708 results about "Automated data" patented technology

Systems and methods for integrating generative artificial intelligence (AI) within Software-as-a-Service (SaaS) platforms to automate data operations, synchronize cross-platform workflows, and enable intent-based interactions. A platform displays table structures of items and characteristics linked to a common objective, provides input interfaces, and enrolls AI agents as credentialed users with read / write privileges. The system prompts agents with column types, structural relations, and role profiles to generate and execute editing instructions that progress workflow objectives, detect missing or inconsistent data, and notify users or request information as needed. Hierarchical access schemes permit multiple agent instances with inherited privileges and resource limits managed through an AI center. Agents can operate as autonomous team members, analyze outputs, and support natural-language explanation sessions. Additional embodiments coordinate inter-service updates, maintain deviation detection tools, and construct tailored products and platform elements. These capabilities improve robust automation, decision support, and operational efficiency in complex SaaS environments.

The present disclosure provides a method and system for orchestrating automated data processing and transformation. A centralized orchestrator receives a request to process a client dataset and initiates a data ingestion process to obtain sample data. A semantic analysis module analyzes the sample data to determine semantic types of data fields. A transformation module generates data transformation instructions based on the determined semantic types. The orchestrator deploys a data processing pipeline to a client-controlled environment and configures privacy preservation parameters to identify and obfuscate potential personally identifiable information. The pipeline applies the transformation instructions and privacy parameters to the dataset. A configuration module determines data storage configurations for the transformed dataset. The transformed dataset is stored according to the configurations in a client-controlled or cloud environment. A machine learning module generates a model based on the transformed dataset, which is stored in a model repository accessible to the client.

The disclosed embodiments provide systems and methods for automated mapping of raw data into a data fabric. An innovative approach leveraging Artificial Intelligence (AI)-powered tools and a data fabric to automate the ingestion, transformation, and integration of raw data into a unified model is introduced. By automating the data mapping process, organizations can reduce reliance on manual methods and accelerate their ability to utilize robust insights for exposure management and attack surface reduction. The disclosed solution provides a scalable architecture for unifying cybersecurity signals across cloud and hybrid environments, enabling real-time decision-making and improved organizational resilience against cyber threats

The invention discloses an automatic NL2SQL data set construction method and system based on a large language model. The method comprises the steps that service-oriented SQL query data collection is carried out; the method comprises the following steps of: collecting SQL query statements related to business through a channel, and cleaning and standardizing an obtained SQL sample by adopting a data preprocessing technology; generating NL2SQL data based on a large language model; a mapping pair between the natural language and the SQL is generated by using a large language model, corresponding natural language description is generated through reverse derivation according to a specific business problem and a table connection mode, and the conversion process from the SQL to the natural language is realized; optimizing the quality of the NL2SQL data set; and dynamically updating a data set for private deployment. According to the method, an automatic SQL sample generation mechanism and a multi-dimensional quality control process are introduced, so that manual intervention is remarkably reduced, and the diversity and accuracy of a data set are improved.

The invention discloses an automatic data compliance check tracking system based on a smart contract, and relates to the technical field of data check, and the system comprises the steps: building a hierarchical mapping database of domain classification samples and compliance check depth; deploying a field identification engine, and analyzing the data packet information according to the field identification engine to determine a field type; identifying a compliance check depth corresponding to the domain type based on the hierarchical mapping database, and loading a corresponding check rule cluster from an intelligent contract strategy library according to the compliance check depth; and performing compliance check according to the check rule cluster, and recording a compliance check state to perform blockchain upchain storage. The technical problems that in the prior art, the execution efficiency of a data compliance check process is low, and traceability cannot be achieved are solved, and the technical effects that the compliance check efficiency and accuracy are improved, and the check process is credible and traceable are achieved.

