731 results about "Visual interaction" patented technology

The invention relates to the technical field of medical image diagnosis, and discloses a medical image automatic diagnosis method and system based on deep learning. According to the method, multi-modal medical image data of a target object is acquired and standardized, a two-channel convolutional neural network is utilized to extract features, the features are processed through cross-modal feature fusion, adaptive attention weight distribution and other technologies, a cascaded two-way long-short-term memory network is adopted for modeling, abnormity is detected based on a probabilistic graph model, and the target object is identified. And the nidus is segmented by a multi-scale context information enhancement module, and finally a diagnosis suggestion is generated by a diagnosis inference engine driven by a knowledge graph. The system comprises a multi-modal image acquisition interface module, a distributed feature calculation cluster, a visual interaction terminal and a security audit module. According to the method, the accuracy and efficiency of medical image diagnosis can be improved, comprehensive diagnosis reference is provided for doctors, and meanwhile data safety and privacy are guaranteed.

The invention discloses an intelligent building design generation system based on multi-source heterogeneous data and an AI large model, and the system comprises a multi-source heterogeneous data collection and processing module which is responsible for automatically collecting the multi-source heterogeneous data related to a project, and carrying out the data processing; the knowledge graph module is used for carrying out structured storage and association on multi-source heterogeneous data by constructing a knowledge network covering upper planning, historical culture, architectural design standards and law and regulation contents; the AI large model fine tuning module is used for performing fine tuning on the AI large model by using data in the special architectural design field; the scheme design module is responsible for generating various design schemes, providing choices of different design styles and function layouts and optimizing the schemes; and the visual interaction and feedback module is used for providing an interaction interface with a user. The method is remarkably superior to the prior art in the aspects of design substantial content processing, multi-source heterogeneous data integration, design generation and optimization, user interaction experience and the like, and a more comprehensive, intelligent and efficient solution is provided for building design.

The invention relates to the technical field of intelligent agriculture, and discloses a visual intelligent agricultural planting control system. The system comprises an environment data acquisition module used for acquiring multi-source environment sensing data; the growth feature modeling module is used for extracting crop growth state features through a morphological analysis algorithm; the environment regulation and control decision module is used for generating an environment regulation and control instruction set by utilizing a dynamic threshold matching algorithm; the visual interaction module is used for generating a three-dimensional farmland live-action simulated diagram through multi-dimensional data fusion processing; the strategy execution module is used for driving agricultural facilities to execute actions by adopting a self-adaptive control algorithm; an abnormity early warning module is further arranged, and abnormity early warning signals are generated through correlation analysis. The system realizes comprehensive acquisition and analysis of agricultural planting environment data, precise environment regulation and control, visual and visual display and abnormal early warning, effectively improves the intelligent and precise level of agricultural planting, improves the crop yield and quality, and assists the development of intelligent agriculture.

The invention relates to the technical field of abnormal monitoring of water-turbine generator sets, in particular to an abnormal monitoring system and method for a water-turbine generator set, and the system comprises a sensor module, an edge computing node, a cloud analysis platform and a scheduling decision center. The edge computing node performs data preprocessing, the cloud analysis platform realizes accurate early warning by applying technologies such as multi-source data fusion, a dynamic early warning model library and adaptive threshold adjustment, the scheduling decision center optimizes scheduling decisions through a resource matching engine and an emergency plan generation module, and the industrial communication network guarantees reliable transmission of data. In addition, the system is also provided with a visual interaction interface and a state evaluation report generation module. According to the invention, comprehensive and accurate monitoring and intelligent early warning of the water-turbine generator set are realized, the fault processing efficiency is effectively improved, the probability of false alarm and missing alarm is reduced, resource allocation is optimized, the operation safety is enhanced, and a powerful guarantee is provided for stable operation of the water-turbine generator set.

