553 results about "Systems analysis" patented technology

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 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.

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 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 technology development analysis method and system based on a patent big language model, and relates to the field of big data analysis. The problems that an existing artificial intelligence patent analysis method is difficult to accurately analyze a technical structure in a patent text, cannot describe a technical hierarchical relationship, is difficult to eliminate technical semantic diversity, cannot systematically analyze a technical evolution process and the like are solved. And the network structure characteristics and the technology node importance are analyzed, and the evolution characteristics of the technology system in different stages, the key technology nodes and the change trend of the technology community structure can be identified, so that technical support is provided for artificial intelligence technology development situation research and judgment and technology layout decision. The method is suitable for a technical analysis scene of million-level patent data.

The invention relates to a dynamic construction method and system for an integrated energy large model knowledge base. The method comprises the steps that a directed graph is constructed according to physical equipment or energy nodes on a target integrated energy system; performing knowledge association and filling on nodes and edges in the directed graph to form a node static knowledge set; accessing multi-source real-time data, and performing anomaly detection and state analysis on the multi-source real-time data to obtain a corresponding event; mapping the event into a directed graph, and updating the directed graph and the node static knowledge set to obtain an updated graph database and an updated vector knowledge base; and constructing an input prompt to the comprehensive energy field large model based on the query statement and the corresponding knowledge set to output an answer. According to the method, a dynamic knowledge base tightly coupling a physical world and an information world is constructed, the systematization degree, the cross-system analysis capability and the real-time performance of comprehensive energy system knowledge are remarkably improved, and powerful support is provided for deep application of a large model in scenes such as fault diagnosis and operation optimization.

Techniques for receiving, storing, analyzing, and utilizing raw content are disclosed. The system receives raw text from a user, including a first tag and second tags. The system identifies and removes formatting attributes in the raw text, including whitespace characters, to generate a normalized input. The system stores the normalized input in target dataset(s) and parses the normalized input for the first tag and the second tags. In this case, the first tag corresponds to one or more topics, and the second tags represent a computing system associated with corresponding portions of the normalized input. The system analyses the first tag and the second tags to identify the topics. The system generates one or more topic maps for the target dataset(s) based on the first tag and the one or more second tags. The topic map(s) include one or more references to content items within the target dataset(s).

The invention relates to the technical field of information extraction, and discloses a long text news event element extraction method and system based on multi-agent collaboration, and the method comprises the following steps: constructing a large language model multi-agent system architecture; the central coordination agent receives the unstructured news text; the text segmentation agent outputs a logic unit and time description thereof; an event extraction agent extracts an event subject, an action and an object, and complements time and entity role information; the scene matching agent judges the fine-grained news scene category to which the scene matching agent belongs; the information extraction agent generates a structured event record; and the central coordination agent performs quality auditing and exception handling on each step of output to ensure the integrity and consistency of the output data. According to the method, the static relationship between the event main bodies can be constructed according to the structured event information through the relationship construction and context sorting agent, the event situation change is summarized based on the timeline, and the feasibility and the visualization of news event analysis of the system are improved.

A system and method for cross-stream asymmetric enhancement combines machine learning-driven asymmetric codebook generation with dyadic distribution algorithms to enable simultaneous optimization of compression efficiency, cryptographic security, and error correction capability. The system analyzes input data characteristics and initializes multiple specialized ML models to generate stream-specific asymmetric codebooks optimized for different objectives. Enhanced dyadic distribution processing creates three pre-conditioned data streams that are processed through parallel asymmetric transformation pipelines: compression-optimized for maximum data reduction, security-optimized for cryptographic strength, and error-correction-optimized for robust recovery capability. Cross-stream optimization coordinates the multiple processing paths to ensure overall system coherence while maintaining individual stream objectives. The system supports multiple operating modes including ultra-high compression using only the primary stream, broadcast quality using primary and secondary streams, and archival mode using all streams for lossless reconstruction. The system supports graduated access control that enables different reconstruction quality levels based on available stream combinations.

