1516 results about "System integration" patented technology

The invention discloses a manipulator grabbing planning system and method based on visual identification, and relates to the technical field of robots, the manipulator grabbing planning system comprises a visual perception module, a coordinate conversion module, a motion planning module, a control execution module, a tail end perception module and a system integration and communication module; the visual perception module realizes multi-view target detection, semantic segmentation and three-dimensional pose estimation through a binocular depth camera; the coordinate conversion module is used for converting a target pose under a camera coordinate system into a world coordinate under a mechanical arm base coordinate system; the motion planning module is responsible for generating a mechanical arm grabbing path, optimizing a strategy and supporting generalization migration of a multi-form mechanical arm; the control execution module drives the six-axis cooperative mechanical arm to complete the grabbing action, and vision-force mixed feedback closed-loop control is achieved. The tail end sensing module monitors the grabbing state in real time through a touch sensor and dynamically adjusts a grabbing strategy; and the system integration and communication module is used for realizing real-time data interaction and cooperative work among the modules of the system.

The invention discloses a hydraulic engineering equipment data intelligent management system based on digital twinning, and belongs to the technical field of hydraulic engineering. Comprising an intelligent perception and data fusion module for realizing real-time acquisition and standardized processing of cross-modal data; the knowledge graph construction and causal reasoning module is used for constructing an intelligent knowledge system capable of autonomously learning and semantic reasoning; the digital twin modeling and simulation module is used for realizing dynamic simulation and scene deduction of a full life cycle and providing limit working condition simulation and risk assessment support; the intelligent prediction and health management module is responsible for performing real-time monitoring, fault early warning and residual service life prediction on the equipment state, and generating personalized intelligent maintenance strategies for different working conditions; the visualization and decision support module is used for visually presenting the equipment operation data and the analysis result and providing intelligent decision recommendation; and the cloud edge collaboration and system integration module realizes cross-platform interoperation and continuous integration through distributed computing and micro-service architecture.

The invention relates to the technical field of smart home, and discloses an automatic coordinated regulation and control system and method for smart home equipment, and the system integrates a plurality of functional modules such as equipment access, environment perception and user behavior analysis. The equipment access module supports access of multiple protocols and endows the equipment with a unique identifier; the environment sensing module collects environment parameters in real time and compares the environment parameters with thresholds. The user behavior analysis module generates a user scene preference model by means of an algorithm, the cooperation strategy generation module synthesizes multiple factors to formulate a regulation and control strategy, and the dynamic priority management module allocates weights according to equipment and scene conditions. And the equipment control module executes the instruction and feeds back the state, and the abnormity detection module monitors the equipment state and triggers a strategy regeneration mechanism when abnormity occurs. The data storage module securely stores various data, and the user interaction module efficiently analyzes instructions. According to the invention, many problems of an existing smart home system are solved, automatic coordinated regulation and control of equipment can be realized, and user interaction experience is optimized.

Disclosed in the present invention is a four-network integration architecture for an unmanned swarm system. The four-network integration architecture has the capabilities of heterogeneous platform resource pooling, intelligent dynamic computing power allocation, and timely decision planning, so as to maximize the overall benefit. The present invention focuses on abstracting and integrating independent submodules to form a mesh topology of a swarm. The present invention designs a four-network integration architecture for an unmanned swarm system, which comprises a computing power network, a perception network, a decision network and a communication network as core modules. The structure aims to achieve efficient cooperation of all parts in the swarm, thereby improving the overall performance and adaptability of the system. The system integrates environmental perception, a swarm network modeling component, a knowledge base and a resource pool, providing an intelligent environmental perception strategy and a network modeling strategy for the interior of the swarm. Therefore, the perception of environments, tasks and networks by nodes can be facilitated, thereby completing establishment of intelligent networks, so as to ensure the characteristics of the stability and flexibility of networks.