The invention relates to the technical field of building reconnaissance, in particular to a high-precision reconnaissance lofting intelligent positioning system and method based on multi-source data fusion, and the system comprises a data collection module, a fusion processing module, an intelligent positioning calculation module, a lofting path generation module, a wireless communication module and a visual terminal module. The data acquisition module synchronously acquires multi-source data; the data fusion processing module carries out space-time alignment and feature level fusion and detects the state of the sensor; the intelligent positioning resolving module outputs three-dimensional coordinates by adopting various algorithms; the lofting path generation module generates an optimized path according to the BIM model; the wireless communication module realizes high-speed data transmission and equipment communication; the visual terminal module provides a visual interaction interface. Multi-source data fusion is utilized, the positioning precision is improved, the reliability of the system is guaranteed through sensor failure detection, the system adapts to the complex environment, the lofting path is optimized, the overall performance of the system is enhanced, and an efficient and accurate solution is provided for building investigation lofting.

The invention provides a method and system for obtaining optimal parameters of a machine tool based on an industrial mechanism AI data model, belongs to the technical field of industrial machine tool application, and solves the problems that an existing machine tool cannot obtain the optimal parameters in real time, autonomous intelligent control of the machine tool cannot be achieved and the like. Comprising the following steps: multi-dimensional high-frequency data fusion acquisition and monitoring; performing data synchronization and storage management; analyzing and mining data; mixing intelligent decision engines; constructing an industrial knowledge base and a model base; training an industrial knowledge base and a model base; intelligent decision making and optimization; equipment is automatically controlled; user visual interaction is carried out; carrying out on-line adaptive parameter optimization; continuously optimizing and iterating; and the production business system works cooperatively. According to the method, online self-adaptive parameter optimization can be realized, a closed-loop control system with rapid sensing, intelligent decision-making and accurate execution capabilities is constructed, the method develops in the direction of sensing-decision-execution full-link autonomous optimization, and finally autonomous intelligent control of the machine tool is realized.

The invention discloses an electrical equipment operation state monitoring method, system and terminal, full life cycle health management of equipment is realized through multi-dimensional perception and intelligent analysis, a composite sensor network can be constructed from the level of the method, and high-frequency current, ultrahigh frequency, fiber grating temperature vibration, multi-parameter gas and acoustic sensors are integrated. Electromagnetic characteristics, mechanical states, environmental parameters and voiceprint characteristics are covered; the adaptive signal processing technology performs classification and noise reduction on multi-source data, and the three-dimensional digital twin model realizes time-space fusion of a temperature field, a vibration field, an electric field and a sound field; a lightweight space-time convolutional network is deployed to fuse a time domain waveform, a spectrogram and spatial distribution characteristics for diagnosis, and a hidden semi-Markov model and a particle filter algorithm are combined to dynamically predict the residual service life of equipment. The system architecture comprises a self-organizing sensor network with edge computing capability, a time-sensitive industrial communication network and a containerized analysis engine, and supports mixed reality visual interaction.

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.

The invention provides a geographic information visualization intelligent analysis system based on unmanned aerial vehicle surveying and mapping, and belongs to the field of surveying and mapping, and the system comprises an unmanned aerial vehicle aerial survey module which carries out regional aerial survey based on a multi-source sensor carried by an unmanned aerial vehicle, obtains multi-modal geographic data, and carries out the preprocessing of the multi-modal geographic data; the data processing module is used for forming an initial geographic data set under a standard coordinate system; the three-dimensional modeling module is used for obtaining the fused geographic feature data, constructing a three-dimensional geographic information model, optimizing the three-dimensional geographic information model and embedding time dimension information to form a four-dimensional spatio-temporal data set; the analysis and decision module is used for generating an analysis report comprising a terrain evolution trend and disaster risk assessment; and the visual platform module is used for visualizing the analysis report through a preset visual interaction interface and adjusting the rendering precision of the optimized three-dimensional geographic information model in real time according to the change of the visual angle of the user. The system provides an efficient and intelligent solution for geographic surveying and mapping and disaster early warning.

