1599 results about "Virtual model" patented technology

The invention discloses a system and a method for task planning of an intelligent robot with a body based on multi-dimensional situation awareness, and particularly relates to the technical field of task planning of the intelligent robot with the body, space-time alignment is carried out on asynchronous heterogeneous data generated by a multi-modal sensor channel, and cross-modal space-time features are extracted through a cross-modal feature fusion network; a fusion situation matrix is generated, dynamic causal modeling is used to update association strength among the multi-modal data, an anti-factual reasoning engine is used to identify and trace abnormities, and an abnormities traceability result is output; dynamically adjusting the reliability weight of each sensing channel through a multi-modal credibility evaluation model by using the fusion situation matrix and an abnormal traceability result; and on the basis of the reliability weight, inputting the fusion situation matrix into a real robot dynamic model and a digital twin virtual model, executing collaborative predictive control, and starting an adaptive rule evolution mechanism when a safety score is lower than a threshold value, thereby solving the problem of fusion matrix distortion in dynamic obstacle avoidance and precise grabbing tasks.

The invention provides an intelligent building energy conservation and emission reduction digital twin management system and method, and relates to the field of building automation and energy conservation control. The intelligent building energy conservation and emission reduction digital twinborn management system comprises a digital twinborn model construction module, an energy consumption prediction and regulation module, a multi-device cooperative control module, a real-time feedback and self-learning module and an edge-cloud cooperative architecture. By integrating a building information model, real-time sensor data, meteorological data and the like, a three-dimensional visual dynamic twinborn model is constructed, historical data playback and thermodynamic diagram analysis are supported, omnibearing virtual mapping of a physical building is realized, a physical system and virtual model data are synchronized in real time through an Internet of Things sensor network, and the real-time real-time dynamic twinborn model is established. The consistency of a virtual model and an actual system is ensured, the precision and reliability of the model are remarkably improved, artificial illumination is dynamically compensated according to natural illumination, lamps are automatically turned off in an unmanned area through infrared induction, and equipment-level energy saving is achieved in combination with air conditioning system optimization.

The invention relates to the technical field of data center management, and discloses a data center digital twinborn simulation and decision-making system oriented to intelligent management. The system comprises a data center physical feature sensing module, a virtual space reconstruction module, an operation situation deduction engine, an abnormal behavior recognition module and a decision instruction generation module. Wherein the physical feature sensing module collects multi-dimensional operation parameters of the infrastructure in real time; the virtual space reconstruction module dynamically constructs a three-dimensional virtual model based on the collected parameters; running a situation deduction engine to simulate a resource scheduling and energy flow process; the abnormal behavior recognition module analyzes the analog data stream to detect an abnormal operation mode; and the decision instruction generation module integrates the abnormal information and generates an optimization regulation and control instruction for the physical equipment. According to the system, intelligent management of the data center is realized through real-time mapping, dynamic deduction and intelligent decision making of physical and virtual spaces, and the management precision and timeliness are improved.

The invention relates to the technical field of data processing, and discloses a steel structure welding seam quality ultrasonic detection method and system. The method comprises the following steps: scanning a welding seam to obtain digital parameters, constructing a virtual model to generate an ultrasonic response database, optimizing a detection parameter forming scheme, executing distributed acquisition to obtain a signal matrix, extracting defect features by using a neural network, and outputting a digital detection report through transfer learning. According to the method, automatic optimization of detection parameters and accurate extraction and intelligent analysis of defect features are realized, the accuracy, efficiency and reliability of detection are improved, and the dependence on artificial experience is reduced.

