1409 results about "Digital mockup" patented technology

The invention relates to the technical field of construction progress monitoring, and discloses a construction progress monitoring method and system based on big data. The method comprises the following steps: forming a space-time alignment data set through multi-source data acquisition, filtering and quality evaluation; performing feature extraction and registration to generate a digital model; target detection classification is performed to form a completion state table; progress evaluation is achieved through component-task mapping; trend analysis and risk identification are performed to generate a prediction result; decision reference is provided for personalized information screening and augmented reality display. Through multi-source data acquisition, fusion and intelligent analysis, accurate perception, objective evaluation, scientific prediction and visual presentation of the actual state of the construction site are realized, so that a comprehensive, accurate and prospective construction progress monitoring method is provided, the construction period delay risk is effectively reduced, and the construction management efficiency is improved.

The invention relates to the technical field of intelligent early warning of digital twinning, and particularly discloses a slope multi-physics field fusion early warning decision-making system based on digital twinning, which is characterized in that physical monitoring data representing the macroscopic state of a slope and microscopic physical response signals reflecting internal damage evolution are synchronously acquired through a multi-modal data sensing module; space-time alignment, standardization and cross-modal fusion analysis are carried out through a damage eigenstate extraction module, and a unique eigendamage variable for quantitatively representing the real-time degradation degree of the material strength is interpreted; the twinborn self-evolution module takes the variable as a core observed quantity, and drives parameters and states of a slope mechanical model to be cooperatively and dynamically updated by adopting a data assimilation method, so that high-fidelity tracking of a digital model on physical reality is realized; and the prospective early warning decision module deduces a future spatio-temporal evolution path of the material strength parameters based on the calibrated model, and realizes graded early warning and intelligent decision support by combining Monte Carlo simulation and quantification of the instability risk probability.

The invention provides a digital twinning-based stereoscopic warehouse goods allocation and sorting scheduling method and system, and relates to the technical field of digital twinning, and the method comprises the steps: constructing a three-dimensional digital model of a stereoscopic warehouse; goods allocation is carried out based on a deep reinforcement learning algorithm, the goods access frequency, the associated purchase probability and the seasonal demand prediction are used as input parameters, and a goods allocation instruction is generated by taking the shortest sorting path and the associated goods centralized storage as optimization targets; warehousing operation is executed, and a sorting order is received; constructing a virtual potential field in the model, and when the repulsive force between stackers exceeds a threshold value, determining a task priority based on the order emergency degree and triggering obstacle avoidance; and calculating an obstacle avoidance track and generating a cooperative scheduling instruction to execute picking operation. According to the invention, efficient goods allocation and intelligent multi-stacker collaborative scheduling are realized.

The invention relates to the field of engineering management, and discloses a wharf steel structure construction progress dynamic management and control and resource allocation system, which comprises a real-time data perception and fusion module for collecting field data and an initial plan and generating a fusion data stream; the dynamic construction digital twinborn construction and evolution module is used for updating the digital twinborn and a dynamic empirical risk layer in real time based on the fusion data flow; the multi-agent self-adaptive decision engine is used for generating a self-adaptive construction scheme based on digital twinborn simulation deduction and feeding back a risk signal; and the man-machine cooperation and closed-loop execution module is used for visualizing the scheme for manual decision making and issuing an instruction to a site. According to the method, the physical field data sensed in real time is fused with the digital model, the dynamic construction digital twin capable of being synchronously updated with the physical construction field state is constructed, and the technical effect that digital information and the physical entity state are kept highly consistent is achieved.