The invention provides an automatic data fitting method and system combined with equipment position calibration, and the method comprises the steps: firstly collecting a multi-source sensor data set of target equipment in a three-dimensional space, including equipment motion track data, position coordinate offset and environment interference parameters, carrying out the feature extraction of the multi-source sensor data set, and carrying out the feature extraction of the target equipment; generating a target feature set containing spatial position correlation features, dynamic trajectory fluctuation features and environment coupling features, then calling a pre-trained adaptive fitting model, inputting the target feature set to perform dynamic parameter matching, and generating an initial calibration parameter set; and generating a dynamic path sequence of equipment position calibration based on the initial calibration parameter set, triggering the equipment to execute automatic position calibration according to the sequence, and feeding back calibrated verification data to the model update parameters, thereby improving the precision, real-time performance and adaptivity of equipment position calibration.

Systems and methods for integrating generative artificial intelligence (AI) within Software-as-a-Service (SaaS) platforms to automate data operations, synchronize cross-platform workflows, and enable intent-based interactions. A platform displays table structures of items and characteristics linked to a common objective, provides input interfaces, and enrolls AI agents as credentialed users with read / write privileges. The system prompts agents with column types, structural relations, and role profiles to generate and execute editing instructions that progress workflow objectives, detect missing or inconsistent data, and notify users or request information as needed. Hierarchical access schemes permit multiple agent instances with inherited privileges and resource limits managed through an AI center. Agents can operate as autonomous team members, analyze outputs, and support natural-language explanation sessions. Additional embodiments coordinate inter-service updates, maintain deviation detection tools, and construct tailored products and platform elements. These capabilities improve robust automation, decision support, and operational efficiency in complex SaaS environments.

The invention relates to a knowledge-intensive visual question and answer automatic data generation method and device, and the method comprises the steps: constructing an original visual data set containing the professional knowledge of a target domain according to a static image, a video stream and multimedia content; extracting a representative frame sequence, converting the audio information into text information, and extracting character information in the static image to construct a structured visual instance database; according to the prompt text meeting the preset professional depth condition, establishing a three-level prompt system containing domain knowledge, an evaluation standard and a generation specification; generating a corresponding visual question and answer pair data set according to the dynamic cooperation of the main agent and the domain expert agent; generating a multi-agent quality evaluation system according to the quality evaluation result; and designing a difficulty grading mechanism according to the negative example sample. According to the method, the professionality, the accuracy and the diversity of the visual question and answer data are remarkably improved, and reliable data support is provided for training and evaluation of a multi-modal large model.

The invention discloses an intelligent PCB sorting process and system based on an unmanned factory, and the process comprises the steps: obtaining surface images and internal structure parameters of PCBs in real time through an image collection module and a scanning module on sorting equipment, and generating a standardized feature data set; the lightweight classification model performs classification and quality grade division on the PCBs in combination with a process grade judgment rule, and dynamically allocates sorting priorities based on MES system order demands to generate a task queue; the path planning algorithm calculates the optimal grabbing path of the mechanical arm and the AGV conveying path according to the priority queue, and a sorting instruction set is formed and issued to the execution unit through the industrial communication network; the mechanical arm completes PCB grabbing and placing operation according to the instruction, and meanwhile, the visual verification module detects a sorting result in real time and outputs a sorting state report and quality data; according to the process, high efficiency, accuracy and unmanned operation of PCB sorting are realized through automatic data acquisition, intelligent classification decision and a closed-loop optimization mechanism.

Example systems and methods described herein relate to the automated generation of sample expressions. Metadata is accessed for each of a plurality of applications. The metadata includes a functional description of each application. Prompt data is provided to a generative machine learning model. The prompt data includes the metadata for each of the plurality of applications and an instruction to generate, for each of the plurality of applications, a plurality of sample expressions corresponding to user input provided to a digital assistant to invoke an action related to the application. One or more responses that were generated by the generative machine learning model based on the prompt data are processed to obtain output data including the plurality of sample expressions for each of the plurality of applications in a structured format. The output data is used to configure the digital assistant.

