946 results about "Smart system" patented technology

The invention discloses an intelligent system and method for early screening of depression based on electroencephalogram-eye movement multi-modal data fusion. According to the system, in a virtual reality situation, emotion and cognitive response of a subject are stimulated through an elaborately-designed cognitive task, and brain electrical activity and sight behavior data are synchronously collected by adopting a wearable EEG device and a high-precision eye tracker. After signal preprocessing and feature extraction, deep fusion of multi-modal data is realized by using a weighted fusion formula or a graph neural network, the weight of each modal is automatically adjusted, interaction features between electroencephalogram and eye movement are extracted, and then the depression risk of a subject is accurately judged through a classifier. Experiments show that the method can effectively capture weak physiological abnormalities of mild depression patients, has the advantages of high sensitivity, high accuracy and real-time online screening, and meets the requirements of non-invasive, portable and intelligent clinical application.

The invention relates to the technical field of intelligent navigation and control of ships. The invention provides a complex channel ship intelligent navigation and emergency decision-making method and system. The method comprises the following steps: acquiring environment data through a multi-source heterogeneous sensor array, and establishing a channel three-dimensional dynamic environment model; establishing a multi-objective optimization function, and performing dynamic path planning by adopting an improved model prediction control algorithm; synchronizing motion state parameters of an actual ship and a virtual ship model in real time, constructing an emergency decision tree in combination with an expert knowledge base, and verifying the feasibility of an emergency decision through Monte Carlo simulation; carrying out local route optimization by adopting edge computing nodes, carrying out multi-ship trajectory prediction through a federated learning mechanism, and generating a corresponding collaborative collision avoidance strategy; and establishing a dynamic priority scheduling mechanism, implementing hierarchical response, and confirming a global avoidance scheme through a distributed consensus algorithm. The problems that an existing inland ship intelligent system is limited in perception, rigid in decision and weak in collaboration in a complex scene are solved.

The invention relates to the technical field of government affair service intellectualization, in particular to a multi-modal retrieval enhanced generation government affair intelligent system which comprises multi-level government affair knowledge base construction, retrieval enhanced agent collaborative fine adjustment, a credible generation mechanism of government affair agents and multi-modal fused personalized interaction application. Multi-source heterogeneous data semantic unification is realized through a dynamic cross-modal alignment framework and a knowledge graph; a dual-path parameter efficient fine tuning architecture is adopted to improve the retrieval and generation capability; the tool chain is verified by integrating logic, law and ethics to ensure the compliance of the generated content; and guiding and optimizing complex scene demand understanding by using distributed user portraits and progressive dialogues. According to the application, the intelligent level and the user experience of government affair services can be remarkably improved, and the government affair services are promoted to transition from passive response to active service.

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 a nuclear industrial robot body intelligent system based on a large model, which realizes autonomous sensing, decision making and execution of a robot in a nuclear industrial environment by integrating a sensing module, a learning model module, an execution module, a communication module and a security module. The sensing module adopts a multi-sensor fusion technology to collect environmental data such as temperature, radiation intensity and gas concentration in real time; the decision-making module dynamically generates a task execution strategy based on an open source large model base in combination with deep learning and reinforcement learning algorithms; the execution module completes complex tasks such as equipment maintenance, fault processing and nuclear waste carrying through a high-precision mechanical arm and a force feedback control technology; the communication module supports data interaction between the robot and a control center, and remote monitoring and emergency intervention are achieved; the safety module monitors the state of the robot in real time, multiple safety protection mechanisms are provided, and stable operation of the robot in the high-radiation environment is ensured. According to the invention, the operation efficiency and safety of nuclear industry facilities are obviously improved.

Integrated intelligent system for beverage production, including distilled spirits and various brewed or fermented products, combining traditional methods with advanced technologies. System incorporates controlled fermentation unit, advanced distillation apparatus, and accelerated aging unit using novel methods like ultrasonic waves and thermal cycling. Real-time analysis system employs multiple sensors for continuous chemical composition monitoring throughout production process. Intelligent control mechanism optimizes processes and adjusts parameters dynamically. Chemical fingerprinting system enables precise quality control and authenticity verification. Flexible interface allows for customization of beverage profiles based on desired flavor characteristics and market demands. Specialized unit creates complex non- alcoholic alternatives. System adapts to different production scales and beverage types. Represents significant advancement in beverage production technology, offering unprecedented control, consistency, efficiency, and rapid product development across various beverage categories.