The invention relates to the technical field of data processing, in particular to a backtracking analysis model construction method based on an attack chain, which comprises the following steps that: a kernel layer security agent acquires process, file and network behavior characteristics in a hardware isolation environment, and generates an event tuple; the tensor network pipeline performs three-dimensional decoupling mapping on the tuple into a behavior fingerprint vector, an orthogonalization noise feature and an asymmetric adjacent tensor, and compresses the behavior fingerprint vector, the orthogonalization noise feature and the asymmetric adjacent tensor into a space-time topology tensor block; the reinforcement learning controller constructs a directed acyclic graph based on the tensor blocks, calculates connectivity loss and outputs an event risk score; the dynamic routing engine constructs a decision tree model according to the risk mark, the burst frequency and the correlation entropy, and implements three-level shunting and a multiple simulation system to generate an anti-interference index; and when the deviation between the physical trajectory and the digital model exceeds the tolerance, the closed-loop feedback weight coefficient updates the loss function parameter and adjusts the channel resource weight. And the problem of threat discovery delay caused by attack chain breakage under massive events is solved.

The invention discloses a UE4-based digital twin model construction system and method, and relates to the technical field of computer vision, and the system comprises a geometric modeling unit which generates three-dimensional model data based on geometric modeling data of a target physical entity, and achieves dynamic LOD rendering level switching through virtualization processing; the real-time data interaction unit is used for analyzing the real-time sensor data flow and generating equipment state data in combination with a transmission operation and maintenance instruction; the simulation engine unit is used for executing rigid body motion and collision simulation based on the equipment state data and outputting a collision event sequence and state transition data by utilizing an event trigger; the machine learning unit is used for training an LSTM (Long Short Term Memory) model based on the collision event sequence and the state transition data, and optimizing state covariance parameters through adaptive Kalman filtering; and the visual interaction unit is used for realizing data synchronization of the command large screen and the mobile terminal through multi-screen collaboration. According to the method, the digital model capable of accurately reflecting physical entity behaviors and characteristics is constructed.

Methods and systems for generating a sharable script related to an input digital model on a digital platform are provided. The method includes receiving a user request indicative of a digital task involving an input digital model, and retrieving a corresponding input digital model file. Then, determining characteristic attributes of the input digital model, where the characteristic attributes include digital artifacts generated from the input digital model file. Then, selecting from a collection of templates, using a machine learning (ML) engine, a template matching the characteristic attributes of the input digital model. The ML engine may be trained on documentations of digital tools integrated into the digital platform, a resource-capability mapping of the digital platform, and sample digital thread orchestration scripts collected through past uses of the digital platform. Finally, the method includes generating the sharable script that implements the digital task, based on the selected template.

The invention provides a high-precision intelligent welding system adapting to complex working conditions, and relates to the technical field of laser welding. The high-precision intelligent welding system adapting to the complex working conditions comprises the steps of S1, based on a high-resolution visual sensor and a laser scanner, S2, based on preprocessed point cloud data, S3, based on a workpiece three-dimensional model, S4, based on an optimized welding path and a material attribute database, S5, based on a dynamic welding parameter set, S6, based on a real-time welding track, and S7, based on multi-sensor monitoring data. S8, based on a defect detection report; and S9, based on a repaired welding result and historical welding data. According to the invention, the geometric data of the workpiece are collected through the high-resolution visual sensor and the laser scanner, the filtering algorithm is applied to remove noise to generate high-quality point cloud data, and a subsequent processing foundation is laid; workpiece features are identified by using a point cloud processing algorithm and a machine learning method based on the point cloud data, and an accurate digital model is generated through a three-dimensional reconstruction algorithm.

The invention relates to the field of motor health management and predictive maintenance, in particular to a motor stator winding insulation state evaluation method and system based on a digital model. Comprising the following steps: S1, collecting high frequency of a motor stator winding, and generating multi-physical-quantity real-time data; s2, calculating a dynamic capacitance reference value according to real-time data of multiple physical quantities; s3, performing subtraction operation on the high-frequency equivalent capacitance measurement value and the dynamic capacitance reference value, and extracting an insulation degradation residual signal; s4, constructing a self-adaptive dynamic detection threshold according to multi-physical-quantity real-time data; s5, judging whether the absolute value of the insulation degradation residual signal is greater than a self-adaptive dynamic detection threshold or not: if so, judging that an insulation degradation event occurs; if not, judging that the operation state is a normal operation state; and S6, in response to the insulation degradation event, updating the insulation degradation index, and generating insulation state evaluation based on the updated insulation degradation index. According to the invention, false alarm under severe load fluctuation is avoided, and the accuracy and reliability of evaluation are significantly improved.

