878 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 invention relates to the technical field of business process compliance inspection systems, in particular to a compliance inspection system and method for an enterprise business process, and aims to solve the problems that in the prior art, an enterprise business process compliance inspection system generally adopts a static risk assessment model and lacks automatic and intelligent data acquisition and processing means, so that the enterprise business process compliance inspection system is inconvenient to use. And the accuracy and timeliness of risk assessment are insufficient. The system comprises a data acquisition module, a risk assessment module, a monitoring and early warning module, a map construction module, an intelligent recommendation module and a report generation module. According to the invention, through automatic data acquisition, preprocessing, risk assessment and monitoring early warning, the efficiency and accuracy of compliance inspection are significantly improved; by utilizing artificial intelligence and big data technologies, the system can quickly identify potential compliance risk points and perform quantitative evaluation, so that errors caused by manual intervention and subjective judgment are reduced, and the efficiency and accuracy of compliance inspection are improved.

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 relates to an automatic data labeling method of an ISP image signal processing visual sensor. The method comprises the following steps: carrying out noise reduction and signal enhancement on an original image data stream acquired by an ISP image signal processing visual sensor to obtain a standardized image sequence; performing time sequence feature extraction to obtain a time sequence feature tensor; performing spatial feature extraction to obtain spatial-temporal feature representation; inputting a double-fold teacher-student network for semi-supervised training to obtain an initial image annotation model; performing quality evaluation based on the prediction probability distribution and the time sequence consistency to obtain a labeling result with a quality score; and storing a high-quality sample in the labeling result with the quality score into a sample buffer pool, carrying out sample screening based on entropy and distance measurement, and carrying out parameter updating on the initial image labeling model through an incremental learning strategy to obtain a target image labeling model. According to the invention, the reliability evaluation of the labeling result is realized, and the accuracy of automatic labeling is ensured.

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

This disclosure relates to a lending system comprising a processor, a memory, and at least one network interface controller configured to enable data exchange with external systems. The memory includes lending management logic configured to execute a personal loan-lending system wherein one or more loans are generated by collecting and preprocessing borrower data from multiple sources, conducting risk assessment and scoring, generating one or more scores based on the conducted risk assessment, and approving loans based on the generated scores. The system leverages machine learning processes to refine scoring accuracy and dynamically adapt to borrower profiles and external conditions. The system may integrate with third-party data sources for enhanced verification and incorporate compliance logic to ensure adherence to regulatory standards. By employing automated, data-driven processes, the system improves efficiency, accuracy, and security in loan origination, servicing, and compliance while enabling dynamic risk management and tailored loan product offerings.

The invention relates to the technical field of computer data processing, in particular to an automatic Excel template data backfilling method based on an LLM semantic understanding technology, which is based on the application of a large language model LLM, a natural language processing NLP, a natural language to NL2SQL and an automatic data filling technology. Through semantic understanding and the NL2SQL technology, efficient and accurate extraction and filling of template data are achieved, the problems that an existing method is low in efficiency and prone to making mistakes are solved, the working efficiency and data quality under the scenes of grassroots social governance and the like are improved, and the burden of workers is relieved.

An automated data extraction pipeline for large language model (LLM) training may include extracting a set of code segments from a set of natural language question-answer (Q&A) combinations that each include a provided input, a provided output, and a provided code segment formatted to transform the provided input into the provided output. The data extraction pipeline may then generate a predicted output from a question portion of a first natural language Q&A combination using a first LLM. A first extracted code segment from the extracted set of code segments may then be executed to generate a first actual output of the first extracted code segment. One or more data samples may then be generated for training a second LLM based on a comparison of the first actual output to the predicted output. The second LLM may then be trained using the one or more data samples.

A system for monitoring and optimizing the performance of an e-commerce platform, which includes the following: a) a data collection and integration module configured to retrieve real-time data from e-commerce platforms, marketing channels, inventory systems and user interactions using APIs, tracking scripts and server-to-server communication; b) a data preprocessing and filtering module for cleaning and structuring the collected data by removing inconsistencies, normalizing formats and detecting outliers; c) a machine learning and predictive analytics machine that uses deep learning, regression models and clustering algorithms to analyze historical and real-time data to predict sales trends, customer behavior and demand fluctuations; d) a performance monitoring dashboard configured to visualize key performance indicators, sales trends and marketing effectiveness, and to provide custom reports and real-time alerts; e) an automated module for optimizing marketing campaigns, which dynamically adjusts advertising strategies, budget allocations, target audience approach and creative elements based on real-time data insights; f) an automated decision and control system configured to execute AI-driven optimization strategies, including bid adjustments, price changes and inventory reallocations without manual intervention; (g) a fraud detection and compliance monitoring module that uses anomaly detection algorithms to identify fraudulent transactions, fake reviews and advertising fraud while ensuring compliance with legal regulations; the system continuously monitoring, analyzing and optimizing the performance of the e-commerce platform through automated data-driven decision-making.