The invention discloses a gravity dam safety risk management and control method and system based on a knowledge graph and a large model. The method comprises the following steps: collecting accident case data of a water conservancy project; performing entity and relationship extraction on all case data in the case library by using the fine-tuned model with domain knowledge to obtain a case data knowledge graph; the obtained case data knowledge graph is converted into natural language description, a text is used for fine tuning of a large language model, and finally a large language model with domain knowledge is obtained; performing information extraction on the engineering general situation information by using an engineering model, forming a natural language description problem by using an extraction result, and inputting the natural language description problem into a large language model with domain knowledge; the big language model answers the questions, and the expert judgment module provides expert opinions according to replies of the big language model. The intelligent system integrating artificial intelligence, reasoning and decision making is formed by utilizing the powerful language and reasoning ability of a large model.

The invention provides a robot brain body-equipped intelligent system based on a large model and a task execution method of the robot brain body-equipped intelligent system. According to the system, a modular distributed architecture is realized, the system is endowed with high expandability and flexibility, and multi-platform deployment is supported. The task planning based on the large model significantly improves the ability of the system to understand and execute complex instructions. Unified sensor data management realizes integration and efficient processing of multi-source data, and enhances the environment perception capability of the system. Meanwhile, a friendly real-time monitoring management interface simplifies monitoring and configuration management of the system. In the aspect of performance, completeness and consistency of task execution are ensured through close cooperation among the modules; the optimized algorithm and model improve the efficiency of task planning and action execution and reduce the system response delay.

The invention relates to the technical field of man-machine collaborative interaction, and discloses a resource allocation method and system in a man-machine collaborative decision-making scene, and the method comprises the steps: generating corresponding task features based on the three dimension indexes of task complexity, knowledge density and intelligent adaptation degree of a to-be-processed task; initializing initial weight coefficients of the three-dimensional indexes, and dynamically optimizing the weight coefficients corresponding to the three-dimensional indexes by using a preset strategy network according to the task characteristics and the real-time system resource state; performing weighted normalization on the three-dimensional indexes according to the weight coefficient to generate a task comprehensive score; task priorities are divided according to the task comprehensive scores, and the tasks are dynamically allocated to manual and / or intelligent systems to execute the tasks according to the priorities, so that the flexibility of artificial experience and the high efficiency of the intelligent systems can be brought into full play, man-machine labor division is flexibly adjusted according to task characteristics, and the universality of cross-task scenes is improved. And the dynamic adaptability, decision reliability and task execution efficiency of the man-machine cooperation system are improved.

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 an intelligent system and method for fusing mental health evaluation in an interest scene based on an education robot. The system realizes non-intrusive mental health assessment by constructing a technical architecture of'multi-modal biological feature anonymization-quantum hybrid encryption-federated learning privacy protection-dynamic risk assessment '. The method is characterized by comprising the following steps of: (1) generating irreversible biological characteristic codes by adopting a chaos random number generator, and realizing anonymized association by combining a double hash chain technology; (2) designing a quantum enhanced hybrid encryption system, fusing an SM4 algorithm, NTRU post quantum cryptography and quantum key distribution; (3) developing a dynamic scene generator, and naturally fusing psychological test questions into interest topics by using a generative adversarial network (GAN) and a curiosity engine; (4) constructing a multi-modal emotion analysis system, and combining an LSTM + CNN hybrid model to realize physiology-behavior-voice data fusion analysis; and (5) deploying a federated learning framework, and protecting model training data through differential privacy noise injection (epsilon = 0.5).

The present disclosure pertains to systems, computer program products, and methods for an automated mesh service-based deployment intelligence system. This system is designed to streamline the deployment of software by integrating a processing device and a non-transitory storage device. The storage device contains instructions that, when executed by the processing device, enable the ingestion of data from various sources such as monitoring systems, databases, and application performance management tools. Once ingested, the data is stored and processed to discern deployment patterns and detect any anomalies. Utilizing a machine learning model, the system anticipates potential deployment issues by analyzing this data. It then orchestrates the deployment of software artifacts accordingly, taking into account the insights gained from the machine learning model. Furthermore, the system is capable of real-time optimization of the deployment process to preemptively resolve any predicted issues, thereby enhancing the efficiency and reliability of software deployment operations.

