1688 results about "Visual interface" patented technology

The invention relates to the technical field of electrical safety, in particular to an electrical operation risk assessment and early warning system based on big data analysis, which comprises a data acquisition module, a data fusion module, a feature extraction module, a risk assessment module, a real-time monitoring and early warning module, a historical data storage and analysis module and a visual monitoring module. Multi-source data are collected in real time by deploying multiple sensors, risk assessment is carried out through Kalman filtering and support vector regression algorithm fusion processing, feature variable extraction and combination of a support vector machine and a random forest algorithm, real-time monitoring and risk level judgment of an electrical working environment are achieved, long-term risk trend prediction is supported, and the risk assessment accuracy is improved. The visual monitoring module provides a basis for optimizing alarm rules and decision support, and displays an evaluation result, early warning information and an analysis result through a visual interface; the system has the advantages of being accurate in data processing, efficient in risk assessment, timely in early warning and the like, and can be widely applied to risk monitoring and safety management of electrical operation.

The invention relates to the technical field of data processing, and discloses a BIM-based building construction quality information intelligent supervision system, which is used for improving the efficiency and accuracy of BIM-based building construction quality information intelligent supervision. The method comprises the following steps: a modeling module performs BIM modeling processing on a building engineering design drawing and building specification data to obtain a building twinborn body; the deployment module carries out field data acquisition system deployment on the building twinborn body to obtain a real-time data acquisition system; the detection module performs anomaly detection processing on the data acquired by the system to obtain a quality anomaly alarm and a priority processing level; the processing module carries out multi-dimensional visualization processing on the quality abnormity alarm and the priority processing level to obtain a visual interface; the decision-making module performs intelligent decision-making analysis on the quality abnormal information in the visual interface to obtain a target processing scheme; and the analysis module performs time sequence analysis and multivariable correlation analysis on the target processing scheme to obtain a dynamic quality evaluation report.

The invention provides a satellite measurement and control data anomaly detection and intelligent diagnosis early warning method and system, and relates to the technical field of satellite measurement and control, and the method comprises the steps: obtaining multi-source heterogeneous data in a satellite measurement and control system, and carrying out the preprocessing of the multi-source heterogeneous data; constructing a multi-level space-time fusion network model, inputting the preprocessed multi-source heterogeneous data into the multi-level space-time fusion network model, and extracting high-dimensional space-time features; based on the extracted high-dimensional spatial-temporal features, designing a dynamic adaptive anomaly detection algorithm, and performing anomaly detection on the satellite measurement and control data; according to an anomaly detection result, performing intelligent diagnosis by using a pre-constructed multi-level satellite system knowledge graph; based on the intelligent diagnosis result, multi-level early warning information is generated; a distributed edge computing architecture is designed, and partial data processing and anomaly detection tasks are deployed to a ground station and a satellite end; and presenting the generated multi-level early warning information through a visual interface, and automatically triggering a corresponding emergency plan according to the early warning level.

The invention relates to the technical field of ecological environment protection, in particular to an ecological environment damage intelligent identification management method based on multi-source data fusion, which comprises the steps of data acquisition and preprocessing, data fusion and feature extraction, ecological environment damage evaluation model construction, damage dynamic analysis and trend prediction, restoration suggestion generation and visual display. The method comprises the following steps: collecting water quality, atmosphere, biological and social economic data in real time through a sensor network deployed in an ecological sensitive area, preprocessing the data, extracting key features by adopting a multi-modal data fusion technology and a deep learning model, and constructing an ecological damage assessment model; the change rule of ecological damage and the potential high-risk area are predicted based on a time sequence analysis method, and an optimal ecological restoration scheme is generated in combination with a particle swarm optimization algorithm. According to the method, the evaluation result and the restoration suggestion are displayed through the visual interface, a scientific basis is provided for decision makers, and the efficiency and the effect of ecological environment restoration work can be improved.

