46 results about "High level analysis" patented technology

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.

The invention discloses an intelligent warehousing dynamic optimization system based on a digital twinborn technology. The system comprises the following steps: S1, creating a digital twinborn model: acquiring basic data of an entity warehouse and constructing an accurate digital model; s2, data collection and integration: deploying Internet of Things equipment and realizing real-time transmission and processing of data; s3, constructing an analysis algorithm: developing an advanced analysis algorithm and implementing a machine learning model; s4, dynamic optimization and adjustment: automatically adjusting a storage layout and optimizing a warehouse-in and warehouse-out process; and S5, prediction and early warning: analyzing and predicting potential problems and setting an alarm mechanism based on historical data. According to the invention, the real-time performance and accuracy of data are obviously improved, the environment monitoring capability is enhanced, the cargo safety is guaranteed, and the space utilization rate is greatly improved.

A method for managing and monetizing performance data for a plurality of users is disclosed. The method involves receiving a first data set related to user performance and / or behavioral information and establishing a first communication link for secure data transfer. The received first data sets, along with additional data from various sources, are stored in a central repository. The method ensures data integrity by verifying and validating its origin and authenticity. Advanced analytics are applied to extract actionable insights, trends, and structured information, enriched with metadata such as events, locations, timestamps, and user identities. A dynamic ontology is created to contextualize and categorize the structured data. When third parties request access, the method employs smart contracts to validate requests, securely transfer data, and compensate users. This approach ensures transparency, control, and equitable monetization of user-generated performance data.

Systems and methods provide for automating the conversion of email orders into structured order entries using generative AI, leveraging an integrated architecture comprising a Real-Time Data Mesh (RTDM), Advanced Analytic and Machine Learning (AAML) Module, and 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 this information into structured entries, and an Integration Gateway that synchronizes the entries 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 enhances order processing efficiency, accuracy, and scalability, enabling businesses to process email orders with minimal manual effort and greater precision.

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 multi-sensor shot detection system and method for accurately identifying and recording gunfire initiated by a user while filtering out ambient gunshots in shared shooting environments. The system integrates motion and acoustic sensors, with data fused into a unified feature vector and processed using a trained classification model to identify valid shot events in real time. The device includes an adaptive calibration process to tune detection parameters to specific firearms and user characteristics. Sensor data is processed locally and may include biometric, environmental, and location-based inputs. Processed event records are stored and synchronized with external applications to support advanced analytics and long-term performance tracking. The system is deployable in wearable and firearm-mounted configurations, enabling high-accuracy detection and context-aware feedback across various use cases.

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.

The invention provides an intelligent and green construction equipment integration system and a cooperative work method, and the system comprises a cloud platform layer which is in communication connection with an edge computing layer and is used for carrying out the storage, advanced analysis and global cooperative scheduling of data uploaded by the edge computing layer; the edge computing layer is in communication connection with the terminal equipment layer and is used for performing real-time processing and low-delay decision making on the data uploaded by the terminal equipment layer; and the terminal equipment layer is used for collecting multi-source heterogeneous data in real time, uploading the data to the edge calculation layer, and carrying out convergence and standardization preprocessing through the edge calculation layer. According to the invention, intelligent construction equipment and green construction equipment of different types and different functions can be deeply integrated, a unified information physical system platform is constructed, and a set of efficient and intelligent cooperative work mechanism is established, so that intelligent management and control, resource optimization configuration and environment-friendly operation of the whole construction process are realized.

The embodiment of the invention provides an obfuscation and reverse protection method and device based on an ROP chain and an opaque predicate, the scheme provides a three-order obfuscation protection framework combining ROP instruction dynamic reconstruction, opaque predicate protection and instruction semantic hiding, the framework compiles a common C / C + + source code into an LLVM intermediate representation IR, and then the original C / C + + source code is compiled into the LLVM intermediate representation IR. And dynamically extracting Gadget and a non-transparent predicate combination based on a user input state to realize deep-level semantic disturbance on a key logic block, a jump structure and a constant field of a program. The control flow reduction chain is thoroughly broken, so that reverse personnel are difficult to analyze the confusion result, controllable, verifiable and high-stability confusion protection on the program is realized, and advanced analysis technologies such as symbolic execution and static analysis are maximally resisted.