The invention belongs to the technical field of electromechanical equipment operation and maintenance, and discloses an electromechanical equipment self-adaptive intelligent early warning system based on multi-source sensing data. The system is composed of a multi-source sensing module, an edge data acquisition and preprocessing module, a data cleaning and multi-dimensional feature extraction module, an equipment state dynamic modeling module, an intelligent fault prediction and trend analysis module, a self-adaptive early warning threshold generation and dynamic adjustment module, and an intelligent decision and remote cooperation module. The system is composed of a multi-source sensing module, an intelligent low-carbon operation and maintenance management and control module and a digital twin system integration and full-period mapping module, multiple sensors are deployed through the multi-source sensing module to acquire multi-dimensional data of equipment, cleaning and calibration are performed through the edge data acquisition and preprocessing module, deep processing is performed through the data cleaning and multi-dimensional feature extraction module, and multi-dimensional feature extraction is performed through the multi-source sensing module. The data integrity and accuracy are ensured; and data are quickly transmitted among the modules, so that the monitoring system can accurately present the running state of the equipment in real time.

The invention relates to the technical field of agricultural traceability, and discloses an agricultural non-point source pollution intelligent traceability method and system based on a knowledge graph, and the method comprises the steps: achieving the system integration of pollution source features through constructing the knowledge graph fusing multi-dimensional data, and building a sky-ground integrated monitoring network to obtain multi-scale dynamic data. An intelligent traceability mechanism is formed based on deep coupling of a knowledge graph and monitoring data, a high-precision pollution identification model is trained in combination with historical data, and real-time response and dynamic traceability of an over-standard pollution area are achieved. And verifying a traceability result through feature matching and semantic reasoning, quantitatively calculating a pollution contribution rate, and finally generating a reliable traceability conclusion containing the position, the type and the contribution rate. According to the scheme, the bottlenecks of data fragmentation, monitoring simplification, extensive analysis and the like of a traditional method are broken through, and the accuracy, timeliness and credibility of pollution traceability are remarkably improved through a series of full-chain technical systems of traceability identification, analysis and positioning.

The invention relates to the technical field of data processing, in particular to a ceramic supply chain quality traceability data management system based on a block chain, which comprises a data acquisition and processing module, a dynamic identifier generation module, an identifier embedding control module, a cross-chain data synchronization module, a multi-modal verification module and a redundancy recovery module. The data acquisition and processing module integrates firing temperature, forming pressure parameters and microscopic image data through Internet of Things equipment and edge computing nodes to generate a structured data set, the system integrated resource scheduling module optimizes node computing resource allocation, and anti-quantum encryption and IPFS distributed storage guarantee data security. And the terminal equipment integration module simplifies the multi-modal data acquisition process, and finally collaborative optimization of the ceramic quality tracing reliability, the operation efficiency and the artistic integrity is realized.

The invention relates to an MCP-based urban rail transit intelligent operation and maintenance service construction method. The method comprises the steps of obtaining an input question of a user; inputting the input question into the large language model for processing and extracting key information; the big language model judges data required for solving the problem according to the key information; acquiring data required for solving the problem from different MCP Clients through an MCP protocol; and outputting a final result by combining the big language model with data required for solving the problem. According to the method, the MCP protocol is introduced into the field of urban rail transit intelligent operation and maintenance for the first time, and a unified model context communication interface is constructed to realize structured interaction between a large model and multi-source operation and maintenance data, so that connection between various operation and maintenance services and intelligent agents is more efficient and standardized, and the operation and maintenance efficiency is improved. The technical problems that in a traditional system, interfaces are heterogeneous, data are closed, and a model is difficult to call external services are solved, and infrastructure support is provided for modular deployment and cross-system integration of an intelligent operation and maintenance platform.

