107 results about "Virtual experiment" patented technology

The invention provides a complex geothermal system digital twinning online experiment method based on multi-source data deep fusion, and solves the problems of precise construction of geothermal system digital twinning and low-delay visualization of virtual-real interaction. The method comprises the following steps: firstly, building a typical geothermal reservoir multi-source heterogeneous data parallel acquisition system, and carrying out fine inversion, high-precision identification and continuous correction on geothermal reservoir parameters by using a large amount of field experiment test data; then, a complete physical model of the geothermal system is established, online simulation is conducted on an actual system through high-performance scientific calculation, and mutual calibration is conducted on the actual system and test data; and finally, building a geothermal system digital twinning on-line experiment platform based on real-time acquired test data, a reliable mechanism model and an artificial intelligence method, and realizing accurate measurement and on-line monitoring of a geothermal exploitation process and iterative evolution of a geothermal reservoir exploitation scheme. According to the method, multi-source data of the geothermal system are deeply fused, high-precision online simulation and low-time-delay virtual-real interaction in the geothermal experiment process are achieved, and geothermal storage and exploitation details can be comprehensively transparentized. And furthermore, intelligent and rapid inversion is performed on the multi-scale seepage and heat transfer process of the geothermal reservoir by carrying out a virtual online experiment, so that the scientificity and efficiency of full-life-cycle geothermal exploitation are greatly improved. Meanwhile, a multi-agent collaborative optimization regulation and control framework provided by the method has relatively high expandability and flexibility, and an optimal well spacing design scheme and an optimal experiment scheme of the geothermal well group can be automatically excavated through adaptive optimization and iterative evolution of a system regulation and control strategy; and the method has a very strong application prospect in the distributed optimization field of the geothermal storage virtual experiment.

The invention relates to the technical field of fluid machinery design, and discloses a fluid machinery through-flow component intelligent design simulation system, which comprises a multi-physics field modeling module, which is used for constructing fluid mechanics, structural mechanics and heat conduction models based on preset boundary conditions and physical attributes; the intelligent optimization decision module is used for constructing and defining a through-flow performance optimization objective function; the cooperative calculation module is used for constructing a comprehensive performance evaluation model based on the candidate design parameter set; the virtual experiment feedback module is used for reconstructing a corresponding through-flow component structure model according to the multi-target evaluation result; and the scheduling control module is used for controlling algorithm path selection and computing resource allocation in the optimization process. According to the method, a method of combining multi-physics field coupling modeling and a dynamic feedback mechanism is adopted, and design parameters are continuously adjusted through simulation data fed back in real time, so that the optimization process can be dynamically updated according to actual operation conditions, and the optimization convergence speed and the overall performance are improved.

The invention discloses a high-precision multi-modal twin model assembly method based on a large language model and a multiple docking optimization mechanism. The whole system framework is composed of three modules: a database, an interaction system and human factors. Wherein the database module integrates a model library module, a user interaction data recording module and a temporary data recording module, and is used for storing and managing various data information required for constructing a virtual scene; the interactive system module is used as a core part, integrates a model dynamic loading module, a model accurate positioning module and a model adaptive assembly module, realizes instruction analysis and model generation by relying on a large language model, and combines a multi-docking optimization mechanism of eye movement tracking, gesture recognition, automatic docking and logic rule binding to realize multi-docking. Efficient and accurate interaction between the user and the database is effectively guaranteed; the method not only provides a solid foundation for virtual experiments and virtual-real symbiosis, but also significantly improves the construction efficiency and precision of digital twin model assembly.