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.

The application provides a data processing system and fluidized bed reactor, comprising: a data acquisition module, a data processing module and a man-machine interaction module; the data acquisition module is used for collecting process parameters of the fluidized bed reactor; the data processing module is used for filtering and engineering unit conversion of the collected process parameters, and real-time control according to a preset process logic; the man-machine interaction module is used for determining a process flow chart, parameter values and alarm information based on the process parameters, and displaying the process flow chart, parameter values and alarm information. In this way, full-link accurate tracing can be realized, the system supports multi-dimensional data association, the root cause can be quickly locked and the problem can be prevented from recurring; data deep value mining can also be realized, and intuitive trend curves, flexible data export and advanced analysis tools are provided.

The invention relates to an online calibration algorithm, in particular to a metering scale online calibration algorithm based on edge calculation. The system comprises an acquisition unit which is used for collecting original data generated in the using process of the metering scale; the data preprocessing unit is used for identifying and eliminating abnormal data points caused by faults of the pressure sensor and external interference; the edge calculation unit comprises a reference calibration module and an on-line self-adaptive calibration module and is used for performing off-line and on-line calibration on the metering scale; and the cloud service unit is used for storing historical calibration data, model parameters and advanced analysis tasks and providing remote management and monitoring functions. Through the edge computing technology, the algorithm can quickly process data at an equipment end, the delay of data transmission to a cloud end is reduced, two strategies of offline reference calibration and online adaptive calibration are combined, the initial accuracy of the metering scale can be ensured, dynamic adjustment can be carried out according to real-time data in the equipment operation process, and the accuracy of the metering scale is improved. And the measurement precision and stability are improved.

Methods and systems for performing advanced analytics based on which features are active by correlating information based on temporal identifiers in feature databases. The method includes receiving a request to change a feature, determining metadata information for the feature, which may include a temporal indicator, a performance metric and a configuration of activation states, and generating a feature change record based on this metadata. The method includes receiving a request to determine a second performance metric, processing the request by determining a second feature change record, determining a configuration and generating a recommendation for display.

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.

The application discloses a kind of high-precision acquisition analysis equipment of multiple type signals, comprising: data acquisition module, central processing unit, storage center and display terminal;Data acquisition module includes thyristor channel and sensor channel, and the channel interface of thyristor channel uses isolated optocoupler and carries out optical isolation;Through thyristor channel and sensor channel, the data acquisition of multiple channels is realized, and the opening and closing of thyristor are alternately controlled by central processing unit, the voltage and working state of all thyristors in thyristor channel are collected, the data acquisition of multiple point positions and multiple potentials is realized, the efficiency and utilization of data acquisition are significantly improved, and isolated optocoupler is used to carry out optical isolation, to avoid the interference between different signals;Synchronous digitization processing and analysis are carried out on the multiple groups of data obtained by using central processing unit, the processing time is saved, and the advanced analysis function in specific field is realized.

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.

The application discloses a sequencing analysis method for single-cell transcriptome based on ONT sequencing and relates to the technical field of gene sequencing analysis.The method comprises the following steps: preparing and constructing a cDNA library of a sample to be measured, sequencing, obtaining three generations of original data, performing quality control on the three generations of original data, obtaining three generations of effective data, performing functional annotation on a reference genome, using software ONT-SC-pipeline to perform data splitting, alignment and quantification, obtaining quantitative data, sequentially performing basic analysis, Marker gene enrichment analysis and advanced analysis on the quantitative data, wherein the basic analysis comprises top gene distribution analysis, hyper-variable gene analysis, clustering and grouping analysis, correlation analysis of each cell group, dimension reduction analysis and Marker gene identification; and the advanced analysis comprises cell annotation analysis, cell communication analysis, cell trajectory analysis, variable splicing analysis and fusion gene 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.