970 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.

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.

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.

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 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.

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.

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.

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.

The present disclosure relates to systems, non-transitory computer-readable media, and methods for generating a content stack utilizing one or more machine-learning models. In some implementations, the disclosed systems generate and provide, to a user account, a content stack that includes content items corresponding to a topic prompt for the user account. For instance, in some implementations, the disclosed systems utilize content-based signals and account-based signals to generate an account-specific stack formulation graph that represents a plurality of content items and relationships of the content items with each other and with the user account. Additionally, in some implementations, the disclosed systems analyze the account-specific stack formulation graph to generate a content stack from the plurality of content items, the content stack comprising a set of content items corresponding to the topic prompt.

A system and method for adaptive data processing combining compression and encryption. The system analyzes input data characteristics, compares probability distributions, and creates a transformation matrix to convert data into a dyadic distribution. It generates a main data stream of transformed data and a secondary stream of transformation information. The system dynamically selects and applies processing techniques, including transformation, encoding, compression, and encryption algorithms, based on analyzed characteristics and real-time performance metrics. It compresses the main data stream using Huffman coding and implements security measures to protect the output. A feedback loop monitors technique effectiveness, updates a knowledge base, and influences future selections. The system can operate in lossless, lossy, or modified lossless modes, adapting to different application requirements. This approach offers an efficient solution for scenarios where both data reduction and security are critical concerns.

A navigation equipment real-time fault diagnosis method based on cross-modal attention fusion belongs to the technical field of power grid equipment fault diagnos.The method comprises the steps that firstly, the type of real-time diagnosis data capable of being obtained by navigation equipment is systematically analyzed, and the real-time diagnosis data is divided into four modals of sensor data, electrical data, acoustic data and text data; after data cleaning is completed, preliminary feature extraction is implemented. Then, constructing a fault diagnosis model based on a cross-modal attention mechanism, constructing a training data set by using the joint features, and carrying out neural network model training; after model training is completed, real-time fault diagnosis of the navigation equipment can be realized through a data pre-processing technology and a fault diagnosis model based on acquired data. According to the scheme, a multi-modal real-time sensing system is constructed, deep feature interaction is realized in combination with a cross-modal attention mechanism, and closed-loop diagnosis capability is established. And core guarantee is provided for real-time state sensing and accurate fault positioning of navigation equipment.

A correlation-aware adaptive codebook compaction system for multi-modal data compression that preserves cross-modal relationships while providing enhanced reconstruction quality. The system analyzes temporal and spatial relationships between different data modalities to generate correlation maps that guide compression decisions. A virtual management layer performs stream characterization and adaptive routing, while a processing pipeline implements primary codebook compression with mismatch handling for novel data blocks. High-entropy data segments receive pre-compression processing before codebook compression. Sequential registration data is processed through matrix factorization and dedicated matrix codebooks. The system continuously monitors data distribution characteristics and automatically retrains codebooks when drift thresholds are exceeded. A neural upsampling subsystem uses correlation information to guide cross-modal enhancement processes through modality-specific networks and attention mechanisms. The unified output includes compressed data streams, correlation maps, synchronization metadata, neural model parameters, and updated codebooks, enabling synchronized reconstruction with preserved cross-modal relationships and enhanced quality through correlation-guided neural upsampling.

The invention discloses a method for evaluating the influence of reservoir-water transfer project combined regulation on downstream ecological flow. The method comprises the following steps: step 1, collecting data of a research area; 2, constructing a water transfer project water source area natural water circulation model WEP-NHA; step 3, constructing a natural-social binary water circulation coupling model WEP-HA; 4, simulating the runoff of the water source area by using a WEP-HA model; 5, the system analyzes the change degree of the key control section water quantity and the ecological flow guarantee rate of the water source area under the condition that the reservoir and the water transfer project are overlapped. According to the method, system simulation of disturbance response of a complex system in a water source area can be achieved, the comprehensive influence of a complex water conservancy project system on water resources and the ecological environment is effectively revealed, high adaptability and predictability are achieved, the problem of regulation and storage effect simulation of a reservoir without data or with little data is effectively solved, and the method is suitable for popularization and application. And decision support is provided for scientifically formulating the water transfer scale and guaranteeing the ecological flow.

The invention discloses a current and voltage signal synchronous correction and output method and system, and relates to the technical field of power system analysis and control. Compared with a previous current and voltage signal correction method, the method solves the problems that traditional multi-channel signal acquisition depends on conventional clock control, and synchronization precision is insufficient; a conventional sampling circuit is weak in anti-interference capability. The problem that an existing scheme lacks efficient error prediction and dynamic optimization capability is solved. Through a multi-channel FPGA clock synchronization and double-differential sampling technology, complete acquisition of high-frequency transient characteristics of voltage and current signals is ensured; a dynamic calibration algorithm and multi-stage composite filtering are combined, so that common-mode interference and non-linear errors are effectively suppressed; and finally, through optical fiber isolation output and intelligent feedback control, signal full-link synchronous correction is realized, the electric energy metering error can be reduced, the fault positioning precision is improved, the characteristics of high precision, strong anti-interference and low energy consumption are realized, and a reliable data reference is provided for intelligent power grid and new energy grid connection.