The invention relates to a data access method and system of an intelligent Internet of Things gateway. The method comprises the following steps: performing feature matching and protocol identification on a plurality of sensors and terminal equipment connected to the intelligent Internet of Things gateway by using an equipment feature library to obtain an equipment protocol type; analyzing the original data to obtain standardized data; performing encryption processing to obtain an encrypted security data packet and an encrypted session key; performing multi-factor transmission channel selection to obtain an optimal transmission channel combination; and based on the optimal transmission channel combination, sending the encrypted security data packet to the Internet of Things platform, verifying the received data confirmation information based on a consensus algorithm, and outputting a data access state report through a data visual interface and an AI voice assistant. The processing strategy can be dynamically adjusted according to the equipment characteristics, the data characteristics and the network environment, and the flexibility and adaptability of the system are improved.

The invention discloses a BIM-based electromechanical installation project progress planning and resource scheduling method and system, and belongs to the technical field of intelligent construction. The method comprises the following steps: 1) collecting multi-dimensional parameters (real-time construction parameters, prediction model parameters and external constraint parameters) in construction in a classified manner, and constructing a dynamic knowledge graph; 2) detecting progress and resource deviation based on a preset threshold value of the BIM model, and dynamically correcting a resource demand curve through the model; 3) in combination with constraint conditions such as policies and weather, optimizing an equipment scheduling path by adopting an algorithm, and screening compliance candidate schemes; 4) performing multi-objective optimization (minimizing progress deviation, maximizing resource utilization rate and controlling supply chain risk) on the scheme by using a genetic algorithm, and verifying through simulation iteration; and 5) outputting the optimal scheme and synchronizing the optimal scheme to a visual interface. According to the invention, real-time closed loop of data is realized through hardware-algorithm cooperation, and an efficient, dynamic and extensible intelligent management scheme is provided for electromechanical engineering.

The invention discloses a building intelligent operation and maintenance management system and method based on big data analysis, and relates to the field of building intelligent operation and maintenance, and the system comprises a data collection layer including high-precision multi-type sensors and adaptive collection and transmission, a data storage layer including distributed storage and high-redundancy cache, and a big data analysis layer including data cleaning and data storage. The operation and maintenance decision-making layer is used for generating a strategy according to analysis and performing dynamic adjustment; the user interaction layer is used for performing visual interface and convenient feedback; the intelligent early warning module is used for performing dynamic threshold and accurate early warning; the analog simulation optimization module is used for performing model simulation and VR / AR auxiliary decision making; and the operation and maintenance management level and the energy utilization efficiency are improved. The system integrates building data, monitors equipment in real time, accurately predicts faults and energy consumption, optimizes operation and maintenance decisions, improves the management efficiency and the energy utilization rate, reduces the cost, and improves the user experience.

The invention relates to the technical field of building data interaction, in particular to a building engineering interaction method and system based on a BIM model and a medium. The method comprises the following steps: collecting construction site environment parameters and structure response data by using a distributed sensor network; preprocessing the data by an edge computing node; generating standardized environment data and key structure indexes; comparing environment threshold values to identify abnormal events; the method comprises the following steps of: displaying spatial positioning and risk levels through a visual interface, receiving a regulation and control instruction input by a user, dynamically adjusting construction parameters of a BIM model, generating an optimized construction progress scheme, comparing the optimized scheme with an original model to identify a construction conflict area, and generating a conflict resolution report. Through the advanced monitoring technology and construction management technology, the response speed and decision-making efficiency of the building engineering project are improved, and the safety guarantee and resource utilization efficiency in the construction process are improved.

The invention relates to the technical field of artificial intelligence and Internet of Things, in particular to an inspection service management system based on artificial intelligence and Internet of Things, which comprises a data acquisition module, an edge calculation module, a task allocation module, a path planning module, an early warning module and a user interaction module. Wherein the data acquisition module is used for acquiring multi-dimensional data of environment and equipment in real time; the edge calculation module is used for carrying out preliminary analysis and processing on the multi-dimensional data; the task distribution module is used for generating inspection tasks according to inspection rules; the path planning module is used for generating an optimal path of the inspection equipment; the early warning module is used for generating an early warning signal when the potential risk is detected; and the user interaction module is used for displaying the state of the system through a visual interface. According to the invention, by realizing real-time data processing, dynamic task scheduling and path optimization, the response speed and efficiency of the system are improved, and meanwhile, the equipment fault prevention and decision-making capabilities of operators are enhanced.