The invention relates to the technical field of image recognition data processing, in particular to a pipeline element library creating method based on a model training and image recognition technology, which comprises the following steps of: acquiring pipeline element image data with depth information through a multispectral imaging scheme, executing Retinex algorithm illumination equalization and morphological restoration through a cascade preprocessing pipeline, and establishing a pipeline element library. A standardized data set associated with the metadata is generated. A multi-task joint learning framework is adopted to integrate ResNet-50, HRNet and Mask R-CNN networks, a bottom convolution feature extraction layer is shared, oil stain and strong reflection antagonism data synthesized by a generative adversarial network is injected, model parameters are optimized in combination with a progressive training strategy, and a lightweight MobileNetV3 model is output. Model parameters and an index structure are dynamically updated, and system self-calibration is achieved in combination with a cross-device calibration protocol and data consanguinity tracking. According to the method, through automatic data labeling, multi-task feature multiplexing and retrieval feedback closed loop, the construction efficiency of the pipeline element library is effectively improved, and the recognition error rate in a complex environment is reduced.

The invention discloses an automatic data modeling and optimizing system and method fusing a knowledge graph and ChatBI, and relates to the technical field of data analysis. In order to solve the problems of high interaction threshold and insufficient analysis depth of a traditional BI tool, the scheme adopted by the invention comprises a data layer which has the capabilities of multi-source data access, domain knowledge graph construction, intelligent mapping recommendation, federal calculation and data mild governance, and realizes data integration and semantic unification; the analysis layer realizes accurate conversion from a natural language to an SQL and semantic reasoning of a knowledge graph through graph vectorization, multi-model cooperation, natural language understanding, intelligent SQL generation and graph dynamic updating, and supports efficient data query and analysis; and the application layer has the functions of natural language interaction, visual recommendation and generation, root cause analysis and intelligent early warning, and provides a visual interaction interface and data display service for a user. According to the invention, intelligent data analysis and visualization based on natural language interaction can be realized.

The invention relates to the technical field of environment monitoring, in particular to an underground water monitoring and early warning method and system based on multi-source data fusion, and the method comprises the following steps: obtaining water quality, flow velocity, temperature and pH value data, adjusting the collection frequency, verifying the state of a sensor, forming a water quality and geologic feature data set, analyzing rainfall, air temperature, permeability and water flow dynamic state, and obtaining a water quality and geologic feature data set. And generating a seepage trend, identifying a pollutant diffusion path, range and main path, evaluating a diffusion rate and influence, and generating a pollution diffusion early warning level in combination with permeability and risk standards. According to the invention, through fusion analysis of multi-source data, determination of the correlation between an underground water flowing mode and water quality change, timely capturing of the influence of meteorological change on underground water, accurate evaluation of pollutant diffusion path, rate and influence range, improvement of timeliness and accuracy of pollution early warning, and improvement of monitoring response speed through automatic data processing. And timely and accurate decision support is provided for groundwater resource management and pollution control.

Certain example embodiments relate to dashboards that help streamline and automate data quality management processes used with machine learning (ML) models and ML-enabled technology. A clean dataset is initialized from a dirty dataset. A search space is the set of all possible combinations of available error detection algorithms and data repair algorithms. A scoring function measures performance of a given error detection algorithm and data repair algorithm combination on the clean dataset. An ML model is trained using the clean dataset. Best error detection and data repair algorithms are selected, based on an optimization on the set of all possible combinations, and the defined scoring function. The selected best error detection algorithm is applied to the clean dataset, and a repaired dataset is generated using the selected best repair algorithm. The clean dataset is set to the repaired dataset. This procedure is repeated until a condition is met.

Various embodiments include methods and systems for automatically evaluating data structures and / or data management documentation for compliance with relevant standards, requirements, and policies. Various embodiments provide a framework for automating compliance reviews of data structures and / or data management against the latest version of standards, requirements, and policies. Methods may include transforming input requests for analysis and data structures and / or data management documents into formats suitable for processing by a large language model (LLM), prompting the LLM to identify concepts and / or elements that are likely subject to a standard, requirement, or policy, submitting a query to a vector database to identify standards, requirements, or policies relevant to the requested analysis and documents, prompting the LLM to assess compliance the data structures and / or data management documents with standards, requirements, or policy, and producing a report on compliance of the data structures and / or data management documents with the relevant standards, requirements, or policies.