The invention discloses a concrete mixing plant automatic control system based on intelligence, and belongs to the technical field of automatic control. Comprising a multi-modal sensing data acquisition module, an intelligent batching optimization module, a digital twin simulation module, a self-adaptive energy consumption management module, a fault self-diagnosis and predictive maintenance module, a dynamic quality tracing module and a multi-target collaborative scheduling module. Real-time synchronization of sensor data and a virtual model is realized in combination with an edge computing technology, dynamic and visual technical support is provided for full-flow simulation of the concrete mixing plant, and complex working conditions in production are reflected more truly; the system predicts a potential problem through a machine learning algorithm, triggers an early warning signal based on a multi-dimensional threshold rule, and generates a preventive maintenance plan in advance; the digital twin platform supports AR and VR interaction interfaces, so that an operator can visually observe the operation states of a virtual model and actual equipment.

The invention relates to the technical field of digital twinning, in particular to a pile foundation construction whole process digital twinning system which comprises a twinning configuration module, a dynamic mapping module, a coupling analysis module and a twinning synchronization module. According to the method, a time axis driving model sequence is constructed based on space-time interpolation, burial depth coordinates, pouring time and segment numbers are dynamically bound, three-dimensional evolution mapping of a pile body model is achieved, axis coordinate deviation is combined with perpendicularity threshold extraction, virtual model coordinates are corrected in real time through point cloud registration, and a pump pressure value and settlement are fused to generate a pile sinking rate index. A bidirectional coupling relation between parameters and stratum response is established in combination with stratum counter-force and injection rate, the thickness of the model is adjusted according to a settlement rate difference value, the similarity of the injection rate and a counter-force curve is matched, a model parameter reverse updating mechanism is formed, and real-time intervention of construction abnormity, dynamic optimization of parameter coupling and model iteration correction are achieved. And the process controllability and the quality tracing precision are improved.

The invention discloses an intelligent cable digital comprehensive monitoring system, and relates to the technical field of electric power engineering, the intelligent cable digital comprehensive monitoring system comprises a digital monitoring center, the digital monitoring center is in communication connection with the following modules: a holographic sensing monitoring module used for deploying an intelligent sensing system and collecting monitoring data including cable operation parameters and environmental data, and the collected data is preprocessed. According to the invention, real-time and comprehensive monitoring of the cable body and the surrounding environment is realized through the holographic sensing monitoring module and the digital twinning technology, the virtual model of the cable is dynamically updated, and the physical state change of the cable is reflected in real time, so that operation and maintenance personnel can find potential problems in cable operation in time, and the working efficiency is improved. And the service life trend and the fault risk of the cable are predicted in advance through the simulation prediction module, and differential maintenance strategies are formulated, so that the possibility of sudden faults of the cable is effectively reduced, the operation reliability of the cable is remarkably improved, and stable operation of a power system is ensured.

The invention discloses a digital twinborn model real-time linkage method driven by information physics fusion, particularly relates to the technical field of digital twinborn cooperative control of an industrial automation production line, and is used for solving the problems of instruction conflict and control failure caused by mismatching of virtual model parameters and dynamic capability of physical equipment in the prior art. According to the method, equipment state and material flow data are collected in real time, equipment dynamic degradation parameters are extracted to generate capability attenuation feature vectors, hidden process conflict path detection and resource preemption probability simulation are combined, a collaborative optimization model of equipment health and process scheduling is constructed, and a control instruction set containing dynamic capability constraints is generated. After the screening instruction is verified through physical constraint matching, parameters are corrected in a closed loop mode based on an execution result, a model is iterated, and self-adaptive matching of the virtual instruction and the physical equipment capacity is achieved; the reliability of the digital twinning control instruction and the stability of a production line are remarkably improved, and the risk of abnormal shutdown caused by an overrun instruction is avoided.

The invention discloses an intelligent campus operation and maintenance management system based on digital twinning, which relates to the technical field of intelligent campus operation and maintenance management and comprises a digital twinning monitoring module, an intelligent task scheduling and resource optimization module and a space optimization and path planning module. According to the system, the visual and intelligent level of equipment management is improved by monitoring the equipment state in real time and constructing an accurate equipment virtual model, the task execution efficiency is improved by optimizing task scheduling and resource allocation, meanwhile, the energy consumption of the equipment is reduced, the service life of the equipment is prolonged, the space conflict between the equipment is solved, and the equipment management efficiency is improved. The cooperative work capability of the equipment is improved, the path planning and task execution sequence is optimized, and the safety and stability of the intelligent campus operation and maintenance system are improved.