The present invention relates to a document analysis system that utilizes artificial intelligence to analyze documents written by group members in their daily lives, extract the key emotions and key keywords of the document writer, and determine relationships among group members. By utilizing the extracted key emotions and key keywords, the system analyzes and intuitively presents whether certain members are connected as a group or close companions, or whether certain members are isolated and not interacting with others.

The invention relates to the technical field of power system analysis, in particular to a distribution network grounding fault information research and judgment method and system, computer equipment and a medium. The method comprises the following steps: acquiring multi-source fault data in a distribution network system and preprocessing the multi-source fault data to obtain a standardized fault data set; extracting fault feature information from the standardized fault data set through a time sequence pattern recognition algorithm; based on the fault feature information, performing clustering analysis based on a topological relation on the fault feature information through a multi-source data fusion research and judgment algorithm, and generating a fault section positioning result; and generating distribution network grounding fault information according to the topological relation identifier and the electrical parameter characteristics in the fault section positioning result. By means of the mode, the technical problem that an existing ground fault research and judgment technology is insufficient in positioning precision in a power distribution network with the high reliability requirement is solved, and the automation and intelligence level of fault research and judgment and the accuracy and usability of a research and judgment conclusion are improved.

The invention provides an intelligent water conservancy monitoring method and system based on the Internet of Things. The method comprises the steps of obtaining heterogeneous water conservancy data of various sources; preprocessing and standardizing the heterogeneous water conservancy data from various sources; constructing a water conservancy domain knowledge graph, and performing multi-dimensional data fusion to generate comprehensive water conservancy situation data; based on the comprehensive water conservancy situation data, constructing a water conservancy digital twinborn body, and realizing real-time driving and hydrological and hydraulic process simulation of the water conservancy digital twinborn body; and multi-modal deep analysis is performed on the comprehensive water conservancy situation data and the water conservancy digital twin, and interpretable intelligent decision support information is generated by using an artificial intelligence technology. According to the method, the problems of scattered data sources, high isomerism, relatively low intelligent degree and insufficient information presentation modes of a water conservancy management system are solved by acquiring and preprocessing the multi-source heterogeneous water conservancy data, fusing and cognizing the water conservancy data, twinning and simulating the water conservancy system and performing analysis and decision intelligent support.

The present disclosure provides a method for selectively encrypting and decrypting portions of a document. The method includes receiving a document containing plaintext, analyzing the document using an artificial intelligence system to identify portions containing sensitive information, assigning predefined security levels or attributes to the identified portions, sending an encrypt file content request to a cryptographic engine, receiving an encrypted file content response, building an encrypted document by replacing portions with masking symbols and storing encrypted portions as metadata, and sending the encrypted document to a client device. The artificial intelligence system uses machine learning models trained on domain-specific data and employs pattern recognition and contextual analysis to identify sensitive content based on semantic understanding and classification rules.

The invention discloses a heat and power load intelligent distribution method of a heat and power cogeneration system, and mainly relates to the field of heat and power cogeneration unit load distribution. Comprising the following steps: S1, establishing a combined heat and power generation unit and performing system analysis; s2, determining an optimization target and a constraint condition of multi-unit thermoelectric load distribution and an operation domain of the units; s3, coupling a thermoelectric load power station economy calculation model by using a random forest algorithm; s4, establishing an improved particle swarm algorithm model; and S5, carrying out heat and power load distribution on the cogeneration system by adopting the improved particle swarm optimization model established in the step S4. The method has the advantages that load optimal distribution of the cogeneration system can be achieved, it is ensured that the system keeps a low coal consumption level during operation, the calculation process is simplified, the scheduling cost of the system is reduced, the improved particle swarm optimization is introduced, the problems that the algorithm is long in operation time and the calculation result is unstable are solved, and the method is suitable for large-scale popularization and application. Therefore, a better optimization result is obtained.