The invention provides an artificial intelligence-based ultrasonic image data classification method and system, and the method comprises the steps: firstly obtaining a real-time ultrasonic scanning signal sequence containing the time sequence change characteristics of a tissue elastic parameter and a hemodynamic parameter, carrying out the noise suppression and motion artifact compensation processing, generating a standardized ultrasonic image sequence, and marking the coordinates of an anatomical boundary; then performing multi-scale anatomical structure decomposition on the ultrasonic image to obtain a local feature map set of different organization levels, inputting the local feature map set into a cascade deep classification network, and realizing cross-frame feature fusion and dynamic weight adjustment through spatial-temporal feature alignment and a multi-granularity attention distribution module to obtain a spatial-temporal feature fusion model; and the abnormal region classification probability distribution and the spatial topological relation graph are output, finally, a multi-modal diagnosis report is generated according to the abnormal region classification probability distribution and the spatial topological relation graph, an interactive three-dimensional visual interface containing risk level labels and treatment suggestions is generated after the multi-modal diagnosis report is compared with historical cases, and ultrasonic image classification accuracy and diagnosis efficiency are improved.

The invention relates to a regional pollution process real-time monitoring, regulation and control system and method based on artificial intelligence. The system is composed of a data acquisition module, a communication module, an artificial intelligence analysis module, an intelligent regulation and control module, an execution terminal and a user interaction module. The data acquisition module acquires multi-source data such as pollutant concentration and meteorological parameters by means of a multi-modal sensor, the data are processed and transmitted by the data communication module, and the artificial intelligence analysis module realizes pollution source tracing and accurate prediction by using technologies such as a space-time diagram convolutional network and an LSTM-Transform hybrid neural network. The intelligent regulation and control module generates a regulation and control strategy based on an NSGA-II algorithm, and the execution terminal is responsible for implementation. The user interaction module provides a visual interface and a manual intervention channel. The method comprises the steps of data acquisition and processing, model construction and prediction, regulation and control strategy formulation and execution and feedback optimization closed-loop operation. According to the invention, comprehensive real-time monitoring and accurate regulation and control of regional pollution are realized, the prediction accuracy is improved, the environmental, economic and social benefits are balanced, and an efficient technical means is provided for regional pollution treatment.

The invention discloses an e-commerce intelligent operation monitoring and collaborative decision-making method based on big data analysis, and the method comprises the following steps: S1, collecting multi-source heterogeneous data, and constructing an original data set; s2, performing data cleaning and characterization processing to generate a feature data set; s3, constructing a knowledge graph and dynamically updating the knowledge graph by using the graph embedding model; s4, generating potential causal paths and weights in combination with the neural relationship reasoning model and the diffusion model, and introducing a microknowledge enhancement layer to generate causal enhancement features; s5, identifying abnormal behaviors in combination with a multi-level anomaly detection mechanism; s6, designing a dynamic game optimization model, determining a collaborative optimal solution under a multi-target conflict through Nash equilibrium, and processing a dominant and subordinate relationship in combination with a Stackelberg game to generate a collaborative optimization strategy; and S7, displaying the monitoring state and the collaborative optimization strategy through a visual interface. According to the invention, big data analysis, a knowledge graph and a dynamic game technology are combined, so that e-commerce intelligent operation monitoring and collaborative decision-making are realized.

