632 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 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 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 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 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 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 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 offshore wind power fault monitoring method based on a knowledge graph and digital twinning, and aims to solve the problems that multi-source data acquisition is dispersed, the transmission delay is high, and real-time convergence and integration are difficult to realize in the operation state monitoring of offshore wind power equipment. According to the invention, the following modules are provided: a multi-source data acquisition layer is used for solving the problems of dispersed data acquisition and transmission lag and ensuring the real-time performance; the knowledge graph construction module is used for solving the problems of extraction, fusion and storage of equipment entity relationships; the digital twin modeling module realizes visual presentation and accurate simulation of the state of physical equipment; the fault monitoring reasoning module solves the problems of anomaly detection and root cause positioning; the intelligent early warning decision module determines an early warning level and efficiently matches a solution; the visual interaction module solves the problems of scattered information display and poor linkage, supports third-party docking, realizes accurate early warning and root cause positioning, improves the decision-making efficiency, adapts to severe environments, supports large-scale wind power plant monitoring, and reduces the operation and maintenance cost.

The invention relates to the technical field of data processing, discloses a digital twin adaptive decision platform for TBM tunneling and a control method, and aims to solve the problems of single sensing dimension, mutual separation of physical and data models, disjunction of early warning and control links and low man-machine interaction efficiency in the prior art. According to the scheme, the method mainly comprises the steps that multi-source real-time data is collected through a hardware sensing layer, after the multi-source real-time data is processed through a data management and fusion layer, a digital twin model layer constructs a physical-data fusion model and completes risk assessment, finally risk visualization is achieved through an augmented reality interaction layer, and an intelligent control decision-making layer generates a control instruction and outputs the control instruction to a TBM main control system. According to the integrated intelligent decision-making platform and the control method, multi-source perception deep fusion, physical and data model online coupling, risk assessment and equipment control second-level closed loop can be realized, and visual interaction can be realized through augmented reality.

The invention discloses an industrial process supervision and management system based on multi-modal data processing, which comprises a data acquisition module (100), a data fusion module (200), an anomaly detection module (300), a decision optimization module (400) and a visual interaction center (500), and is characterized in that the data acquisition module (100) transmits multi-source sensing data to the data fusion module (200) in real time through an industrial Ethernet; the data fusion module (200) forms a multi-modal feature vector and sends the multi-modal feature vector to the anomaly detection module (300), and the anomaly detection module (300) presets a threshold value and sends a joint alarm signal to the decision optimization module (400) and the visual interaction center (500). The method has the advantages that breakthrough is achieved in the aspects of detection speed, positioning precision and decision reliability through multi-modal data fusion and dynamic modeling, and the technical bottlenecks of time sequence misalignment, single-source misjudgment, insufficient causal analysis and the like existing in a traditional industrial monitoring system are effectively solved.

The invention discloses a sewage treatment information intelligent management method, system and equipment based on the Internet of Things, and relates to the technical field of sewage treatment information management, and the method comprises the steps: 1, collecting multi-source water quality data, initializing sensor equipment, and carrying out the primary processing and caching of the data; step 2, carrying out edge intelligent prediction on pollutant concentration and pollution load sudden increase; step 3, establishing a prediction and control response model, introducing a control strategy generation mechanism based on a rule engine and priority tradeoff, and calculating a dosing dynamic sensitivity value; 4, adjusting frequency conversion of a fan, a dosing module and a reflux pump, and feeding back, collecting and uploading in real time; and step 5, performing artificial intelligence strategy center verification and a digital twinborn simulation strategy, analyzing multi-plant load scheduling and implementing optimization measures, and performing visual interaction in real time. The problem of energy and medicament waste caused by lack of a feed-forward pre-judgment mechanism is solved.

The invention relates to the technical field of intelligent operation and maintenance management of an energy storage power station, and provides an energy storage power station efficient operation and maintenance management system fusing AI and the Internet of Things, and the system comprises an Internet of Things data collection module which is used for obtaining multi-source heterogeneous data of a battery pack, an inverter and an environment sensor in the energy storage power station in real time; the AI analysis module comprises an anomaly detection unit, a performance prediction unit and a scheduling decision unit which are respectively used for anomaly probability calculation, equipment performance degradation trend prediction and optimal charging and discharging strategy generation; the dynamic optimization control module receives an instruction of the scheduling decision-making unit and adjusts operation parameters of the energy storage power station through a dynamic load adjustment algorithm; and the visual interaction module integrates an equipment state three-dimensional mapping interface and an alarm pushing unit, and displays the decision result of the AI analysis module and the equipment health degree score in real time. The operation and maintenance management efficiency can be improved, the reliability and safety of equipment operation are enhanced, the operation and maintenance cost is reduced, and the service life of equipment is prolonged.

The invention provides an intelligent task scheduling and optimizing method and system, a medium and equipment, and the method comprises the steps: analyzing task metadata through a database storage process, generating a task dependency graph according to an obtained task dependency matrix, and converting the task dependency graph into a visual interaction interface; identifying a key link with longest time consumption and a bottleneck task on the key link through the created time prediction model, and generating a multi-dimensional optimization suggestion based on an identification result; automatically adjusting a task scheduling script according to the multi-dimensional optimization suggestion and generating a standardized configuration file, dynamically allocating resources according to the standardized configuration file, and monitoring an execution state of a task through a fault-tolerant mechanism; and performing intelligent early warning according to the task log data collected in real time and the system performance index, and generating a multi-dimensional analysis report. The system is an intelligent scheduling system integrating dependency analysis, link optimization and execution monitoring, and automatic analysis and optimization of a complex task network are achieved.

The invention relates to the field of man-machine interaction, and particularly discloses a multi-mode man-machine interaction application system for sky-eye insight based on a pathologist visual angle, which is characterized in that firstly, a candidate focus thermodynamic diagram is generated through rapid scanning of a full-slice image, low-power lens global browsing of a doctor is simulated, and the doctor is guided to lock a key area interactively through professional judgment; therefore, the processing efficiency of the oversized image is greatly improved. And then, the system only performs high-resolution deep analysis on the focus confirmed by the doctor, and performs multi-modal fusion on the extracted microscopic visual features and the patient text information to generate a preliminary report with an interpretable basis, so that the problem that the multi-modal function deviates from a clinical core task is solved. Finally, the doctor can check and finalize the report through visual interaction, and the dominant position and the final decision making right of the doctor in the diagnosis process are ensured, so that the bottlenecks of black box operation and low clinical acceptability of a traditional AI system are overcome.

The invention discloses an enterprise production risk hidden danger identification method and system based on AI, and the method comprises the steps: building a dynamic risk map through a deep space-time attention mechanism according to the production environment multi-source data collected in real time; based on the dynamic risk map, carrying out risk propagation modeling by adopting a dynamic edge weight adjusted graph neural network to obtain a risk node set and a diffusion path thereof; according to the risk node set, constructing a dual-stage reinforcement learning model to optimize the disposal schemes, and outputting an optimized disposal scheme set containing priority ranking; based on production state data fed back in real time, a risk disposal instruction set is generated, the dynamic risk map is synchronously updated, and visual early warning signals and disposal path guidance are output through a multi-modal interaction interface. By utilizing the embodiment of the invention, full-process closed-loop management of multi-source data fusion, dynamic risk propagation modeling, intelligent disposal scheme optimization and visual interaction can be realized, so that the intelligent level of enterprise production safety management is improved.