527 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 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.

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.

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.

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 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.

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 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 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.

The invention relates to the technical field of data analysis and processing, in particular to an automatic data standardization management method based on a large language model, a medium and equipment, and the method comprises the following steps: a data receiving step: receiving data from various data sources; a data source analysis step: identifying field semantics and standardization requirements; a standardized rule generation step: based on the semantic understanding result of the large language model, automatically generating a standardized rule suitable for a corresponding data source; a standardization rule application and optimization step: performing application and dynamic optimization; an intelligent monitoring and verification step of data quality: performing real-time quality detection of the data by using a large language model; and a data restoration and suggestion step: automatically deducing a correct value or giving a restoration suggestion when the data does not meet the standardization requirement. According to the method, the problems of non-uniform standards, high manual rule maintenance cost and lack of dynamic adaptivity caused by inconsistent formats and semantics of the multi-source data are solved.

A computing server retrieves a list of custom-defined categories of a database and accesses a plurality of training samples for training a machine-learned encoder model. The computing server trains the machine-learned encoder model that generates embeddings of data instances. The machine-learned encoder model is trained to separate a plurality of embeddings of positive data instances belong to the target category from a plurality of embeddings of negative data instances. The computing server receives a target data instance that is to be imported to the third-party data platform and generates features of the target data instance to prepare the target data instance for further processing. The computing server applies the machine-learned encoder model to the target data instance to determine an assignment of a category from the list of custom-defined categories. The computing server exports the target data instance including the determined assignment to the third-party data platform.

The invention provides a flood prediction method and device, electronic equipment and a computer readable storage medium, and relates to the technical field of urban drainage pipe networks. The method comprises the following steps: dividing a target point location in a rainwater pipe network into a plurality of clustering blocks; performing classification according to a plurality of preset waterlogging risk levels based on the ponding depths in the clustering blocks; training a basic flood prediction model based on the rainwater pipe network data in the clustering blocks until the prediction precision is reached, and obtaining a flood prediction model; according to the method, nodes in a rainwater pipe network are clustered, the rainwater pipe network is divided into multiple types of blocks according to different types, historical data in different blocks are utilized to train a prediction model comprising a double-layer long-short-term memory neural network, and the trained model has intelligent and automatic data capability and can meet the requirement of quick response; especially under extreme conditions, when large-scale data is processed, the calculation time is shortened, and the response speed is increased.

The invention relates to the technical field of electric digital data processing, and discloses an intelligent data processing method for clinical research. The method comprises the following steps: constructing a basic clinical knowledge graph fused with an international medical ontology; the method comprises the following steps: performing deep learning driven entity recognition and knowledge graph linking on original data from heterogeneous sources such as an electronic medical record and an inspection system; executing cross-source entity alignment and knowledge fusion based on the graph attention network; performing automatic data quality verification and restoration according to clinical logic rules embedded in the atlas; and finally, extracting and generating a standardized analysis ready data set from the enhanced atlas according to research requirements. According to the technical scheme, high-quality, automatic and semantic integration and flexible delivery of clinical research data are realized, and the data processing efficiency and research reliability are improved.

According to the photovoltaic power generation prediction system and method based on the smart power grid provided by the invention, the efficient fitting advantage of a gradient boosting decision tree model on high-dimensional static characteristics is utilized under a long-period stable weather condition, and the capturing capability of a long-short-term memory network on time sequence nonlinear dynamics is fully exerted in a weather sudden change period; weather categories are judged in real time and weights are dynamically adjusted through an environmental condition classifier, so that the RMSE of a final prediction result in four typical scenes of sunny days, cloudy days, cloudy days and rainy days is obviously reduced, and the RMSE is obviously superior to that of an existing fixed weight combination model; meanwhile, through an automatic data cleaning chain formed by an isolation forest and Lagrange interpolation, the data anomaly rate is controlled to be 0.1% or below, and the dimensional difference is eliminated by adopting minimum-maximum standardization, so that the model training stability is ensured; the parallel hybrid architecture realizes minute-level rolling prediction under the acceleration of the GPU, and the real-time requirement of a provincial power grid dispatching center is met.

The invention provides a three-axis six-direction impact test simulation method and system based on an F-DEM, and the method comprises the steps: building a full-size model frame, coupling the F-DEM, carrying out the dynamic solving of a model, and carrying out the post-processing of automatic data. The invention further provides a triaxial six-direction impact test simulation system based on the F-DEM, a three-dimensional full-scale numerical model is established based on basic parameters of a waveguide rod and a rock sample in a TEHB test system, and the three-dimensional full-scale numerical model comprises a discrete element rock sample model and a finite element waveguide rod model. According to the method, efficient coupling modeling of the finite element waveguide rod and the discrete element rock sample is achieved, the method has good stress wave propagation precision, the dynamic response behavior of the rock sample under triaxial six-direction loading can be accurately simulated, and efficient and verifiable technical support is provided for impact response analysis and engineering application research of the rock mass in a complex stress environment.

