1286 results about "Systems analysis" patented technology

A system and method are disclosed for generating hyperspectral images from multi-modal sensor data including RGB, LiDAR, thermal, and near-infrared inputs. Training data includes hyperspectral images and corresponding multi-modal measurements. Spectral band grouping is performed based on correlation coefficients. A multi-modal decomposition network with cross-modal attention mechanisms generate reconstructed hyperspectral images by fusing complementary sensor information. A fine-tuning network creates reconstructed RGB images. A comprehensive quality assurance system analyzes spectral consistency, cross-modal coherence, and fusion artifacts to generate quality metrics. Missing data compensation strategies handle corrupted sensor inputs using information from other modalities. The system includes temporal integration for video sequences and multi-resolution processing for different sensor resolutions. Quality metrics guide network weight adjustments to improve reconstruction accuracy while maintaining robustness to sensor failures and environmental variations.

The invention provides an operation and maintenance alarm processing method and system based on a knowledge graph enhanced large model, and relates to the technical field of knowledge graphs, and the method comprises the steps: collecting multi-source operation and maintenance alarm historical data, carrying out the adaptive hierarchical structured processing of the data, and constructing a space-time semantic multi-dimensional feature matrix; constructing a multi-dimensional alarm association network based on a dynamic weight fusion algorithm and forming a hierarchical knowledge graph; constructing a root cause inference rule base in combination with time sequence evolution, topology propagation and business dependence characteristics of the alarm; and when a real-time alarm is received, a diagnostic reasoning system is started to analyze a propagation link, a root cause is identified, and a processing suggestion is generated. The method can accurately identify the alarm root cause, improves the operation and maintenance efficiency, and reduces the false alarm rate.

Examples of the present disclosure describe systems and methods for automatically detecting and repairing reliability issues in operating systems and applications using a generative artificial intelligence (“AI”) system. In examples, a generative AI system receives a request to evaluate a detected issue in a software service or application. In response, the system analyzes error information associated with the detected issue to build an error context for the software code that caused the detected issue. The error context is used to identify the location of the software code file that comprises the software code. The error context and the software code file are used to identify a prompt. The prompt, the error context, and / or the identified software code are provided as input to a language model. The language model provides an output that is responsive to the user request and may perform actions to further evaluate or repair the detected issue.

Method and system for analyzing software or firmware of computing systems to assess security properties includes loading predicate device input data including characteristics about predicate devices; translating predicate device input data into predicate device model data describing characteristics or dependencies of the predicate device input data relevant to the analysis; determining digital twin configuration data used to configure digital twin; loading the digital twin configuration data onto the digital twin; storing configuration data in the memory; instructing the digital twin to configure itself to implement the loaded digital twin configuration data; determining security analysis to be carried out on the digital twin; simulating the predicate device; executing security analysis on the digital twin; generating output data describing the result of execution of the security analysis; storing output data pertaining to the result; and determining if the result satisfies a predetermined condition, and if so, executing action corresponding to the result.

An AI-powered content generation system that creates consistent, coherent, and engaging multi-modal content by integrating multiple specialized AI components. The system analyzes user input, identifies key elements, and maintains continuity throughout the generation process. It incorporates a feedback loop to learn and adapt based on user preferences, enabling personalized content experiences. The modular architecture allows for seamless integration of AI components focusing on text, images, audio, and interactive elements. The system ensures consistency across modalities and over extended periods, while managing rights, licenses, and royalties using blockchain technology. This advanced platform revolutionizes content creation, consumption, and management in the digital age.

Various embodiments leverage artificial intelligence in identifying and potentially resolving compliance issues (e.g., with regulatory requirements, client-specified requirements, certification conditions, etc.), or preventing violations of law, rules and regulations. The AI can be configured to automatically generate requests for information. For example, a system analysis component can be configured to identify a specific compliance target (e.g., a branch location) and select or automatically generate questions to collect responsive information to ensure compliance, identify potential violations, and define any evidence required to identify or resolve issues (e.g., prove compliance, support potential violations, flagged issues, etc.). According to one example, the system can use trained AI models to analyze a set of rules and / or requirements to efficiently build questionnaires to address or demonstrate compliance.

