673 results about "Smart system" patented technology

The invention belongs to the technical field of financial data management, and particularly discloses a financial multi-source protocol self-adaptive fusion system based on AI semantic understanding and a knowledge graph, which comprises the steps of avoiding fusion errors caused by semantic misunderstanding through deep semantic analysis and a conflict resolution decision based on the knowledge graph; when protocol version updating is detected, incremental learning and model adjustment are carried out based on a newly added sample and historical experience, and a fusion protocol standard is dynamically updated, so that the high adaptive capacity to dynamic change of a financial protocol is realized; an exception monitoring and repairing mechanism is introduced, data missing, format errors and other exceptions are found in time and repaired online based on historical data modes and business logic, and negative influences of abnormal data on downstream risk control, transaction decision making and other key business links are avoided; through deep collaboration and information feedback among the intelligent modules, an intelligent system capable of self-learning and evolution is constructed.

The invention discloses a digital twinning-based heterogeneous body-equipped intelligent equipment collaborative management system and method, and belongs to the technical field of digital twinning and body-equipped intelligent systems. The system comprises a digital twin platform, a communication module, a task scheduling module, a cooperative control module and an edge computing node. The digital twinborn platform constructs a high-fidelity three-dimensional model of a physical environment and digital twinborn bodies of the intelligent devices with the bodies; the communication module is used for realizing bidirectional data interaction between the platform and the intelligent equipment; the task scheduling module performs task allocation and path planning by adopting a reinforcement learning algorithm based on the global dynamic situation map; the cooperative control module models an equipment space-time relationship through a graph neural network, predicts conflicts in real time and generates a dynamic avoidance strategy; and the edge computing node carries out real-time preprocessing on the equipment sensing data. According to the method, global optimization scheduling and multi-device intelligent cooperation are realized, and the overall efficiency, robustness and adaptability of the system are remarkably improved.

The embodiments disclose a cryptographically governed system for contextual memory management in artificial intelligence including a Contextual Orchestration and Scoped Memory Protocol (COSMP) and a Decentralized Memory Wallet (DMW), which enforce secure, auditable, and policy-driven access to AI memory. Traditional token-based memory tracking is replaced by memory capsules secure data units with embedded access policies and tamper-evident logs 5400 organized via a distributed ledger. Access control is managed through decentralized identifiers (DIDs) and private cryptographic keys, ensuring that all memory interactions are signed, verifiable transactions. This architecture establishes a universal trust layer for AI, enabling post-hoc compliance checks and privacy-preserving audits. Applicable across domains such as national security, healthcare, and autonomous systems, the invention enforces rigorous behavioral constraints and access governance within AI memory and decision-making processes.

The invention discloses a multi-model game method for multi-scene intelligent safety management. The method comprises the following steps: acquiring multi-domain original data and generating information structure features; the heterogeneous indexes are uniformly mapped into information loss components based on the features, and stable routing of the game template is carried out in combination with sequential statistics and hysteretic decision; performing game payment-oriented decision sensitive subspace compression on the unified semantic state output after routing; a dynamic risk-to-go (dynamic risk surplus) time difference method is adopted to generate step risk signals meeting total amount conservation, and the step risk signals are injected into a compression space; and through online estimation of the spectral radius of the Jacobian matrix of the optimal response operator, an equilibrium vulnerability index is generated, and stability management and closed-loop adjustment of an equilibrium point are realized. According to the invention, the decision adaptability, stability and robustness of the intelligent system in a dynamic confrontation environment can be improved.

The invention relates to the technical field of genome data analysis, and discloses an intelligent system, method and equipment for assisting multi-step genome data analysis, and the system comprises a dialogue agent which is used for generating a corresponding answer according to a question of a user, or reading an analysis plan file generated by a workflow agent, generating an analysis interpretation text for the analysis plan file; the workflow agent is used for generating a structured task execution plan according to the to-be-executed analysis task and executing the to-be-executed analysis task; and the modeling analysis agent is used for generating a configuration file and a script based on the user request, constructing a model and generating an analysis result corresponding to the user request in combination with the workflow agent. Through multi-agent cooperation, task division and cooperative scheduling are realized, each agent independently completes task planning, execution control, model analysis and other functions, the bottleneck problem of processing of a traditional single model in a complex process is avoided, error accumulation is reduced, and the execution efficiency and stability of the whole process are improved.