To provide methods and systems for improving a process of casting an object by predicting a probability distribution of local properties of a casting.SOLUTION: A method comprises using a single casting process simulation (2) and a process variability parameter (40) to predict a probability distribution of local microstructure-based mechanical properties (8) and local damage factors (9); and applying the corresponding local damage factors (9) to the local microstructure-based mechanical properties (8) at each part of the casting to predict local weakening in the corresponding material performance and a probability distribution thereof. The method further includes statistical analysis for a single cast part or a virtual experimental design method using parametrized sets of input variables in order to determine a local defect probability and process capability for each part of the casting.SELECTED DRAWING: Figure 1

The invention discloses an intelligent catalyst design method and system based on multi-objective optimization and deep learning, and aims to solve the problems of limitation of single-objective optimization, high computing resource consumption and the like in traditional catalyst design. According to the method, graph data are constructed by fusing atomic-scale and macroscopic features, multi-target prediction is carried out by utilizing a multi-task graph neural network (GNN), and collaborative optimization of adsorption energy and stability loss is realized by combining an NSGA-II multi-target optimization algorithm and a dynamic weight adjustment strategy of Bayesian optimization. A closed loop is verified through active learning and virtual experiments, candidate materials are dynamically selected, the model is updated, and the efficiency and precision of material design are remarkably improved. The method is suitable for rapid discovery and optimization of the high-performance catalyst, and has a wide application prospect.

A method for predicting probability and distribution of local mechanical properties of a casting using a single casting process simulation and a process variability parameter to predict the probability distribution of local microstructure based mechanical properties and local damage factors; and applying respective local damage factors to the local microstructure based mechanical properties at each part of the casting to predict local weakening in the respective material performance and a probability distribution thereof. The method further includes statistical analysis for a single cast part or a virtual Design of Experiments using parametrized sets of input variables to determine local defect probability and process capability for each part of the casting.

The invention relates to the technical field of public management, in particular to a scene demonstration evaluation method and system for public management virtual experiment cases, and the method comprises the following steps: S1, data collection: obtaining scene demonstration data; s2, data preprocessing: generating a structured scene data set comprising space-time labels; s3, dynamic feature modeling: generating a three-dimensional feature vector; s4, feature matching: generating a feature matching matrix including case similarity, a deviation index and an optimization weight; s5, real-time evaluation generation: generating real-time evaluation parameters according to the feature matching matrix; and S6, evaluation visualization: mapping the real-time evaluation parameters into a dynamic evaluation map, and synchronously driving topological structure optimization of the virtual experiment scene. According to the invention, the scientificity, real-time performance and operability of evaluation are improved, the transparency of decision making is enhanced, resource waste or insufficiency is avoided, and the intelligent management level of the public management field is improved.

The invention provides a multi-modal fusion virtual simulation experiment teaching system, and relates to the technical field of virtual simulation, and the system comprises an information collection module which is used for obtaining the interaction information of a learner; the information fusion module is used for constructing a multi-modal input data set; the instruction analysis module is used for analyzing and generating an operation instruction sequence; the teaching intervention strategy generation module is used for generating a self-adaptive teaching intervention strategy; the teaching intervention strategy execution module is used for executing a self-adaptive teaching intervention strategy; and the learning effect evaluation module is used for quantifying the learning effect evaluation score. The technical problems that in traditional virtual simulation experiment teaching, multi-modal operation behaviors of learners are difficult to accurately capture in real time, personalized teaching intervention is lacked and learning effect evaluation is incomplete can be solved, and virtual experiment simulation and intelligent teaching intervention driven by multi-modal information fusion are achieved. And the experimental interaction experience and the learning effect evaluation precision are improved.

The invention relates to the technical field of virtual reality experiment teaching, and discloses an experiment teaching simulation method and system based on virtual reality. The method comprises the steps that a continuous behavior sequence generated by user operation is collected, and a teaching behavior chain is generated through discretization; constructing a state vector by identifying the space-time state of the behavior unit, and comparing the state vector with a standard template to mark an abnormal behavior unit; tracing the context to generate an abnormal behavior description block, and querying an error knowledge base to obtain error information; meanwhile, analyzing a state change event of the virtual experiment equipment; and finally, fusing the error information and the state event to generate a simulation teaching evaluation result. According to the method, refined multi-dimensional analysis of experimental operation behaviors and intelligent error influence quantitative evaluation are realized, and the accuracy and guidance of teaching feedback are improved.

