9 results about "High level analysis" patented technology

The invention provides a power grid data restoration method and system based on Huber norm tensor decomposition, and belongs to the technical field of intelligent power grids and high-dimensional data processing. The method comprises the following steps: constructing multi-source and multi-dimensional power grid load data into a high-dimensional tensor structure; a Huber norm-based tensor decomposition model is adopted as a core reconstruction engine, through differential punishment of different residual errors, random noise is effectively suppressed, key abnormal modes such as electricity stealing are reserved, and excessive smoothing of abnormal features by a traditional method is avoided; efficiently solving the low-rank factor matrix of the tensor by using a stochastic optimization algorithm; and finally, realizing accurate filling of massive missing values in the original data through the reconstructed factor matrix. According to the method, the restoration precision and robustness of the power grid data containing noise and abnormal values can be remarkably improved, a high-quality data basis is provided for subsequent advanced analysis tasks such as electricity larceny detection and load prediction, and the method has important practical application value.

We provide a system that improves decision-making capabilities. [Solution] System 300 includes a server that provides a Single Pane of Glass User Interface (SPoG UI) and a Real-Time Data Mesh (RTDM) module for ingesting and standardizing data from multiple sources. An Advanced Analytics and Machine Learning (AAML) module analyzes the data to provide predictive analytics, anomaly detection, and personalized recommendations. The SPoG UI presents real-time data and insights through interactive visualizations, enabling resellers to take actions such as creating quotes, placing orders, and managing customer accounts. The system supports real-time pricing negotiation, compliance management, and integration with external systems via APIs, generating a real-time, end-to-end view of interactions between both suppliers and end-user customers.

Computerized systems and methods are described for enabling resellers to manage their end-user business within their own business environment on a distribution platform. The system includes a server configured to provide a Single Pane of Glass User Interface (SPoG UI) and a Real-Time Data Mesh (RTDM) module for ingesting and standardizing data from multiple sources. Advanced Analytics and Machine Learning (AAML) models analyze the data to provide predictive analytics, anomaly detection, and personalized recommendations. The SPoG UI presents real-time data and insights through interactive visualizations, enabling resellers to perform actions such as creating quotes, placing orders, and managing customer accounts. The system supports real-time negotiation of pricing, compliance management, and integration with external systems via APIs. The method and system generates a real-time, end-to-end view of both supply and end-user customer interactions.

The invention discloses a battery data hybrid compression and reconstruction system and method based on end-cloud collaboration, and the method comprises the steps: a vehicle-mounted end firstly collects battery state data, carries out the differential processing, automatically judges whether the current state is in a steady state or a complex working condition through the calculation of a nonlinear fluctuation index of data in a sliding time window, and generates a corresponding identifier; if the working condition is a steady-state working condition, compressing the data by adopting algebraic iteration linear regression to obtain a slope and an intercept coefficient, and packaging the slope and the intercept coefficient into a steady-state data packet to be uploaded; and if the working condition is a complex working condition, extracting a hidden space feature vector of the data by using a lightweight quantization encoder, and packaging the hidden space feature vector into an abnormal data packet to be uploaded. And the cloud server respectively calls a linear generator or a high-precision decoder according to the data packet identifier, reconstructs the received parameters or feature vectors into original differential time sequence data segments, and finally seamlessly splices all the data segments according to a time sequence to restore complete and continuous battery state time sequence data for storage and advanced analysis.

The environment measures the value of data sources as input to the identity graph in terms of the impact of their inclusion or removal. Combinations of candidate sources are sent to the sandbox environment to produce the desired output. The person process, person + touchpoint process, and activity value process are executed. Results include whether a person was added or removed, whether a person created a point of failure, and whether a person was merged or split. The output provides an analysis of the evolution of the identity graph within the entity resolution system based on the selection of data sets used to build the graph.

This invention relates to the field of facility operation and maintenance management technology, and discloses a remote monitoring and intelligent operation and maintenance management system for charging pile clusters, including a physical charging pile layer, an edge digital twin layer, a dual-chain evidence storage layer, and an intelligent decision-making layer. The physical layer has a built-in anti-tampering data acquisition module that acquires static and dynamic data and sends it to the edge layer; the edge layer constructs a lightweight digital twin, generates a dynamic health baseline model, and outputs anomaly warnings; the dual-chain evidence storage layer stores static identities and dynamic hash digests separately through device chains and operation and maintenance chains; the intelligent decision-making layer combines a cloud-based advanced analysis engine to schedule edge nodes to perform distributed fault simulation and generate a comprehensive health credit score. This invention ensures data anti-counterfeiting and tamper-proofing from the hardware source, and distinguishes between individual hardware faults and common environmental interference through a cloud-edge collaborative simulation and identification mechanism, achieving accurate diagnosis and reducing operation and maintenance costs.

Disclosed are systems and methods that provide a decision-intelligence (DI)-based, computerized framework for automatically and / or dynamically managing connectivity and / or operation of assets on / over an electronic network. The disclosed framework functions in relation to providing network management, inclusive of inventor and network reconciliation, via a network data quality management tool (NDQ). The NDQ framework is configured for advanced network data discovery, provisioning analysis, discrepancy resolution, monitoring processes, and the like. The disclosed NDQ framework serves as a comprehensive, automated solution for ensuring accurate network data and seamless network management by integrating advanced analytics, multi-platform compatibility, and automation capabilities. The framework operates to provide a unified, high-quality network data environment by aligning asset provisioning data with network provisioning data, identifying and resolving discrepancies, and enabling ongoing monitoring to maintain network integrity.

The present invention provides a system and method for automating the conversion of emails into structured order items using generative AI, which utilizes an integrated architecture comprising a real-time data mesh (RTDM), advanced analytics and machine learning (AAML) module, and a single pane of glass (SPoG) user interface. The system includes an email parser that extracts order information from emails, an order generation engine that converts the information into structured items, and an integration gateway that synchronizes the items with external systems. The RTDM manages data flow and transformation, while the AAML provides predictive analytics and process automation. The SPoG UI performs real-time data visualization and user interaction. The system improves order processing efficiency, accuracy, and scalability, enabling businesses to handle email orders with minimal human effort and higher precision.

We provide systems, methods, and devices that use artificial intelligence (AI) and machine learning (ML) technologies to ingest, integrate, and analyze data from multiple sources, delivering comprehensive and actionable insights in real time. [Solution] System 300 includes a single-pane-of-glass user interface (SPoG UI) that serves as the primary interface for the user, providing a unified view of data and insights from various modules within the system. The real-time data mesh (RTDM) module facilitates continuous data flow and harmonization, ensuring data consistency and accuracy across the platform. The advanced analytics and machine learning (AAML) module employs a predictive analytics engine, an anomaly detection engine, and a recommendation engine to generate insights and provide actionable recommendations.