A modular open system architecture for common intelligence picture generation is disclosed. The system receives intelligence requirements through a multimodal artificial intelligence system and calculates collection feasibility across multiple intelligence sources based on physical and temporal conditions. The system develops integrated collection plans through the containerized analytics workbench using containerized analytics modules and processes intelligence through GPU-accelerated deep learning models for automated target recognition. Satellite collection is orchestrated through satellite data acquisition optimization platform by evaluating weather conditions, orbital parameters, and sensor capabilities, while space domain awareness is maintained through space domain awareness system for real-time collection asset management. Multi-source intelligence data is fused through multi-source intelligence fusion system to populate a common intelligence picture. The system implements automated workflows for intelligence analysis and dissemination while maintaining security controls, with the containerized analytics workbench providing pattern of life analysis and dynamic exploitation through containerized microservices.

The invention relates to the technical field of agricultural remote sensing information processing, and particularly discloses a crop planting pattern spot intelligent extraction system based on remote sensing information, which constructs a multi-scale feature vector by fusing pattern spot area, compactness, time sequence vegetation index fluctuation and field ridge slope variation coefficient, and combines dynamic threshold adjustment and spatial semantic verification to obtain a multi-scale feature vector. According to the method, automatic correction of abnormal fragments, giant spots and topological conflicts is achieved, in complex scenes such as Yunnan terraced fields, through vertical field and ridge error response suppression and dynamic graph reconstruction, the pattern spot boundary precision and topological rationality are remarkably improved, and a classification result in a GeoJSON format is output.

The invention discloses an old city boundary intelligent extraction and texture calculation system based on vector topology analysis and semantic segmentation. According to the method, the topological semantic association module provides a multi-dimensional association basis for boundary extraction by integrating spatial structure information of vector topology and functional attribute tags of semantic segmentation. In the coarse extraction stage, the objective space law of the topological features and the subjective function orientation of the semantic tags are mutually verified, so that the misjudgment of'similar forms but inconsistent functions' possibly caused by single dependence on the topological structure or the deviation of'tag coverage but space breakage 'possibly caused by only dependence on the semantic tags is avoided; in the fine correction stage, the module further dynamically calibrates the boundary range through historical-current situation space-time correlation and multi-source data conflict detection, so that the continuity of historical stable elements is reserved, the updating requirement of current situation semantic tags is included, the final boundary better fits the essential characteristics of'active inheritance 'of the old city, and the accuracy of the final boundary is improved. And human experience interference and errors are obviously reduced.

An information technology system for a distributed manufacturing network includes an additive manufacturing platform configured to manage workflows for a set of distributed manufacturing network entities associated with the distributed manufacturing network. The information technology system includes a set of digital twins generated by the additive manufacturing platform. The information technology system includes an artificial intelligence system configured to be executed by a data processing system in communication with the additive manufacturing platform. The artificial intelligence system is trained to generate process parameters for the workflows managed by the additive manufacturing platform using data collected from the set of distributed manufacturing network entities. The information technology system includes a control system configured to adjust the process parameters during an additive manufacturing process performed by at least one of the set of distributed manufacturing network entities.

The invention belongs to the technical field of crossing of artificial intelligence and computer system architecture, particularly relates to a computing power center equipment power supply intelligent system of a knowledge graph, and aims to solve the problem of low energy efficiency caused by extensive power supply management, unbalanced energy consumption and lagged load response. The system comprises a task analysis module, a dynamic load prediction module, a multi-source power supply scheduling module, an equipment-level power supply control module and a feedback optimization module, through load prediction and multi-source power supply cooperative scheduling driven by task semantics, voltage and current dynamic adjustment and sleep wake-up control are achieved, and a strategy is optimized online based on operation feedback. The scheme improves energy efficiency, equipment life and system stability.