The invention belongs to the technical field of polymer composite materials, and discloses a carbon fiber interface modifier aided design method and system based on collaborative optimization, and the method is used for designing a carbon fiber interface modifier based on collaborative optimization of machine learning and molecular dynamics simulation. By constructing a closed loop iteration process of generation-prediction-verification-learning, interfacial modifier molecules meeting multi-target performance requirements are quickly and accurately obtained under the condition that a large amount of early-stage chemical synthesis and experimental screening are not needed. According to the method, through deep combination of high-throughput prediction of machine learning and virtual experiment verification of molecular dynamics, efficient and automatic design of the interface modifier molecules is achieved, experimental synthesis and trial and error links are greatly reduced, the research and development efficiency is improved, and the method is widely suitable for design and optimization of the interface modifier molecules of the carbon fiber / resin-based composite material.

The invention is suitable for the field of injection molding, and provides a virtual detection evaluation method and system for injection molding of automobile plastic parts, and the system comprises a data acquisition and real-time monitoring module, a temperature field model construction and thermal analysis module, a performance change analysis module, and a virtual experiment and performance evaluation module. According to the method, through accurate thermal field simulation and stress analysis, the performance and reliability of the injection molding part under the actual use condition can be systematically evaluated. Besides, by combining the fatigue theory and oxidation reaction analysis, a scientific basis is provided for pre-judging the service life and performance change of the material, so that the prediction result is more accurate and reliable. The method not only can effectively identify the potential failure mode, but also can provide data support for design optimization and material selection so as to improve the product quality and durability. Meanwhile, by adopting the virtual detection technology, the cost and time of actual testing are reduced, the research and development efficiency is improved, and the process of intelligent manufacturing is promoted.

The present invention discloses a stable control method for excavation face of cable shield tunnel in water-rich strata, which relates to the field of shield tunnel excavation control. The method comprises: using virtual experiment technology to establish a tunnel excavation face model; using the tunnel excavation face model and discrete element method to simulate the deformation and settlement law of soil in the excavation face stratum of the support structure under different support force distribution conditions, and analyzing the ultimate support force of the support structure and the stability of the excavation face according to the soil deformation and settlement law; selecting the optimal support force distribution form, predicting the mechanical properties of the shield machine according to the shield machine structural parameters, and generating the excavation face stability control value in combination with the optimal support force distribution form; adjusting the excavation parameters of the shield machine according to the excavation face stability control value, and applying the adjusted shield machine to real-time tunnel excavation work. The present invention can effectively reduce the potential safety hazards such as excavation face instability and soil collapse during excavation, and further ensure the smooth progress of the project.

The invention relates to a digital twinborn scene rapid construction method based on cross-modal generation. The method is composed of a cross-modal data generation framework and a digital twinning construction framework. The cross-modal data generation framework comprises a character generation module, an image generation module and a model reconstruction module; the digital twinning construction framework comprises a digital twinning model construction method, a digital twinning model assembly method, a digital twinning model fusion method and a digital twinning model verification and calibration method. According to the method, a rapid virtual modeling strategy in a low-sample application scene is defined, a basis is provided for virtual experiments and virtual-real symbiosis, the construction efficiency of a twinborn scene is improved, and a new possibility is provided for datamation of the scene in a resource-constrained environment.

The invention provides a virtual reality system for a rock uniaxial compression experiment, and the system comprises an offline calculation module which is used for generating a numerical simulation sample set based on a random simulation scheme generation service and an elastic brittle damage constitutive model driven numerical simulation system; the online calculation module is used for constructing and training a numerical simulation process agent model based on the numerical simulation sample set and the U-Net network; and the mixed reality visualization and interaction module is used for applying the trained numerical simulation process agent model to a mixed reality scene to complete a mixed reality experiment. According to the virtual experiment system provided by the invention, different accurate experiment results can be obtained according to different parameters, meanwhile, real experiment operation experience is obtained, the experiment cost can be greatly saved, and the environmental protection property is improved; the experiment risk is reduced, and the safety is improved; the space-time limitation is broken, and the self-learning enthusiasm of students is stimulated.