The invention discloses a virtual-real combination-based power generation side industrial control system network security target construction method. The method comprises the following steps of: constructing a virtual-real combination target environment consisting of a physical equipment layer and a virtual model layer; the heterogeneous industrial control protocol between the physical equipment layer and the virtual model layer is analyzed, protocol semantic information is extracted, and a bidirectional dynamic mapping rule of a physical equipment state and a virtual model state is generated based on the protocol semantic information; based on a state change event of the physical equipment layer, according to a bidirectional dynamic mapping rule, synchronizing changed equipment state data to the virtual model layer in real time, and simulating protocol behavior logic corresponding to the equipment state data in the virtual model layer according to a security test requirement; and based on the attack instruction or the abnormal state signal generated by the virtual model layer, according to the protocol specification format of the target physical equipment, converting the instruction or the signal into an executable control command, and driving the physical equipment layer to execute an operation corresponding to the control command.

The invention provides a pipe jacking construction digital twinning optimization system and method based on high-precision physical simulation, and relates to the technical field of underground pipe network pipe jacking construction. Construction data of multiple physical entities during pipe jacking construction are collected in real time through a physical simulation engine module and then transmitted to an established digital twin model, virtual mapping synchronously updated with the construction data of the physical entities is established, and an optimization and decision module optimizes construction parameters by adopting a joint simulation algorithm fusing finite element analysis (FEA) and computational fluid dynamics (CFD), so that the construction parameters are optimized. The construction feedback module realizes automatic parameter adjustment based on PLC control according to optimized construction parameters, the analysis report module analyzes time sequence data and generates a construction efficiency evaluation report, a stress concentration area is accurately simulated, the simulation precision is improved, high synchronization of a virtual model and physics is realized, and the precision and real-time performance of system execution are improved.

The invention relates to a fault diagnosis method for a server. Efficient fault positioning and processing are achieved by constructing a multi-dimensional intelligent diagnosis system. Constructing a server virtual model at each node and establishing a connection relationship, and collecting various data and mapping the data to the virtual model; performing weighted fusion on the data through correlation analysis, and extracting fault features including time and space correlation by using a bidirectional long-short-term memory network; fault probability distribution is obtained by means of a dynamic fault knowledge graph and graph neural network reasoning, and a fault evaluation result is visualized in combination with a virtual model; and finally analyzing the behavior deviation through a neural network and generating a processing strategy. Real-time processing of multi-source data, intelligent extraction of fault features and dynamic deduction of fault propagation are achieved, the accuracy, the real-time performance and the automation level of server fault diagnosis are effectively improved, and predictive maintenance and intelligent decision making of a complex server system are achieved.

The invention discloses a logistics transportation intelligent prediction scheduling method and system based on digital twinning. The method comprises the steps that transportation carrier data and cargo data are acquired in real time through Internet of Things sensing equipment; constructing a logistics transportation digital twinborn model, wherein the logistics transportation digital twinborn model comprises a physical entity layer, a data fusion layer, a virtual model layer and a decision service layer; simulating a logistics transportation process, and deducing and predicting resource demands in a future time window; the method comprises the following steps: dynamically adjusting marshalling configuration and loading and unloading operation time sequence of transportation carriers by constructing a digital twin-driven elastic resource scheduling mechanism; and training and optimizing the logistics transportation digital twin model based on feedback data and scheduling execution deviation data in the logistics transportation process to form a closed-loop optimization system. The method has the advantages that logistics transportation data are simulated through the digital twin technology, logistics carrier scheduling is predicted, resource demand prediction and elastic scheduling are achieved, and the transportation efficiency is optimized. And the logistics process is ensured to efficiently and flexibly cope with uncertain factors.

