79 results about "High level analysis" patented technology

System and methods are provided for real-time data integration, analysis, and notification within a Mobile App system. Embodiments include initializing a data layer and preprocessing phase, leveraging distributed database strategies to store structured and unstructured data. The Real-Time Data Mesh (RTDM) continuously draws data from various platforms, employing signal processing methods and machine learning techniques for noise removal and data feature extraction. The Advanced Analytics and Machine Learning (AAML) engine processes data, while decision constructs derive suitable actions. The Push Notification Service activates based on an Event-Driven Architecture (EDA) or a Publish-Subscribe (Pub-Sub) system, delivering customized notifications. Technical precision ensures timely and pertinent information reaches end-users. The system optimizes operations through adaptive feedback mechanisms and secures data through encryption.

Computerized systems and methods are described for integrating real-time insights across various entities involved in distribution processes. The system includes a Real-Time Data Mesh module for ingesting and harmonizing data from multiple sources, a Data Lake for storing harmonized data, and an Advanced Analytics and Machine Learning (AAML) module for generating insights using predictive analytics, anomaly detection, and recommendation engines. A Single Pane of Glass User Interface (SPoG UI) provides visualizations of these insights through interactive dashboards. The system supports customer, vendor, reseller, and associate systems, enabling efficient data exchange and synchronization. It employs natural language processing (NLP) for sentiment analysis, topic modeling for key theme identification, and clustering algorithms for customer segmentation. Continuous learning mechanisms ensure the system adapts to new data in real-time, enhancing decision-making and operational efficiency.

Systems and methods for a programming language-agnostic data modeling platform that is both less resource intensive and scalable. Additionally, the programming language-agnostic data modeling platform allows for advanced analytics to be run on descriptions of the known logical data models, to generate data offerings describing underlying data, and to easily format data for compatibility with artificial intelligence systems. The systems and methods use a supplemental data structure that comprises logical data modeling metadata, in which the logical data modeling metadata describes the logical data model in a common, standardized language. For example, the logical data modeling metadata may comprise a transformer lineage of the logical data model.

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.

The invention relates to the technical field of water meter flow, in particular to an ultrasonic water meter flow curve fitting system, which is characterized in that an edge computing device is used for primarily processing original data at a data acquisition end, a mean filtering algorithm is used for removing noise data, and the data after acquisition is subjected to filtering, denoising, abnormal value elimination and normalization processing; a dynamic evaluation model is constructed by adopting a time sequence prediction algorithm, the model calculates and predicts various data of future ultrasonic water meter flow by continuously inputting real-time data, and the model is optimized according to a prediction result, so that errors of the model are continuously reduced in the training process, and the prediction accuracy of the model is improved; modeling and fitting are conducted on the flow curves, calculated by the neural network model, of the ultrasonic water meter in different working states through the fitting model, the fitted curves can serve as a basic data source of a user water consumption behavior and system load characteristic advanced analysis model, and energy-saving analysis and fault positioning are facilitated.

Systems, methods, and other embodiments associated with contribution metric-based pruning of a neural network are described. In one embodiment, an example method includes accessing a trained neural network that has a plurality of channels. The neural network is to be evaluated for pruning of the channels. The example method may also include determining contribution metrics for the channels by measuring changes in error of the convolutional neural network with individual channels removed in turn. The contribution metrics are determined based at least in part on higher order analysis of the changes. And, the example method may also include pruning out of the convolutional neural network a set of the channels for which the contribution metrics do not satisfy a threshold.

Systems and methods for a programming language-agnostic data modeling platform that is both less resource intensive and scalable. Additionally, the programming language-agnostic data modeling platform allows for advanced analytics to be run on descriptions of the known logical data models, to generate data offerings describing underlying data, and to easily format data for compatibility with artificial intelligence systems. The systems and methods use a supplemental data structure that comprises logical data modeling metadata, in which the logical data modeling metadata describes the logical data model in a common, standardized language. For example, the logical data modeling metadata may comprise a transformer lineage of the logical data model.

The invention discloses an enterprise supply chain side evaluation method and system based on big data, and provides an innovative solution for the problems of low data processing efficiency, weak analysis and prediction ability, insufficient causal relationship analysis, decision support lag and the like in supply chain management. The method comprises the steps of integrating and processing real-time data, deeply analyzing the data by using an advanced analysis and prediction module in combination with a mathematical model and a machine learning algorithm, deeply understanding a causal relationship in a supply chain through a causal relationship analysis and optimization module, and finally accelerating a decision process through a visualization and decision support module. According to the system, through modular design and an advanced algorithm, the supply chain management efficiency and scientificity are remarkably improved, and comprehensive evaluation and optimization of the supply chain are achieved. The invention aims to solve the problem of improving the efficiency, accuracy and scientificity of enterprise supply chain management under the background of big data so as to adapt to rapidly changing market demands and a complex supply chain network.

Systems and methods for a programming language-agnostic data modeling platform that is both less resource intensive and scalable. Additionally, the programming language-agnostic data modeling platform allows for advanced analytics to be run on descriptions of the known logical data models, to generate data offerings describing underlying data, and to easily format data for compatibility with artificial intelligence systems. The systems and methods use a supplemental data structure that comprises logical data modeling metadata, in which the logical data modeling metadata describes the logical data model in a common, standardized language. For example, the logical data modeling metadata may comprise a transformer lineage of the logical data model.