The invention belongs to the technical field, and discloses a film coating processing technology simulation and optimization system based on digital twinning. The system is composed of a data acquisition module, a feature recognition and analysis module, a behavioral digital twinborn simulation module, a coating uniformity evaluation module, a process parameter optimization module and a coating distribution prediction module. The method comprises the following steps: acquiring a three-dimensional roughness digital model of a base material, accurately identifying a micron-sized local rough region and extracting geometrical characteristic parameters of the micron-sized local rough region; constructing a coating growth behavior digital twinborn model based on the geometrical characteristic parameters and the process parameters, and accurately simulating the coating growth process of the local rough area; and establishing a global coating uniformity quantitative evaluation system, and optimizing a process parameter combination by applying machine learning and a genetic algorithm. The system breaks through the limitation of traditional static shadow hypothesis, real-time tracking and accurate simulation of microstructure evolution in the deposition process are achieved, and the coating growth prediction accuracy is remarkably improved.

The invention relates to the technical field of BMS and the like, and provides a battery management system based on adaptive digital twinning, a physical layer of the battery management system comprises a battery pack, a sensor network and an edge computing node, and is responsible for data acquisition and preprocessing; the digital twin layer comprises a self-adaptive multi-scale model and a real-time data engine, battery behaviors are dynamically simulated by coupling electrochemical, thermal and aging models, a future state trajectory prediction result is output, and the real-time data engine fuses sensor data, historical data and simulation data to drive model updating; the intelligent decision-making layer comprises a reinforcement learning controller and a fault prediction module which are deployed in a local server, the reinforcement learning controller dynamically optimizes a charging and discharging strategy according to a prediction result of the digital twinborn layer and issues and executes the charging and discharging strategy, and the fault prediction module analyzes multi-source time sequence data based on an LSTM network so as to early warn thermal runaway and short circuit risks in advance. According to the invention, long-term accurate mapping and adaptive adjustment between the battery physical entity and the digital model can be realized.

The present invention relates to an adaptive manufacturing method for building components, particularly in prefabrication, modular construction, and panelized construction. The invention minimizes cumulative errors in building component production, enhances precision, and facilitates automated assembly using real-time digital updates. The method includes creating a digital information model of a building, containing data on the dimensions and positions of building components; manufacturing at least one building component based on initial model parameters; scanning the manufactured component using laser scanning, structured light scanning, or ultrasound-based scanning, selected based on the material properties, to generate a digital representation of its actual dimensions and shape; updating the digital model with the scanned data to dynamically adjust parameters for subsequent components; recalculating and adjusting the parameters of unmanufactured components based on detected deviations; transmitting adjusted parameters to an automated manufacturing system for real-time modifications in production. The system comprises a processor executing adaptive manufacturing instructions, a scanning module, a processing unit, and a recalculation module that updates component dimensions based on deviations, ensuring seamless integration of prefabricated elements.

The invention discloses an additive manufacturing intelligent repairing and surface optimizing method for a complex special-shaped curved surface, and particularly relates to the technical field of additive manufacturing. A three-dimensional digital model is obtained, and a complex special-shaped curved surface defect area is recognized; extracting a local morphology parameter set; constructing a repair path generation model based on a graph neural network, and performing collaborative optimization of forming precision, residual stress and deposition stability on the path through a multi-objective optimization algorithm; additive manufacturing equipment is controlled to perform material deposition according to the optimized path; carrying out online error detection by adopting a structured light system, judging repair quality through a fuzzy decision tree model, and triggering path reconstruction if the repair quality exceeds a threshold value; after repairing is completed, surface micro texture factors are extracted, and functional texture regulation and control are conducted through the laser micromachining technology; according to the method, high-precision intelligent repair of the complex curved surface area and collaborative improvement of surface function performance are achieved, and the method is suitable for the fields of aerospace, energy, high-end equipment manufacturing and the like.