The invention discloses a data center machine room AI energy-saving control method and system, a digital twin model of a machine room operation state is constructed through a holographic perception and heterogeneous data fusion technology, centimeter-level monitoring of an equipment state and environmental parameters is realized, and the system integrates a laser radar array, an acoustic sensor and a gas sensor network. The time-space alignment of multi-modal data is completed by combining edge computing nodes, holographic mapping including thermodynamic characteristics, vibration characteristics and gas leakage risks is formed, historical temperature control strategy characteristics are extracted by adopting a variational auto-encoder based on a dynamic strategy generation mechanism of generative artificial intelligence, and a load trend is predicted by combining a long-short-term memory network. Constructing a self-adaptive strategy pool; the multi-agent reinforcement learning framework enables temperature control, equipment scheduling and power grid response to form game optimization, the strategy robustness in a complex scene is improved, and the system innovatively fuses power grid real-time electricity price and carbon transaction data so as to establish a multi-target decision system.

The invention discloses a process automation execution method based on a large language model, and belongs to the technical field of artificial intelligence and process automation. User intention is analyzed through multi-modal input, and a structured task definition is constructed; the semantic reasoning layer is used for performing task layering, complexity evaluation and sorting optimization; the task execution layer completes subtask scheduling and execution; and the feedback and optimization layer performs performance evaluation and model updating based on execution data to realize closed loop and continuous optimization of the process, so that the technical problems of dynamically analyzing unstructured instructions, automatically optimizing a complex task dependency relationship and adapting to business changes in real time by a process automation tool are solved; according to the method, end-to-end conversion from an unstructured instruction to a structured task is realized, a subtask execution path is dynamically optimized, cross-platform tool calling is supported, the existing system integration cost of an enterprise is reduced, visual display task decomposition logic and prediction and execution time consumption comparison are provided, and the system credibility is enhanced.

The invention discloses an energy management multi-agent collaborative optimization method and system based on a large model agent architecture. The system comprises a multi-agent architecture module, a model processing module, a data acquisition and management module, a system integration and interaction module and a collaborative optimization control module. The model processing module comprises a large model and a plurality of small models and is used for realizing task processing and analysis; the data acquisition and management module acquires and processes multi-source data; the system integration and interaction module realizes multi-platform integration and visual interaction; and the collaborative optimization control module ensures collaborative work and system optimization of the intelligent agents. The system has the advantages of autonomy and decentralized control, isomerism and modularization, intelligent collaboration and the like, instrument faults can be accurately diagnosed, energy management is optimized, production is intelligently controlled, enterprise energy utilization efficiency, instrument operation stability and the overall operation management level are improved, enterprises are assisted to achieve green and low-carbon transformation, and the system is suitable for popularization and application. The method is suitable for energy management and energy equipment maintenance scenes of various industrial production enterprises.

Computerized systems and methods are described for converting traditional technology products into an “As a Service” (AaS) model, facilitating the transition from capital expenses (CapEx) to operational expenses (OpEx). Methods include receiving user inputs for technology product selections and accessing a Real-Time Data Mesh (RTDM) to retrieve data. An Advanced Analytics and Machine Learning (AAML) Module analyzes user inputs and market data, optimizing the conversion into subscription-based services. Process results are displayed to the user through a Single Pane of Glass User Interface (SPOG UI). An AaS Conversion Module performs transition of products into customizable subscription packages. This method emphasizes dynamic pricing based on usage, flexibility, and / or scalability of services. Methods are provided for real-time reporting, subscription management, and vendor system integration, enabling a comprehensive AaS conversion process suitable for modern technology products and services.

The invention discloses a digital twin pumped storage power station three-dimensional real-time modeling fusion method which comprises the following steps: (1) obtaining three-dimensional modeling basic image data through a video monitoring system, and constructing a three-dimensional modeling basic image database; (2) performing real-time three-dimensional modeling and model fusion; and (3) system integration and real-time updating. According to the invention, deep learning is utilized to improve the image quality of a video monitoring system, and the precision of a three-dimensional model in a digital twin system can be improved; and new equipment in the image is identified by combining deep learning, real-time modeling of single equipment is completed on the premise of not increasing the model acquisition cost, and low cost, simplification and intelligentization of refined three-dimensional real-time modeling are realized.

