11 results about "Physical plant" patented technology

The invention provides an intelligent building energy-saving processing control method and system, and relates to the technical field of industrial automation. The method comprises the following steps: dividing a factory into a plurality of areas, and collecting operation data and energy consumption data of production equipment; constructing a digital twin model comprising an equipment energy consumption physical model and a production process logic model, and establishing an initial energy consumption benchmark; generating a cross-regional scheduling scheme through a hierarchical control architecture, inputting the scheduling scheme into a digital twinborn model for simulation, dynamically adjusting an energy consumption reference and scheduling parameters according to a simulation result, and verifying whether an energy-saving index meets a preset threshold value or not; deploying the optimized scheduling scheme to a physical factory, controlling equipment start-stop and operation parameters through an automatic control system, and updating a digital twin model and an energy consumption benchmark based on real-time feedback data; accurate management and control, efficient scheduling and dynamic optimization of intelligent building production energy consumption are realized, the energy consumption cost is effectively reduced, and the service life of equipment is prolonged.

This invention provides a manufacturing factory management system based on a digital twin platform, belonging to the field of factory management technology. It includes: a physical factory layer for real-time collection of equipment operation, process parameters, material consumption, and employee work reports; a digital twin platform layer deployed on a cloud or local server, including a data acquisition and integration module, a model building and management module, and a simulation and prediction module, constructing a high-fidelity, real-time synchronized digital twin and identifying dynamic bottlenecks based on constraint theory; and an application service layer including sub-modules such as intelligent scheduling and dynamic dispatching, human-machine collaborative work assignment, precise quality control, predictive equipment maintenance, and intelligent logistics and warehouse scheduling. This invention achieves real-time mapping and interaction between the physical factory and virtual space through the digital twin, solving problems such as data silos, delayed response, and disconnect between planning and execution in existing systems, thus realizing adaptive optimization and precise control of the production process.

Generating intelligent drawings includes obtaining a current set of design drawings for a physical plant comprising a plurality of components, generating rasterized drawings from the current set of design drawings, and applying an object detection model to the rasterized drawings to extract a set of shapes corresponding to components in the drawings. A trained library is applied to the extracted shapes to obtain a classification of components in the drawings, and the classification is propagated to instances of the components in the current set of drawings.

A computing device (102) constructs plant descriptions for a plurality of water units of a physical plant (110) and determines a water balance correlation based on the plant descriptions. Each water balance interrelation is associated with a connection associated with one or more water units. Each water balance correlation may be a water input or a water output. The computing device (102) constructs a water balance model (208) based on the water balance interrelation, receives the water consumption data from the physical plant (110), and analyzes the water consumption data using the water balance model (208). Analyzing the water usage data may include balancing water input and water output for each water unit according to the water usage data.

This invention proposes a collaborative control system and method for anaerobic fermentation based on digital twins and hybrid intelligence. The system comprises a physical layer, a digital twin layer, a hybrid intelligent decision-making layer, and a collaborative execution layer. The physical layer is the source of data generation and the final execution target of control commands. The digital twin layer constructs a complete, dynamic, and high-fidelity digital copy of the physical plant in virtual space. The hybrid intelligent decision-making layer uses intelligent algorithms to find the optimal control strategy within the digital twin environment. The collaborative execution layer receives the optimal control strategy, decomposes it into specific, time-sequential control commands for each actuator, and distributes them to the corresponding actuators in the physical layer for execution. Simultaneously, the execution results are fed back to the digital twin layer, forming a closed-loop control system. This invention achieves an intelligent leap from stable operation of a single tank to maximizing the economic benefits of the entire plant, significantly improving the overall operational efficiency and intelligence level of the biomass energy station.

The present invention provides a steel hot rolling control system and method based on digital twins, comprising: a perception module for sensing the physical plant environment, equipment operating conditions, and hot-rolled slab quality attributes; a data module for collecting, storing, fusion, transmitting, and analyzing the perceived environment, equipment, and hot-rolled slabs; a presentation module for representing the production conditions of the actual physical plant using virtual twin images through three-dimensional graphics rendering and augmented reality technology based on the analyzed data; and a control module for generating decision-making control recommendations based on a fault knowledge base and issuing control instructions, according to control objectives and corresponding control rules. The present invention uses a fault diagnosis method based on a multi-scale convolutional neural network to monitor the operating status of the equipment, effectively identifying abnormal conditions in the equipment and matching the corresponding fault knowledge base to provide repair recommendations.