The invention belongs to the technical field of laser welding, and discloses a real-time stress monitoring laser welding clamp with a force sensor and a control system, and the real-time stress monitoring laser welding clamp comprises a transverse connecting rod, a supporting column, a servo motor, an adjusting supporting column, a stress rod, a force sensor protection shell and the force sensor; the force sensor is additionally arranged on the welding clamp, welding deformation in the welding process is effectively monitored, in turn, deformation data is obtained to optimize the process of a welding machine, searching of a welding process window can be greatly shortened, and welding quality and welding efficiency are guided and improved. In the welding experiment process, when data change of deformation force in the welding process is obtained, system analysis software analyzes the deformation force and deformation prediction in the welding process, the welding process optimization direction in the welding process is further analyzed, and the welding quality is improved. The process is optimized from the aspects of welding clamping force, laser power, welding speed, welding laser beam focus scanning path and the like.

The invention provides a multi-modal hydrological data and underlying surface element data processing method and a computer system, and the method comprises the steps: collecting multi-source observation data in a hydrological cycle process through distributed observation nodes, and carrying out the spatial-temporal feature dynamic decoupling operation of the multi-source observation data, separating to obtain a hydrological dynamic feature set with a time sequence characteristic and an underlying surface static feature set with a spatial distribution characteristic; calling a preset underlying surface parameter adjustment model, performing parameter dynamic optimization on the underlying surface static feature set in combination with the hydrological dynamic feature set, and generating a dynamic parameter configuration set containing a geographic response coefficient; hydrological-underlying surface coupling correction processing is carried out based on the dynamic parameter configuration set, and a hydrological variable set is obtained through optimization; and performing spatio-temporal evolution law analysis according to the hydrological variable set, and outputting a hydrological system evolution trend map and a disaster risk early warning index. According to the invention, a more reliable basis can be provided for hydrological system analysis and disaster risk management.

The invention discloses a reactor core nuclear thermal coupling calculation method based on an embedded physical knowledge neural network. The method comprises the following steps: 1, dividing reactor core nodes of a nuclear reactor; 2, initializing neutron physical field parameters and thermal hydraulic parameters of the reactor core; 3, building, training and storing an embedded physical knowledge neural network model; 4, predicting the neutron number density of each node of the reactor core by using the stored model to obtain neutron flux distribution; 5, calculating reactor core power distribution according to neutron flux distribution; 6, calculating and updating the thermal hydraulic parameters of the reactor core at the tn + 1 moment by using a system analysis program; 7, calculating and updating reactivity of each node of the reactor core; and 8, judging whether the appointed calculation time is reached, if so, ending, otherwise, returning to the step 4. The method is based on an embedded physical knowledge neural network method, an accurate and efficient calculation method is provided for solving distribution of the neutron physical field and the thermal field in the reactor core, and the method has important significance on reactor design optimization.

The invention discloses a power grid overhaul cost factor identification method and system based on multi-dimensional feature analysis, and belongs to the field of power system analysis and management, and the method comprises the steps: obtaining multi-source data and historical cost data of a power grid overhaul project, and carrying out the preprocessing of the cost data; the method comprises the following steps: constructing a multi-dimensional entity and an associated knowledge graph, carrying out dynamic weight calculation on influence factors through a graph neural network, establishing a factor quantification model, modeling a risk conduction path, identifying high-order association among key influence factors, establishing a cost reference library and a compliance verification rule system, and detecting abnormal cost items in project budget. And generating root cause analysis and optimization suggestions. The method breaks through the limitation of a traditional static analysis model, significantly improves the accuracy and evaluation efficiency of cost recognition, reduces the cost waste caused by human deviation, provides intelligent decision support for an overhaul project under a novel power system, and assists the lean management of assets in the whole life cycle.

A system and method for optimizing data compression and encryption using reinforcement learning. The system analyzes incoming data streams to extract statistical features and data characteristics, which are processed by a reinforcement learning engine to automatically configure a multi-stage compression pipeline. Each compression stage transforms data into optimized distributions, applies Huffman coding, and maintains full encryption using homomorphic operations. A performance monitor tracks compression efficiency, processing speed, and output quality in real-time, providing feedback to continuously improve the reinforcement learning model's decisions. The system can dynamically adjust between one to five compression stages and select appropriate compression methods, including traditional algorithms or neural network-based approaches, based on data characteristics and performance requirements. All processing occurs on encrypted data without requiring decryption, ensuring complete data security throughout the pipeline. The adaptive nature of the system enables optimal compression performance across diverse data types while maintaining encryption integrity.

The invention discloses an axle coupling system analysis method based on a physical information neural network, relates to the technical field of vehicle-axle coupling systems, and aims to solve the problem that the calculation precision of the dynamics problem of the vehicle-axle coupling system is not high. The axle coupling system analysis method based on the physical information neural network comprises the following steps: establishing a vehicle model and a bridge model, and establishing an axle coupling system according to the vehicle model and the bridge model; taking the motion equation of the axle coupling system as a constraint condition, taking integration of an initial condition and a boundary condition of the axle coupling system as a loss function, and constructing a physical information neural network; selecting a plurality of random points in the axle coupling system to carry out iterative calculation on the physical information neural network to obtain a trained neural network; and calculating the motion equation of the axle coupling system through the trained neural network to obtain the vibration response of the axle coupling system.