The invention discloses a BIM rule specification extraction method based on a large model, and the method comprises the following steps: S1, BIM standard document content extraction: extracting all contents in a document from a BIM standard document, and carrying out the corresponding processing, thereby converting the contents into a form which can be used for the subsequent semantic understanding of the large model and the BIM rule specification extraction; s2, semantic disassembly of BIM standard document content: based on the content extracted in the step S1, extracting entities and relationships thereof in the content; s3, BIM rule specification fusion and processing: based on the extracted entities and the relationships thereof, performing knowledge fusion and knowledge processing to form BIM rule specifications; and S4, BIM rule specification regularization and semantization: through BIM rule specification regularization, realizing system integration of BIM rule specifications from the BIM rule specification knowledge graph to a BIM platform. According to the method, the BIM rule specification is efficiently, rapidly and accurately extracted from the BIM standard, so that the time cost and the error possibility of manual reading and rule document arrangement are greatly reduced, the heavy workload of manual reading understanding and BIM standard execution is solved, the BIM efficiency in an actual engineering project and the BIM standard system implementation efficiency are improved, and the BIM standard system implementation efficiency is improved. And the accuracy of standardizing the BIM rule to an actual engineering project is improved.

The invention discloses a cargo scheduling and distribution management system suitable for a stocker three-dimensional warehouse, and relates to the technical field of logistics warehousing, and the system comprises an intelligent scheduling module which employs a multi-algorithm fusion and dynamic switching mechanism, plans the path of a stocker according to an order and the condition in the warehouse, and distributes tasks; the accurate inventory management module is used for guaranteeing the accuracy of inventory data by applying a multi-sensor fusion and intelligent identification technology and combining environment self-adaption and intelligent auditing; the system integration module is used for realizing multi-system integration through a standardized interface and middleware and has a cluster deployment, fault processing and interface management mechanism; an equipment fault handling module; the energy management module is used for optimizing energy utilization based on an energy-efficiency balance model in combination with hybrid energy and an intelligent switching technology; according to the invention, through close cooperation of a plurality of modules and comprehensive application of innovative technical means, efficient, intelligent and reliable operation of the stacker three-dimensional warehouse cargo scheduling and distribution management system is realized.

The invention discloses a grabbing control system for robot visual guidance, and belongs to the technical field of grabbing control systems.The grabbing control system comprises a visual perception module used for collecting workpiece images, extracting workpiece feature information and calculating accurate coordinates of workpieces in space; the robot main body module is used for receiving a system instruction to drive a carrier to move to a target position; the grabbing execution module is used for being in contact with the workpiece, grabbing the workpiece stably and transferring the workpiece to a designated position to be placed without damage; the system integration module is used for guaranteeing the safety, operability and stability of the feeding and discharging process; according to the system, workpiece information can be accurately collected, the posture of the grabbing execution end is analyzed based on the workpiece position, the grabbing posture is corrected in real time according to the grabbing dynamic state, and the workpiece grabbing precision and stability are improved.

The invention discloses a multi-channel meteorological risk early warning information self-adaptive targeted publishing system and method based on low-altitude flight, and solves the problems that existing low-altitude meteorological early warning is low in temporal-spatial resolution, single in risk identification and insufficient in information pertinence. The system integrates air-based, space-based, foundation, social and topographic data, and generates a low-altitude meteorological dynamic database through fusion; micro-scale risks such as wind shear are extracted and graded through an AI model, a digital twin simulation response is constructed in combination with the state of the aircraft, and a risk grid model and coordinates are output; calculating a comprehensive risk index, generating evasion guidance, converting into multi-role early warning information, generating a three-dimensional thermodynamic diagram, and finally adaptively selecting a publishing channel according to user roles, terminals and network states. According to the invention, the accuracy and timeliness of low-altitude flight meteorological risk early warning are improved, and the safety of low-altitude flight is guaranteed.