The invention provides a tunnel rock mass dynamic disaster monitoring system based on digital twinning, and relates to the technical field of tunnel rock mass dynamic disaster monitoring based on digital twinning. By integrating a multi-mode sensing network, the system comprises a self-adaptive infrared structured light scanner, a distributed micro-seismic sensing array and a fiber grating strain sensor; comprehensive and real-time monitoring of the rock mass state is realized; meanwhile, an intelligent analysis algorithm based on deep learning is introduced, abnormal conditions are automatically recognized, a rockburst risk comprehensive index is generated, in addition, a physical entity and a virtual model are combined through a dynamic digital twinborn modeling technology, and visual analysis of rock mass state changes is achieved; and finally, an edge-cloud cooperative communication technology is adopted to ensure low delay and reliability of data transmission and ensure that the monitoring system can quickly respond and timely take emergency measures.

The invention discloses an inspection robot collaborative planning method and system based on digital twinning, and the method comprises the steps: constructing a virtual simulation environment of a large-scale component through a point cloud modeling technology, and integrating a motion capture system to achieve the real-time mapping of a physical environment and a virtual model; defining a cooperative mechanism of the scanning robot and the positioning robot, providing global coordinates and path guidance by the positioning robot, and correcting position errors in real time by the motion capture system; simulating a dual-machine motion track in the digital twin model, and identifying a potential collision risk through a conflict prediction model; designing a collaborative planning algorithm under coupling constraint, and performing optimization by taking path optimization and scanning visual angle coverage as a joint target; based on a twin intelligent framework with a body, the path and the visual angle of the robot are dynamically adjusted by combining reinforcement learning with real-time feedback, and self-adaptive collaborative planning is achieved. Through double-machine division cooperation and real-time correction, the scanning efficiency and precision of the large-size component can be remarkably improved, and the collision risk is reduced.

The invention discloses a digital twinning system and a construction method thereof, and relates to the technical field of digital twinning, and the construction method comprises the steps: carrying out the classified collection of multi-source heterogeneous data of a digital twinning entity, carrying out the filtering and denoising through a self-adaptive wavelet threshold, mapping the data into a multi-dimensional feature vector, screening key features, and outputting the key feature data; the method comprises the following steps: establishing a physical mechanism layer based on a general physical rule, defining core physical parameters and a constraint equation, initializing a particle swarm to establish a data driving layer, constructing an LSTM time sequence prediction module, executing particle filter state calibration, and selecting a model to configure a communication protocol to establish a virtual-real interaction layer, so as to realize state mapping and instruction feedback of a digital twin entity and a virtual model; and calculating virtual and real state deviation in real time and performing attribution diagnosis, adjusting model parameters or key features according to a deviation source, generating an optimization parameter combination based on a long time sequence prediction result and performing simulation verification in a virtual environment, and controlling entity parameter adjustment through an instruction feedback channel so as to realize predictive optimization.

The invention discloses a traffic protection facility state detection method and system based on digital twinning, and relates to the field of data processing methods for prediction purposes. Constructing a characteristic parameter matrix in the virtual model layer; performing adaptive decomposition on the characteristic parameter matrix to obtain a parameter subspace; establishing a parameter subspace index chain; analyzing the change correlation degree among the state parameters in each parameter subspace, and constructing a correlation degree network; calculating correlation evolution characteristics of the parameter subspace to obtain an evolution characteristic sequence; identifying an associated abnormal region, and positioning an abnormal source; performing state prediction simulation on the virtual model layer to obtain a state prediction simulation result; and predicting a parameter change curve, and when any value in the parameter change curve exceeds a preset safety threshold, outputting an early warning signal to the physical entity layer through the data interaction layer. The method and the device are used for improving the accuracy of traffic protection facility state detection, so that the preventive maintenance effect of the traffic protection facility is improved.