The invention discloses a multi-field coupled microwave deicing intelligent system and an energy efficiency dynamic regulation and control method, and relates to the field of material surface ice layer desorption behavior research and deicing technology experimental testing. In order to solve the technical defects in the prior art that the existing microwave deicing technology still has obvious defects in the aspects of power adjustability, field distribution uniformity, interface depth temperature measurement, real-time mechanical test and the like, the technical scheme provided by the invention is as follows: the microwave deicing device comprises a microwave box body which is a metal closed cavity and is provided with a waveguide interface; a stirrer is arranged inside; the refrigerating system is connected with the back surface of the bearing plate of the microwave box body; a pressure head at the tail end of the mechanical loading module applies thrust to the ice layer; the device and the method are used for realizing construction, microwave heating and real-time mechanical testing of a low-temperature icing environment on the same platform. The method can be applied to dynamic monitoring and research work in multi-physics coupling deicing scenes such as aviation, traffic and new energy.

The embodiment of the invention provides a control method, device and equipment for an intelligent unmanned aerial vehicle with a body and a storage medium. The method comprises the steps of receiving a target task issued by an intelligent platform; in the process of controlling the flight of the unmanned aerial vehicle main body according to the target task, acquiring current environment information collected by a sensor; fusing the current environment information collected by different sensors to obtain a target environment model, wherein the target environment model is used for describing the current environment of the intelligent unmanned aerial vehicle; generating a behavior instruction according to the target environment model and the target task; and controlling the unmanned aerial vehicle body and the execution mechanism to execute the target task according to the behavior instruction. According to the embodiment of the invention, through the intelligent decision-making capability of the intelligent system and the intelligent platform and the efficient cooperation of the unmanned aerial vehicle main body and the execution mechanism, the real-time environment information collected by various sensors is fused, so that the intelligent unmanned aerial vehicle with the body has the capability of coping with a complex environment; and closed-loop control of real-time sensing, decision making and execution of the intelligent unmanned aerial vehicle with the body is realized in an unstructured environment.

Systems, methods, and instrumentalities associated with inter-connectivity of data flows between various surgical devices and / or systems are disclosed. The surgical devices and / or surgical systems may be interrelated or independent smart surgical devices and / or surgical systems. Data sourced by a first surgical device / system may be communicated to and / or accessible by a second surgical device / system for interactive use and storage. The data exchange may be bi-directional or unidirectional, which, for example, may enable the surgical devices / systems to interface and use each other's data. The data exchange may affect one or multiple surgical device / system's operation.

The invention provides an intelligent ring and an intelligent system with the same. The intelligent ring is of an annular structure and is provided with an opening, and the intelligent ring comprises a shell part arranged on the annular outer side of the annular structure, a first contact part arranged on the annular inner side of the annular structure and a second contact part arranged on the annular opening side of the annular structure; the first contact piece is arranged on the main body part of the annular structure, a gap space is formed between the shell piece and the first contact piece, and a plurality of electronic elements are contained in the gap space. According to the scheme provided by the invention, the shape of the ring can be more adjustable through the design of the annular opening of the intelligent ring, so that the ring is more in line with human engineering, dynamic adjustment is realized through the design of the movable opening, the wearing flexibility is improved, the ring can adapt to fingers of various different sizes, a user can wear the ring conveniently, and the user experience is improved. Meanwhile, damage caused by extrusion or collision is effectively avoided in an exquisite electronic element integration mode, and comfort, intelligence and adaptability of the intelligent ring are guaranteed.

Systems, methods, and instrumentalities associated with adaptation of operation associated with monitoring of surgical devices. A first surgical device may monitor operation of a second surgical device. The monitoring of the second surgical device may affect the operation of the first surgical device (e.g., impact data generation). The first surgical device or the second surgical device may change its operating configuration, for example, based on the monitoring.