The invention discloses an intelligent management system for nuclear power plant personnel situation prediction and risk assessment, and relates to the field of intelligent safety management systems, and the system comprises a data collection unit, a multi-dimensional situation awareness unit, a risk prediction and assessment unit, an intelligent decision intervention unit and a visual interaction unit. And multi-source data acquisition, real-time situation construction, dynamic risk prediction and evaluation, intelligent early warning intervention and information visualization are realized. According to the invention, real-time monitoring, dynamic risk prediction and intelligent management of the safety state of the operating personnel can be realized, and the defects of real-time monitoring, dynamic prediction and intelligent management of the operating personnel in a high-risk area in the prior art are overcome, so that the safety management level is improved, the life safety is guaranteed, and the accident occurrence probability is reduced.

The invention relates to the technical field of power system operation training, and discloses a virtual reality-based power grid fault simulation training system and method, and the system comprises a virtual scene modeling module which is used for constructing a three-dimensional model of power grid equipment and an operation environment thereof, and providing a vivid training environment for subsequent modules; and the interaction and operation module is connected with the virtual scene modeling module and realizes interaction between the trainee and the virtual environment by using a VR head-mounted display, a handle and a motion capture device. By adopting a high-precision three-dimensional modeling technology, PBR material rendering and an LOD optimization strategy, high simulation of power grid equipment and an operation environment is realized, students can experience a vivid visual effect and immersion in a virtual reality scene, and meanwhile, by combining a gesture recognition technology realized by motion capture equipment and a convolutional neural network, the real-time simulation of the power grid equipment and the operation environment is realized. Operation actions of trainees can be accurately captured, a natural and visual interaction mode is provided, and the authenticity and operation experience of the training system are greatly improved.

The invention belongs to the technical field of automatic industrial sorting, and discloses an automatic sorting digital twin system and a construction method. The construction method comprises the following steps: constructing physical entities for executing different operation functions in automatic sorting based on a physical entity layer, and sending the physical entities to a digital twin platform layer; the digital twin platform layer constructs a three-dimensional virtual model synchronous with different operation functions executed by the physical entity through three-dimensional design software; the three-dimensional virtual model is dynamically updated in real time; and loading the three-dimensional virtual model to a three-dimensional engine of a visual application layer through a communication protocol or a transmission network, monitoring the real-time states of different operation functions of the physical entity in an industrial automatic sorting simulation scene, and visually displaying the real-time states through a user interface. The digital twinning platform of the automatic sorting system is constructed, and visual interaction, real-time data monitoring and virtual and real synchronous operation of the automatic sorting digital twinning system are achieved.

The invention relates to the technical field of XR extension, and discloses an XR augmented reality-based educational culture multi-mode interactive learning system, which comprises a culture scene intelligent construction module used for constructing an interactive historical scene through LiDAR scanning, ancient books and literature and AI generation technologies, dynamically associating cultural relics, buildings, historical events and scientific principles based on a knowledge graph technology, and establishing a multi-mode interactive learning model based on the knowledge graph technology; the multi-modal interactive learning system comprises an AR (Augmented Reality) subsystem, a VR (Virtual Reality) deep learning workshop, an AI (Artificial Interaction) real-time error correction function and an interdisciplinary fusion education platform, wherein the AR subsystem supports scanning textbooks to trigger a 3D (Three-Dimensional) scene and explores an internal structure of a cultural relic through gesture operation, and the VR deep learning workshop provides force feedback gloves to simulate non-abandoned tool operation and AI (Artificial Interaction) real-time error correction function. According to the educational culture multi-mode interactive learning system based on the XR augmented reality, visual interaction of historic building mechanical analysis and process mathematical modeling is achieved, a cross-region cooperation space is achieved, the educational culture multi-mode interactive learning system based on the XR augmented reality forms a dynamic optimization closed loop, and the teaching efficiency and the learning effect are remarkably improved.