The invention discloses an emergency response decision optimization system and method for an intelligent refrigerant system, and the method comprises the steps: carrying out the automatic data collection, crawling and collecting the text data of an emergency plan, a historical emergency event and a risk factor related to the refrigerant system, carrying out the intelligent text preprocessing, and carrying out the decision optimization. Potential association relationships in the data are mined through an association rule mining algorithm, and a multi-layer complex network model is constructed. The multi-layer complex network model is input to a convolutional neural network through an embedded layer, key features are automatically extracted, and the model is trained, so that optimal emergency response strategies in different risk scenes are learned. The system receives risk input data in real time through an application program interface, and rapidly outputs optimized emergency resource configuration and coping measure combination to assist a decision maker to make an optimal decision in an emergency.

The invention relates to the technical field of shipbuilding production site management, in particular to a digital intelligent factory management method and system, and the method comprises the steps: building a configurable product structure model, a work decomposition structure model and a technological process model for each ship type, and carrying out the real-time sensing and automatic data collection of parameters of a production site; establishing a uniform resource pool, and marking attributes of resources; dynamically evaluating the requirements of different ship types on shared resources in combination with plans and real-time production states; carrying out resource allocation by using an optimization algorithm; the execution state of each process task is monitored in real time, and key production data is automatically collected; and visually displaying each ship type, the construction progress of each section, the critical path, the resource state and the risk early warning information in real time through a billboard, an instrument panel and a report form. According to the invention, through multi-ship-type unified modeling, real-time data perception and intelligent optimization scheduling, collaborative management, cost reduction and benefit increase in the ship building process are realized.

The invention discloses an enterprise data asset entry system based on artificial intelligence, and the system comprises an original document receiving module which is used for generating an original document set; the multi-modal data construction module is used for constructing a multi-modal input data set; the artificial intelligence identification module is used for identifying a field set with data asset attributes; the structured asset modeling module is used for converting the identification field set into a structured data asset object; the rule matching and template selection module is used for generating a field mapping rule and table entry trigger configuration; the field standardization mapping module is used for generating a structured data entry record set; the table entry metadata supplementing module is used for adding a template number, a mapping timestamp and an operator identifier for each data table entry record; the ledger writing and auditing module is used for writing the data entry record into the enterprise asset ledger system; and the table entering state management module is used for generating a state mapping table. According to the invention, automatic data asset entry is realized.

The invention discloses a metallographic detection method and system based on image recognition, and belongs to the technical field of metallographic detection. The detection method comprises the following steps: image acquisition and preprocessing: acquiring a metal metallographic image by an optical microscope, performing median filtering and denoising, and extracting a grain boundary by adaptive binarization; and field-of-view and scale calibration: selecting a corresponding scale according to the shooting magnification. The multi-parameter automatic measurement comprises grain size analysis, grain size rating, second phase analysis and intelligent report generation, a report is automatically generated based on a preset template and comprises an original image, a binary image, measurement data and a histogram, and report fonts and layout can be adjusted in real time through a visual control. According to the detection method disclosed by the invention, the efficiency, the precision and the reliability of metallographic structure analysis of the metal material are remarkably improved through the full-process design of image acquisition standardization, preprocessing intellectualization, data analysis automation and report generation templating.

The invention is suitable for the field of data processing and analysis, and provides a medicine cold chain on-line fast verification system and a real-time data processing and intelligent analysis method thereof.The medicine cold chain on-line fast verification system has the beneficial effects that the medicine cold chain on-line fast verification system comprises a wireless temperature and humidity recorder, an intelligent management host and upper computer server platform software, and the wireless temperature and humidity recorder adopts an SHT40 sensor; the wireless management host receives encrypted data of the recorder, then transfers the encrypted data to the W25Q256JVEIQ storage chip, and encrypts and uploads collected environment parameters and equipment state information to the upper computer server, an online real-time verification technology is introduced, instant monitoring and response to overtemperature early warning can be achieved, rapid deviation correction can be achieved, repeated verification of traditional offline verification is avoided, and the verification efficiency is improved. And the verification period is greatly shortened. The automatic data processing system realizes full-chain unmanned intervention, reduces manual operation time, guarantees data authenticity, and improves verification efficiency and result objectivity.

