33 results about "High level analysis" patented technology

The invention relates to a differential privacy-based competitive intelligence analysis system and method. The differential privacy-based competitive intelligence analysis system comprises a differential privacy processing and budget management module, a differential privacy feature engineering module, a privacy protection analysis and prediction module and a differential privacy synthetic data generation module. According to the design of the invention, an end-to-end advanced analysis framework with mathematics provable privacy protection capability is constructed, so that accurate, timely and reliable insight of market dynamics and competitor strategies is realized on the premise of strictly protecting enterprise sensitive commercial confidentials.

A system is provided for managing safety of bridge cranes in an industrial environment. The system comprises: (a) a first real-time locating component for generating a first location data stream about a crane within the industrial environment; (b) a second real-time locating component for generating a second location data stream about mobile persons and objects within the industrial environment; (c) one or more processors in communication with the first real-time locating component and the second and configured to: synchronizing the first location data stream and the second location data stream, converting the first location data stream into a coordinate of the second time locating component, and determine a collision event between the crane and the movable object; and (d) optionally a component using optical / infrared sensors (camera, LIDAR, etc.) backed by artificial intelligence for visualization and high-level analytics.

Computerized systems and methods are described for managing vendor-independent configuration-on-order (CTO) and offer-on-order (QTO) processes. A real-time data grid (RTDM) is provided for aggregating, normalizing, and normalizing real-time data from different sources. A single pane view user interface (SPoG UI) facilitates dynamic interaction and visibility to vendor performance. An advanced analysis and machine learning (AAML) module analyzes product compatibility, optimizes a pricing strategy and predicts a market trend. A vendor independent CTO / QTO integration module (VACIM) includes a process standardization engine and a vendor data conversion gateway to ensure consistency of different vendors. According to the method, automatic data processing and data conversion gateway integration are carried out to ensure data consistency, and decision making is carried out by adopting a rule engine driven by machine learning, so that a retailer process is simplified in an extensible and high-adaptability framework, the expandability is enhanced, and a pricing strategy is optimized.

An anomaly detection system for e-commerce platforms can integrate multiple user touchpoints via a unified interface. Real-time data ingestion may process and store information in an anomaly data lake. A graphical database can link user interactions, devices, accounts, and transactions. A neural network may analyze this data to predict identities and detect anomaly patterns, potentially providing risk assessments. Machine learning models can generate risk scores for accounts and transactions. An anomaly orchestration system may combine assessments and scores, applying dynamic rules to implement prevention actions. The system can capture data throughout the user journey, enabling adaptive protection against evolving anomaly tactics. By integrating advanced analytics and real-time processing, the system may make informed decisions to block, allow, or challenge user actions across the e-commerce platform.

Computerized systems and methods are described for managing multi-tiered subscription-based models of technology products, including hardware, software, and cloud services. The system performs automated optimizing of subscription models and pricing using a Real-Time Data Mesh (RTDM) and Advanced Analytics and Machine Learning (AAML) Module. The system employs a Single Pane of Glass User Interface (SPoG UI) to enhance user interaction and subscription management. Automated processes facilitate dynamic adjustment of subscription terms, pricing, and configurations based on real-time data and analytics. API integrations for perform data exchange for comprehensive management of multi-tiered subscriptions within a unified ecosystem.

A system for remote access to active shipping containers may gather information and analytics on containers during transit, and allow automated and manual configuration changes to be transmitted, from remote users, to the container during transit. A remote access device capable of long range communication over cellular or satellite networks may be coupled with a control system of the active shipping container, in order to send information from the control system to a remote server, and to receive and provide configuration changes to the control system. The remote access device may also include sensors, or may be coupled with a plurality of sensors that may be placed in an around the container to provide additional and more detailed sources of sensor data. Sensor and status data from containers may trigger automatic configuration changes or notifications, and may also be aggregated to provide high level analytics related to transit lanes.

The invention provides a diabetic foot data inspection and early screening method and system based on an AI model. Belongs to the technical field of disease screening. The method comprises the steps that diabetic foot data of a patient are collected in real time through multi-modal data collection equipment, and the collected diabetic foot data are transmitted into an edge computing network to be processed; the processed diabetic foot data are transmitted into a cloud space, a multi-modal AI model is constructed through a convolutional neural network, and key features are extracted from the diabetic foot data based on the multi-modal AI model. The edge computing network can reduce cloud burden and accelerate data processing speed by processing data, and cloud computing is responsible for training and optimization of a deep learning model, so that advanced analysis of data is realized, and prediction precision is improved.

