special analysis module to perform semantic analysis and parameter extraction on the natural language request so as to generate an operable analysis instruction; calling a business intelligent system data interface to execute the analysis instruction, and obtaining corresponding structured data from a business intelligent system data warehouse; and generating a comprehensive analysis report which is matched with the identified intention type and the corresponding structured data and comprises visual elements and analysis explanations based on the identified intention type and the corresponding structured data. According to the method, the intention type of the natural language request of the user is understood through the large language model, so that the user with a non-professional background can easily perform complex data exploration, and the use threshold is reduced.

The invention provides a data probing method and system. The method comprises the steps of metadata basic information probing, metadata field security attribute probing and metadata blood relationship probing. Null value, NULL value and non-null-duty ratio exploration, data volume and distribution exploration, field attribute and distribution exploration and security exploration are carried out; data security analysis, data integrity and distribution analysis, and data relationship and distribution analysis are carried out. According to the invention, through intelligent automatic analysis and identification, null values, redundant information and performance bottlenecks in data are found in real time, and accurate support is provided for subsequent data governance; the probing task can be automatically completed only by inputting basic database connection information and related parameters, so that the efficiency is improved, and manual intervention is reduced; the universality is high, different database environments and data scales are adapted, and the probing process is efficient and accurate; the structure, the use condition and the potential risk of enterprise data assets can be deeply known, so that data management is accurate and efficient, and digital transformation and intelligent decision making are promoted.

The invention relates to the technical field of geophysical electromagnetic data exploration, in particular to a three-dimensional time-frequency domain electromagnetic joint inversion method based on a finite element algorithm. The method comprises the following steps: respectively acquiring time domain and frequency domain electromagnetic observation data of a to-be-measured region; dividing a to-be-measured region to obtain a non-structural tetrahedral mesh to construct a time-frequency domain inversion initial model; performing forward modeling calculation on the time-frequency domain inversion initial model to obtain time domain and frequency domain electromagnetic response data; obtaining a time domain electromagnetic data residual error and a frequency domain electromagnetic data residual error, respectively obtaining corresponding adaptive weights, and establishing a joint inversion objective function; performing iterative solution on the joint inversion target function, and calculating an update vector of the inversion initial model; and iteratively updating the initial inversion model until a set condition is met, and completing the time-frequency domain electromagnetic joint inversion. According to the method, the accuracy of three-dimensional inversion is effectively improved through joint inversion, so that the practical effect of three-dimensional inversion is enhanced.

The invention discloses a multi-dimensional data association analysis system and method based on unified modeling, and relates to the technical field of big data intelligent analysis. Comprising the following steps: 1, constructing a semantic model; step 2, carrying out query compiling and optimization; 3, performing query execution and scheduling; 4, interactive analysis and visualization are carried out, wherein a multi-dimensional report designer MRD is used for providing end-to-end full-process visualization display, and interactive drilling and narrative analysis are supported; step 5, performing closed-loop optimization; according to the method, the complexity of an underlying data structure is shielded through the semantic model, and automatic correlation analysis of a cross-fact table is supported, so that business personnel can independently complete deep data exploration and decision support without mastering professional technologies such as SQL (Structured Query Language) and the like.

A computerized method provides secure and controlled access provisions for data exploration in managed compute datasets. A workspace on a compute cluster is created for a user with permissions to the workspace. The compute cluster that includes a code analyzer is provisioned within a cloud computing infrastructure. The code analyzer is configured to: receive a data query from a task executing on the compute cluster; identify a query operation included in the data query; compare the query operation to a list of prohibited query operations for the workspace to which the user has the permissions; and terminate the data query without execution when the query operation is in the list of prohibited query operations for the workspace to which the user has the permissions.

Embodiments of the present invention provide an approach for exploring interesting data patterns in structured tables through recommending aggregate questions in a conversational data exploration. Specially, interesting features and operators are selected that are used to frame aggregate questions based on user intent and the data. The aggregate questions are ranked based on user persona and interestingness of the questions. The approach dynamically adapts and improves the recommendation of interesting and relevant aggregate questions for the user based on user feedback iteratively.

The invention relates to the technical field of data monitoring, and particularly discloses a data quality monitoring system and method, and the system comprises a data quality rule obtaining module which is used for defining data quality rules to obtain a data quality rule set; the data quality inspection report generation module is used for performing data exploration and analysis on the source data to obtain a data quality inspection report; and the source data quality grade evaluation module is used for carrying out quality evaluation on the data quality inspection report based on the set of the data quality rules to obtain a quality evaluation result, and the quality evaluation result is used for representing a quality grade label of the source data. Therefore, the data quality inspection report can be obtained by automatically acquiring and processing various data of different types and formats, and the data quality inspection report is analyzed by utilizing multiple data quality rules to realize quality evaluation of the source data, so that the quality grade label of the source data is determined.