An AI-powered data quality monitoring and automatic alerting system (100) for cloud-based data warehouses, comprising: a. an AI-driven data quality monitoring module configured to continuously assess data in the cloud-based data warehouse for accuracy, completeness, consistency, and timeliness; b. an anomaly detection and warning system configured to automatically detect data anomalies based on the assessment performed by the data quality monitoring module and to generate real-time alerts based on detected anomalies; c. an automated data cleansing and remediation workflow that triggers corrective actions in response to detected anomalies, including data imputation, record merging, and validation to maintain data integrity.

A platform which provides a system and method for intelligent document processing with anomaly detection and predictive analysis comprising a user interface which allows platform users to upload documents, a data acquisition engine that leverages one or more machine and / or deep learning algorithms to classify, validate, and enforce compliance of the uploaded documents, and an artificial intelligence engine that constructs and maintains the models developed from the machine and / or deep learning algorithms. The platform may utilize various bespoke APIs to integrate validated data with third-party systems when an authorized entity initiates the process. The platform can function as a system of record and central, secure repository for an applicant's documentation and information required for various application processes. In some embodiments, the platform utilizes a trained generative AI model to assist platform users and to provide predictive analysis responsive to user submitted queries.

The invention relates to the technical field of automatic acquisition, in particular to an automatic riverbed material acquisition control system and method based on Beidou navigation. The method comprises the following steps: acquiring a Beidou satellite multi-dimensional data set; performing original data preprocessing on the Beidou satellite multi-dimensional data set to obtain a Beidou multi-dimensional standard data set; performing multi-source data fusion based on the Beidou multi-dimensional standard data set to obtain riverbed multi-source fusion data; generating a riverbed sampling decision by using the riverbed multi-source fusion data; therefore, through comprehensive application of multi-source data fusion, path optimization and automatic data evaluation, the problems of incomplete data, unreasonable sampling path and low report generation efficiency in the traditional riverbed data sampling process are solved, and the riverbed data acquisition precision, efficiency and intelligent level are improved.

The invention provides an automatic data management method and system based on a large model, and the method comprises the steps: obtaining a native data set corresponding to a to-be-managed data field, carrying out the cross-domain feature association mining of the native data set based on a pre-trained large model, generating a semantic feature map of data entities and an implicit association link between the data entities, and carrying out the data management through the implicit association link. Calling a pre-trained large model, performing quality dimension evaluation based on the semantic characteristic spectrum and the implicit association link, outputting a quality defect vector of a data entity, performing source tracing analysis based on the quality defect vector, and determining a data quality defect type existing in a to-be-treated data field and a cross-entity propagation path of the data quality defect; a strategy evolution module in the pre-training large model is driven to generate a self-adaptive governance strategy containing defect positioning anchor points, and the self-adaptive governance strategy is deployed to a data governance execution environment to complete automatic data governance, so that the governance strategy can better adapt to different data quality scenes; and the pertinence and effectiveness of data management operation are improved.

The invention relates to the technical field of fire alarm, and discloses a self-adaptive threshold fire-fighting buzzer alarm method and system based on fuzzy logic, and the method comprises the following steps: S1, environment data collection; s2, data preprocessing; s3, performing fuzzy processing; s4, performing fuzzy reasoning and alarm threshold calculation; s5, optimizing an alarm threshold value; s6, real-time feedback and dynamic adjustment: according to the real-time fire suppression result and the environment change data, adjusting an alarm threshold to form a dynamic feedback mechanism; s7, applying a fire propagation model; and S8, alarm execution. By adopting distributed data storage, redundant backup, encryption technology and automatic data management, efficient and safe data processing, reliable data recovery and accurate alarm decision are realized, and the response speed and stability of the system are improved.