The invention discloses a highway bridge bearing capacity detection device and a use method thereof, and belongs to the technical field of highway bridge detection. The device comprises a multi-source data acquisition module for realizing omnibearing real-time data acquisition of bridge structural stress, environmental parameters and apparent characteristics; the edge calculation processing module is used for executing basic data processing, primary feature extraction, intelligent data compression and abnormal preliminary screening at a data source, and ensuring the balance between the data transmission efficiency and the processing real-time performance; the knowledge graph management module is used for constructing and maintaining a multi-level knowledge graph system covering bridge structure features, damage modes and historical cases; the intelligent analysis decision module is used for realizing intelligent decisions of bridge bearing capacity evaluation, risk early warning and detection strategy optimization; and the visual interaction module is used for realizing visual display, intelligent interaction, report management and authority management.

The invention relates to the technical field of production scheduling, and discloses a production scheduling visualization method based on big data. According to the method, multi-dimensional production data such as equipment real-time states, material inventory, personnel skills and order delivery periods are collected, and a four-dimensional dynamic constraint model is generated through a dynamic constraint modeling algorithm; and obtaining an initial scheduling scheme by using a mixed integer programming and improved genetic algorithm dual-drive optimization engine, inputting the initial scheduling scheme into a visual interaction module, displaying a result by using a drag-and-drop Gantt chart, and detecting resource conflicts in real time. The feasibility of the adjusted scheduling scheme is verified through a dynamic re-calculation algorithm, and then the model is updated. According to the method, multi-dimensional data are integrated, production scheduling optimization and visualization are realized, production resource conflicts can be effectively solved, the production efficiency is improved, the cost is reduced, and an efficient solution is provided for enterprise production management.

The invention provides a modular construction method of a low-code visual interaction system, and relates to the technical field of low-code development. The method comprises the following steps: constructing an edge cognitive topology network, packaging each node into a reconfigurable meta-module, analyzing a user operation track by using an intention perception interface to generate a meta-intention vector, and carrying out context correlation analysis through a semantic behavior chain to generate a potential behavior sequence. And establishing an asymmetric coupling relationship based on an implicit context mapper, forming a dynamic semantic resonance field, triggering distributed intention resonance and activating a matching element module, and generating a collaborative logic cluster under the constraint of edge computing node resources. And dynamically adjusting an interaction rule and a resource allocation strategy of the logic unit. According to the method, automatic construction and high-precision intention adaptation of complex business logic are realized, the elastic expansion capability and the real-time response efficiency of the system in an edge computing scene are improved, and the risk of logic fault and resource scrambling caused by node isomerism is reduced.

The invention discloses an industrial internet of things-oriented equipment digital twin operation and maintenance management system, and relates to the technical field of equipment management. The system comprises a data acquisition and preprocessing module, a digital twin model construction module, a data transmission and storage module, a state monitoring and fault diagnosis module, an operation and maintenance decision and optimization module and a visual interaction module. The data acquisition module adaptively acquires data through a sensor and preprocesses the data; the model construction adopts multi-scale and multi-model fusion; a hybrid network architecture and an encryption technology are used for transmission and storage; performing feature fusion and transfer learning for monitoring diagnosis; reinforcement learning and multi-agent collaboration are used for decision optimization; visual interaction supports VR / AR fusion. According to the invention, multiple modules work cooperatively, accurate acquisition, efficient transmission and storage of data are guaranteed, and accurate fault diagnosis and scientific operation and maintenance decision are realized through an advanced algorithm; the operation experience is improved through visual interaction; the method also has energy consumption optimization and supply chain cooperation capabilities, and can improve the operation and maintenance efficiency of industrial equipment and enterprise benefits.

