318 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.

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 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 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.

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.

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.

The invention discloses a building structure explosion multistage early warning and damage assessment integrated analysis method, which relates to the field of building structure safety, and comprises a BIM-FEM model conversion module, an FEM explosion response and damage assessment module, an Internet of Things gas sensor acquisition module, an early warning analysis and grading early warning module and a data storage module. And an efficient automatic model conversion interface between the BIM and the FEM is developed. The interface realizes bidirectional data conversion between the BIM model and the finite element analysis model; a gas explosion load is applied to finite element analysis software, dynamic response and damage of each component under the gas explosion load are studied through numerical simulation, and anti-explosion damage evaluation attributes of each component are formed. The problem of low modeling efficiency of a complex structure in finite element analysis software is effectively solved, and automatic data extraction, model mapping and result write-back between the BIM model and the FEM model are realized.

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.

A trouble ticket associated with an incident is automatically triggered. A task is initiated to collect system event logs from one or more servers based on the trouble ticket. A system document is obtained that includes an event list. The event list and the system event logs are provided to an Artificial Intelligence (AI) engine for analysis. An AI diagnosis result is generated by the AI engine. The AI diagnosis result is received from the AI engine. A remote action is executed to resolve the incident based on the AI Diagnosis Result. Action result logs are obtained in response to executing the remote action. A result document is generated that includes a result of the executing the remote action. The result document is provided to the AI engine for training a model used by the AI engine to generate the AI diagnosis result.

The invention discloses a large and small model collaborative robot autonomous assembly method and system, and belongs to the field of advanced manufacturing technology intellectualization. The problems that traditional robot assembly depends on manual programming, adaptability is poor, and an algorithm cannot be optimized autonomously are solved. The method comprises the following steps: receiving an assembly project request submitted by a user in a natural language; the request is decoupled into a structured action primitive through an arrangement agent, and tasks are allocated; an algorithm design agent retrieves and adapts an algorithm from an open source library based on the action primitive, and completes training in a sandbox environment; the verification agent is responsible for automatic data acquisition and labeling, and carrying out overall performance test and iterative optimization on an integrated algorithm in a simulation environment; and finally generating an executable field control script. According to the method, the problems of algorithm autonomous design, simulation verification and closed-loop optimization when the robot faces a complex assembly task are solved, the development threshold and the deployment risk are reduced, and the intelligent level and the flexible adaptive capacity of an assembly system are remarkably improved.

The invention relates to the technical field of data processing, particularly provides a data acquisition system, method and equipment based on an authorized webpage resource access level and a medium, and aims to solve the problem of low network data processing efficiency. In order to achieve the purpose, the data acquisition system comprises a resource access level acquisition module used for acquiring a resource access level based on webpage response data and webpage serialization characteristics of an authorized target webpage, and a data acquisition module used for acquiring the resource access level if the resource access level is greater than a preset access level. If yes, generating a robot process automation data collection script based on the webpage response data, the webpage serialization features and the resource access level, and obtaining target data of the authorized target webpage based on the robot process automation data collection script; if the resource access level is smaller than or equal to the preset access level, the target data of the authorized target webpage is obtained based on the automatic browser frame and / or the preset data collection interface, and the optimal collection scheme can be dynamically selected.

The invention relates to the technical field of low-voltage power distribution operation and maintenance, and discloses a low-voltage transformer area road and line checking method and system, and the method comprises the steps: obtaining a reference electrical characteristic value of each low-voltage branch line in a transformer area in a stable operation state, executing a preset disturbance operation sequence on a target low-voltage branch switch, and collecting full-period target response electrical data; calculating a multi-dimensional feature difference value of each branch line based on the reference electrical feature value, and dynamically determining a feature matching threshold by combining a load type identification result; performing accurate matching judgment on the dynamic threshold value and the multi-dimensional feature difference value, and generating affiliation relation data of the target branch switch and each branch line; and a full-flow mechanism of automatic data acquisition, intelligent feature analysis and dynamic threshold value adaptation is adopted to complete branching line checking. According to the invention, through collaborative innovation of active disturbance response acquisition, dynamic threshold adjustment of load type adaptation and multi-dimensional feature matching, comprehensive improvement of low-voltage transformer substation road-distinguishing line checking precision, adaptability and operation safety is realized, and reliable technical support is provided.

The present invention relates to a system and a method for measuring and analyzing minimal residual disease (MRD) in pediatric B-cell precursor acute lymphoblastic leukemia (B-ALL) using multiparameter flow cytometry (MPFC). The invention finds application in clinical diagnostics and hematology-oncology for quantifying MRD in B-ALL patients with high sensitivity and specificity, needed for risk stratification, monitoring treatment response, and informing therapeutic decisions. The system comprises interconnected subsystems including an acquisition subsystem with an MPFC instrument, a control and file generation subsystem, and an analytical subsystem. The analytical subsystem incorporates modules for sequential data reduction, automated data cleaning, automated unsupervised data clustering, and interactive cluster analysis. Key advantages include high MRD detection sensitivity (e.g., 10⁻⁵ or 0.001%) and high specificity, without reliance on reference samples or supervised machine learning models, making it applicable in laboratories with different measuring equipment and using different panels of antibodies for identification of leukemic cells.