The invention discloses an interactive biological virtual experiment teaching system based on deep learning, and the system comprises a dynamic scene intelligent construction module, a multi-modal operation perception module, a real-time feedback guidance module, a comprehensive report generation module, and a self-adaptive learning optimization module. Component interaction, state simulation and parameter adaptive adjustment are supported, learning progress and difficulty data are received to realize linkage, a knowledge graph contains principle, specification, equipment characteristics and safety knowledge, staged loading, dangerous operation early warning and real-time knowledge node pushing are supported, and the knowledge graph is used for learning. The multi-mode operation sensing module integrates computer vision and sensor technologies to collect action data, identifies an operation sequence and a pre-judgment intention based on a space-time sequence model, adopts an environment adaptability algorithm to adjust collected parameters, and achieves autonomous optimization of the parameters through historical data analysis.

The invention provides a ceramic card production control method and system based on AI real-time feedback, and the method comprises the following steps: obtaining multi-dimensional production data in a ceramic card production process, and transmitting the multi-dimensional production data to a data processing center in real time for preprocessing; according to the method, multi-dimensional data are accurately collected through a sensor, data association of each link is analyzed by means of a graph neural network, an accurate scheme is generated, a model is constructed by using a digital twin module to carry out a virtual experiment to provide an optimization strategy, and the quality is predicted by relying on an AI intelligent decision model in combination with real-time data and the optimization scheme. The system automatically and quickly generates an adjustment instruction and feeds back the instruction to the equipment through edge calculation, and meanwhile, the reinforcement learning algorithm is used for dynamically scheduling the equipment to run, so that the problems of raw material errors, manual experience dependence, uneven temperature control and the like are effectively solved, and real-time dynamic adjustment in the production process is realized; the performance consistency, the quality stability and the overall quality of the product are improved, and the production requirements for high quality and high performance of the ceramic card are met.

A method for predicting the spatiotemporal state information of resources based on time series belongs to the field of entity state prediction in virtual experiments. It addresses the problems of data transmission delay and packet loss in distributed virtual experiments. The method includes: acquiring non-negative n time series state data of n variables over the first m time steps, and analyzing them to obtain N strongly correlated time series state data; processing each strongly correlated time series state data to generate an r-order cumulative generation sequence; establishing an N-order whitening differential equation based on the r-order cumulative generation sequence, and solving for the identification parameters composed of the coefficients and constant terms of the N-order whitening differential equation; then establishing the time response function of a fractional-order multivariate grey model EFMGM(1,N) at time K based on the solved identification parameters; and finally, performing cumulative subtraction to restore the time response function at time K to obtain the predicted value of the state data at time K. This invention is used for predicting the spatiotemporal state information of resources.

According to the stress analysis method and system for physics teaching provided by the invention, through integration of a parameterized virtual experiment template, physical simulation engine real-time solution, stress intelligent analysis and a multi-modal dynamic visualization technology, abstract mechanical concepts and relationships can be converted into visual, dynamic and interactive visual graphical representation and data flow; the problems that in traditional teaching, stress analysis is static and abstracted, and real-time data association and interactivity are lacked are effectively solved. The system not only can automatically and intelligently judge and mark the power or resistance attribute according to the relationship between the friction force direction and the object movement direction, vividly illuminate the dual roles of the friction force, but also can support the exploration type and collaborative learning through the synchronous and independent interaction mode of the master control and the multi-student terminal, thereby improving the learning efficiency. And meanwhile, the contrastive analysis, process recording and playback functions are provided, so that teaching explanation and knowledge consolidation are powerfully assisted, and the intuition, interactivity and teaching efficiency of physics teaching are remarkably improved.

The invention relates to the field of experiment guidance, and particularly provides a virtual experiment environment guidance information generation method and system, a terminal and a medium, and the method comprises the steps: capturing operation data of a student in a virtual experiment environment in real time to extract operation intention features, carrying out the compliance check of parameter setting, and carrying out the recognition and judgment of an equipment state and an experiment result, the method comprises the following steps: firstly, carrying out scene analysis on a scene, forming a uniform situation representation vector, carrying out rule matching through a predefined rule based on the vector, predicting and identifying operation errors, logic errors, safety violations and potential experiment risks through a machine learning model, and outputting a final diagnosis result by adopting a conflict resolution mechanism; and according to the error type, the risk level, the historical performance of the student and the learning preference, dynamically generating and outputting guidance information in a text, vision, voice or animation form. According to the invention, the accuracy and comprehensiveness of error recognition are improved, the safety protection capability of the experiment process is enhanced, and real-time and accurate guidance of student operation is realized.