The invention provides a cross-domain multi-dimensional query feature index selection method, which comprises the following steps of: judging a query field and a query purpose of a user through query subject classification and query intention identification according to historical query behavior data of the user, and obtaining a field tag and an intention tag of the query; a navigation path of knowledge links is optimized, semantic correlation between nodes and small world index algorithm characteristics are comprehensively considered, an optimal knowledge navigation path is recommended for a user, and an optimization strategy is dynamically adjusted according to query historical behaviors and feedback of the user; a knowledge navigation result is presented in a visual interaction mode, a multi-dimensional attribute screening and sorting function is provided, exploration type browsing and deep mining of a user are supported, and personalized knowledge recommendation service is provided according to a query scene and preference of the user.

The invention discloses a UE4-based digital twin model construction system and method, and relates to the technical field of computer vision, and the system comprises a geometric modeling unit which generates three-dimensional model data based on geometric modeling data of a target physical entity, and achieves dynamic LOD rendering level switching through virtualization processing; the real-time data interaction unit is used for analyzing the real-time sensor data flow and generating equipment state data in combination with a transmission operation and maintenance instruction; the simulation engine unit is used for executing rigid body motion and collision simulation based on the equipment state data and outputting a collision event sequence and state transition data by utilizing an event trigger; the machine learning unit is used for training an LSTM (Long Short Term Memory) model based on the collision event sequence and the state transition data, and optimizing state covariance parameters through adaptive Kalman filtering; and the visual interaction unit is used for realizing data synchronization of the command large screen and the mobile terminal through multi-screen collaboration. According to the method, the digital model capable of accurately reflecting physical entity behaviors and characteristics is constructed.

The invention discloses an AGV (Automatic Guided Vehicle) scheduling system based on a soft bus protocol of a swan gap system and star flash wireless communication, and relates to the technical field of AGV scheduling planning, the AGV scheduling system comprises an AGV scheduling management center, and the AGV scheduling management center is in communication connection with a task management module, a communication connection management module, a global task collaborative planning module and a visual interaction module; and the task management module is used for receiving task requests, classifying, sorting and distributing tasks, and dynamically adjusting task priorities according to real-time production requirements and AGV states. According to the invention, low-delay and high-reliability communication between AGVs and between the AGVs and the scheduling management center is realized by the AGV scheduling system through a soft bus protocol of the swan Mongolian system and a star flash wireless communication technology, the communication delay is effectively reduced, the communication interruption caused by electromagnetic interference is avoided, the real-time performance of task scheduling and path planning is ensured, and the service life of the AGV scheduling system is prolonged. A solid guarantee is provided for stable operation of the AGV, and the overall response speed and reliability of the system are remarkably improved.

The invention relates to the technical field of artificial intelligence, can be applied to business scenes of financial science and technology, medical health and the like, and discloses an operation guidance and optimization method, device, equipment and medium, and the method comprises the steps: obtaining multi-source heterogeneous data to construct a dynamic knowledge graph, and generating an initial guidance scheme containing target operation, and in combination with the visual interaction model, presenting a target operation in real time, collecting real-time dynamic data, generating operation correction information based on data differences, updating display contents of the visual interaction model, and dynamically optimizing a guidance scheme according to an effect analysis value. According to the method, the dynamic knowledge graph is constructed by fusing multi-source heterogeneous data, real-time presentation and data acquisition are performed in combination with a visual interaction model, operation correction information is generated based on data differences, and a guidance scheme is dynamically optimized, so that personalized, dynamically updated and real-time feedback guidance scheme generation and optimization in multi-field application are realized; and the operation accuracy and adaptability are improved.