The invention relates to the technical field of production line scheduling management, in particular to a bamboo chopstick production line data real-time intelligent scheduling management system which comprises a data acquisition module, an intelligent scheduling module, an anomaly detection module, an equipment collaboration and optimization module and a decision support module. The data acquisition module acquires data such as equipment operation state, production progress, raw material consumption and yield statistics; the intelligent scheduling module generates a production scheduling scheme by using a federated learning algorithm and integrating multiple factors; the anomaly detection module monitors anomaly by means of a depth auto-encoder; the equipment collaboration and optimization module optimizes equipment collaboration based on the two; and the decision support module generates a visual interface and an intelligent analysis report. Real-time intelligent management of the production process is achieved, production efficiency is effectively improved, resource utilization is optimized, product quality is guaranteed, management decision is assisted, intelligent development of the bamboo chopstick production industry is promoted, and enterprise competitiveness and sustainable development capacity are improved.

The invention provides a soybean planting fertilization management system, and relates to the technical field of management systems.Soil physicochemical properties, a meteorological microenvironment, a plant growth state and agricultural machinery operation data are collected in real time through a field sensor network and an unmanned aerial vehicle multispectral imaging technology, and a farmland dynamic portrait is constructed; the intelligent decision-making unit uses a deep learning model and a GLCM texture analysis technology to dynamically identify plant nutrient deficiency pathological characteristics, determines a nutrient deficiency type in combination with an element concentration threshold and triggers early warning; the scheme decision module dynamically optimizes the nitrogen-phosphorus-potassium ratio through a reinforcement learning algorithm, generates a field-level fertilization prescription map by using Kriging interpolation, recommends the optimal fertilization opportunity and dosage, and the precise execution module is linked with a variable fertilizer applicator to realize integrated precise application of water and fertilizer. The user interaction module supports remote monitoring and intervention of farmers through a GIS visual interface and a mobile terminal APP, can dynamically adapt to environmental changes and crop requirements, and significantly improves the nutrient utilization efficiency.

The invention discloses an unmanned aerial vehicle trajectory tracking system based on multi-sensor fusion, which relates to the technical field of unmanned system autonomous navigation and control, and comprises an unmanned aerial vehicle data acquisition module, an unmanned aerial vehicle feature extraction module, a trajectory prediction deviation scoring module, a trajectory dynamic correction module and a trajectory monitoring module. The unmanned aerial vehicle data acquisition module acquires and preprocesses unmanned aerial vehicle and GPS data, the unmanned aerial vehicle feature extraction module constructs a trajectory tracking feature sequence table, the trajectory prediction deviation scoring module outputs three-dimensional trajectory prediction coordinates and deviation scores, and the trajectory dynamic correction module generates correction parameters. Based on the judgment logic, the judgment logic is converted into an unmanned aerial vehicle correction instruction and attitude feedback quantity, and the track monitoring module constructs a visual interface. According to the method, the trajectory tracking precision of the unmanned aerial vehicle in a complex environment is remarkably improved, real-time detection and dynamic correction of abnormal deviation are realized, the state monitoring capability is enhanced by a three-dimensional visual interface, the trajectory tracking error is reduced, the misjudgment rate is reduced, and the flight safety and the control efficiency are guaranteed.

The invention belongs to the technical field of data detection, and particularly relates to an efficient data anomaly automatic detection and alarm system. The detection and alarm system comprises a sensor and data acquisition device, a data processing and analysis module, an alarm module, a monitoring center and visual interface, a communication and network module, a storage and backup module, an intelligent decision support module and a user feedback and adjustment module. According to the system, data can be monitored and analyzed in real time, abnormity can be quickly found, an alarm is given, manual monitoring requirements are reduced, data misjudgment is avoided, data accuracy is ensured through data cleaning and processing, an intelligent decision support module provides optimization suggestions based on AI, and a user can adjust rules to adapt to requirements. The system has the functions of data processing, anomaly recognition and trend prediction, the monitoring center visually displays the state, the storage and backup module ensures long-term storage of data, the automatic mechanism improves the response speed and the operation benefits, and the adaptability is high.

