730 results about "Smart system" patented technology

The invention relates to the field of building intellectualization, in particular to a digital twinborn mixed cloud edge collaborative intelligent house building group operation and maintenance intelligent system, which establishes a digital twinborn scene database by collecting building basic information, equipment operation data and environmental parameters, synchronizes the digital twinborn scene database to edge equipment, and uses BIM, GIS, Internet of Things, 5G and AI technologies to establish a digital twinborn scene database, so as to realize the intelligent operation and maintenance of a building group. A virtual-real combined digital intelligent building scene is constructed, real-time synchronization of a virtual scene and a physical environment is realized through AR / VR equipment, and an operation and maintenance module comprises multi-source heterogeneous data fusion, edge intelligent analysis decision, adaptive model training and iterative optimization, a predictive maintenance algorithm of virtual-real mapping and a multi-level collaborative decision and autonomous scheduling mechanism. And the monitoring module monitors the state and operation condition of the edge equipment, provides data service and supports visualization of management decisions, and the system effectively improves the intelligence and digitization level of operation and maintenance of the building group.

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 an air flotation vacuum pump performance prediction system based on a specific calculation model, and relates to the technical field of mechanical engineering. Comprising a multi-parameter fluctuation sensing module, a weak feature sensitive extraction module, an abnormal coupling atlas construction module, an instability precursor identification module, a prediction model regulation and control module and a model adaptive optimization module, and carrying out fusion in a preset time window, constructing a multi-parameter synchronous fluctuation sensing model, and outputting a synchronous anomaly index. By introducing multi-parameter synchronous fluctuation perception, weak feature extraction and an abnormal coupling map, accurate identification and early warning of the early instability trend of the air floating vacuum pump are realized, and an intelligent system with real-time perception and closed-loop prediction capabilities is constructed by combining model regulation and control and a self-adaptive optimization mechanism, so that the operation safety and stability are improved.

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 drug production safety robot inspection method, which comprises the following steps: acquiring multi-source heterogeneous data of a drug production safety state in real time, including environment sensor data, intelligent video data and equipment operation parameters; based on the multi-source heterogeneous data, constructing and dynamically updating a production safety risk assessment model; according to the production safety risk assessment model, a self-adaptive inspection task is generated and adjusted, and the task defines an inspection target, a path, a period and an execution maneuvering terminal; scheduling the maneuvering terminal to execute the inspection task, driving the multi-mode AI potential safety hazard identification engine by the returned safety state data and outputting an identification result; and an identification result is fed back to the production safety risk assessment model to form closed-loop control. The invention also discloses an intelligent inspection system for pharmaceutical production safety. According to the invention, prospective risk early warning, self-adaptive task scheduling and closed-loop self-optimization can be realized, so that higher requirements of the modern pharmaceutical industry on personnel safety, public health and compliance production can be met.

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 relates to the field of health management, and discloses a health propaganda and education method and system based on an intelligent system, and the method comprises the steps: collecting environment data and multi-dimensional health data of a service object, and creating a scene perception layer; generating a causal relationship chain by using a preset Bayesian network optimization algorithm; determining an adaptive propaganda and education opportunity of the service object based on the health state and the scene type; constructing a self-management scoring device of the service object, and generating personalized propaganda and education contents of the service object; and creating a multi-scene collaborative propaganda and education platform of the service object and a corresponding effect evaluation system thereof. According to the invention, multi-scene collaborative propaganda and education can be realized, and targeted health propaganda and education contents are provided.

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 relates to the technical field of steel wire preparation, in particular to a high-strength prestressed steel wire preparation device and a use method thereof.The device comprises a pay-off mechanism, a pretreatment unit, a drawing mechanism, a heat treatment furnace, a cooling device, a stress applying mechanism, an online stress detector and a take-up mechanism; the stress applying mechanism comprises a tension roller set driven by a servo motor, a hydraulic damper and an on-line stress detector, and the on-line stress detector monitors the strain of a steel wire in real time through an optical fiber strain sensor and is linked with the PLC control system; dynamic prestress control and a multi-stage heat treatment process work cooperatively, so that the tensile strength of the steel wire stably reaches 1570 MPa or above, a closed-loop control tension system and an online detection technology are adopted, the prestress applying precision is high, automatic optimization of process parameters is achieved through an intelligent PLC system, the product percent of pass is greatly increased, and the production cost is reduced. And the energy consumption and the rejection rate are greatly reduced.

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 provides a domain self-adaption-based generative attack and defense confrontation method. Directional interference on a black-box intelligent system is realized by constructing a generative confrontation network framework. The method takes a domain self-adaption theory in transfer learning as a core, builds a dynamic mapping relation between a white box model group and a black box target system, designs a dual-channel generative adversarial network, jointly supervises sample authenticity and cross-model transfer consistency through cross entropy loss and KL divergence constraint, builds a dynamic boundary feedback system, and achieves the dynamic boundary feedback. And a disturbance generation strategy of the generator is adjusted according to the real-time attack effect, and a self-adaptive attack and defense game closed loop is formed. Compared with a traditional black box attack method, the method has the advantages that migration robustness of an adversarial sample in a model heterogeneous scene is remarkably improved, stable attack efficiency can still be maintained when a defense system has protection mechanisms such as adversarial training and input reconstruction, and the method can be applied to the fields such as penetration testing of an intelligent security system and the like. And a technical support is provided for vulnerability evaluation of the intelligent system in a complex environment.

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 relates to the technical field of intelligent traffic, and discloses an intelligent servo barrier gate management system. According to the system, vehicle identification information, detection information, environmental parameters and a barrier gate equipment state are acquired in real time through the data acquisition unit, comprehensive monitoring of traffic flow is ensured, the processing and analysis unit processes the acquired data by using a deep learning model, a control instruction is quickly generated, and the vehicle passing efficiency is evaluated, so that the traffic flow is ensured. The servo control unit is combined with an adaptive control algorithm to dynamically adjust the switching speed, the torque and the acceleration of the barrier gate, the vehicle passing experience is optimized, the queuing time is reduced, the bidirectional communication capability of the communication unit realizes remote monitoring and adjustment of the state of the barrier gate, and the traffic management decision making efficiency is improved. A manager can timely respond to sudden traffic conditions, the response speed and accuracy of the system are improved, the intelligent system effectively relieves the bottleneck of traditional barrier gate management, and the intelligent level of urban traffic is improved.

The invention discloses a task planning method based on an intelligent agent and an intelligent system, and belongs to the field of artificial intelligence. Through the technical scheme provided by the embodiment of the invention, the main agent performs task planning on the target task based on the task description information of the target task, the first multi-modal information of the multi-modal sensor and the task reference information obtained through the long-term memory library to obtain the first task planning information, the process is combined with the task reference information, and the task planning efficiency is improved. And the accuracy of the obtained first task planning information is higher. The main agent sends the first task planning information to the plurality of sub-agents, each sub-agent executes the corresponding action based on the state information acquired from the short-term memory library and the first task planning information, and the state information is combined when the sub-agents execute the action, so that the actions executed by the plurality of sub-agents are not easy to generate conflicts, and the task planning efficiency is improved. And the completion effect of the target task is better.

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.