The invention discloses a lake-warehouse integrated system and a data processing method based on the lake-warehouse integrated system. The lake and warehouse integrated system comprises a data exploration layer, the data exploration layer is used for accessing a target data source, constructing a first custom function and configuring a target data pipeline, the target data source comprises to-be-processed original data information, and the target data pipeline calls the first custom function to process the original data information; the lake and warehouse integration is used for executing the target data pipeline so as to process the original data information to obtain first data information, and the lake and warehouse integration is further used for storing the first data information; and the first object calls the first data information through the data application layer. According to the method and the device, the technical problem of relatively low data processing efficiency caused by the fact that a user needs to manually configure information such as a data source, a table structure and resources to process and store data in lake and warehouse integration in related technologies is solved.

Disclosed in the present disclosure are a data lakehouse system and a data processing method based on the data lakehouse system. The data lakehouse system comprises: a data exploration layer used for accessing a target data source, constructing a first user-defined function and configuring a target data pipeline, wherein the target data source comprises original data information to be processed, and the target data pipeline calls the first user-defined function to process the original data information; a data lakehouse used for executing the target data pipeline so as to process the original data information to obtain first data information, and further used for storing the first data information; and a data application layer, wherein a first object calls the first data information by means of the data application layer. The present disclosure solves the technical problem in the related art of low data processing efficiency caused by the fact that the data lakehouse requires a user to manually configure information such as data sources, table structures and resources before processing and storing data.

In various examples, an exploratory data analytics tool obtains a natural language query and generates a structured data query for execution on a sample of a dataset based on the natural language query. In an example, an intent is determined for the query and the intent is used, at least in part, to determine the most appropriate sample. In addition, the intent, in some examples, is used to generate recommended queries. A user interface of the exploratory data analytics tool, for example, can display the recommended queries and / or the results of the structured data query on the sample.

The invention discloses a holographic flow monitoring method and system based on AI and BI, and relates to the technical field of network security monitoring. The method comprises the steps of flow collection and data processing, realization of multi-dimensional feature extraction, multi-model detection and dynamic decision by an AI engine, realization of trend, region and situation analysis by a BI engine, realization of anomaly detection based on a behavior baseline, and realization of AI detection result optimization and triggering of BI cross-dimension analysis by an adaptive weight algorithm. The holographic flow monitoring system is constructed through big data driving and fusion of an AI big model, millisecond-level real-time analysis of a security data flow is realized, immediate response of emergency events such as 0day attacks and the like is met, static rule limitation is broken through, the detection accuracy of unknown threats and APT attacks is improved, an AI analysis result and BI visualization capability are fused, and the system has a good application prospect. Multi-dimensional insight of the traffic situation is realized; low-latency interactive query is provided, and ad hoc data exploration of security analysts is supported.

This invention discloses a method for delivering three-dimensional geographic information data for power transmission and transformation projects, relating to the field of power engineering technology. The method includes: acquiring and preprocessing digital orthophoto image data and digital elevation model data; using an information review platform to review the accuracy of the geographic information data; and converting the geographic information data format for unified delivery. The method provided by this invention ensures consistency in design depth requirements across different levels of departments by limiting the design accuracy of digital orthophoto image data and digital elevation model data. It reviews the accuracy of the data map to meet the geographic information data requirements of line projects and converts the format to adapt to the standardization of the surveying and mapping industry and local units. Through accuracy control during the review process and the use of integrated scale and principal component analysis, the method ensures that the data meets engineering needs and that the depth of data exploration is consistent across different levels of units.

The application provides a sensitive data exploration method and device for Internet of Vehicles, a medium and an electronic device, and belongs to the field of data processing. The embodiment of the application can respond to a sensitive data exploration instruction for a vehicle, determine a vehicle sensitive data source corresponding to the sensitive data exploration instruction, and establish a connection with the vehicle sensitive data source; then detect the vehicle sensitive data source based on at least one pre-configured sensitive data type dictionary to determine vehicle sensitive data in the vehicle sensitive data source; and finally generate sensitive data statistical information of the vehicle based on the vehicle sensitive data and output the sensitive data statistical information. The embodiment of the application uses at least one pre-configured sensitive data type dictionary to comprehensively and accurately detect the vehicle sensitive data source, thereby effectively exploring various types of vehicle sensitive data and reducing the risk of leakage of vehicle sensitive data.

Method, system, and computer-readable storage media for generating a data visualization. A plurality of algorithmic visualizations is received for a dataset. Based on the plurality of visualization matrices, a matrix is generated. Each visualization matrix of the plurality of visualization matrices corresponds with an algorithmic visualization of the plurality of algorithmic visualizations. A synthetic matrix is generated by randomly shuffling a plurality of values in each column of the matrix. A random forest classifier is trained to generate a random forest for distinguishing shuffled data of the synthetic matrix from unshuffled data of the matrix. Further, an ensemble visualization graph is generated using the random forest. From the ensemble visualization graph, an embedding vector corresponding to each sample of the dataset is extracted to generate an embedding matrix. Based upon the embedding matrix, the method includes generating the data visualization by harmonizing the plurality of algorithmic visualizations.