The invention discloses an end-to-end zero code AI model automatic training and deployment system and method, and belongs to the technical field of artificial intelligence and machine learning engineering, and the system comprises a user interaction layer which is used for providing a graphical user interface and receiving a model training instruction configured by a user; the business logic layer responds to the instruction, encapsulates the instruction into a structured work order, automatically executes data management, model training and model conversion tasks based on the work order, and generates a model deployment package capable of directly running on at least one target hardware platform; and the resource management layer is used for monitoring and scheduling bottom computing resources to support task execution of the business logic layer. According to the method, a Web-based graphical interface is provided, all operations are completed through clicking, dragging and pull-down selection, dependence on programming and command line operations is thoroughly eliminated, and complete AI workflow zero-code operation from data to deployment in a single system is achieved.

In various examples, a technique for neural motion control includes generating a first trajectory associated with a first end effector of an articulated object based at least on a first reference trajectory and one or more transformations between one or more reference poses for one or more objects and one or more new poses for the one or more objects. The technique also includes generating a second trajectory associated with a second end effector of the articulated object based at least on a second reference trajectory and the one or more transformations. The technique further includes determining that a task is successfully completed via execution of the first trajectory and the second trajectory and updating one or more parameters of a machine learning model based at least on the first trajectory and the second trajectory to produce a trained machine learning model.

The invention relates to the field of air tightness testers, and discloses an automatic data calibration method and system for an air tightness tester, and the method comprises the steps: obtaining a temperature sensor value T, a pressure sensor value P and a humidity sensor value H from the air tightness tester; performing three-dimensional coordinate positioning in the three-dimensional coordinate space to obtain a coordinate position C1; mapping the coordinate position C1 to a distribution model for coordinate mapping by using a coordinate transformation matrix M; obtaining a compensation parameter set S1 based on the error characteristic value E and the compensation parameter combination S; adjusting the compensation weight of each environment parameter according to the compensation parameter set S1; fusing the adjusted compensation weight with the error characteristic value E and the compensation parameter combination S to obtain a comprehensive compensation coefficient Kc; and performing correction processing on the original measurement data by using the comprehensive compensation coefficient Kc. The method has the advantages that automatic extraction and dynamic adjustment of calibration parameters are achieved when the test environment changes.

The invention discloses an automatic data cleaning and anomaly recognition method for financial auditing, and relates to the technical field of financial auditing. Extracting fields from the multi-source heterogeneous data by using a regular expression, and unifying the format through semantic mapping; repairing numeric missing data by using LSTM, and checking logic errors by using a rule engine; identifying abnormal transactions based on an isolated forest algorithm in combination with business rules; constructing a risk assessment matrix to divide risk levels; dynamic knowledge graph analysis implicit association can be constructed, an incremental learning optimization model is utilized, and an auditing evidence chain and block chain evidence storage are recorded. According to the invention, automatic processing of financial audit data is realized, the data recovery success rate is improved, and the anomaly identification accuracy is improved; hidden risks are mined through the knowledge graph, audit traceability is guaranteed through block chain evidence storage, manual annotation cost is reduced by 40%, and audit efficiency and reliability are improved.

A system and method for strategic airspace deconfliction is disclosed, centered on a Cooperative Multi-Agent Platform (CMAP) embedded within an Automated Data Service Provider (ADSP) operating within a federated Unmanned Aircraft Systems (UAS) Traffic Management (UTM) network. The system's inventive feature resides in a specific technical architecture that synergistically integrates a real-time safety constraint into a strategic negotiation engine. A Regulatory Compliance Monitor (RCM) generates a real-time Conflict Risk Score that is incorporated directly into the Local Observation Vector of a Multi-Agent Reinforcement Learning (MARL) policy network. The MARL engine is thereby configured to select a strategic negotiation primitive (e.g., BID, YIELD, TRADE) that is dynamically determined based on this real-time Conflict Risk Score. This transforms abstract resource allocation into a safety-aware, risk-adaptive protocol, allowing an ADSP agent to maximize fleet efficiency while guaranteeing collective safe separation

The invention relates to the technical field of data cleaning, in particular to an automatic data cleaning method and system based on a large language model.The method comprises the steps that original unstructured text data to be cleaned is preprocessed to generate standardized data blocks, and vector indexes are established for the data blocks; performing error detection on the data block based on a retrieval enhancement generation technology; in response to an error detection result, calling a large language model to generate a plurality of candidate repair schemes for the identified errors, and performing score sorting on the candidate repair schemes based on the multi-dimensional features; carrying out comprehensive confidence evaluation on the sorted candidate repair schemes, and executing a grading decision according to an evaluation result; and storing the result of the grading decision, and establishing a mapping relationship between the repair result and the original data to form a traceable data cleaning version record. Quantitative evaluation and three-level automatic decision making of the repairing result are achieved, and the problem that the result is uncontrollable due to the fact that LLM is directly used is solved.