The invention relates to an image auxiliary data processing method and system suitable for the neurology department. The method comprises the following steps: acquiring multi-temporal neurology image data and clinical data to generate a standardized spatio-temporal data set; establishing an incidence matrix of lesion areas and symptoms by extracting dynamic features of the standardized spatio-temporal data set; inputting the matrix into a prediction model to obtain a lesion diffusion direction and symptom risk prediction result, visually displaying prediction information through a dynamic indication diagram, and performing closed-loop verification and model optimization in combination with clinical actual data to realize dynamic tracking of a neurological lesion evolution process and intelligent prediction of a symptom development trend. The continuity of illness state analysis and the reliability of a prediction result are remarkably improved, sequential decision support is provided for clinical diagnosis and treatment, and meanwhile the interpretability of a pathological mechanism is enhanced through visual interaction.

The invention belongs to the technical field of three-dimensional geological modeling, and discloses a real-time three-dimensional geological modeling system and method based on groundwater dynamics. The system comprises a geological data acquisition module, a complex geological structure modeling module, a geological body internal attribute simulation module, an underground water monitoring module, an underground water simulation module, a geological evolution simulation module, an underground water-geological body simulation module and a visual interaction module. By integrating multi-source data and applying advanced data cleaning and quality control technologies, the accuracy and consistency of the data are ensured, then by utilizing advanced geologic structure modeling and geologic body internal attribute simulation technologies, a high-precision three-dimensional geologic body model is constructed, and the accuracy and consistency of the geologic body model are improved. A reliable space frame is provided for dynamic simulation and analysis of underground water, and timely and accurate information support can be provided for water resource management and environmental protection. The method has a wide application prospect and an important practical value in the fields of dynamic monitoring, analysis and simulation of underground water.

The invention discloses a multi-mode intelligent linkage 3D visual data center operation and maintenance system and method, and relates to the technical field of data center operation and maintenance. The system comprises a multi-modal data fusion module used for mapping physical sensor data and video monitoring and operation and maintenance logs to a unified three-dimensional coordinate system and constructing a multi-modal fusion feature tensor; the three-dimensional particle modeling module is used for constructing a particle space structure based on a Voronoi diagram and Delaunay triangulation and adaptively adjusting the particle resolution; the multi-modal score analysis module is used for calculating a particle multi-dimensional score and generating a semantic heat map to recognize an abnormal region; the structured noise prediction module is used for simulating future responses under different instructions based on a diffusion model; the instruction generation and regulation module is used for realizing causal analysis and instruction optimization in combination with a knowledge graph; and the three-dimensional visual interaction module supports real-time rendering and interaction operation at a client. According to the method, the intelligence, the real-time performance and the visualization level of data center operation and maintenance can be improved.

The invention discloses a software test automation report generation optimization method which comprises the following steps: automatically registering and authenticating a multi-source test data access endpoint, uniformly collecting and normalizing test data with a multi-dimensional attribute tag, and forming a high-quality grouped data set through structured modeling and automatic clustering; further extracting event and result feature nodes, and establishing an interpretable tree rule dependency relationship; according to the method, the minimum redundancy and reusable customized rule path is generated by applying a dependency tree traversal and optimization algorithm and automatic reasoning, and the continuous adaptive evolution of the rule base among multiple projects is realized by combining conflict detection and visual interaction, so that the efficiency and expandability of data processing in a complex test scene are improved, and the test efficiency is improved. And the flexibility and automation level of report customization rule configuration are enhanced.

The invention relates to the technical field of neural engineering and brain-computer interfaces, in particular to a closed-loop multi-modal nerve stimulation system and method based on time interference, and the system comprises a multi-modal stimulation module which is used for integrating electrical stimulation, magnetic stimulation and optical genetic stimulation, and generating a time interference field domain; the neural state sensing module is used for collecting real-time electroencephalogram signals, blood oxygen concentration and neural metabolite level data; the neural control center is used for fusing neural state data and stimulation parameters, and dynamically adjusting time interference frequency and stimulation intensity through an adaptive algorithm; the time sequence cooperation engine predicts a neural response time phase based on a deep learning model, and optimizes a stimulation time sequence and a mode switching strategy; and the visual interaction platform is used for rendering the nerve activation thermodynamic diagram and the stimulation parameter adjustment curve in real time. Therefore, the problems of adjustment strategy solidification, low adjustment precision, insufficient energy conversion efficiency and the like in the prior art are solved.