The invention relates to a humanoid robot double-arm body operation method based on reinforcement learning, belongs to the field of robot cooperative control, and is characterized in that a strategy of trajectory block prediction and time integration fusion is used for double-arm cooperative control, and continuity and stability of double-arm operation are improved; in reinforcement learning control, a three-dimensional pose track generation and correction module is introduced, condition generation and denoising correction of a three-dimensional space are carried out on a track block layer, and geometric consistency and naturalness of a generated track are guaranteed; the invention further provides a reinforcement learning optimization framework and a simulation-reality migration process, and through system integration of reward, value guidance and migration processes, strategy deployability and safety are guaranteed; compared with the prior art, the method has the advantages that the naturalness, the collaboration and the success rate of double-arm operation can be remarkably improved, the generalization ability is high, and the good simulation-to-reality migration ability is achieved.

The invention relates to the technical field of artificial intelligence and virtual reality, and discloses a virtual digital human generation and interaction optimization system based on multi-modal data fusion, and the system comprises a multi-modal input fusion module which is used for collecting and fusing multi-modal data from audio, video and touch; the adaptive behavior adjustment module is used for adaptively adjusting the behavior and response strategy of the virtual digital human according to the user input and the interaction history; the real-time dynamic rendering module is used for generating and rendering an image of the virtual digital human and synchronizing actions, expressions and language output of the virtual digital human in real time; the personalized customization interface module is used for providing personalized customization functions of the appearance, the sound and the behavior of the virtual digital human for the user; and the system integration and optimization module is used for integrating the modules. By fusing data (such as audio, video and touch) of multiple modes, the system can comprehensively understand behaviors and emotional states of the user, interaction delay is reduced, and the user can experience a smoother interaction process.

The invention relates to an indoor robot navigation method based on multi-modal feature fusion. The method comprises the following steps: constructing a semantic map based on visual observation environment information; the method comprises the following steps: acquiring an RGB image of an indoor scene object, converting the RGB image into point cloud data, and preprocessing the RGB image and the point cloud data; image multilayer semantic features and point cloud features in the RGB image and the point cloud data are extracted respectively, and initial fusion is carried out; performing weighted fusion on the multi-layer semantic features and the point cloud features of the image by adopting space-channel-cross-modal multi-attention dynamic cooperation; and predicting a long-term target in a map space from top to bottom based on the fused feature map and the semantic map, and performing navigation path planning based on the current position and the long-term target. Under low-cost hardware configuration, the robustness of environment perception, the real-time performance of decision response and the usability of system integration in a dynamic complex environment are comprehensively improved, and the method is particularly suitable for application scenes such as indoor service robots.

The invention relates to the technical field of robot control, in particular to an energy consumption optimization and automatic charging control system for an intelligent pet robot. The system comprises a robot data acquisition module, an energy consumption state evaluation module, an energy efficiency optimization module, an automatic charging scheduling module and a battery health management module which are in communication connection in sequence. The system is driven by multi-dimensional sensing data, energy consumption characteristics are extracted in combination with potential energy field mapping and sparse modeling, and an energy consumption influence map is constructed; joint optimization of the path and the servo strategy is realized by using a quantum behavior particle swarm algorithm; scheduling a charging path through a graph attention network; and the battery health state is dynamically predicted based on an incremental learning algorithm, and the operation efficiency, the cruising ability and the system intelligence of the robot are improved. According to the method, a closed-loop control process is constructed from energy consumption evaluation to energy efficiency optimization, automatic charging and battery health management, good system integration and intelligent response capability are achieved, and the cruising ability and operation stability of the pet robot in a complex application scene are remarkably improved.