A system and method for neural network weight compression with intrusion detection capabilities that optimizes model storage and transmission while providing security. The system analyzes weight characteristics to identify statistical properties within different neural network layers, generates optimized encoding schemes based on the analysis, and creates reference distributions for security verification. The compression process employs a multi-resolution approach that produces a progressive representation with base and enhancement layers, enabling flexible deployment across diverse computing environments. Security markers and statistical fingerprints can be embedded throughout the encoded representation, allowing for detection of unauthorized modifications during transmission or deployment. The system monitors encoded weight streams, measures distribution divergence against reference baselines, and generates alerts when statistical anomalies indicate potential tampering. This approach achieves superior compression ratios while maintaining model performance and providing robust protection against increasingly sophisticated attacks targeting neural network weights.

The invention relates to a follow-up network type control smooth switching method based on a generalized operation short-circuit ratio index. The follow-up network type control smooth switching method comprises the following steps of 1, introducing a generalized operation short-circuit ratio (GOSCR) as a switching index, and proposing a GOSCR-based hysteresis control mode switching judgment index under different power grid strength and power output working conditions; step 2, establishing a unified new energy grid-connected control unit model: constructing a unified grid-connected control model in two modes of network following control and network construction control, systematically analyzing oscillation characteristics of a grid-connected unit in the two modes, and defining boundary conditions of mode switching; 3, proposing an LADRC-based undisturbed switching control strategy: observing and estimating system current disturbance parameters online by utilizing LESO, realizing real-time compensation of disturbance quantity, and reducing dynamic sudden change in the switching process; the method has the advantages that GOSCR switching indexes are introduced to comprehensively evaluate the strength, a unified grid-connected control model is established, and an undisturbed switching control strategy is provided.

An intelligent anomaly detection system (100) for IoT networks using edge computing and deep learning, comprising: (a) a data collection and pre-processing module configured to collect data from a variety of heterogeneous IoT devices and perform pre-processing operations, including normalization, denoising, and feature extraction; b) an edge intelligence and model deployment module configured to deploy deep learning models optimized for edge devices to perform local data analysis; (c) an anomaly detection and classification module configured to identify abnormal behavior in the processed data using deep neural networks and to classify the anomalies into predefined threat categories; (d) an adaptive learning and model update module configured to update the deployed models through incremental or federated learning mechanisms without transmitting raw data to a centralised server; (e) an alert and response management module configured to generate alerts and execute predefined mitigation actions based on the type and severity of the detected anomalies; f) and a system monitoring and visualization module configured to display real-time insights, alerts and system analysis through a user interface, g) the system operates in a decentralised manner using edge computing to achieve scalable and privacy-preserving anomaly detection in IoT environments in real time.

The invention discloses a stability analysis method of a network tracking / constructing hybrid station based on an impedance model, and relates to a modeling and stability evaluation technology of a wind power plant grid-connected system. The method comprises the following steps: firstly, respectively establishing sequence impedance models of a following network type direct-driven fan and a construction network type direct-driven fan based on a harmonic linearization method, and verifying the accuracy of the established models in different frequency bands by combining an impedance sweep frequency method; secondly, on the basis of considering the influence of line impedance, deducing to form an impedance network structure of a hybrid wind power plant grid-connected system, and constructing a stability judgment condition suitable for system analysis; and finally, based on the established impedance stability criterion, identifying possible oscillation risks of the system in subsynchronous and supersynchronous frequency bands. The method is suitable for wind power system stability analysis in various hybrid access scenes, and provides theoretical basis and technical support for subsequent system parameter setting, control strategy optimization and oscillation suppression measures.