The invention discloses a system for realizing crop intelligent identification based on a CLIP multi-mode method, and belongs to the technical field of crop identification, and the method specifically comprises the steps: collecting real-time images, text description and environment data of crops; performing image processing on the real-time image; a CLIP model is used for carrying out feature extraction on the image and text description, visual features are extracted from the image, semantic features are extracted from the text, the text description comprises crop types and symptom description of diseases and pests, the environment data and the visual features are fused, and a multi-modal feature vector is generated; the multi-modal feature vectors are classified by using a pre-trained classifier, specific crop types and pest and disease damage types are identified according to extracted multi-modal features, identification results are displayed to users through a visual interface, and information is fed back to a monitoring center; according to the method, visual, semantic and environmental features are fused, so that the accuracy of crop disease and insect pest recognition is remarkably improved.

The invention relates to the technical field of data processing, in particular to a whole-process data monitoring and visual management method and system for an air separation process, and the method comprises the steps: collecting key parameter data in the air separation process, and input data vectors and output data vectors among equipment; constructing a parameter correlation model to analyze the input data vector and the output data vector to obtain data deviation; if the data deviation exceeds a dynamic threshold value, triggering a multi-level alarm mechanism, and generating a block chain log record; generating a hash value for the key parameter data, and marking an abnormal link; a bimodal anomaly detection model is constructed to analyze an abnormal link, an equipment performance attenuation coefficient and an inter-equipment anomaly influence relation coefficient are obtained, and a comprehensive anomaly score is generated through a fusion technology; when the comprehensive abnormal score is greater than a preset threshold value, constructing a fault tree model to reversely trace an abnormal data link for the abnormal link, and positioning a fault source; and full-process data monitoring and management are realized through a visual interface.

The invention provides a workflow condition node management method based on a dynamic rule engine, and the method comprises the steps: obtaining a business circulation demand, configuring a task node attribute, and carrying out the adaptation of the business circulation demand according to the task node attribute, and obtaining a process template model; setting rule conditions, binding the rule conditions with the task nodes, and processing the flow template model; generating a process instance according to the updated process template model, analyzing rule conditions, initializing task allocation logic, and pushing a to-do task to a user; the user executes a to-do task, triggers the rule engine to carry out condition judgment, and updates a process instance state to obtain an updated process instance state; and according to the updated process instance state, generating a visual interface to display the process progress, and carrying out real-time early warning on an overtime or abnormal task to obtain a monitoring report and the process instance state after abnormal processing. Tasks can be decomposed, so that complex tasks are easier to manage, and the execution difficulty is reduced.

The invention discloses an intelligent working place dust monitoring and early warning system. The system comprises a dust sensor module, a data processing and storage module, a wireless communication module, an alarm module and a visual interface. The dust sensor detects the dust concentration in the air in real time, dust with different particle sizes is included, the data processing module carries out concentration calculation, trend analysis and acceleration monitoring, and dynamic threshold adjustment and trend prediction are supported. The system has a multi-stage alarm function, and when the dust concentration reaches a preset threshold value, early warning signals with different intensities are triggered to remind a user to take protective measures in time. The system not only improves the real-time performance and accuracy of dust monitoring, but also has automatic learning and intelligent early warning functions, is suitable for various high-dust environments such as mines, construction sites, workshops and the like, and remarkably improves the safety management level of workplaces.

The invention discloses a railway intelligent construction site safety penetration type management messenger platform which comprises a multi-modal data fusion processing module, an edge computing node cluster module, a three-dimensional visual penetration type management interface module, an intelligent early warning and emergency response module, a self-adaptive network transmission module and the like. Real-time cleaning, alignment and correlation analysis are realized by using a dynamic data calibration algorithm, and a data island is broken; the edge computing node cluster carries out localization preprocessing and the like on data in a key area, so that the load of a central server is reduced; the three-dimensional visual interface is based on a digital twinborn construction model, supports drilling type viewing and realizes three-dimensional monitoring; the intelligent early warning system adopts a reinforcement learning model to automatically trigger multi-channel early warning; and the adaptive network transmission module dynamically switches communication modes to ensure low-delay transmission of key data. The platform realizes real-time acquisition and integration of construction site data and reduces manual intervention.