The invention discloses a customer service robot system integrating knowledge base question and answer retrieval and work order processing, which comprises a natural language understanding and generating module, a knowledge retrieval module, a business system integration module, a dialogue management and decision module and a cue word management module, and relates to the technical field of artificial intelligence. Through cooperation of a large language model and a dialogue management module, the system realizes multiple rounds of deep understanding of a natural language, and can actively guide a user to clarify a demand, such as gradually inquiring an equipment model and fault details during repair, memorizing a dialogue context and avoiding information omission. Compared with traditional regular customer service, the method has the advantages that the accuracy of understanding complex problems is remarkably improved, open dialogue scenes such as technical consultation and fault diagnosis are supported, user interaction experience is closer to manual service seats, and the problems that multi-round dialogue understanding is limited, progress data cannot be obtained in real time, multiple knowledge bases are fused, and the automation degree of the business process is low are solved.

The invention discloses a bus duct full life cycle health management system based on digital twinning, and relates to the technical field of health management. The multi-source sensor collects operation parameters and static information, the digital twin modeling and mapping module constructs a physical model and associates real-time data, and a thermal-electric coupling equation is used for simulating temperature; the state monitoring and fault diagnosis module compares data to judge states and diagnoses faults by means of methods such as a fault tree, the health assessment and decision making module constructs an index system to assess health and makes a maintenance decision, the data management and interaction module stores data and realizes visual interaction and system integration, and the intelligent optimization module performs intelligent optimization based on operation and maintenance data. And optimizing model parameters and a decision strategy. According to the invention, intelligent health management of the bus duct is realized, multi-source data acquisition is accurate and comprehensive, fault diagnosis is more accurate and prediction is more timely through combination of digital twinning and an algorithm, and intelligent assessment assists scientific maintenance decision; and the operation and maintenance efficiency and the power transmission stability are improved through system integration and edge calculation.

The invention provides a cloud environment active defense system based on dynamic honey spots. The system comprises a honey spot deployment and management module which generates and deploys honey spots, manages honey spot layout and provides honey spot information; the dynamic defense control module monitors network flow, perceives an attack path, analyzes attack behavior characteristics, adjusts honey point layout, generates an adjustment instruction and generates alarm information according to the attack behavior characteristics; the attack chain tracking and analyzing module is used for acquiring attack event data for attack behavior tracking, constructing an attack graph for attack path analysis and attack intention prediction and generating a threat intelligence report; and the system integration and management module monitors the running state and the resource use condition of each module, dynamically distributes system computing resources, and sets an interaction unit to provide interaction. According to the system, a complete deception defense mechanism is constructed, a deception environment is constructed by utilizing honey points, the honey points are dynamically adjusted according to attacks, and quick response and accurate countering are ensured through a flexible defense strategy and multi-layer cooperation.

The invention relates to the technical field of environmental governance and remediation, in particular to a combined contaminated site soil remediation data management method, which comprises the following steps of: firstly, integrating multi-source heterogeneous data through a unified space-time coordinate system to generate a fusion data pool, and then driving a pollution assessment model to generate a multi-dimensional space-time pollution map; and then intelligently matching a candidate repair process chain based on an associated knowledge base, performing iteration deduction through a dynamic multi-objective optimization engine to obtain an optimal repair scheme, converting the optimal repair scheme into a digital regulation and control instruction, and finally monitoring an execution state and environmental response data in real time and feeding back and updating a data pool to realize closed-loop optimization. According to the method, the data quality and the pollution evaluation accuracy are improved, the adaptability of the repair process and the cost performance of the scheme are optimized, the repair effect is ensured to be stable and reach the standard, and the efficiency and reliability of the repair project are improved.

The invention relates to the technical field of data center interaction, and particularly discloses an information interaction method and system for computing power and electric power collaborative scheduling of a data center, which integrates multi-source heterogeneous data and constructs a space-time correlation model to realize accurate prediction and dynamic modeling of resource demands of the data center. The real-time learning ability of the DRL and the discrete decision advantage of the MIP are utilized to realize multi-objective synchronous optimization, the energy efficiency and economy of the data center are significantly improved, the multi-objective optimization can realize "computing power-electric power-heating power" coupling, the block chain technology ensures that data cannot be tampered and is transparent and credible, the security and auditing performance of the system are improved, and the system performance is improved. Carbon footprint tracking, green power authentication and PUE index dynamic energy efficiency optimization are supported, green power use is directly stimulated, the carbon footprint and operation cost is reduced, thermal data is used for cooling control, power data is used for task scheduling, resource conflicts are avoided, the limitation of traditional single resource scheduling is broken through, and the response speed is increased.