The invention discloses an automatic data calculation method and system based on configuration driving, relates to the technical field of automation of configuration driving, and solves the problems that although standardized data acquisition of multi-source heterogeneous equipment is realized in the prior art, data calculation modeling depends on hard coding; the same function is difficult to reuse so that the development period is long; computational logic is embedded in a program, debugging and modification are difficult, system retransmission is needed for demand change, and the maintenance cost is high; the technical problems that a large amount of manual intervention exists in a calculation link, rules cannot be dynamically adapted according to field requirements, system universality and expandability are poor, project efficiency is restricted, and multi-scene energy data analysis requirements are difficult to meet are solved. According to the method, common model measuring point configuration is set; after common model measuring point configuration information is read, a function registration mechanism is executed; carrying out callback function binding; and setting a function operation mechanism to solve the technical problem.

Industrial automation data staging and transformation (e.g., using a computerized tool) is enabled. For example, a system can comprise executable components comprising: a model condition component that determines whether a defined data connection condition, applicable to an industrial asset model, has been satisfied, and a device interface component that in response to the defined data connection condition being determined to be satisfied, determines an industrial data source that comprises industrial data associated with the defined data connection condition, sends a request to the industrial data source determined to comprise the industrial data to provide the industrial data in a second data format determined to be compatible with the industrial asset model, and retrieves the industrial data from the industrial data source, wherein the industrial data has been converted by the industrial data source from a first data format to the second data format.

The invention provides an enterprise supply chain financial risk prediction method fusing a dynamic knowledge graph and a graph neural network. The method comprises the steps of multi-source data loading and preprocessing, supply chain knowledge graph construction, financial feature extraction, risk label generation and the like. Aiming at the problems that multi-subject information of core enterprises, suppliers, customers and the like in supply chain data is dispersed and is inconsistent with independent financial data structures, the method solves the problems that manual integration is low in efficiency and prone to errors through automatic data cleaning, entity matching and relation mapping, and a unified and structured enterprise supply chain relation knowledge graph is constructed; the limitation of manually designing network features is overcome by utilizing a graph attention network; an effective feature fusion strategy is designed, and joint feature representation with higher discriminative force is formed; and constructing an end-to-end evaluation framework, training classifiers such as a support vector machine to learn the fusion features, realizing accurate and stable classification of high-risk and low-risk enterprises, and providing probability output to enhance result interpretability and decision support capability.

The invention relates to the technical field of traffic management, in particular to a control system and method for unmanned traffic guidance during road maintenance, and the control system is composed of a satellite data acquisition module, a data processing unit, a communication module, a traffic display device and a storage module. The control method based on the control system comprises the following steps: S1, data acquisition; s2, calculating time; s3, dynamic adjustment; and S4, generating a traffic guidance signal. According to the invention, by constructing a full-process automatic closed-loop command system of data acquisition-processing-decision-execution, firstly, unmanned operation is realized, and dependence on field personnel is eradicated, so that labor and management costs are thoroughly saved; secondly, the safety risk of personnel working in the traffic flow is avoided, and the working safety is improved; in addition, through automatic data driving decision and a dynamic adjustment mechanism based on queuing length change, adaptive response to real-time traffic is realized, congestion is reduced, and traffic efficiency is improved.

A system for detecting and addressing anomalous data is disclosed. The system receives a first application data from a source device. The system receives a request to perform an operation on the first application data. In response to receiving the request, the system generates a second application data by performing the operation on the first application data. The operation includes transferring the first application data from a first data format to a second data format. The system determines that a portion of the second application data is anomalous. The system compares the second application data with a historical application data that is associated with a solution instruction. Based on the comparison, the system determines that the second application data corresponds to the historical application data. In response, the system executes the solution instruction to address the anomalous portion of the second application data.

The invention discloses an intelligent management system and method for rubber production, and the system comprises a system user interface, a business process management module, a yield calculation service center and a resource library, and the data output end of the system user interface is connected with the data input end of the business process management module. The data output end of the business process management module is connected with the data input end of the yield calculation service center, the data output end of the yield calculation service center is connected with the data input end of the system user interface, and the yield calculation service center comprises an incoming material management module, a discharging management module, a yield calculation module and a production progress management module. And the yield calculation module is in data connection with the resource library. The invention aims to reduce the dependence on a large amount of manpower, reduce the labor cost, simplify the management process and improve the production efficiency; through real-time automatic data acquisition, storage and analysis, a complete production data chain is established, and an enterprise is helped to carry out accurate monitoring and datamation management on each link from raw materials to finished products.