The invention provides a property system virtual-real mapping inspection method, device and equipment based on digital twinning and a medium, and belongs to the technical field of property inspection. Real-time mapping of an equipment entity and a virtual model is achieved through layered design of a physical layer, a data layer, a twinning layer and an application layer; collecting static / dynamic data and reducing noise, and establishing a global coordinate system; constructing a 1: 1 parameterized model and binding equipment attributes; the state analysis model is used for evaluating the equipment health degree, and virtual-real identification and work order visualization are triggered when abnormity occurs; the inspection path is dynamically optimized, and the processing efficiency is improved in combination with AR assistance and dual-stage verification; and finally, updating the model weight and adjusting the inspection strategy through data-driven acceptance feedback. Real-time synchronization and intelligent decision making of the equipment state are realized, the inspection efficiency is improved, the fault omission ratio is reduced, the resource allocation is optimized, and the operation and maintenance transparency and reliability are enhanced.

The invention relates to the technical field of wind turbine generator operation and maintenance, in particular to a wind turbine generator probability fatigue life prediction method and device, equipment and a medium. The method comprises the following steps: constructing a digital twinborn model of a wind turbine generator; based on operation load data, collected in real time, of the wind turbine generator, a digital twinborn model is adopted to determine corresponding stress data; and inputting the stress data into a pre-trained probabilistic fatigue life prediction model to obtain a current probabilistic fatigue life result of the wind turbine generator. According to the method, the current stress data of the wind turbine generator are calculated by constructing the digital twinborn model of the wind turbine generator, and the current probability fatigue life result of the wind turbine generator is obtained based on the pre-trained probability fatigue life prediction model. Therefore, according to the method, through real-time interaction of the physical unit and the virtual model, accurate prediction of the fatigue damage of the wind turbine generator is achieved, that is, the method can reflect the operation state and the fatigue damage condition of the wind turbine generator in time, and the real-time performance is high.

The invention provides a tunneling equipment autonomous navigation positioning method and system based on multi-source data, and relates to the technical field of inertial navigation, the method comprises the following steps: connecting multi-source monitoring equipment, obtaining a multi-source data set, carrying out space-time alignment, and establishing a monitoring data network; butting the transparent geologic model, and fusing the transparent geologic model with a monitoring data network to construct a virtual model; fitting the physical structure parameters and the operation state parameters of the tunneling equipment to the virtual model, positioning the tunneling equipment, and performing tunneling path optimization search; performing attitude and inertia analysis in the virtual model based on the planned path to obtain a path attitude strategy; and carrying out operation navigation control on the tunneling equipment. According to the method and the device, the technical problem of high difficulty in long-time navigation and positioning in a complex environment due to lack of autonomous navigation of the tunneling equipment is solved, and accurate positioning of the tunneling equipment in the complex environment is realized through fusion of multi-source monitoring and the virtual model, so that the tunneling efficiency and safety are improved.

The invention discloses an intelligent diagnosis method for composite faults of a permanent magnet synchronous motor based on digital twinning, relates to the technical field of fault diagnosis, and solves the technical problem of low diagnosis accuracy caused by the fact that fusion of multi-source information is not considered when a twinning model is adopted for fault diagnosis in the prior art. The method comprises the following steps: acquiring prophet data through a physical entity layer and transmitting the prophet data into a twin data layer; virtual current is generated through the virtual model layer and transmitted to the twinborn data layer; the twin data layer performs time-space synchronization on the received data to construct a virtual current signal reference library; the application layer makes a difference between predicted current output by the virtual model layer and current collected by the physical entity layer in real time, whether an electrical fault occurs or not is preliminarily judged in combination with a virtual current signal reference library, and corresponding fault features are input into a classification network according to a judgment result for fault diagnosis; the electromechanical composite fault diagnosis of the permanent magnet synchronous motor is realized by combining a multi-source information fusion technology.