The invention discloses a medical experiment teaching remote supervision system based on virtual reality and Internet of Things, and relates to the technical field of Internet of Things teaching, and the system comprises a laboratory Internet of Things sensing layer; the gateway layer is used for receiving data information of the laboratory Internet of Things sensing layer; the background management platform is used for sorting and displaying data information of the laboratory Internet of Things sensing layer, performing remote management and remote operation on experimental equipment and managing experimental data of the experimental equipment; the VR display module is used for displaying data information of the laboratory Internet of Things sensing layer in real time; the VR display module is also used for displaying an experiment operation process and an equipment monitoring state; and the VR display module is also used for displaying experimental operation specification information. According to the application, VR and the Internet of Things are deeply fused, actual environment data in a laboratory can be acquired in real time in a virtual experiment environment, experiment details are observed, and the experiment effect and learning experience are improved.

The invention discloses a CFD (Computational Fluid Dynamics)-based simulation analysis method for flow and evaporation characteristics of a falling film of a microstructure vertical pipe. According to the method, a geometric model is firstly constructed, grid local encryption and independence verification are carried out, then a control equation set containing continuity, momentum, energy and component transportation is constructed, an interface phase change model based on Rankine-Hugoniot jump conditions is particularly introduced, mass and energy transfer of a gas-liquid interface is accurately calculated through a user-defined function, and the gas-liquid interface phase change control method based on the Rankine-Hugoniot jump condition is obtained. And finally analyzing the film forming characteristic, flow field distribution and evaporation performance of the liquid film through post-treatment. According to the method, the physical mechanism of the microstructure induced secondary flow and the reconstructed thermal boundary layer can be analyzed, the heat transfer enhancement effect of different microstructures can be quantitatively evaluated, and the problem that a traditional empirical formula cannot predict the microstructure enhancement mechanism is effectively solved; and a low-cost and high-efficiency virtual experiment platform is provided for the optimal design of the high-performance vertical falling film evaporator.

The application relates to the technical field of virtual reality, in particular to a virtual reality-based experiment safety training method and a training system thereof. The method is applied to a training system configured with a virtual reality device, wherein a virtual experiment environment is constructed, the virtual experiment environment comprising a virtual cleaning tool and a virtual experiment instrument; the method comprises the following steps: playing a trajectory demonstration animation of performing a virtual hand cleaning operation in the virtual experiment environment; synchronously performing a real hand cleaning operation on a real hand of a trainee in a real environment; in a process in which the trainee performs an experiment operation on the virtual experiment instrument, randomly presenting a sudden risk event and an event occurrence moment according to a preset trigger condition; collecting a position point sequence of the trainee in a training process in real time; and evaluating a risk handling capability of the trainee based on the position point sequence. The method reduces the dependence on high-precision modeling and interactive design, improves the effectiveness of experiment safety training while maintaining a low development cost and cycle.

The invention discloses an assistant teaching method and system based on an artificial intelligence large model, and the method comprises the steps: collecting the learning behavior data of a student, building a student learning condition model based on the learning behavior data, and carrying out the real-time diagnosis and analysis of a learning state; a knowledge graph covering teaching contents is established, so that the relation and reasoning between concepts can be conveniently searched; a hidden knowledge structure is found by using a graph neural network technology, and the organizability of teaching contents is improved; teaching content and steps are dynamically adjusted according to the learning condition and progress of the student, and personalized learning experience is provided; teaching content suitable for the learning characteristics of the target student is generated for each student; a virtual experiment environment simulating a real situation is constructed, so that students can perform practical operation; teaching strategies are adjusted in real time according to feedback of students, and high learning participation degree is kept; learning condition analysis and improvement suggestions are provided for teachers, and continuous improvement of teaching quality is promoted. The learning effect is improved, the learning experience is enhanced, and the teaching management is optimized.