The invention discloses an unmanned vehicle path planning method based on real-time sampling, and relates to the field of intelligent systems and automatic control. The method comprises the following steps: dividing a regional map into a plurality of concentric sampling regions by taking an unmanned vehicle starting point and an unmanned vehicle target point as symmetry axes, and performing partition search in the plurality of concentric sampling regions through a dynamic density adjustment algorithm to obtain an initial global path; performing path trimming on the initial global path, and performing path smoothing processing on the initial global path after path trimming to obtain a global path; in the improved DWA algorithm, path selection is carried out based on the global path, the weight of the trajectory evaluation function, the unmanned vehicle initial state and unmanned vehicle model parameters, and a lowest-cost path is obtained; the weight of a trajectory evaluation function in the improved DWA algorithm is dynamically generated by adopting a fuzzy controller; and determining the path with the lowest cost as the path of the unmanned vehicle. The method can improve the reliability of unmanned vehicle path planning.

The invention provides an end-to-end intelligent system development method and system based on an AI large model. The method comprises the following steps: S1, receiving a system demand described by a user through a natural language; s2, analyzing the requirements through an AI large model and converting the requirements into formalized specifications; s3, automatically generating a system architecture and dividing components; s4, generating each component code in a recursive manner; s5, dynamically detecting and repairing the generated code problem; s6, automatically integrating all components to form a complete system; and S7, continuously optimizing the system based on system feedback or user feedback. According to the method, work of multiple stages such as demand analysis, architecture design, module division, code writing and testing is automatically completed, distributed integration and smart contract are performed, distributed integration verification is performed through a block chain smart contract and a graph database, data consistency, interface matching and security among components are ensured, the integration process is optimized, and the integration efficiency is improved. Cross-domain cooperation is achieved, and the development efficiency is high.

Data is the “fuel” that powers the machine learning “engine” for Artificial Intelligence. However, identifying high quality data that can catalyze smarter AI, AGI, and SuperIntelligent systems is becoming an increasingly challenging bottleneck for machine learning. This invention not only describes novel methods for identifying the most valuable data, but it also presents an entirely new framework for understanding the information content of AI-relevant datasets. The methods can be used by intelligent systems autonomously or in collaboration with humans. Novel methods for accelerating AI learning, and for updating the knowledge of AI systems in real-time, are also disclosed. Consistent with the view that human survival may depend on the fastest path to AGI also being the safest path, the invention describes catalysts which help maximize alignment between the values of AGI and humans. These innovative catalysts increase not only the intelligence, but also the safety, of AI systems.

The invention provides an energy storage electrical intelligent system of a self-adaptive control strategy, and relates to the technical field of electric power grids. According to the invention, multi-source operation data of a power grid, a load, new energy and an energy storage system are collected in real time through the sensing layer, the decision-making layer realizes accurate synchronization and updating of the system operation state by using a digital twin model, and distributed optimization of swarm intelligence and extreme scene testing of confrontation deduction are fused. Dynamically generating and screening out an optimal control instruction which shows robustness under various working conditions; and finally, the execution layer accurately controls the energy storage system to form a sensing-decision-execution-feedback adaptive closed loop, so that the control strategy of the energy storage system can be automatically and accurately adjusted along with the fluctuation of the running state of the power grid, the stiffness defect of a fixed strategy is overcome, the capacity of the power grid for dealing with source load fluctuation and sudden faults is enhanced, and the energy storage system is ensured to be more stable. The self-adaptive matching of the energy storage system control strategy and the power grid operation state is realized, and the dynamic stability and the operation safety of the power grid are improved.

The invention belongs to the technical field of artificial intelligence, and discloses a large model memory management and optimization system and method based on a vector data lake. Comprising a multi-dimensional memory vectorization coding and hierarchical management module, an intelligent retrieval and dynamic updating module, and a memory optimization strategy and interactive evaluation and cyclic adjustment optimization module based on a reflection and backtracking mechanism. According to the large model memory management and optimization system and method based on the vector data lake, a multi-dimensional and hierarchical memory storage and dynamic retrieval framework is constructed, collaborative management of long-term / short-term memory is supported, efficient reasoning, knowledge reuse and continuous learning ability of an intelligent agent in a complex dynamic task are achieved, and the system and the method have the advantages that the system and the method are simple and convenient to operate. Therefore, the robustness, the adaptability and the task migration capability of the intelligent system are remarkably improved.