The invention provides a power flow convergence method and system based on direct current power flow preprocessing and staged decoupling, and belongs to the field of power system analysis and control. Comprising the following steps: performing multi-stage verification and parameter adjustment on original power flow data, executing active balance preliminary verification of a power system, and generating a node active power set initial value set; setting an initial value set based on the active power of the nodes to carry out direct-current power flow calculation, obtaining an initial phase angle of each node, identifying a heavy-load line with a power angle difference exceeding a preset threshold value, and carrying out adjustment to obtain initial phase angle distribution; in combination with a preset initial voltage amplitude and the initial phase angle distribution, performing staging decoupling load flow calculation and adjustment to obtain load flow parameters; and taking the power flow parameter as an initial value, and carrying out full-coupling power flow calculation to obtain a final power flow solution meeting the convergence precision requirement. According to the method, the physical root of the convergence problem can be intelligently diagnosed and positioned, and the convergence and robustness of load flow calculation under complex working conditions are effectively improved through small-range targeted adjustment.

The invention discloses a thermodynamic system analysis method and system based on a physical information neural network. The method comprises the following steps: constructing a thermodynamic system mathematical model; converting a solving problem of the mathematical model of the thermodynamic system into an optimization problem taking a minimum control equation set residual error as a target function; constructing a physical information neural network; constructing a composite loss function based on the control equation set residual sum of squares, and performing end-to-end training on the physical information neural network to obtain a trained physical information neural network; taking the trained physical information neural network as an approximate solver, and for any given thermodynamic system input working condition, predicting a group of approximate solutions as initial predicted values through one-time forward propagation; and inputting the initial predicted value into a traditional numerical solver for accurate solving to obtain a to-be-solved state variable. According to the method, a feasible technical path is provided for applications with strict requirements on the analysis and calculation speed of the thermodynamic system, such as real-time simulation, online optimization and digital twinning.

The invention relates to the technical field of computer data processing, in particular to an intelligent model system for automatically generating a standard protocol interface, which comprises the following steps: acquiring demand information input by a user through a demand acquisition module and converting the demand information into standardized demand parameters; the protocol knowledge base module outputs a compliance rule set according to the target protocol type, and the interface generation module automatically generates an interface source code, a configuration file and an interface document according to the standardized demand parameters and the compliance rule set. The verification module verifies and optimizes the generated interface, and the self-learning module updates system analysis logic and rule content according to a verification result. The system adopts a natural language processing and structured analysis double-path fusion technology to realize accurate analysis of requirements, realizes intelligent matching of protocol rules through a protocol knowledge graph, completes automatic generation of codes based on a symbol reasoning and neural code generation technology, forms a complete closed loop of requirement analysis, protocol adaptation, code generation and verification optimization, and realizes automatic analysis of the requirements. And the interface development efficiency and quality are effectively improved.

Described herein is a system that detects ransomware infection in filesystems. The system detects ransomware infection by using backup data of machines. The system detects ransomware infection in two stages. In the first stage, the system analyzes a filesystem's behavior. The filesystem's behavior can be obtained by loading the backup data and crawling the filesystem to create a filesystem metadata including information about file operations during a time interval. The filesystem determines a pattern of the file operations and compares the pattern to a normal patter to analyze the filesystem's behavior. If the filesystem's behavior is abnormal, the system proceeds to the second stage to analyze the content of the files to look for signs of encryption in the filesystem. The system combines the analysis of both stages to determine whether the filesystem is infected by ransomware.

The invention belongs to the technical field of online education, and discloses an AI digital human-computer interaction method and system applied to art design education and a medium, and the method comprises the steps that a multi-mode sensing module carries out input data collection and analysis; the digital person engine module realizes communication between virtual digital persons and students; during artistic creation of students, the generation type creation engine assists the students in creation; when a student inputs a keyword or uploads a reference picture, the creative inspiration excitation module carries out inspiration resource recommendation; the real-time cooperation module supports online synchronous editing of design works by multiple persons; the teaching evaluation system analyzes learning processes and works, constructs ability portraits, and recommends personalized learning paths and course packages. According to the invention, multiple interaction modes are supported through the multi-mode perception module, and the creative inspiration motivating module broadens the visual field of students and motivates creation inspiration through intelligent recommendation and inspiration communities; the teaching evaluation system scientifically constructs student ability portraits, recommends personalized learning paths and course packages, and meets personalized learning requirements.