The invention discloses an AI intelligent collaborative monitoring management system for a multi-index water volume system, and relates to the technical field of monitoring management, the collaborative monitoring management system comprises a data acquisition module, a water volume system analysis module, a collaborative monitoring module, an emergency early warning module, an early warning scheme module and a data visualization module, all the modules are connected through electric signals. According to the AI intelligent collaborative monitoring management system for the multi-index water volume system, abnormal changes in water quality, water volume and water ecological indexes are identified through anomaly detection, an early warning mechanism is triggered according to the detected anomaly, early warning information is sent to management personnel through various modes such as a visual interface, a short message and an email, and the management personnel can carry out early warning on the water quality, the water volume and the water ecological indexes. According to the invention, the time interval from the occurrence of the abnormity to the discovery of the abnormity is greatly shortened, and valuable response time is provided for managers, so that further deterioration of the abnormity is effectively restrained, and the safety and sustainable utilization of water resources are guaranteed.

A platform for dynamically generating application experiences. The platform comprises a design management system, an agent orchestration system, an analytics system, a model management system, a user management system, and databases for storing design elements and templates. The design management system provides a portal for application owners / designers to create UX / UI designs, allowing them to select design elements from a set of categories or templates. The platform gathers existing websites / applications to identify common design patterns, stored in a design catalogue database, and suggests historical interfaces for design exploration. It enables the generation of templated applications that integrate with legacy systems. The agent orchestration system parses user specifications, selects generative AI systems, and generates UX / UI content based on the specifications. The analytics system collects and analyzes data to provide insights for improving UX / UI design and optimizing website performance. The model management system trains and maintains generative AI models used for content generation.

The invention relates to the technical field of aviation information, in particular to a flight data change publishing system and method, and the system comprises a flight state monitoring module, a network modeling and analysis module, a flight influence prediction module, an optimization scheduling decision module, a scheduling execution and state updating module, and an information publishing and updating module. According to the method, the state change of the flights and the airport is monitored in real time, passengers and airport operators are ensured to obtain real-time flight information, key nodes and influence chains in an aviation network are identified by analyzing the mutual influence relationship among the flights and constructing an interaction relationship graph among the flights, and flight faults and changes are predicted and coped with. According to the method, system analysis is carried out on the influence of flight change, the flexibility and efficiency of flight scheduling are optimized, inconvenience to passengers and operation chaos caused by inaccurate information and untimely updating are reduced, and the experience and satisfaction degree of the passengers are enhanced by updating an airport information system and navigation facilities and pushing real-time updating to the passengers.

The invention discloses a refrigerator refrigerating capacity adjusting method which comprises the following steps that S1, environment data are collected in real time through a plurality of sensors, and future environment changes are accurately predicted through a machine learning algorithm; s2, constructing an environment change prediction model by using a machine learning algorithm; s3, the refrigerator intelligent refrigeration adjusting system analyzes and models use habits of the user, and predicts future refrigeration requirements; s4, according to the prediction data obtained in the step S1 and the step S3, the refrigerant flow and the working mode of the compressor are dynamically adjusted; s5, the refrigerator intelligent refrigeration adjusting system dynamically adjusts a refrigeration strategy according to comprehensive analysis of the real-time temperature data and the prediction result, and the operation mode of the refrigerator is continuously optimized; and S6, regularly storing the use data and the environment data of the user in a local or cloud storage. According to the method, future refrigeration requirements can be accurately predicted, the refrigeration strategy is dynamically adjusted, and the energy efficiency is improved to the maximum extent.

A system and method for dynamically recommending set of potential courses of actions for a user within a search query are disclosed. The system receives search queries from a user and determines dialogue attributes and context variables based on these queries. It identifies query parameters and additional parameters, The system analyzes user preferences, and determines entities and variants based on the user's preferences and the conversation context. The system then determines the types of set of potential courses of actions to generate for the search queries. Further, the system retrieves set of potential courses of, associated applications, and integration parameters from databases based on the determined types. Using large language models (LLMs) in generative AI or conversation AI environments, the system generates responses and clickable elements corresponding to the search queries, incorporating the recommended set of potential courses of actions, applications, and deep integration parameters.