The invention discloses an optical fiber distributed sound wave sensing data visualization processing system based on PyQt, which belongs to the technical field of optical fiber sensing, seismic data processing, traffic monitoring and signal processing, and comprises a data access module, a user interface module, a data conversion module, a basic processing module and an advanced analysis module. The user interface module performs visualization operation, the data conversion module realizes format unification of sound wave sensing data in different formats, and the basic processing module and the advanced analysis module jointly realize processing and visualization of the distributed sound wave sensing data. According to the system, the functions of data loading, visualization, signal processing and advanced analysis are integrated through a graphical interface, and multi-field application is supported.

The invention provides an AIGC-based financing and leasing industry analysis report writing method and system. The method comprises the following steps: collecting and preprocessing multi-source heterogeneous data; a step of constructing and updating a dynamic knowledge graph; a step of AIGC model training and fine tuning; a complex statistical model integration step; a step of generating a multi-mode content; reinforcing learning optimization report logic; a step of real-time data incremental learning; performing domain expert interactive verification; a step of personalized customization of the report; and carrying out automatic distribution and feedback collection. According to the method, report writing efficiency is remarkably improved, and labor cost is reduced; the report depth is enhanced through an advanced analysis model, and accurate decision making is supported; content innovation is ensured by the dynamic knowledge graph and reinforcement learning, and homogenization is avoided; the timeliness and reliability of the report are improved through real-time data updating and interactive verification; on the whole, the financial lease industry is promoted to be transformed to intelligence and data drive, and the enterprise competitiveness is enhanced.

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 for managing vendor-independent order configuration (CTO) and quote-to-order (QTO) processes. [Solution] A method for streamlining vendor processes, improving scalability, and optimizing pricing strategies within a scalable and adaptable framework, wherein a single-pane-of-glass user interface (SPoG UI) 705 facilitates dynamic interaction and visibility into vendor performance. An advanced analytics and machine learning (AAML) module 715 analyzes product compatibility, optimizes pricing strategies, and predicts market trends. A vendor-independent CTO / QTO integration module (VACIM) 720 includes a process standardization engine 725 and a vendor data conversion gateway 730 to ensure uniformity across vendors.

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.

The embodiment of the application provides a kind of based on ROP chain and the method and device of obfuscation and anti-reverse protection of opaque predicate, this scheme proposes a kind of three-order obfuscation protection framework of combining ROP instruction dynamic reconstruction, opaque predicate protection, instruction semantic hiding, after the ordinary C / C++ source code is compiled into LLVM intermediate representation IR, dynamic extraction Gadget and opaque predicate combination based on user input state are used, the deep level semantic disturbance of key logic block, jump structure, constant field of program is realized. To completely break the control flow restoration chain, it is difficult for reverse personnel to analyze the obfuscation result, realize the controllable, verifiable, high-stability obfuscation protection of program, while maximizing resistance to symbolic execution and static analysis and other advanced analysis techniques.

This invention discloses an intelligent warehouse dynamic optimization system based on digital twin technology, comprising the following steps: S1: Creating a digital twin model: acquiring basic data of the physical warehouse and constructing an accurate digital model; S2: Data collection and integration: deploying IoT devices and achieving real-time data transmission and processing; S3: Constructing analysis algorithms: developing advanced analysis algorithms and implementing machine learning models; S4: Dynamic optimization and adjustment: automatically adjusting storage layout and optimizing inbound and outbound processes; S5: Prediction and early warning: anticipating potential problems and setting alarm mechanisms based on historical data analysis. This invention significantly improves data real-time performance and accuracy, enhances environmental monitoring capabilities, ensures cargo safety, and greatly improves space utilization.

This invention belongs to, but is not limited to, the field of analytical technology, and particularly relates to an advanced analysis method and system for an S-CO2 dual-turbine power generation system, comprising: S1, constructing an S-CO2 dual-turbine power cycle simulation model using the process simulation software Aspen HYSYS; S2, performing conventional analysis on the S-CO2 dual-turbine power generation system; S3, performing advanced analysis on the system, analyzing the potential for performance improvement of the system and its components; S4, exploring the effects of irreversible losses within the system; and S5, proposing a priority order for component improvements. This invention constructs an S-CO2 dual-turbine power generation system, uses Aspen HYSYS software as a tool to simulate system operation, calls the REFPROP database for property calculations, performs advanced analysis on the system, analyzes the potential for performance improvement, explores the effects of irreversible losses within the system, discusses the priority order for component improvements, and compares the results with those of conventional analysis.

An anomaly detection system for e-commerce platforms can integrate multiple user touchpoints via a unified interface. Real-time data ingestion may process and store information in a anomaly data lake. A graphical database can link user interactions, devices, accounts, and transactions. A neural network may analyze this data to predict identities and detect anomaly patterns, potentially providing risk assessments. Machine learning models can generate risk scores for accounts and transactions. A anomaly orchestration system may combine assessments and scores, applying dynamic rules to implement prevention actions. The system can capture data throughout the user journey, enabling adaptive protection against evolving anomaly tactics. By integrating advanced analytics and real-time processing, the system may make informed decisions to block, allow, or challenge user actions across the e-commerce platform.