The invention relates to a simulation calculation method for researching the influence of pore structure deformation on the hydrogen storage efficiency of an underground aquifer, through the combination of a solid mechanical simulation process and a CFD (computational fluid dynamics) simulation process, the simulation system can simulate the influence on the hydrogen storage efficiency of the underground aquifer due to the production of internal fluid and the reduction of pore pressure in the reservoir exploitation process. The process that the original stress balance state of the reservoir is damaged, and consequently the pore structure of the reservoir is deformed is illustrated, and meanwhile, the transportation and storage process of hydrogen in the aquifer under the condition that the pore structure is deformed is illustrated. The distribution condition of the fluid at different time is analyzed by calculating the flowing speed of the fluid and the hydrogen storage amount in the porous medium, and then systematic analysis is carried out on improvement of the underground aquifer hydrogen storage technology. The method is accurate in result, high in convergence, easy to operate and high in reliability, and shows good application value in simulation research of underground aquifer hydrogen storage.

The invention discloses a main and distribution network collaborative analysis method, system and device and a storage medium, and belongs to the technical field of power system analysis and calculation. The method comprises the following steps: abstracting a main power distribution network integrated model into a heterogeneous graph and generating a fusion node feature matrix; a time-space diagram neural network model is constructed and trained, the model ensures that a prediction result conforms to a power system physical law by introducing physical information neural network constraints, and multi-task parallel prediction is completed by adopting a diagram self-attention pooling and multi-task learning architecture; performing cross-domain interaction verification on a collaborative analysis result output by the model, wherein the cross-domain interaction verification comprises boundary consistency verification and energy / power balance verification; and dynamically adjusting calculation granularity or model parameters according to a verification result, and accelerating a calculation process based on a linear self-attention mechanism. According to the method, the problems of model splitting and inconsistent calculation results caused by independent analysis of the traditional main and distribution networks are solved, and global consistent, physically credible, efficient and collaborative power grid analysis is realized.

Systems and methods are disclosed for managing a cryptocurrency mining system. In some examples, a system analyzes contextual data associated with a mining system to identify a condition associated with at least one component of the mining system (of a plurality of components of the mining system). The contextual data includes at least one measurement of at least one characteristic of the at least one component. The system selects, based on the condition associated with the at least one component being identified, at least one indicator (of a plurality of indicators of the mining system) to activate. The plurality of indicators corresponds to the plurality of components of the mining system. The system activates the at least one indicator to indicate the condition associated with the at least one component.

A computer-implemented system analyzes program and data flows in a software system comprising code written in multiple programming languages using a generative large language model (LLM) directed by programming-language-specific prompts. The LLM identifies functional components within the code, generating labeled graph nodes that include a node type, a node name, and dependency information. A graph construction computer system processes the labeled graph nodes to generate a directed graph, where nodes represent functional components and directed edges represent dependencies. The system stores the graph in a database and provides a web-based interface for visualization, allowing users to explore, query, and analyze program and data flows across the software system. The system enables automated, language-agnostic dependency mapping, facilitating software analysis, debugging, and modernization.

The invention discloses a surrounding rock damage data analysis system and method based on multiple factors, and relates to the technical field of surrounding rock damage data simulation. A to-be-tested rock material is selected to prefabricate a closed crack group to form a stable roadway; setting different fatigue load intensities and cycle times, analyzing data changes of a stress field, a displacement field and acoustic emission under the action of the fatigue load based on an acoustic emission monitoring system, and determining a bearing performance change model; and real working conditions are simulated based on the bearing performance change model, a difference value between simulated data and real data is formed, an output target of fatigue load strength is intelligently corrected, and data compensation is realized for the bearing performance change model. According to the method, original defects such as joints and cracks in the rock mass and damage evolution under cyclic loads are included into an analysis framework, a reliable basis is provided for long-term safety and stability judgment of a roadway under cyclic load working conditions such as compressed air energy storage, and safe and efficient production of a coal mine is effectively supported.