A system and method for efficient data storage, transfer, synchronization, and security using automated model monitoring and training. The system analyzes test datasets to detect data drift, retraining encoding and decoding algorithms as needed. New data sourceblocks are created and assigned codewords, compiling an updated codebook for distribution to connected devices. A novel dyadic distribution subsystem simultaneously compresses and encrypts data by transforming input streams into a dyadic distribution. This process generates a compressed main data stream and a secondary stream of transformation information, which are combined into a secure output. The system includes a network device manager for optimizing codebook distribution based on device resource usage. Operating in both lossless and lossy modes, the system offers flexible, efficient, and secure data handling across various network configurations.

The invention provides a similar picture retrieval method, which comprises the following steps of: constructing a hierarchical semantic-visual representation framework, and decomposing query and images into three levels, namely a core concept, attribute modification and relation description; a context-aware professional term understanding system is achieved, the professional field background and purpose of query are analyzed, context feature vectors are generated, and term ambiguity is eliminated; establishing a semantic-vision bidirectional mapping and optimization framework, realizing forward mapping from semantics to vision and reverse mapping from vision to semantics, and generating an optimization mapping matrix and a candidate set through iterative optimization; and performing multi-modal similarity calculation, converting the context feature vector into a hierarchical weight vector, fusing the hierarchical weight vector with the multi-level similarity into a comprehensive similarity score, and sorting and outputting a retrieval result set according to the comprehensive similarity score. According to the method, the accuracy of similar picture retrieval in the professional field is improved.

Techniques are described for validating build integrity of software products, such as applications or containers. More specifically, this disclosure describes a build integrity validation system that analyzes build artifacts resulting from a software build process to create source code assertions, and compares the assertions against the source code from which the build artifacts were produced. The build integrity validation system validates that a particular build artifact is producible by the source code to ensure that no additional code was introduced during the build process. The build integrity validation system may also reverse the analysis to validate that the source code is able to produce the build artifacts to ensure that no code was removed or modified during the build process. The build integrity validation system identifies and reports identified discrepancies between the source code and the build artifacts resulting from the software build process of the source code.

A method of dynamically updating a frequently asked questions knowledge base for agent assist according to an embodiment includes receiving, by a computing system, a transcript of a contact center interaction between a contact center agent and a client, analyzing, by the computing system, the transcript to generate a plurality of question-answer pairs based on an artificial intelligence model, determining, by the computing system, whether each question-answer pair of the plurality of question-answer pairs is stored in a dynamic frequently asked questions knowledge base, determining, by the computing system, whether a question-answer pair of the plurality of question-answer pairs is frequently asked in response to determining that the question-answer pair is not stored in the dynamic frequently asked questions knowledge base, and updating, by the computing system, the dynamic frequently asked questions knowledge base to include the question-answer pair in response to determining that the question-answer pair is frequently asked.

An AI-powered augmented reality (AR) system for optimizing product placement and customer loyalty. The system includes: • a user interaction data collection unit configured to monitor consumer behavior in real time in any retail scenario (point-of-sale POS, online e-commerce and shopping areas within a public virtual technology space), with the unit analyzing consumer movement patterns, engagement metrics, eye-tracking and purchase intent; • an intelligent spatial mapping module to build a contextual understanding of high-traffic zones, underutilized areas and high-quality product placements using heatmap analysis, other spatial computer algorithms and the live movement of consumers; • A model-based product placement recommender based on machine learning, which uses one or more DL-based predictive models trained by importing current sales data, historical text interaction data, and consumer preference data to suggest optimal product placement so that customers see the advertisement and are likely to click through it to ensure a conversion. • a customer-centric, real-time AR visualization interface configured to allow customers to experience digitally enhanced product presentations directly via mobile AR applications, smart glasses, or in-store digital kiosks, enabling customers to preview product placements, rotate and resize objects, and visualize interactive advertising content before purchasing; • a customized advertising and sales promotion engine configured to generate augmented reality-based marketing content and provide users with tailored promotions and interactive ads based at least in part on an analysis of consumer sentiment by the system, as well as information on location data or consumer interaction history, wherein the customized advertising and sales promotion engine personalizes the configuration instructions for advertising and sales promotion to encourage consumer interaction and achieve higher sales revenues; • A secure, cloud-based data processing and analytics unit that uses an automated engagement model to link online and in-store sales information with established activity models for product placement decisions. The unit leverages AI-powered consumer insights to continuously recommend product placement changes based on a variety of programmatic retail trends; • a privacy-friendly AI module based on federated learning and on-device AI processing, providing consumer behavior analytics while complying with global data protection regulations and preventing the use of open data; • a processing framework characterized by a low-power mode suitable for AR rendering and AI calculations, and implemented via dynamic voltage scaling and energy management techniques for seamless and sustainable operation; The system according to claim 1, wherein the user interaction data capture unit further comprises an eye-tracking and sentiment analysis sub-module, wherein the sub-module detects facial expressions, emotional reactions, and attention levels of consumers in real time to determine the effectiveness of product placements and advertising strategies.