The present invention provides a platform that enables codeless generation of online mass spectrometry data exploration portals. The platform facilitates upload of generic spreadsheets containing processed mass spectrometry results (e.g., peak tables) and enables on-the-fly hierarchical organization of data. Following data upload, platform users can select individual visualizations to add to their custom web portal from a menu of options. Based on these selections, a complete webpage is constructed with all associated functionality embedded. These custom web portals can then be shared with collaborators and other researchers at the discretion of the creator via a developed user permissions sharing scheme.

The invention discloses a multi-dimensional data exploration system and method based on large model enhanced embedding, the system comprises a front end and a rear end, the front end comprises a multi-dimensional data exploration interface, and the rear end comprises a large model, a pre-training model and an embedded network model; the large model converts multi-modal data into a text, an entity is extracted from the text and is subjected to semantic embedding through semantic matching, classification standards and label spaces are guided to be generated, and reasoning hierarchical labels are obtained; the pre-training model embeds text generation features; the embedded network model is used for performing embedded learning on the labeled data to generate a new embedded representation fusing embedded information and label information; and the multi-dimensional data exploration interface is used for organizing and classifying the multi-dimensional embedded data generated at the rear end, and displaying the multi-dimensional embedded data on a screen in a graph or image form to realize data analysis, query and interactive data exploration. According to the method, a multi-dimensional exploration mechanism is provided, and high-quality embedded expression and interpretable analysis capability are realized.

The invention provides a method and system for probing the data quality of original logs, and relates to the technical field of data probing, and the method comprises the steps: obtaining a plurality of original logs from a data source, and caching the original logs to a local server; the local server forms a probing process according to a plurality of probing rule nodes preset by probing personnel, and performs rule probing on each original log in sequence according to the probing process; and the local server performs processing according to rule probing results of all the original logs to obtain multiple data indexes, generates a probing analysis report and displays the probing analysis report. The method has the beneficial effects that an automatic probing process consisting of a plurality of probing rule nodes is constructed, so that low-efficiency and error-prone manual one-by-one verification is completely replaced, and the probing efficiency and accuracy are greatly improved; a plurality of data indexes and quality reports are generated based on a comprehensive rule exploration result, a set of objective and systematic data quality evaluation mechanism is established, logs with substandard quality are effectively prevented from entering a subsequent processing link, and high later repair cost is avoided.

The present invention relates to the technical field of AI behavior recognition, and discloses a method and system for identifying garbage classification and placement behavior based on an AI algorithm, wherein the method comprises: collecting and preprocessing garbage placement video data to obtain a preprocessed data set; performing data exploration and augmentation processing on the preprocessed data set to obtain an extended data set; performing feature extraction and behavior characterization on the extended data set to obtain a behavior feature vector; constructing and training a behavior classification model according to the behavior feature vector to obtain a trained classification model; performing global re-detection and long-term behavior recognition based on the trained classification model to obtain a behavior recognition result; based on the behavior recognition result, performing edge-cloud collaborative computing and reinforcement learning on the trained classification model to obtain a target classification model. The method can accurately identify garbage classification and placement behavior, and at the same time has a real-time and scalable AI algorithm, thereby improving the efficiency and accuracy of garbage classification.

Computer-implemented method for processing data requests is presented. The method comprises receiving a first request defining a first set of search parameters, predicting search results to requests for different sets of search parameters, determining one or more sets of search parameters from the different sets of search parameters, transmitting a first response comprising at least a part of the one or more determined sets of search parameters with one or more associated predicted search results, receiving a selection of a second set of search parameters, retrieving actual search results for the second set of search parameters, and transmitting a second response comprising the actual search results. A corresponding computing system and a computer program are provided, too.

The present invention proposes a deep reinforcement learning path planning method and system based on reward function improvement, which relates to the field of path planning technology. The method includes constructing a deep reinforcement learning training environment model; collecting feasible state information and position state information of an autonomous mobile robot set in the deep reinforcement learning training environment model; constructing a state space model corresponding to a deep deterministic policy gradient algorithm and a policy learning algorithm, and making the state space model output a state space function and an action space function; obtaining a reward function corresponding to all optional state actions of the autonomous mobile robot; storing the state space function, the action space function and the reward function in an experience pool, and selecting samples from the experience pool to update the deep deterministic policy gradient algorithm and the policy learning algorithm; repeating the above steps until the autonomous mobile robot moves to the target point to complete the path planning. The present invention helps to improve the convergence speed of path planning and the efficiency of data exploration.