The invention discloses a river model multi-data processing analysis method and system, and relates to the technical field of hydraulic engineering, and the method comprises the steps: extracting a motion feature vector from a digital twin model, and carrying out the preprocessing to generate a standard river motion data set; according to the method, the sediment space distribution concentration Nsc (x, y, z, t) in a three-dimensional coordinate grid is calculated through a standardized data set, and the change trend of the sediment space distribution concentration Nsc (x, y, z, t) along with time is evaluated. When the sediment space distribution concentration Nsc (x, y, z, t) is larger than 0, highlighting marking is conducted on the abnormal area through a visual interface; when the sediment space distribution concentration Nsc (x, y, z, t) is less than or equal to 0, the sediment concentration is normal. For an abnormal area, the method further calculates the sediment conveying capacity Qs and the sediment deposition rate S, accurately predicts the moving path, the sediment deposition area and the sediment deposition speed of the sediment, dynamically analyzes the sediment movement condition of the river, and can track the sediment change trend in real time in a complex dynamic environment.

The invention discloses a metadata real-time self-adaptive standardization system for multi-field data sharing. The metadata real-time self-adaptive standardization system comprises a metadata information layering module, an intelligent information acquisition module, a real-time self-adaptive standardization module and a visual management module, the metadata information layering module identifies sources of various metadata, and sequentially performs cleaning, classification and layering preprocessing on the metadata; the intelligent information acquisition module uniformly acquires metadata information and converts the acquired data into standardized intermediate representation; the real-time adaptive standardization module is used for realizing rule dynamic adaptation and real-time optimization in a metadata standardization process, is internally provided with a dynamic rule library, is used for storing dynamically generated metadata mapping rules and supports automatic loading, updating and semantic extension of the rules; and the visual management module realizes management, analysis and dynamic adjustment of metadata through a visual interface. The method is high in accuracy of processing unknown data, can adapt to different data scenes in real time, and is high in semantic interoperation capability.

The invention discloses a PCB defect detection method based on a small target enhanced feature pyramid, and the method comprises the steps: S1, data collection and processing: downloading a PCB defect data set for screening, carrying out the data processing of a screened picture, and dividing the processed data set into a training set and a verification set; s2, constructing and training a model: constructing an improved RT-DETR-R18 model and carrying out model training to obtain the weight of the improved RT-DETR-R18 model, and then carrying out model verification; and S3, detection and visualization: deploying the verified model to a visual interface for real-time detection of PCB defects. According to the method, an improved RT-DETR-R18 model algorithm is adopted, and higher accuracy and robustness are achieved; and the improved network structure also has relatively high hardware adaptability and compatibility, and can be used in PCB defect detection tasks in different scenes.

The invention discloses a road defect detection method based on a DRR module and an SDFM. The method comprises the following steps: S1, data acquisition and processing; s2, constructing a model: replacing a DWR module with a DRB module, and replacing a BottleNeck residual module of a backbone network part C2f through the DRR module; downsampling of a backbone network and a neck is replaced by an LDConv convolution module, a neck structure of an SDFM improved model is used, and features of different scales are fused to serve as input of a detection head; s3, model training: training the improved network model by using the made training set by setting training hyper-parameters; s4, model verification: setting verification parameters to verify the trained model; and deploying the verified model to a visual interface to realize real-time detection of road defects. According to the method, the model is improved, so that the detection algorithm has higher accuracy and robustness, and can adapt to different road defects and environmental conditions.

The invention discloses an AI intelligent collaborative monitoring management system for a multi-index water volume system, and relates to the technical field of monitoring management, the collaborative monitoring management system comprises a data acquisition module, a water volume system analysis module, a collaborative monitoring module, an emergency early warning module, an early warning scheme module and a data visualization module, all the modules are connected through electric signals. According to the AI intelligent collaborative monitoring management system for the multi-index water volume system, abnormal changes in water quality, water volume and water ecological indexes are identified through anomaly detection, an early warning mechanism is triggered according to the detected anomaly, early warning information is sent to management personnel through various modes such as a visual interface, a short message and an email, and the management personnel can carry out early warning on the water quality, the water volume and the water ecological indexes. According to the invention, the time interval from the occurrence of the abnormity to the discovery of the abnormity is greatly shortened, and valuable response time is provided for managers, so that further deterioration of the abnormity is effectively restrained, and the safety and sustainable utilization of water resources are guaranteed.