The invention provides an automated mobile-money value-capture mechanism designed to help users accumulate savings by setting aside small amounts from every outgoing transaction. The system operates on any mobile-money wallet or integrated financial service and uses a logic engine that applies two variables to each transaction: a percentage-based deduction and a flat "coins" amount. When a user initiates a transfer, the mechanism automatically diverts the combined captured value into a separate user-owned holding wallet before the transaction is completed. This interception ensures the savings are secured at the point of transfer without requiring additional user action. The solution may function as an independent application or as an embedded feature within a bank or mobile-money operator platform. By automating micro- savings from routine transactions, the invention enables consistent value accumulation and improved financial discipline for users

Techniques are disclosed relating to extracting data from a document, using a large language model (LLM), to populate fields in a data structure. A computer system may receive a request to populate multiple fields of a data structure with data extracted from text of a document. The computer system parses the text using an LLM (as well as regular expressions or other parsing techniques in some embodiments). The parsing includes issuing, to the LLM, a sequence of queries targeting individual ones of the multiple fields. The computer system applies a validation algorithm to results received from the LLM in response to the sequence of queries. The validation algorithm confirms the presence of results in the text of the document and populates the data structured with the validated results. In various embodiments, the computer system performs an optical character recognition (OCR) on the document to determine the text for parsing.

Embodiments of the present application provide a method, medium and electronic device for automatic data analysis, the method comprising: obtaining an original data file and a current analysis target; obtaining a target data table according to initial data characteristics of the original data file; obtaining updated data characteristics corresponding to the original data file according to the target data table; inputting the updated data characteristics and the current analysis target into a pre-constructed analysis method recommendation knowledge base to obtain a candidate analysis method set; performing effect simulation pre-evaluation on at least part of the analysis methods in the candidate analysis method set according to the target data table to obtain real-time performance evaluation indexes of each method; obtaining a target analysis method selected for the current analysis target based on a reinforcement learning decision model; and analyzing the target data table using the target analysis method to generate an analysis result.

The present disclosure provides a kind of income processing method and system based on event-driven and multilayer parallel architecture, the method comprises: receiving and processing multi-source trigger signal, generates the standardized trigger context of income calculation task;According to the standardized trigger context, the income calculation task is split into multiple independent subtasks, encapsulated as a standardized income event message and published;Subscription and processing standardized income event message, split each independent subtask into atomic subtask, execute distributed income calculation, generate customer income raw results;Customer income raw results are converged and persisted to unified income accounting table.The present disclosure effectively solves the bottleneck of traditional system in performance and expansibility through event-driven and parallel computing architecture, and further through the innovative initiative audit repair mechanism, builds endogenous, automated data quality guarantee system, realizes the overall upgrade of reliability, accuracy and operation efficiency of income processing system.

The invention discloses a data storage query method and system for industrial automation. The method comprises the following steps: defining a synchronization process from a cache layer to a real-time analysis layer, from the real-time analysis layer to an offline analysis layer and from a metadatabase to each layer, and determining a data flow direction and a trigger condition; consistency and integrity of cross-layer data are ensured through mechanisms such as version control, transactional messages and account checking compensation; the metadatabase records each layer of metadata, such as a data version, a synchronization state and a timestamp, drives a synchronization process and verifies consistency. According to the scheme of the invention, full-link collaboration of industrial automation data is realized.

The application discloses a kind of open source data semi-automatic data labeling method and system based on multi-modal large model, belong to data labeling technical field.The method includes: enhancement processing;Determine image-text-category data pair training set and image-text-category data pair test set;Adjust SimCSE text encoder parameter;Adjust CLIP-SimCSE model parameter;Adjust CLIP-ViT model parameter;Test in multi-modal large model, to adjust multi-modal large model parameter;Classified labeling is carried out to determine the different image-text data pairs in the labeling result of CLIP-SimCSE model and CLIP-ViT model;Different image-text data pairs are corrected.The application can guarantee the accuracy of professional field data category labeling result, improve data labeling efficiency, significantly reduce the cost of artificial data verification.

Assessments of guardrails of LLMs, whether used by an application or within an AI / LM stack, must be dynamic to protect against the ongoing engineering of jailbreaking prompts. An assessment framework has been created that facilitates assessment of language model guardrails. The assessment framework includes a prompt generator and has access to sensitive data (e.g., source code, trade secret, confidential documents, etc.) that occurs in training data of a model being assessed. The framework provides the prompt generator jailbreaking strategies and categories of the sensitive data (e.g., program code, trade secret, confidential document.). With the data categories and the strategies, the prompt generator generates different prompts and submits them to the AI-powered application or LM stack being assessed. The assessment framework then analyzes the outputs / responses from the AI-powered application or LM stack to determine whether guardrails have been subverted and any of the sensitive data has been exfiltrated.

In one general embodiment, an apparatus includes a guidance system configured for coupling to a frame of an automated data storage library, the guidance system including a guide member coupled to a rail and configured to move along the rail in a first direction. A robot is detachably coupled to the guidance system and physically configured to be moved out of the automated data storage library in a direction about perpendicular to a plane of intended movement directions of the robot. The guidance system is configured such that the guide member remains engaged with the rail upon detachment of the robot therefrom.