The invention relates to the technical field of intelligent decision making, in particular to a digital twin command and decision feedback system for war game deduction based on multi-agent game confrontation. Comprising a digital twin modeling unit; a multi-agent game confrontation unit; a command instruction generation unit; a decision feedback evaluation unit; and a data interaction unit. According to the design of the invention, the precision of the model is adaptively adjusted through the digital twinborn modeling unit according to the criticality of the deduction scene, and the digital twinborn model can adapt to different criticality deduction requirements of a strategic layer, a tactical layer, a key confrontation scene and the like in combination with multi-scale division and cross-scale parameter coupling transmission; realizing cross-level dynamic association between the physical entity and the digital model; a real-time perception-decision closed-loop mechanism constructed based on a multi-agent game confrontation unit enables the agents to realize collaborative confrontation based on a real-time state autonomous evolution strategy of a digital twinborn scene, and solves the problem that the agent decision is disjointed from a physical scene state.

The invention discloses an intelligent life prediction and optimization system and method for a steam turbine rotor welded joint. The system comprises a multi-source data acquisition module, a digital twin modeling module, a health state evaluation and life prediction module, a risk early warning module and an operation collaborative optimization module. The multi-source data acquisition module acquires the multi-dimensional physical quantity of the rotor welding joint in real time. The digital twin modeling module establishes a high-fidelity virtual model and realizes real-time synchronization and correction of a physical entity and a digital model. And the health state evaluation and life prediction module is used for calculating a damage accumulation rate and a health index based on fusion of a physical model and an LSTM neural network so as to realize residual life estimation. And the risk early warning module performs graded early warning according to the dynamic trend of the health state. And the operation collaborative optimization module adaptively adjusts operation parameters and optimizes the unit efficiency based on a reinforcement learning algorithm. All the modules are interconnected through an industrial network to form a closed loop, and real-time monitoring, intelligent evaluation and active service life management of the rotor welding joint are achieved.

The invention discloses a digital twinning-based building full life periodicity performance evaluation method and device, and belongs to the field of building construction.The evaluation method comprises the following specific steps that I, in the building planning and design stage, a building foundation digital model is constructed, meanwhile, various construction data are collected in real time through a multi-modal sensor, and block chain storage is established; iI, performing real-time sensor data preprocessing and updating based on the building basic digital model so as to perform multi-physics field coupling simulation, and dynamically updating and predicting a building state; the method overcomes the limitation that traditional simulation is large in calculation amount and difficult to update in real time, improves evaluation reliability, achieves dynamic calibration and precision enhancement, enables material aging and repair prediction to be more accurate, provides a solid basis for durability evaluation, improves the economy and reliability of the whole life cycle of a building, and has a wide application prospect. And a complete closed loop of coverage planning-construction-use-maintenance is formed, so that the performance evaluation can be continuous, dynamic and traceable.

The invention provides a real-time 3D printing defect detection method and system based on artificial intelligence, and aims to solve the problems of dependence on single visual information, lack of dynamic tracking and insufficient model generalization ability in the prior art. According to the method, the printing process is comprehensively perceived through multi-modal data fusion, and defects are analyzed and classified in real time by using a deep learning model. The model output comprises defect category, position, abnormal score and severity evaluation, and the defect development trend can be dynamically tracked and early warning can be carried out in advance. Through comparison with a printed digital model, the system can accurately identify geometric deviation, and early intervention is realized in combination with trend analysis. When serious defects are detected, the system automatically triggers response measures, such as printing pause, parameter adjustment or correction scheme provision, to form a complete quality control closed loop. According to the method, the detection precision and robustness are remarkably improved, and incremental learning is supported to adapt to different printing environments.