The invention provides an automatic data exception root cause positioning and playback repair method and system and related equipment. The method comprises the following steps: constructing a dynamic heterogeneous topological graph containing data logic nodes and physical resource nodes in real time, and establishing real-time directed dependency connection between the nodes; in response to a monitored abnormal signal, generating an anti-fact intervention set assuming that an upstream node is in a reference state by using a Do operator based on the dynamic heterogeneous topological graph; the anti-fact intervention set is substituted into a causal reasoning model for simulation, and the abnormal maintenance probability of the abnormal signal still existing under the condition that the anti-fact intervention set takes effect is obtained; and calculating a causal contribution degree according to the exception maintenance probability to lock a root cause node, and transmitting state data of the root cause node to a repair module to trigger playback repair. According to the invention, the accuracy and stability of abnormal root cause positioning are improved.

The invention discloses a market intelligence-oriented automatic data acquisition and decision support system, particularly relates to the technical field of artificial intelligence and information processing, and comprises a structure rendering module, a semantic extraction module, and a credible evaluation and execution feedback module. The system extracts structural anchor points through image rendering, generates semantic vectors in combination with context modeling, calculates a structural consistency index and a semantic validity index, outputs a five-level credibility level result through a fuzzy logic device, refreshes a collection task scheduling priority, and updates a pre-training language model based on semantic offset feedback. Self-iteration semantic generalization and efficient decision support under a cross-platform context are realized; according to the method, stable recognition of the webpage structure is achieved through structure rendering, multi-dimensional semantic feature generation is achieved by means of semantic extraction, the credibility level is output in combination with double-index evaluation and fuzzy logic, then collection scheduling and model self-adaptive updating are optimized, and the obtaining precision and decision reliability of market information data are effectively improved.

The invention relates to the technical field of big data management and artificial intelligence application, in particular to an automatic data quality inspection system and method based on a big model and data flow orchestration, and the method comprises the following steps: system architecture construction: employing a three-layer architecture design which comprises a data access layer, an intelligent quality inspection layer and a flow orchestration layer; the method has the beneficial effects that the full-link automatic quality inspection of structured and unstructured data is realized by integrating the intelligent analysis capability of a large language model (LLM) and the data processing pipeline of a low-code process arrangement tool. The system combines a rule engine and large model dynamic reasoning, supports customized quality inspection rule generation, abnormal data intelligent identification and quality inspection process adaptive optimization, improves the automation level and complex scene adaptability of data quality detection, and is suitable for data governance scenes of multiple industries such as government, finance, medical treatment, e-commerce and the like.

The invention provides a building structure intelligent design method and system based on 3D modeling, electronic equipment and a storage medium, and relates to the field of building intelligent design, and the method comprises the following steps: obtaining three-dimensional data through an unmanned aerial vehicle, oblique photography and laser scanning, and carrying out data cleaning, conversion and screening; performing feature extraction and semantic segmentation on the point cloud data by using a deep learning model; performing intelligent registration and fusion on the two types of point clouds by adopting a deep learning model; a three-dimensional model is generated based on the registration data, a parameter form is generated in combination with the building planning data, and a component model is constructed; collision detection, smooth verification and mechanical simulation are carried out on the whole model, parameter adjustment and optimization are carried out according to verification results, through automatic data collection, multi-source point cloud fusion, intelligent parameter modeling and multi-dimensional verification and optimization, the problems that a traditional method is low in efficiency and insufficient in precision are solved, and the design efficiency and precision are remarkably improved.

The invention discloses a data filling automation system and method based on a large model, and relates to the field of electric digital data processing, and the method specifically comprises the steps: monitoring and collecting the input behaviors of a user in different platform forms in real time, constructing a semantic state vector, and carrying out the aggregation to form a semantic global vector; constructing a structural disturbance tensor model, modeling the stable position of each field, and obtaining a stable state representation vector of each target field under disturbance through a tensor projection mode; and constructing a path cost function according to a matching relationship between the semantic global vector and a structure mapping result, solving an optimal execution path for field filling and reporting, and driving a browser to perform automatic filling and reporting according to a path sequence, thereby realizing automatic operation of data filling and reporting under semantic reasoning and structure disturbance. Automatic and intelligent cross-platform data filling and reporting are achieved, filling and reporting efficiency is effectively improved, platform independence, semantic adaptability and structure fault-tolerant ability are achieved, and data consistency and stability of the filling and reporting process are ensured.