Targeted dynamic analysis of code is provided by utilizing static analysis and dynamic analysis. A static code analysis system can operate to generate an initial analysis of code. The results of the initial analysis may be used to target a dynamic analysis system to analyze portions of code identified by the static analysis as potentially containing defects. In some cases, a preliminary dynamic analysis may be used to generate code inputs that may be utilized in future analyses to determine the inputs necessary to target particular code paths identified by a static analysis as potentially defective. Future updates to the code base may utilize the generated data to target analysis to code paths affected by the updates.

The invention discloses a safety detection system for a tool magazine of a gantry machine tool, relates to the technical field of gantry machine tools, and solves the problems that an object entering a blind area cannot be quickly recognized to belong to a human body, and the comprehensiveness of recognition needs to be improved. By means of two-dimensional coordinate system analysis, central point overlapping and zooming operation, calculating an overlapping area proportion so as to judge whether the area is a human body area or not; the scientific judgment mode improves the accuracy and reliability of recognition, reduces the false alarm rate, ensures the accurate transmission of early warning information, and can quickly send out an early warning signal through the early warning end to remind an operator to take measures in time to avoid danger once the system judges that a change area belongs to a human body area; the real-time response mechanism can effectively reduce the probability of accidents and kill potential risks in the bud.

A system for adaptive data compression uses content-aware analysis and dynamic feedback to optimize compression quality. An adaptive quantization subsystem analyzes content characteristics of input data and determines appropriate quantization parameters. A bit allocation engine distributes available bits across different portions of the input data based on the analyzed characteristics. A quality assessment subsystem monitors the compressed output and generates parameter adjustment signals based on measured quality metrics. A feedback control subsystem then modifies the quantization parameters in response to these signals. The modified parameters are used to create optimized compressed output data from the input dataset. This dynamic, content-aware approach enables improved compression quality while maintaining efficient data reduction.

The invention provides a power grid wiring diagram automatic analysis system and method based on deep learning. The method comprises the steps of S1, data preprocessing and model training; s2, carrying out equipment identification and association; step S3, topology reasoning and verification are carried out; s4, standardized output: converting the verified power grid network model into a regulation and control cloud platform standard graphic file, and optimizing graphic arrangement through an adaptive layout algorithm; step S5, performing closed-loop optimization; and S6, performing manual intervention and iteration. According to the method, the wiring diagram analysis efficiency and accuracy can be remarkably improved, manual intervention is reduced, standardized graph generation of a power grid regulation and control cloud platform is supported, and a reliable data basis is provided for power system analysis and decision making.

A computer implemented system and method for generating offer ranges for candidates in an interviewing process is disclosed. The system generates an AI-based interviewer simulating human-based interactions for conducting an ongoing interview with candidates. The system analyzes data associated with candidates obtained during ongoing interview. The system process analyzed responses of candidates to determine contextual attributes associated with responses using ML models. The system automatically generates follow-up interview questions to be delivered to candidates during ongoing interview based on analyzed responses from candidates, by applying AI model to contextual attributes associated with responses. The system generates recruitment scores for candidates based on analyzed responses, contextual attributes, and interpreted non-verbal cues, associated with candidates, using AI model. The system generates offer ranges for candidates based on recruitment scores using AI model. The system provides information associated with selected candidates, and offer ranges generated for selected candidates, to users.