The invention discloses a digital twin technology-based digital factory collaboration system for a sewage treatment equipment control cabinet. The digital twin technology-based digital factory collaboration system is characterized by comprising a digital twin module, a comprehensive plan management module, a design management module, a production management module and a material management module, the modules are interconnected and intercommunicated through the digital management cloud platform; based on the digital twinning system, a user can put forward product requirements and establish an engineering project on a digital platform through the Internet, then construction drawing deepening design is conducted on the engineering project through a digital twinning model in combination with an enterprise family library, virtual preassembly is conducted in a virtual factory through the digital twinning system, and the engineering project can be subjected to construction drawing deepened design through the digital twinning model. According to the invention, electrical circuit design can be automatically carried out, material preparation of the electrical cabinet can be automatically carried out according to a design drawing, a worker only needs to carry out installation according to a layout diagram, the working efficiency is improved, and the professional knowledge requirement of the wiring worker is reduced.

The invention relates to the technical field of intelligent manufacturing and digital twinning, in particular to an intelligent factory production visualization method and system based on digital twinning. Comprising the following steps: S1, multi-dimensional data acquisition and fusion: acquiring equipment state, production parameters, material information, environmental data and personnel data of a physical factory through a sensor, an RFID, a PLC and an industrial camera by utilizing an Internet of Things technology, performing data cleaning, conversion and noise reduction preprocessing through an edge computing gateway, and fusing through a data bus to form a standardized data stream; through multi-dimensional data acquisition and high-fidelity visualization, real-time visibility of all elements of people, machines, materials, methods and rings is realized, a manager can quickly master the global production state, the problem of information black box of a traditional factory is solved, based on virtual and real synchronous data and abnormal alarm, the production abnormality response time is shortened by more than 50%, the equipment fault positioning efficiency is improved by 60%, and the production efficiency is greatly improved. And the shutdown loss is greatly reduced.

The invention provides a method and a system for automatically inspecting a semiconductor factory by an intelligent agent, which are applied to the technical field of semiconductor manufacturing, and the method comprises the following steps: firstly, constructing and configuring an inspection intelligent agent at least comprising an equipment interface, a knowledge base and a workflow engine through an intelligent agent configuration platform; then, in a 2.5 D on-line semiconductor factory where physical factory layout and equipment are mapped, the intelligent agent is driven to execute the defined inspection process, and sensor data are obtained in real time; and finally, the workflow engine controls the intelligent agent to carry out anomaly recognition and root cause analysis on the data, and an inspection report is automatically generated in combination with a knowledge base, so that 7 * 24-hour uninterrupted intelligent and automatic factory inspection is realized, a large amount of vigor of engineers originally invested in repeated inspection is effectively released, and the inspection efficiency is improved. According to the method, engineers are enabled to concentrate on key areas such as high-value decision analysis and process optimization, the labor cost is remarkably reduced, the operation efficiency is improved, and stable operation of a semiconductor factory is ensured.

The invention provides a dynamic information anchoring method, device and equipment in a digital twinning factory, and relates to the technical field of industrial automation and digital twinning. The method comprises the following steps: determining a target device in a plurality of devices in a virtual environment; acquiring physical coordinates of the target equipment in the physical factory; and determining a virtual coordinate of the target device in the virtual environment according to the physical coordinate and a preset space mapping formula. And based on the virtual coordinates, determining an initial attachment position of an information display panel used for associating and displaying the related information of the target equipment in the virtual environment. And if it is determined that the initial attachment position does not meet the preset visibility condition, adjusting the initial attachment position to a final display position according to a preset obstacle avoidance strategy. The method is used for achieving the effect of improving the spatial positioning and interaction efficiency of information display in the virtual environment.

This invention discloses a safety instrument network acquisition and big data analysis and early warning system, belonging to the field of industrial safety monitoring technology. The system includes: a safety instrument network acquisition module, a data preprocessing and storage module, a digital twin modeling module, a reinforcement learning decision-making module, a risk early warning and adaptive control module, and a human-machine interaction module. This invention achieves comprehensive, real-time acquisition and unified management of various safety parameters at the production site through the safety instrument network acquisition module. The digital twin modeling module enables real-time synchronous mapping and risk diffusion simulation between the physical factory and the virtual system, allowing for early identification of hidden safety risks and prediction of accident development trends. The reinforcement learning decision-making module generates dynamically optimized control strategies, solving the problem that traditional fixed strategies are difficult to adapt to complex working conditions. The risk early warning and adaptive control module enables the coordinated execution of early warning and control, shortening response time and avoiding interference from human factors.