Disclosed is a system to generate an adversarial pattern. The system receives an input specifying data describing a target object, an objective indicating whether to reduce detectability of the target object or to induce a false detection of a target type, and context parameters. The system generates candidate adversarial patterns for the target object using an AI-based generative algorithm. Each candidate adversarial pattern represents a potential solution for achieving the objective under context parameters. The system simulates a representation of the target object with candidate adversarial pattern in a virtual environment that models a plurality of sensor modalities. The simulation may use machine learning object detection models. The system analyzes the target object and generates, for each target object, a performance metric for ranking the candidate adversarial pattern based on an effectiveness of the simulated representation. The candidate adversarial pattern with highest rank is generated as an optimized adversarial pattern.

The invention belongs to the technical field of dental pulp blood supply reconstruction, and relates to a multi-omics data fusion analysis method for EDTA (Ethylene Diamine Tetraacetic Acid) induced cell differentiation in dental pulp blood supply reconstruction. A space-time matched multi-level data basis is provided for analyzing a dynamic rule; then through standardization processing and difference analysis, screening out differential molecules which realize corresponding modes and are strongly related to phenotypes, focusing on core regulation and control candidate objects, and avoiding limitation of single omics; the method comprises the following steps: constructing a dynamic multi-layer regulation and control network comprising a transcription layer, a protein layer, a metabolism layer and interlayer connections thereof, finally identifying key nodes and modules through network topology analysis, tracking a time evolution rule in combination with dynamic trajectory analysis, and finally screening a core regulation and control module and predicting a cross-level key path. System analysis from multi-omics data to a hierarchical regulation and control mechanism and a dynamic rule is realized, and the limitation of single-dimension research is effectively broken through.

The invention relates to the technical field of information technology and data decision making, and discloses a multi-dimensional quantization system construction method and system for medical instrument replacement decision making. The method comprises the following steps: acquiring and processing multi-dimensional original data of a medical instrument; constructing an inter-dimension correlation model by utilizing machine learning, obtaining an interleaving relation matrix, identifying potential influence paths, and generating a marked influence path set; performing simulation prediction on the multi-level chain reaction through random forest and other algorithms to obtain a reaction chain index representing a chain influence degree, constructing a multi-dimensional quantization framework according to the reaction chain index, and calculating an influence effect value of each candidate alternative scheme among dimensions; and constructing a system analysis graph by taking the influence effect value as an edge weight, generating a decision path graph which is verified and optimized by multiple scenes, and outputting a medical instrument replacement decision scheme with better adaptability. According to the method, quantification, visualization and closed-loop optimization of the medical instrument replacement decision from data to a scheme are realized, and the scientificity and operability of the decision are improved.

An artificial intelligence system for military planning and decision support is disclosed. The system comprises a central coordination agent, multiple specialized agents focused on specific military domains, and a dynamic learning module. The system analyzes inputs, activates relevant specialized agents, coordinates information exchange, and synthesizes outputs into cohesive solutions. It incorporates security and ethics verification, multi-modal interfaces, and can operate in both local and remote configurations. Advanced capabilities include multi-lingual operation, predictive maintenance, and generation of multiple courses of action with sophisticated modeling and simulation. The system is not limited to this architecture, and may instead comprise of a single AI system consolidating these functions. The system continuously learns and adapts to new scenarios while maintaining alignment with military doctrine and objectives.

The invention provides a gait evaluation method and system based on a human body nonlinear system analysis technology, and the method comprises the steps: collecting a gait cycle six-channel high-precision time sequence signal outputted by a wearable inertial measurement unit, eliminating noise and gait difference through wavelet threshold denoising and Z-score standardization, extracting a chaotic feature vector composed of a Lyapunov index spectrum and a Kolmogorov entropy value, and carrying out the recognition of a gait signal, a wavelet neural network is adopted to realize nonlinear mapping of chaotic features and phase-space reconstruction parameters, and a self-adaptive feedback mechanism is introduced to dynamically optimize modeling parameters, so that the accuracy and personalized matching capability of gait pattern recognition and stability evaluation are effectively improved; quantitative characterization of the gait chaos level and real-time online model optimization can be achieved, and high-robustness support is provided for rehabilitation training and exercise aided decision making.