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 invention presents an energy management system for power distribution networks. It features a hierarchical network with multi-level metering nodes, each measuring energy usage. Data from these nodes are transmitted via a secure communication gateway to a cloud-based platform, which includes servers for processing and storage. A key component is a machine learning algorithm within the server, designed to analyze energy data, normalize and transform inputs, and identify anomalies. The algorithm generates anomaly scores for each power meter, reflecting potential operational issues. A user interface, accessible through client devices, displays the processed data and anomaly scores. This allows users to effectively monitor and manage the energy distribution, making informed decisions for optimization. The system enhances the efficiency and reliability of power distribution, offering advanced analysis and user-friendly monitoring capabilities.

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.

Computerized systems and methods are disclosed for automating Configure to Order (CTO) and Quote to Order (QTO) processes. Methods include receiving user inputs for desired product configurations, retrieving corresponding data from a bill of materials database, and calculating optimized pricing through intelligent rules based on real-time market data. Automated quotes are generated and transferred to orders in a vendor system, selected based on pre-set criteria like vendor reputation and delivery time. Validation steps reduce errors, and real-time reports are generated. The system integrates a Real-Time Data Mesh for data aggregation, a Single Pane of Glass User Interface for user interactions, and Advanced Analytics and Machine Learning Modules for implementing rule-based and learning algorithms. The system is accessible across various devices and standardizes data for uniform consumption, while also employing machine learning models to continually optimize processes. Notifications are sent to users upon successful execution of orders or completion of quotes.

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 belongs to the technical field of safety management and information of nuclear power plants, and discloses a nuclear power plant emergency response data management system which comprises a data integration module, a data processing and analysis module, a data visualization module and a system management and configuration module. The integration module is used for integrating data and carrying out unified access and standardized processing on the data, the data processing and analysis module is used for carrying out preprocessing and advanced analysis on the data processed by the integration module so as to extract key information required by emergency response, and the data visualization module is used for dynamically displaying the analyzed data in various visualization forms. And the system management and configuration module is used for performing user authority management, system log recording and system custom configuration. The data utilization rate can be increased, the emergency response efficiency can be improved, the decision support capability can be enhanced, and the system flexibility and expandability can be improved.

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.

The invention discloses a shale nanopore multi-scale evolution method based on deep learning, and belongs to the technical field of shale pore evolution, and the method comprises the following steps: S1, robot cluster injection; s2, multi-modal data acquisition is carried out; s3, constructing and training a deep learning model; s4, carrying out multi-scale evolution characterization; s5, dynamic evolution simulation; s6, performing entity model comparison; in-situ dynamic holographic detection of shale pores is realized through swarm intelligence and multi-mode sensing of the nano-robot, a complex detection network is formed through swarm intelligence, and the system can efficiently move in a whole shale sample and collect data; the multi-mode sensor captures feature information of the nano-robot at different positions and under different conditions, in-situ dynamic holographic detection of the shale pores is achieved, the deep learning technology optimizes and extracts detailed features of the shale pore structure by conducting advanced analysis on collected data, and the shale pore structure is obtained. The multi-physics field simulation further verifies and supplements the result of the deep learning algorithm.

A system and method for the predictive management and automated compliance of industrial flares are disclosed. The system integrates multi modal data from optical and thermal cameras, alongside physical sensors, to create a real time digital twin of the flare stack. An artificial intelligence engine analyzes this data to perform predictive analysis, detecting anomalies such as incomplete combustion, gas leaks, and structural degradation, and forecasting potential failures. This enables predictive maintenance, optimizing operational efficiency and safety. The system features a secure, blockchain powered module for generating tamper proof, auditable compliance reports aligned with environmental regulations. It also includes an automated alert system, a comprehensive management dashboard, and advanced analytical reporting. This integrated approach provides a holistic solution for flare management, moving beyond simple monitoring to proactive and secure operational control.

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.

A method for providing advanced analytics and domain-specific insights related to a site. The method includes receiving data related to a site, building a graph database from the received data, querying the graph database, generating a response based on the query, displaying the generated response within a graphical interface on a screen, and performing a site action based on the displayed response. Generating the response may include reviewing a chat history for the response related to the query, displaying the response on the screen when the response is found in the chat history, and sending the query to a supervisor when the response is not found in the chat history. After the query is sent to the supervisor, the supervisor may delegate the query to one or more sub-agents which in turn may generate the response.

The application discloses a method and system for improving the defect detection precision of smart contract code, which verifies the smart contract code from multiple aspects by means of vulnerability rules and security attributes, improves the detection precision of the smart contract code, and combines contract basic analysis information and contract advanced analysis information to comprehensively analyze the smart contract code and further improve the vulnerability detection precision of the smart contract code.