Digital model platform that enables the streamlined creation and management of digital twins and physical twins by leveraging external feedback and artificial intelligence (AI) is disclosed. Methods and systems for a model collaboration platform that facilitate the integration of external feedback, whether from physical, virtual, or human sources, into the streamlined design, validation, verification, certification, assembly, operations, and maintenance processes of complex systems. Updated results are output to those external sources, including physical twins, other digital twins or models and simulations, and / or human users. External feedback includes feedback data from physical prototypes and their environment, virtual prototypes, simulations, and subject-matter experts. Embodiments are directed to integrating external feedback into the assembly and system-level assessment of digital twins, physical twins, digital threads, and the iterative design of constituting digital engineering models, including constructing, maintaining, and improving digital engineering models for the design, validation, verification, certification, operations, and maintenance of complex systems.

The invention discloses a medical equipment full-life-cycle management system based on a digital twinning technology. The medical equipment full-life-cycle management system comprises four modules. The digital twinborn model construction module constructs and updates a simulation medical equipment digital model in real time; the intelligent data acquisition and monitoring module is responsible for data acquisition, transmission, monitoring and interface design; the intelligent decision and maintenance module realizes fault prediction, maintenance plan generation and record management; and the comprehensive analysis and optimization management module performs data analysis, optimization decision and life cycle planning. The system has the remarkable advantages that when equipment is used, the running state of the equipment is adjusted in time through real-time monitoring and intelligent analysis, and the fault rate and the maintenance and replacement frequency are reduced; in the aspect of cost control, a maintenance strategy and a purchase decision are optimized, and expenditure is reduced; at the medical service quality level, stable and efficient operation of equipment is guaranteed, patient safety is improved, and reasonable utilization of medical resources is achieved.

The invention discloses a digital modeling steel structure multi-dimensional collaborative optimization design method and system, belongs to the technical field of steel structure design, and aims to solve the problems that multi-dimensional comprehensive consideration and automatic optimization tools are lacked, and complex multi-objective optimization requirements are difficult to quickly respond. Comprising the steps of establishing a full-parameterized digital model containing construction process parameters and environmental protection indexes, constructing a multi-objective evaluation system of structural safety, economical efficiency, construction feasibility and environmental influence, performing automatic optimization by adopting an improved multi-objective evolutionary algorithm, selecting 10 groups of candidate schemes, generating a visual interface, supporting human-computer interaction decision, and generating an optimization report. According to the method, multi-dimensional collaborative optimization is realized by constructing a multi-target evaluation system of structural safety, economy, construction feasibility and environmental influence, construction process parameters and environmental protection indexes are integrated through the digital modeling unit, it is ensured that factors of all parties are comprehensively considered in design decision, and the design efficiency is remarkably improved.

The invention discloses an intelligent warehousing dynamic optimization system based on a digital twinborn technology. The system comprises the following steps: S1, creating a digital twinborn model: acquiring basic data of an entity warehouse and constructing an accurate digital model; s2, data collection and integration: deploying Internet of Things equipment and realizing real-time transmission and processing of data; s3, constructing an analysis algorithm: developing an advanced analysis algorithm and implementing a machine learning model; s4, dynamic optimization and adjustment: automatically adjusting a storage layout and optimizing a warehouse-in and warehouse-out process; and S5, prediction and early warning: analyzing and predicting potential problems and setting an alarm mechanism based on historical data. According to the invention, the real-time performance and accuracy of data are obviously improved, the environment monitoring capability is enhanced, the cargo safety is guaranteed, and the space utilization rate is greatly improved.