Disclosed is a mutually generative artificial intelligence system based on multi-dimensional spatiotemporal information vector graphics, relating to the field of artificial intelligence and engineering applications. Based on a geographic information system (GIS) or a computer-aided design (CAD) platform and a data source, a multi-dimensional vector spatiotemporal large model terminal, a multi-dimensional spatiotemporal information processing agent terminal, and an intelligent information system application terminal are constructed. For multi-dimensional spatiotemporal data such as two-dimensional and three-dimensional vectors and temporal states, a multimodal spatiotemporal large model having an understanding capacity for an engineering professional knowledge system, a data processing flow, multi-dimensional vector graphics, and thematic graphics-text documents is pre-trained to achieve the mutual expression and generation of engineering multi-dimensional vector graphics and thematic graphics-text documents and to form an intelligent engineering graphic data processing application.

The invention discloses an intelligent system and method for realizing multi-mode psychological counseling and seamless connection based on an education robot. A multi-mode psychological counseling system is constructed aiming at the problems of insufficient privacy protection of a traditional campus psychological counseling room, shortage of psychological counseling resources of mountainous schools and the like. The system provides psychological counseling modes such as an education robot, real person remote and man-machine collaboration, can be combined with counseling environments such as a simple psychological counseling room and a VR counseling room to become multi-modal psychological counseling, supports seamless switching of each modal and spatial form, and achieves the intelligent scheduling technology through a multi-modal data real-time synchronization engine and reinforcement learning driving. And data encryption transmission and millisecond-level service form switching are realized. A dynamic auxiliary screen system is innovatively integrated, and psychological archives are dynamically associated with a knowledge graph through a domain-customized named entity recognition model (NER, the accuracy rate is greater than or equal to 95%), so that the remote real expert key information retrieval time is less than or equal to 2 seconds, and the decision-making efficiency is improved by 78%. The system meets the requirements of various laws and regulations such as the personal information protection law.

The invention discloses a simulation test method and system for an unmanned aerial vehicle control algorithm, relates to the field of intelligent system simulation test, and can solve the defects of an unmanned aerial vehicle simulation system in high real scene rendering and physical engine precision at the present stage. The method solves the problems that the real characteristics of a visual sensor and a flight platform cannot be accurately simulated, the performance of an algorithm in a complex real scene is difficult to effectively evaluate, and the adaptability of the algorithm to a hardware environment is difficult to verify, and comprises the following steps: constructing a high-trueness virtual environment according to scene requirements and rigid body dynamics simulation requirements of an unmanned aerial vehicle simulation test; constructing a simulation model according to the physical motion characteristics of the unmanned aerial vehicle hardware and the performance of the sensing equipment; importing the simulation model into a high-reality virtual environment, and performing parameter setting on the simulation model; and testing and verifying a control algorithm of the unmanned aerial vehicle based on the high-trueness virtual environment and the simulation model completed by parameter setting. The method is used for performing simulation test and verification on the control algorithm of the unmanned aerial vehicle.

The invention discloses an intelligent sports event decision-making system based on causal-driven multi-modal fusion and spatio-temporal dynamic reasoning. The system comprises a multi-modal causal data acquisition and preprocessing module; a causal-oriented multi-modal knowledge graph construction and updating module, wherein the causal-oriented multi-modal knowledge graph construction and updating module is provided with a causal structure learning algorithm and an online learning framework; a space-time perception hybrid agent module; the causal constrained dynamic decision optimization module is provided with a deep reinforcement learning algorithm; and the interpretability analysis and visualization module is used for receiving the decision strategy sent by the space-time perception hybrid agent module, generating decision explanation according to the decision strategy and visually presenting the decision explanation. The objective of the invention is to construct an intelligent system capable of providing high-precision, interpretable and adaptive decision support by integrating causal reasoning, multi-modal learning and reinforcement learning, so as to significantly improve scientificity and effectiveness of sports event analysis and decision.

An AI-based platform for enabling intelligent orchestration and management of at least one operating process is provided herein. The AI-based platform includes an artificial intelligence system that is configured to generate a prediction of an energy pattern associated with the at least one operating process. The AI-based platform is also configured to manage the at least one operating process based on the prediction of the energy pattern.