The invention discloses an intelligent safety management method for a gas operating enterprise. According to the invention, through deep fusion of Internet of Things perception and intelligent decision technologies, the full-period management capability of safety risk prevention and control is significantly improved. On the aspect of beforehand prevention, the global sensing network and the dynamic risk assessment system form double defense lines, hidden danger signals such as pipe network pressure abnormity and equipment aging are captured in real time, the risk evolution trend is pre-judged in combination with meteorological environment changes, and accurate deployment of protection resources is guided. The autonomous inspection robot cluster breaks through the space-time limitation of manual inspection, continuously obtains high-precision detection data under complex terrains and severe weather, and ensures that hidden danger recognition is free of dead angles. The user side safety enabling system converts professional protection knowledge into visual interaction guidance, so that residents can check hidden dangers of gas appliances by themselves, and an enterprise-user linkage basic protection net is constructed.

The invention discloses a network security situation awareness and analysis platform based on AI, and relates to the technical field of network security situation awareness and analysis, and the platform comprises a multi-source data collection module which integrates flow, logs, assets and threat intelligence data, and carries out encryption transmission and standardization; the data preprocessing module purifies and optimizes data, and guarantees data quality and sensitive information security; the AI situation awareness analysis module extracts features through a deep learning model, dynamically evaluates the situation and identifies threats; the threat early warning and decision-making module triggers graded early warning and generates a targeted emergency response scheme; the visual display and interaction module displays information in multiple dimensions and supports query and report generation; and the data storage and tracing module adopts a mixed storage architecture, so that the data security and traceability are ensured. The platform integrates multi-source data and realizes situation accurate perception and intelligent decision by means of an AI technology; the early warning is accurate, the visual interaction is convenient, and the intelligent and efficient level of network security protection is comprehensively improved.

The invention provides an automatic management system for tower crane ground control, and relates to the technical field of tower crane ground control management. A dynamic multi-dimensional cost model is constructed by collecting spatial obstacle data, environmental risk indexes and time efficiency parameters, and an environmental protection reference value is generated and transmitted to a credit evaluation unit. The credit evaluation unit calculates an equipment health green credit score based on an environmental protection reference value and real-time operation data, and dynamically adjusts a green weighting factor of task allocation. And the task allocation unit constructs an economic decision matrix in combination with the cost parameter and the credit evaluation result, generates an optimal task allocation scheme through priority ranking, and triggers path optimization. And the task constraint unit optimizes the operation path and feeds back the energy recovery benefit to the cost model in real time. The visual interaction unit controls and manages in real time through a three-dimensional thermodynamic diagram and supports manual intervention. According to the system, resource optimization configuration and dynamic risk management and control of tower crane operation are achieved, and the construction efficiency and economical efficiency are improved.

The invention discloses an electronic sand table personalized recommendation system in a virtual construction system based on GraphRAG. The electronic sand table personalized recommendation system comprises a data acquisition module used for acquiring user behavior data, scene perception data and industry knowledge data; the data processing and analysis module is used for analyzing user behavior data and scene recognition and comparison; the GraphRAG retrieval enhancement module is used for constructing a knowledge graph, retrieving a template and a component library and enhancing the retrieval capability; the personalized recommendation module is used for recommending sand table templates and sand table components; the personalized explanation generation module is used for generating personalized explanation for the recommended templates and components by utilizing a GraphRAG technology; the visual interaction module comprises a template, component interaction and dialogue interaction; and the dynamic adjustment updating module is used for scene change identification and user feedback analysis. According to the method, the behavior data of the user and the three-dimensional scene information are combined, and the industry knowledge graph is utilized, so that scene perception and personalized recommendation are realized, and the intelligent level of virtual construction is effectively improved.