The disclosed invention provides a system and method for processing air traffic control (ATC) communications to ensure real-time validation of aircraft operations and improve pilot logbook accuracy. The system receives ATC voice communications through a digital radio system, converts them to text using a natural language processing model trained on aviation terminology, and extracts key operational data, including aircraft identifiers, instructions, and corrections. This data is correlated with Automatic Dependent Surveillance-Broadcast (ADS-B) and GPS data to verify compliance with ATC instructions. The system creates a continuous transcript of ATC-pilot communications, stitching together conversations across multiple frequencies. Deviations from ATC instructions are detected, generating alerts and logging the events. The method further provides automated reporting, archiving of communications, and enhanced safety metrics in immutable pilot logbooks, using blockchain technology for secure storage. This invention improves operational safety, transparency, and flight data integrity.

The invention discloses a real-time and efficient basin flood inundation simulation method and system, and relates to the technical field of flood simulation. Multi-source data such as hydrology, remote sensing, weather and terrain are collected in real time, and cleaning, interpolation processing and format standardization are carried out; a data fusion technology and a dynamic weight distribution algorithm are utilized, the system integrates multi-source data to generate a high-precision data set, and the weight of each data source is dynamically adjusted according to the real-time performance, the precision and the space coverage degree of the data; factors such as gradient, elevation and land utilization types are considered, correlation coefficients are quantified, the inundation range and water depth distribution in the drainage basin are estimated, and visual display and early warning release are carried out through the GIS technology. In addition, the system assists in decision making, submerging risk index calculation, emergency evacuation path planning and resource scheduling scheme generation; the real-time performance and accuracy of flood simulation are effectively improved, the flood early warning and emergency response capabilities are enhanced, and powerful support is provided for decision makers.

The invention provides an underwater robot intelligent navigation and environment sensing system based on a multi-sensor fusion collaborative technology. The system integrates a plurality of sensors such as sonar, optical, electromagnetic and the like, and realizes comprehensive data acquisition and real-time perception of an underwater environment. By applying advanced algorithms such as Kalman filtering and a neural network, the system performs synchronization, preprocessing and deep fusion on multi-source data, and the consistency and accuracy of information are remarkably improved. In addition, the system also has adaptive navigation and environment perception capabilities, can dynamically adjust a navigation path and a perception strategy according to fused data, and adapts to a complex and changeable underwater environment. And the intelligent decision support system provides decision assistance for an operator in combination with an environment perception result and a task target. And due to the design of a user interaction and monitoring interface, the usability and real-time monitoring capability of the system are ensured. According to the implementation of the invention, the precision and efficiency of the underwater robot in tasks such as monitoring and exploration are greatly improved.

The invention discloses an old people falling risk assessment and dynamic protection intervention system and method, and relates to the technical field of computer vision, and the system comprises a hardware composition module, a software and algorithm module, an intervention mechanism module, a system integration and optimization module, and an application scene module. The hardware composition module is used for collecting motion postures, gaits and environment data of old people in real time through a multi-mode sensor, hardware support is provided for risk assessment and protection, deep fusion of vision, inertia and environment data is achieved through an attention mechanism, spatial-temporal features are rapidly extracted in combination with a lightweight CNN-LSTM network, and the accuracy of risk assessment and protection is improved. And then the dynamic relationship between the mass center and the supporting surface is accurately calculated based on a biomechanical model, so that the real-time performance of attitude analysis and the accuracy of fall risk assessment are effectively improved, the prediction accuracy of the system on the fall risk of the old people is greatly improved and the false alarm rate is reduced while the consumption of calculation resources is reduced, and a reliable basis is provided for a hierarchical protection strategy.