The invention discloses an automatic data acquisition and intelligent analysis system, which relates to the technical field of factory management, and comprises a data acquisition module, a data storage module, a data analysis module, a result display module and a system management module. The data value is deeply mined through the advanced analysis module, the comprehensive processing mode helps to monitor the production state in real time, timely discover potential problems and improve the controllability and predictive ability of the production process, and the analysis module and the display module are combined, so that deep data insight is provided, and the production efficiency is improved. The analysis result is visually displayed to a user through a visual tool, so that the safety and the stability are improved, the management, the operation and the maintenance of the system are more efficient, all modules of the system are tightly matched, the process from data acquisition to result display is highly automatic, a real-time feedback mechanism helps to quickly identify and solve problems, and the system is more convenient to use. And continuous improvement and optimization are promoted.

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.

Computerized systems and methods are described for managing vendor-agnostic configure-to-order (CTO) and quote-to-order (QTO) processes. A Real-Time Data Mesh (RTDM) is provided for aggregating, standardizing, and normalizing real-time data from various sources. A Single Pane of Glass User Interface (SPoG UI) facilitates dynamic interaction and visibility into vendor performance. An Advanced Analytics and Machine-Learning (AAML) Module analyzes product compatibility, optimizes pricing strategies, and predicts market trends. A Vendor-Agnostic CTO / QTO Integration Module (VACIM) includes a Process Standardization Engine and a Vendor Data Transformation Gateway to ensure uniformity across vendors. Methodologies within the invention automate data processing, integrate transformation gateways for data consistency, and employ rule engines driven by machine learning for decision-making, thereby streamlining vendor processes, enhancing scalability, and optimizing pricing strategies in a scalable, adaptable framework.

The invention provides a comprehensive 16S automatic analysis method and process based on Web, and from upstream sequencing data processing to downstream data analysis, the most common analysis items at present and 16S advanced analysis are included. And meanwhile, the analysis content is modularized, the data mining method and the like are diversified and deep, and different analysis modules can be selected according to individual needs. The method not only can help researchers without any biological information background to carry out 16S research on flora micro-ecology, but also can overcome the defects of some existing 16S analysis processes, can obtain an accurate and comprehensive analysis result, and meets various analysis requirements.

The invention discloses an event recording method of an intelligent flexible adjusting unit. According to the method, through precise deployment of the sensor network, monitoring of various parameters is comprehensively covered, and the multi-element load monitoring accuracy is improved; through deep learning and edge calculation, voltage fluctuation and potential faults can be rapidly identified, and early warning accuracy and response speed are improved. Through automatic recording and anomaly classification, automatic recording of threshold data is realized, and event processing sorting is realized based on random forest algorithm classification. On the basis of a hot and cold data layering strategy and an encryption technology, instant response and long-term safe storage are ensured; through authority management, based on role access control and dynamic adjustment, flexibility and security are enhanced; efficient data interaction is realized based on RESTful API and WebSocket through a remote query and analysis tool, and an advanced analysis model is integrated; through side-to-side communication and adaptive learning, communication stability is ensured based on MQTTCP / IP or Zigbee, an adaptive learning mechanism continuously updates a model, and the self-optimization capability of the system is improved.

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.

Systems and methods for a programming language-agnostic data modeling platform that is both less resource intensive and scalable. Additionally, the programming language-agnostic data modeling platform allows for advanced analytics to be run on descriptions of the known logical data models, to generate data offerings describing underlying data, and to easily format data for compatibility with artificial intelligence systems. The systems and methods use a supplemental data structure that comprises logical data modeling metadata, in which the logical data modeling metadata describes the logical data model in a common, standardized language. For example, the logical data modeling metadata may comprise a transformer lineage of the logical data model.

Systems and methods for a programming language-agnostic data modeling platform that is both less resource intensive and scalable. Additionally, the programming language-agnostic data modeling platform allows for advanced analytics to be run on descriptions of the known logical data models, to generate data offerings describing underlying data, and to easily format data for compatibility with artificial intelligence systems. The systems and methods use a supplemental data structure that comprises logical data modeling metadata, in which the logical data modeling metadata describes the logical data model in a common, standardized language. For example, the logical data modeling metadata may comprise a transformer lineage of the logical data model.

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.

Computerized systems and methods are disclosed for automating on-order configuration (CTO) and on-order offer (QTO) processes. The method includes receiving a user input for a desired product configuration, retrieving corresponding data from a bill of materials database, and calculating an optimized pricing through intelligent rules based on real-time market data. Automated offers are generated and transmitted to orders in a vendor system, selecting based on preset criteria, such as vendor reputation and delivery time. The verification step reduces errors and generates a real-time report. The system integrates a real-time data grid for data aggregation, a single glass pane user interface for user interaction, and an advanced analysis and machine learning module for implementing a rule-based learning algorithm. The system is accessible on various devices and standardizes the data for unified use, while also employing a machine learning model to continuously optimize the process. And sending a notification to the user after the order is successfully executed or the quotation is completed.