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 relates to the technical field of language teaching, and discloses an intelligent system applied to Chinese language teaching, and the system comprises a voice recognition module which is used for collecting a voice signal of a learner and converting the voice signal into a phoneme sequence; the pronunciation difference analysis module is used for performing alignment analysis on the dialect phoneme sequence and the mandarin phoneme sequence of the learner to generate pronunciation difference data; the knowledge graph module is used for constructing a multi-layer knowledge graph of dialects and mandarin and describing the relationship among phonemes, grammar rules and vocabularies; and the personalized learning path generation module is used for dynamically generating a personalized mandarin learning path based on the pronunciation difference data and the knowledge graph of the learner. A multi-task learning framework based on a pre-training model Wav2Vec 2.0 is adopted, pronunciation difference analysis is performed through joint recognition of mandarin and dialect voices in combination with a dynamic time warping algorithm, and the technical effect of accurately capturing pronunciation characteristics and differences of learners is achieved.

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.

An intelligent system for a photovoltaic cleaning robot includes: a control terminal, an intelligent control center, a stain detection database, and an intelligent device; where the intelligent device includes a camera shooting module, a motion flight module, and a cleaning module; the intelligent device and the intelligent control center are integrated on the photovoltaic cleaning robot; the intelligent control center and the control terminal are connected by a wireless communication; the intelligent control center is connected to the stain detection database through the wireless communication. In the intelligent system for the photovoltaic cleaning robot, even in a distributed photovoltaic power station with a harsh environment and terrain, it can also do the unmanned intelligent cleaning operation and maintenance work of the photovoltaic plate, where the operator only needs to operate the control terminal according to the attenuation of power generation of the power station to issue simple instructions.

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.

The invention relates to the technical field of artificial intelligence, particularly provides wearable equipment and an operation method thereof, a wearable intelligent system and a storage medium, and aims to solve the problem of how to timely and accurately obtain sentences which are not talked out by a user due to jamming and prompt the sentences. The method provided by the invention comprises the steps of collecting an electroencephalogram signal of a user and a target voice signal in an environment where the user is located, wherein the target voice signal comprises a first voice signal of the user; analyzing the speaking state of the user according to the first voice signal; when the speaking state is a pause state, according to the electroencephalogram signal and the target voice signal, predicting a missing utterance of the user during pause, and outputting prompt information; or, the electroencephalogram signal and the target voice signal are sent to the server, prompt information sent by the server is received and output, and the prompt information is generated by the server according to the utterance missed by the user during the jamming. By means of the method, the sentences which are not taught by the user due to jamming can be timely and accurately obtained and prompted.

This invention relates to a computer-implemented method and system for performing security evaluation on a machine learning (ML) model. The method includes determining a taxonomy of the ML model and of the environment in which the machine learning model is implemented at one or more stages in the model's lifecycle. The method additionally includes generating, based on the determined taxonomy, a set of assumptions about the ML model and the environment. An adversarial test attack is performed on the ML model at a stage in its lifecycle, based at least in part on the set of assumptions, and one or more failure modes in the ML model are identified based on the result of the first adversarial attack. The system may include a threat modelling component, an assessment component, a reporting component, and a risk mitigation component.

The invention relates to the technical field of body intelligence, and discloses a body intelligence-oriented sensing active control method and system, and the method comprises the steps: obtaining multi-modal sensing data, and determining a feature extraction unit and a control center node based on a heterogeneous data fusion framework; selecting a target coding module according to the spatial-temporal correlation index of the adaptive coding module, and generating a feature fusion path; determining a signal synchronization moment by combining the dynamic response delay and the sub-layer coupling degree, and marking a real-time fusion topological graph; and detecting a signal phase conflict, and planning a multi-modal control instruction based on a conflict result. The technology also relates to perception mode priority grading, feature fusion path dynamic generation, spectrum interference detection, path re-planning and the like. Microsecond-level time-space stamp marking and modal feature decoupling are realized through a distributed heterogeneous sensor array. According to the invention, the multi-modal data fusion precision, the signal synchronization control and the multi-modal coordination decision ability are improved, and the environmental adaptability and robustness of the intelligent system are enhanced.

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.