The invention relates to the technical field of industrial automation, in particular to a weftless tape machine process parameter self-adaptive acquisition system fused with machine vision. The system comprises a data acquisition module which is arranged on a production path of the weftless tape machine and is used for capturing a digital image of a weftless tape flowing through a view field of the weftless tape machine in real time; the data processing and parameter extraction module is used for receiving and processing the digital image and extracting process parameters from the digital image; the control module is used for receiving the process parameters, comparing the process parameters with preset target parameter values to generate deviation signals, generating control instructions according to the deviation signals and sending the control instructions to a controllable execution part of the weftless tape machine so as to drive the controllable execution part to carry out adjustment. By constructing an intelligent system from high-precision visual perception to real-time closed-loop control, the product quality consistency is improved, the rejection rate is reduced, and high automation and intelligence of the production process are achieved.

The invention discloses an intelligent system for bid invitation document risk prompting, and relates to the technical field of bid document risk identification, and the intelligent system comprises the steps: extracting a semantic feature matrix of bid invitation document terms containing any risk keyword in a risk term knowledge base; obtaining a risk probability according to the semantic feature matrix and the first weight vector; the first weight vector comprises a risk identification weight of each vocabulary in the plurality of vocabularies, and the weight of the risk-related vocabularies is higher than that of the non-risk vocabularies; the loss influence probability is obtained according to the semantic feature matrix and a second weight vector, and the second weight vector is obtained based on historical dispute cases and corresponding loss label training and comprises the loss association weight of each vocabulary; the marking module is used for marking the clauses as high risks if the risk probability and the loss influence probability both exceed preset threshold values; otherwise, marking the risk as low risk, and outputting risk explanation. The invention provides an intelligent system capable of combining semantic understanding, historical case analysis and multi-dimensional risk assessment.

The invention discloses a method of a terminal strip field alignment intelligent system based on augmented reality. The system collects a terminal strip image / video in real time through a mobile device, identifies a terminal number and a wire sleeve label by using an AI algorithm of an edge server, and compares the terminal number and the wire sleeve label with a structured CAD drawing in real time. Based on a comparison result, the system visually superposes a matching state on a real-time picture through an augmented reality technology, and supports intelligent interaction for manual correction. According to the method, high-real-time and high-accuracy alignment can be realized in a network-free environment, the efficiency is remarkably improved, and the method is suitable for a complex field environment.

The invention discloses a multi-dimensional comprehensive evaluation method for a highway intelligent capacity expansion control strategy effect. A dynamic analysis database is constructed based on real-time traffic data of a target road section; a dynamic weight adaptive traffic state quantification mechanism is provided, and historical data and real-time fluctuation characteristics are combined to generate a comprehensive congestion index so as to improve scene adaptability of different time periods; designing a capacity expansion strategy simulation framework, optimizing and reconstructing road network capacity parameters through physical lane adjustment and an intelligent system, and generating traffic response data after capacity expansion; a multi-dimensional evaluation model is provided, the traffic efficiency improvement effect, the safety risk coefficient and the environmental influence index are fused, and a three-stage coupling analysis chain is constructed; and dynamically correcting the evaluation weight based on a fuzzy decision algorithm, and outputting the comprehensive optimization priority of the capacity expansion strategy. According to the method, the technical bottlenecks of a traditional method in the aspects of dynamic traffic response, multi-dimensional index collaborative evaluation and real-time data driving are solved, and a scientific and quantifiable evaluation system is provided for intelligent expansion of the expressway.

The invention relates to a self-adaptive tuning inerter damping device and method for suppressing vertical micro-vibration of a structure. The self-adaptive tuning inerter damping device comprises a bearing base, a tuning mass block, a vibration system connecting the bearing base and the tuning mass block, a pneumatic control system and an intelligent measurement and control system. The vibration system is formed by connecting an outer side air spring set and a central air spring in series with a hydraulic inerter unit, an adjustable damping branch and a fixed inerter branch which are connected in parallel are integrated in the hydraulic unit and are respectively provided with an electro-hydraulic proportional valve and a spiral pipe type hydraulic inerter, so that adjustable damping and mass amplification are realized. The pneumatic system adjusts the air pressure of each air spring through an independent air path and a proportional valve so as to change the rigidity. An intelligent system monitors vibration through a sensor, a central controller identifies excitation frequency and optimizes parameters based on a kinetic model, and rigidity and damping are synchronously adjusted. According to the invention, the problem that a mechanical inerter is insufficient in response to micro-vibration is solved, the defects that a traditional passive device is narrow in frequency band and easy to detune are overcome, and broadband, self-adaptive and high-performance suppression of vertical micro-vibration of a structure is realized.