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.

The present disclosure provides a system for generating customized messages to a prospect. The system comprises: a data input module configured to receive prospect data, wherein the prospect data comprises data related to a prospect and data gathered from a plurality of sources; an input data processing module configured to select a subset of the data to create an input data; a first model trained to generate one or more sentences based on the input data; and a second model trained to determine whether the one or more sentences are unacceptable, wherein upon determining the one or more sentences are unacceptable, directing the input data processing module to select another subset of the data to create a new input data.

The invention relates to the technical field of flow detection, in particular to a fluid-based online calibration method, device and equipment, a storage medium and a computer program product. The method comprises the following steps: determining a calibration fluid according to physical properties of a current fluid; fluid parameters of calibration fluid in the flowmeter to be processed are obtained, wherein the fluid parameters comprise pressure, temperature and flow velocity; according to the calibration model and the fluid parameters, the reading error of the flowmeter to be processed is determined; based on the reading error, the flowmeter to be processed is calibrated, the fluid parameters are collected in real time, the error is calculated in combination with the calibration model, rapid and efficient calibration is achieved, and time waste is reduced; calibration fluid is dynamically selected, the flowmeter is adjusted in real time, and the adaptability of the method is improved; through automatic data acquisition, error calculation and calibration adjustment, the calibration time and cost are reduced, and the calibration efficiency is 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 discloses an automatic data analysis system and method based on artificial intelligence, and relates to the technical field of data management, and the method comprises the following steps: S1, data collection and preparation; s2, data annotation and classification; s3, feature selection and extraction; s4, data standardization and normalization are carried out; s5, model selection; s6, training and optimizing the model; s7, performing model evaluation; s8, model interpretation and visualization are carried out; s9, generating an automatic report; and the system is constructed based on the steps. According to the automatic data analysis system and method based on artificial intelligence, by setting a classification and labeling model, a training optimization mechanism and a dynamic model evaluation method, the efficiency and precision of automatic data analysis are remarkably improved, and the quality of data preprocessing is improved; meanwhile, through self-adaptive hyper-parameter adjustment and optimization, the efficiency of the training process and the model performance are optimized; the dynamic evaluation mechanism ensures that the model is more accurate in performance under different data characteristics.

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.

Various embodiments relate generally to data science and data analysis, computer software and systems, based on graph-based data arrangements, among other things, and, more specifically, to a computing platform configured to generate configurable automation templates and automation executable programs that automate workflows based on process models to effectuate automated data governance of datasets, including graph-based data stored in one or more graphs, such as a knowledge graph, a data catalog, etc., whereby in at least one example, automated workflows can provide automatic enrichment of datasets, such as multi-layered knowledge graph data that may include data catalog data. In some examples, a method may include receiving data representing activation of an automated workflow template configured to perform a process workflow, receiving configuration data, linking an automated workflow template to executable instructions, and retrieving data representing a type of process flow, which may data including enriched graph data to modify graph data.

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.

Currently, technical expertise is required to construct data maps between data sources and targets within integration processes. Disclosed embodiments enable automated generation of data maps using semantic comparisons and artificial intelligence. In particular, data may be converted into reference vector embeddings that define a semantic location of the data within a multi-dimensional vector space. User requests may be converted into input vector embeddings, based on source and target information. The input vector embeddings may be semantically compared to the reference vector embeddings, potentially using a graph database, to identify semantically similar data. This semantically similar data may then be used to generate a prompt, which may be input to a generative language model to automatically produce a data map for use in an integration process.

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.

A computer-implemented method for automated data record resolution includes: receiving an unmapped data record comprising a plurality of data fields, wherein at least one data field of the plurality of data fields causes a mapping error between the unmapped data record and a plurality of validated data records; generating, from the plurality of validated records, a resolution candidate record for the unmapped data record based on detecting the mapping error between the unmapped data record and the plurality of validated data records; and automatically re-assigning one or more data records of the computer database that are digitally associated with the unmapped data record to the resolution candidate record.

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 provides an automatic data verification system in carbon asset transaction, and relates to the technical field of carbon asset management, the system comprises a data acquisition layer, a data integration layer, a verification model layer, an interaction and security layer, and a sliding window technology of an LSTM time sequence analysis unit increases the number of training samples. The SVM classification checking unit processes non-linear data through kernel function skills and optimizes parameters through cross validation, through the synergistic effect of the kernel function skills and the SVM classification checking unit, trend prediction and abnormal judgment of the carbon data are more reliable, misjudgment and missed judgment conditions are greatly reduced, and therefore the accuracy and reliability of the whole automatic data checking system are improved, and the data checking efficiency is improved. The method provides powerful guarantee for the fairness of the carbon asset transaction, can effectively collect and integrate the carbon asset transaction data, uses an advanced algorithm model to carry out accurate verification, guarantees the safety and credibility of data interaction through the block chain and the intelligent contract, and provides powerful technical support for the healthy and stable development of the carbon asset transaction market.

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.