The invention discloses an urban road mobile source intelligent monitoring method and system based on multi-source data coupling, and relates to the technical field of environment monitoring and intelligent traffic. Utilizing a machine learning algorithm to construct a pollutant emission, carbon emission and energy consumption prediction model; acquiring vehicle inventory data in the city, and calculating the pollutant emission, energy consumption and carbon emission of the whole city; the method comprises the following steps of: constructing a dynamic distribution diagram of automobile emission in a city by using real-time position data of vehicles and urban road network information, introducing an atmospheric diffusion model, simulating pollutant migration by combining urban geographic information and environmental data, comparing and verifying a simulated migration result with actually measured data of a national control site, and optimizing parameters of the atmospheric diffusion model; and storing the result into a real-time database, and displaying the pollutant distribution, emission, energy consumption and carbon emission of the urban road network in real time through a visual interface. According to the invention, the temporal-spatial resolution and prediction precision of data are effectively improved.

The invention discloses a power equipment data acquisition and processing system, which comprises a multi-protocol compatible acquisition module, an intelligent network detection module, a system service performance monitoring module and a high-concurrency processing module, the multi-protocol compatible acquisition module is responsible for adaptation and data acquisition of different power equipment protocols and normalization processing of the acquired data; the intelligent network detection module monitors a network link state, diagnoses a network fault reason and pushes fault information in real time; the system service performance monitoring module collects key performance indexes of a server, displays performance data through a visual interface, and triggers an alarm when the indexes are abnormal; the high-concurrency processing module compresses the collected data, realizes efficient pushing and priority processing of the data through a message queue, and guarantees the data processing efficiency in a high-concurrency scene; comprehensive monitoring of power equipment communication and system performance is realized, and data acquisition efficiency and system reliability are improved.

The invention discloses an optimization method for spatial distribution of garden plants, which comprises the following steps: generating a high-precision 3D model of a garden terrain by adopting a plant spatial adaptive layout algorithm and utilizing a laser radar or unmanned aerial vehicle technology, generating a weight matrix according to soil types, illumination intensity and drainage characteristics, and applying a dynamic weight Voronoi algorithm in the terrain model to optimize the spatial distribution of the garden plants. The method comprises the following steps: generating a planting range of each plant, establishing a root distribution model by using an underground imaging technology, simulating distribution dynamic of moisture and nutrients among root systems in combination with underground hydrological data, collecting sensor data in real time by using a wireless network, designing a dynamic irrigation scheme, and optimizing the planting density and irrigation range of the plants. Data such as terrain and climate are integrated into a virtual garden, the virtual garden is ensured to be consistent with an actual state through interaction of a sensor and a digital twinborn model, plant growth, ecological benefit changes and disaster risks are predicted by using the digital model, and a user is allowed to modify and optimize the garden layout in real time through a visual interface.

The invention relates to the field of physiological status real-time monitoring, provides a virtual patient physiological status real-time monitoring method and system based on digital twinning, and is used for solving the problems of non-linear combination deviation and multi-source data synchronization lag of virtual patient physiological status monitoring caused by dependence on a static model and one-way feedback. The method comprises the steps that a valve three-dimensional model is built based on aorta CT data, and a simulation model containing motion conditions is built in combination with a fluid mechanics equation; the blood flow pressure difference and vortex parameters of valve opening and closing are calculated in real time, an optical fiber virtual sensor is embedded in the topological surface of the valve to simulate blood flow deformation, and a valve state mechanical signal is output; parameters and signals are input into a digital twin engine, particle trajectories and refractive indexes are adjusted to generate a visual interface, blood flow velocity-pressure gradient spatio-temporal data are extracted, model boundary conditions are reversely corrected, and valve movement is synchronized with the physiological state of a patient. According to the technical scheme, heart valve movement is simulated in real time, and the real heartbeat rhythm of the patient is automatically matched.