The invention provides a building energy consumption real-time monitoring traceability evaluation method and device and electronic equipment, and relates to the field of data processing. According to the method, photovoltaic inverter, subitem load, intelligent electric meter, topology and meteorological irradiation data are collected and preprocessed to generate a unified measurement flow and topology connection matrix, a cross-layer digital twinborn body is constructed, photovoltaic, load and energy channel characteristic flow is output, a phase dislocation event is identified, and a bias loss evolution track is generated in combination with a virtual experiment. And performing causal intervention to obtain a responsibility chain and an attribution weight, outputting a governance instruction, generating a strategy effect file through translation closed-loop checking, and finally forming a visual traceability report and an operation and maintenance execution list, thereby realizing energy consumption real-time monitoring traceability evaluation. By implementing the technical scheme provided by the invention, the accuracy of real-time monitoring, tracing and evaluation of the energy consumption of the building can be improved.

The application relates to a cladding tube explosion simulation method, device, equipment, storage medium and product. The method comprises the following steps: acquiring a pressure boosting explosion experiment parameter of a cladding tube; applying a pressure load to a virtual model corresponding to the cladding tube according to the pressure boosting explosion experiment parameter, performing a cladding tube explosion simulation experiment, generating a pressure boosting explosion simulation parameter, and analyzing the pressure boosting explosion simulation parameter to generate a pressure boosting explosion simulation result. The scheme replaces the real pressure boosting explosion experiment with a virtual experiment method, simulates various working conditions of the real pressure boosting explosion experiment through corresponding various pressure boosting explosion experiment parameters, avoids waste of the cladding tube material caused by a large number of experiments in the real pressure boosting explosion experiment, reduces the cost of the pressure boosting explosion experiment, and improves the economy of the pressure boosting explosion experiment.

The invention discloses a virtual analogue simulation system used in a fracturing crack fluid experiment. The system comprises virtual reality equipment, space positioning equipment and an analogue simulation device, the virtual reality equipment is used for receiving simulation operation of a user for any virtual experiment equipment model and displaying fracturing crack fluid experiment data; the spatial positioning device is used for detecting the position of a user; and the analogue simulation device is used for determining the state and / or mode of the operated virtual experimental equipment model and the logic relationship and reference relationship of virtual data between the operated virtual experimental equipment models according to the simulation operation, and obtaining fracturing fracture fluid experimental data by combining standard experimental data and the user position. According to the system, virtual simulation of a fracturing crack fluid experiment is realized, virtual simulation of related complex experiments can be carried out under the condition that actual hardware is not used, an operator is helped to be familiar with related equipment and operation processes in advance, and related data display is carried out at the same time.

The utility model discloses a pair of intelligent glasses for virtual experiments, which comprises a pair of VR glasses and a connecting frame, and a power storage structure for supplying power is arranged in the VR glasses. According to the utility model, the VR glasses are rotatably connected with the connecting frame through the hinge and are adsorbed and fixed through mutual adsorption of the first magnetic attraction block and the second magnetic attraction block, and when an experiment condition needs to be recorded, the VR glasses are lifted upwards along the hinge by shifting the shifting piece, so that the eyes of a user can be exposed to facilitate recording; in order to prevent the connecting frame from being separated from the face of the user when the poking piece is poked, the bandages are arranged on the two sides of the connecting frame, and the motors capable of tightening and preventing the bandages from loosening are arranged, so that the tightness of the bandages can be adjusted more conveniently, and the use comfort is improved.

The application belongs to the technical field of internet distance education, and discloses a remote virtual experiment system and method combined with the internet of things, which comprises a near-end virtual reality module, a cloud-end management and analysis module and a remote internet of things experiment module; the near-end virtual reality module is used for restoring the modeling remote laboratory scene, experiment action and experiment visual data, and executing element control instructions through a control handle; the cloud-end management and analysis module is used for realizing the centralized management of the internet of things equipment, data collection, analysis and judgment of the control instructions issued to students, recording of student experiment information and judgment of experiment results; and the remote internet of things experiment module is used for realizing sensor data uploading and control instruction issuing, and controlling and identifying the action of the laboratory executing element. The application has strong experiment safety, and all instruction operations are completed through network communication, so that the action of other laboratory equipment is reduced, the safety and reliability of student experiments are ensured, and dangerous experiments are avoided.