The invention discloses an electronic material life cycle quality tracing method based on digital twinning, and relates to the technical field of industrial Internet of Things, the digital twinning of an electronic material is constructed, a material constitutive equation, a process parameter threshold library and historical quality data are integrated, and a multi-dimensional virtual model is formed; a production line real-time data stream including an equipment state, environmental parameters and material attributes is collected. According to the method, the virtual model containing the material constitutive equation and the process parameter threshold library is constructed, the real-time data flow dynamic evolution is combined, and the graph calculation and the causal reasoning algorithm are applied, so that the interaction effect of the equipment state, the environmental parameters and the material attributes can be associated, the core influence factor chain of the quality abnormality can be positioned, the single-point alarm limitation is broken through, and the quality abnormality can be accurately detected. The quality problem is deeply analyzed from the angle of multi-factor coupling, a comprehensive and systematic analysis framework is provided for accurate attribution, the source of the quality problem can be quickly and accurately found, and the efficiency and accuracy of quality tracing are improved.

The invention relates to the technical field of equipment intelligent diagnosis, in particular to a generator digital twinning diagnosis method based on temperature field online simulation, which comprises the following steps: respectively constructing a three-dimensional virtual model, a temperature field simulation model and an intelligent agent diagnosis model; the digital twin is in data connection with the physical entity; data alignment is achieved through a unified space-time coordinate system, a mapping relation is established between a vertex of the three-dimensional virtual model and a grid node in the temperature field simulation model, and a fault probability matrix output by the intelligent agent diagnosis model is matched to a three-dimensional grid of the three-dimensional virtual model through spatial interpolation; according to a fault probability output by the intelligent agent diagnosis model, correcting a boundary condition of the temperature field simulation model, triggering local grid adaptive encryption of the temperature field simulation model, and marking a fault position in real time through a three-dimensional virtual model; and the simulation result of the temperature field simulation model verifies the fault probability output by the intelligent agent diagnosis model. Through the method, the fault identification accuracy can be effectively improved.

The invention discloses a hard alloy production control method and system based on digital twinning, and relates to the technical field of digital twinning. A hard alloy production control system based on digital twinning comprises a twinning processing model building module and a hard alloy production control module. According to the method, the digital twinborn model is constructed for the full-process alloy production equipment, the historical production data and the equipment loss data are introduced for calibration regulation and control, the virtual model can truly map the equipment operation state and the process behavior, and the model prediction precision and adaptability are remarkably improved; on the premise that the quality target and the cost constraint are met, the candidate process paths are generated in the multiple equipment clusters, and screening is performed in combination with the raw material feature prediction portrait and the historical database, so that the scientificity and operability of path planning are improved, and the blindness and resource waste of process path selection are effectively reduced.

The invention provides a spacecraft full-cycle monitoring and operation and maintenance decision-making system based on digital twinning, and belongs to the field of spacecraft full-life-cycle intelligent operation and maintenance. Structural data and environmental data are monitored in real time through a multi-source sensor network deployed on a spacecraft structure; fusing physical entity monitoring data and damage evolution rule virtual data in a twin database, constructing a structure damage resistance and mechanical property evaluation model, and executing geometric modeling, physical attribute analysis, behavior response simulation and damage evolution rule modeling; based on a simulation result of the virtual model, life prediction and reusability evaluation are carried out, and an operation and maintenance decision instruction is generated; and feeding back the operation and maintenance decision instruction to the physical entity to execute control, and updating state data in the twin database to perform iterative optimization. According to the method, full-scene coverage from design demonstration to scrap recovery is supported, the problem of stage splitting in the traditional technology is solved, and a closed-loop optimization mechanism is formed through real-time interaction between the physical entity and the virtual model.

The invention relates to the technical field of intelligent war game deduction, in particular to an intelligent war game deduction simulation system based on a digital twin fusion large model. Comprising a digital twin modeling unit which adopts a dynamic precision adaptive modeling mechanism; a large model decision unit; a deduction execution unit; and a dynamic interaction unit. A dynamic precision self-adaptive modeling mechanism is adopted by the digital twin modeling unit, the model precision can be dynamically switched on demand, the detail simulation demand and the computing resource consumption are balanced, and meanwhile, the sub-second-level dynamic synchronization of a virtual model and a war game deduction scene is realized through a time sequence calibration algorithm; and space confrontation, time sequence decision and rule triggering features are fused through the multi-modal feature analysis module, and the Wargame rule verification module is combined to embed entity performance boundaries and scene rule constraints, so that compliance confrontation decision parameters conforming to Wargame deduction logic can be generated, and the problem of insufficient model precision adaptation and decision constraints is solved.