The invention provides a data processing method and system based on a business intelligence system. The method comprises the following steps: receiving request data, and standardizing the request data to obtain standardized data; analyzing the standardized data based on a pre-constructed dynamic semantic map to obtain an analysis result; the dynamic semantic graph is a directed attribute graph constructed based on business terms, data entities and business concepts; performing logic compiling on the analysis result to generate an executable operation chain; the executable operation chain comprises sequenced atomic operation steps, and the atomic operation steps comprise data processing logic and metadata binding information; and running the executable operation chain to obtain an analysis result corresponding to the request data. The method comprises the following steps: analyzing request data based on a dynamic semantic map; and the analysis result is logically compiled, an executable operation chain is generated, and the data processing process is decomposed into sequenced atomic operation steps, so that the data processing flow becomes clear, and the accuracy and efficiency of request data processing are improved.

There are provided systems and methods for intelligent detection and acquisition of authentic product reviews for cross-platform availability. A service provider may provide computing services to merchants and users for processing various interactions, such as purchasing items electronically. When items or other products are purchased, users may leave reviews. To determine an authenticity of the reviews, the service provider may utilize an intelligent system that may include an LLM or other generative AI. The system may automatically generate questions for users that may be designed by the LLM to elicit responses that verify whether a review is authentic and / or relevant. This may include receiving responses and scoring those responses to verify the user is providing an authentic review. If so, the review may be written to a blockchain, which may allow the review to be pushed to users and / or automatically injected to product pages and checkout flows.

The invention discloses a verifiable generation and causal optimization education intelligent system and method oriented to teaching and evaluation integration. The system comprises a data access and learning portrait, a knowledge retrieval and knowledge graph, VGE (Verifiable Generation Environment), consistency / contradictory rate / coverage rate verification, multi-expert agent collaboration and arbitration, causal structure learning and anti-factual optimization, a context protocol and an edge-cloud double-loop scale block. The illusion is reduced through evidence chain binding and thresholding, and the content quality is guaranteed through multi-Agent voting and negative right; optimal intervention is selected based on a causal objective function, and a teaching plan, a calendar and a task list are written back in a closed loop, so that end-to-end optimization of question setting, reading, lecture and teaching intervention is realized, learning gain is improved, and fairness and teacher workload are considered.

The invention provides an offline reinforcement learning diffusion trajectory planning method based on dynamics enhancement, and belongs to the technical field of artificial intelligence and robot control. According to the method, a forward dynamic model is introduced in a diffusion trajectory generation process, and dynamic optimization of trajectory distribution is realized by establishing a mismatch degree between a planned trajectory and a dynamic prediction trajectory and applying gradient correction in a reverse sampling stage. Modeling is carried out on a state transition relation by combining a variational auto-encoder, starting and target constraint conditions are superposed, and a track conforming to a real dynamic law is guided to be generated. According to the method, the problem of error accumulation caused by modeling errors in traditional diffusion type offline reinforcement learning can be effectively relieved, and the performability and stability of the long-time-domain trajectory are improved. In a D4RL-based continuous control task, an experiment result shows that the method has good adaptability and generalization performance, the planning and control capability of the intelligent agent in a complex environment can be remarkably improved, and reliable support is provided for application of related intelligent systems.

The invention belongs to the technical field of model-driven system engineering and intelligent system modeling, and particularly relates to a test requirement formalized design method based on primitive intelligent extension. The method comprises the following steps: obtaining a semantic map, and defining semantic nodes in the semantic map as type modules or intelligent modules; instantiating the type module to obtain a type model instance node, and instantiating the intelligent module to obtain an intelligent model instance node; organizing the type model instance nodes and the intelligent model instance nodes into a model graph; constructing an extended state primitive, constructing a plurality of interactive combination states based on the extended state primitive, defining an intelligent migration mechanism and constructing an extended state machine diagram; respectively testing the model diagram and the expansion state machine diagram, and if the model diagram and the expansion state machine diagram are tested to be qualified, exporting the model diagram and the expansion state machine diagram; the method can solve the problems of dynamic behavior description fragmentation, multi-agent interaction mechanism stiffness and insufficient model reusability of a traditional system modeling language in intelligent system modeling.