The invention relates to a weather situation intelligent analysis method based on artificial intelligence multi-modal causal reasoning, and the method comprises the following steps: obtaining multi-modal meteorological data, and carrying out the preprocessing of the multi-modal meteorological data, and obtaining the processed data; processing the processed data through a pre-constructed multi-modal causal reasoning neural network model to obtain a weather situation intelligent interpretation result; performing feedback correction on the weather situation intelligent interpretation result through an automatic feedback correction mechanism based on a large model so as to update the weather situation intelligent interpretation result; and generating a weather situation analysis result based on the updated weather situation intelligent interpretation result, and visually displaying the weather situation analysis result. Through the cooperative work, the weather system can be automatically identified, the causal relationship between the systems can be analyzed, a professional weather situation analysis report can be generated, and multi-mode forecast comparative analysis is supported. According to the invention, the efficiency and accuracy of weather situation analysis can be improved, and reliable technical support is provided for weather forecast service.

The invention provides an urban rainfall emergency processing method and system, and relates to the technical field of rainfall disaster emergency processing, and the method comprises the steps: obtaining historical urban rainfall data and drainage information, and generating an urban rainfall simulation model through the three-dimensional modeling and rainfall simulation technology; furthermore, a disaster tree model is constructed in combination with a machine learning algorithm and a fault tree analysis method, and association and influence relationships of historical rainfall disasters are identified. And based on the disaster template data and a preset historical emergency processing scheme, systematically analyzing and optimizing a disaster prediction model, and realizing real-time prediction of disaster information and generation of the emergency processing scheme. An emergency processing scheme is optimized by adopting a simulated annealing algorithm, and optimal response under different disaster levels is ensured. Through multi-level data analysis and algorithm optimization, the accuracy and efficiency of emergency response are improved, and the occurrence probability and loss of disasters such as urban waterlogging are reduced.

Machine learning based processing systems and techniques are described. In some examples, a machine learning based processing system analyzes text-based input to extract a plurality of natural language elements from the text-based input. The text-based input is associated with an object. The machine learning based processing system generates a prompt from at least a subset of the plurality of natural language elements. The machine learning based processing system analyzes the prompt by using a trained machine learning model to generate a response. The response is responsive to the prompt. The machine learning based processing system analyzes the response to extract a plurality of media content elements from the response. The plurality of media content elements corresponds to different aspects of the object.

The invention discloses an electric power analysis decision calculation method and system based on a diffusion process graph neural network, and belongs to the technical field of electric power system analysis, and the method specifically comprises the steps: carrying out the subproblem construction of a calculation architecture, and obtaining an electric power graph intelligent calculation framework based on diffusion process induction; constructing a graph structure of the power system according to the physical topological structure and the real-time operation data of the power system; aiming at the obtained parameter change characteristics of the graph structure of the power system in iteration, designing a node embedding method fused with physical constraints to obtain a complete node embedding representation; gNN iteration training is carried out on the obtained complete node embedding representation, node state diffusion and convergence are realized through multiple times of iteration calling, and a convergent reinforcement learning model is obtained; performing offline training on the obtained reinforcement learning model; and inputting real-time data into the trained reinforcement learning model, predicting to obtain a future state of the power system, realizing power analysis decision calculation, and generating a control decision based on the future state.

There are provided systems and methods for a domain-knowledge guided agent framework for automated system analysis. An online transaction processor or other service provider may provide computing services and platforms to entities, which may require compliance enforcement for different policies, regulations, and the like. To provide compliance review, investigations, and enforcement in a computing system of a service provider, the service provider may implement an intelligent and automated agent and framework that may utilize different large language models for processing compliance investigation requests and queries. The agent may utilize the models to plan and execute tasks using an available toolkit of computing operations and capabilities for compliance investigation. A data guard module may also be used to ensure data privacy and security is maintained. Within a main task, sub-tasks may be executed by models with specific domain knowledge.