The invention provides a 3D modeling method based on a digital twin city, and belongs to the technical field of 3D city modeling. Through a multi-source semantic data fusion acquisition means, space-time semantic tags are added to various types of data, the problem of multi-source data isomerism is solved, and semantic unification and efficient fusion of different types of data are realized; by means of hierarchical dynamic modeling means, a building component network is constructed based on triple association of geometry, functions and semantics, so that a model structure better conforms to the logic of the building industry, and the reasonability and efficiency of modeling are improved; a virtual-real two-way mapping means is applied, incremental updating of the model is triggered through a cross-modal semantic comparison algorithm, limitation of traditional static modeling is broken through, and dynamic synchronization of the virtual model and the physical world is achieved; an intelligent optimization means is adopted, a multi-constraint-condition generative adversarial network is combined with a visual attention mechanism, it is ensured that generated textures conform to building geometric features and material specifications, and meanwhile intelligent distribution of rendering resources is achieved.

The invention discloses a self-adaptive switching system and method for power supply and distribution, and the system comprises a data collection module, an analysis module, a modeling module, a decision module, an execution module and an early warning module, and the data collection module generates a real-time monitoring signal; the analysis module performs dynamic weighted evaluation according to the real-time monitoring signal and generates an evaluation signal; the modeling module constructs a virtual model of the power supply system and generates an analog signal; the decision signal generates a control signal containing a path selection instruction; the execution module controls the semiconductor device to switch the physical connection state of the power supply according to the control signal and generates a switch feedback signal; and the early warning module forms an equipment health assessment result according to the switch feedback signal and an abnormal arc signal when the execution module switches the connection state and transmits the result to the decision module. The self-adaptive switching system for power supply and distribution can solve the problem that a traditional power supply and distribution switching system depends on a single threshold to judge the state of a power supply and cannot comprehensively evaluate the quality of electric energy.

The invention discloses a live-action three-dimensional dynamic change modeling method based on digital twinning. The method comprises the following steps: collecting multi-source data; carrying out space registration and time synchronization processing, and outputting a fusion data set; carrying out three-dimensional modeling, optimizing model parameters, and generating a live-action three-dimensional model; virtual-real mapping is carried out, and generated virtual space data and state parameters thereof are stored; performing differential analysis to generate a change detection result data set; performing dynamic change modeling to generate an updated virtual model; executing geometric accuracy optimization and semantic consistency check, and outputting an optimized virtual model; bidirectional data flow association and dynamic updating are executed, and the live-action three-dimensional dynamic change modeling process is completed. The real scene dynamic updating is realized by adopting the improved Gaussian sputtering modeling, and the method has the advantages of high precision, strong real-time performance and virtual-real synchronization.

The invention provides a five-axis series-parallel numerical control machine tool machining process virtual monitoring simulation method and system based on digital twinning, and the method comprises the steps: building an information interaction mechanism between a physical machine tool and a digital twinning model, enabling a server side to collect sensor data, and transmitting the real-time data to a client side through a communication protocol; the client processes the data and drives the digital twin model. And aiming at potential errors of twin system forecast, tool setting error compensation can be carried out in real time, so that the machining precision is improved. Meanwhile, the system supports an offline simulation function, combines an inverse kinematics algorithm and an NC code, fuses a material removal model, simulates a machining process, evaluates machining performance and errors, and provides an optimization basis for machining strategies of workpieces with different geometrical shapes and materials. According to the method, bidirectional interaction between the physical entity and the virtual model is achieved, the real-time monitoring and visualization capability of the machining process is enhanced, a reliable simulation evaluation and optimization means is provided for machining of complex parts, and the method has wide industrial application prospects.