The invention discloses a dynamic digital twinning system based on 3D Gaussian splashing. The dynamic digital twinning system comprises a flexible electric power inspection robot, a 3DGS dynamic enhancement module and a fusion digital twinning background centralized control system. The flexible electric power inspection robot is used for collecting 2D images and environment point cloud data of substation equipment; the 3DGS dynamic enhancement module is used for carrying out 3DGS dynamic enhancement and generating a 3D Gaussian model; the standard digital twinborn centralized control platform is used for storing and calling data and digital twinborn bodies of the traditional static substation digital model; the model fusion port carries out space alignment and attribute interpolation fusion on the 3D Gaussian model and the static digital twin; and the visual terminal is used for displaying the fused enhanced digital twinborn body and the equipment state evaluation result. According to the invention, the dynamic enhancement of the digital twinborn model of the substation equipment can be realized, and the operation state and change condition of the equipment can be reflected more truly and intuitively.

The invention discloses a thermal insulation color steel sandwich composite board product quality detection system based on digital twinning, and particularly relates to the field of quality control. The system comprises a composite board full-dimension three-dimensional digital model construction module, a production digital twinborn body creation module, a total-factor real-time data acquisition module, a data preprocessing module, a twinborn data driving and model calibration module, a quality intelligent analysis decision module and an optimization execution module. The model construction module constructs a reference model and embeds a process standard and a quality threshold value; the twin creation module builds a virtual production line, standardizes a data interface and assigns an ID to a plate; the data acquisition module acquires real-time data; the preprocessing module removes abnormal values, standardizes data and associates the data; the calibration module updates the twin body, calculates the deviation and calibrates the model; the analysis module realizes compliance judgment, defect analysis and quality prediction; and the optimization module generates and executes an instruction, so that the problems of low efficiency, data splitting and no closed loop of traditional detection are solved, and the quality control precision and efficiency are improved.

The invention relates to the technical field of power system monitoring, in particular to a power system parameter dynamic verification method, system and device based on digital twinning and a storage medium. Constructing a digital twinborn model of the power system, establishing a state mapping relation between a physical system and a digital model, realizing bidirectional dynamic mapping between the physical system and the digital model through a digital mapping mechanism, and obtaining system operation characteristics; constructing a parameter verification model based on the operation characteristics, obtaining a verification objective function by fusing physical constraints and data driving, and dynamically adjusting parameter values to generate parameter correction values by evaluating model errors in real time by adopting an adaptive parameter correction algorithm; based on a dynamic optimization mechanism of multi-objective optimization, an optimization strategy is adaptively adjusted according to the running state, and the verification process is continuously optimized; and performing multi-dimensional evaluation on the verification result through the verification precision evaluation system to generate a parameter verification result. The technical problems that a traditional method is inaccurate in modeling, low in response speed and poor in adaptability are effectively solved.

Methods and systems enabling multimodal inputs for interacting with a live digital object are provided. The system receives the live digital object which includes a digital artifact extracted from a digital model file through a model representation. The system receives a user's security level and determines the user's access permission and modification permission to access and modify the digital artifact. The system accesses a multimodal interface configured to receive a conversational input and a spatial input, outputs the digital artifact to the multimodal interface based on the access permission, receives from the multimodal interface a conversational input and a spatial input from the user, and generates a modified digital artifact from the digital artifact via the digital model representation, based on the modification permission and on the conversational or spatial input. The multimodal interface may include conventional interfaces (GUIs / APIs), conversational interfaces (text / voice), and spatial computing interfaces (VR / AR / MR / gestural).

The invention relates to the field of visual measurement of parts, in particular to a non-contact micropore form and roughness measurement method and system. The method comprises the following steps: carrying out initial contour scanning on a precision part, carrying out part detection base point analysis, and extracting an optimal coordinate base point; performing full-coverage scanning planning and panoramic image splicing based on the optimal coordinate base point to obtain a panoramic fusion image; geometric structure point depth identification is carried out on the panoramic fusion image, three-dimensional point cloud modeling is carried out, and a part CAD digital model is constructed; carrying out micropore shape key size calculation based on a part CAD (Computer Aided Design) digital model, carrying out geometric comparison calculation, and extracting an error value of each measurement point; and performing deviation visual mapping and comprehensive evaluation according to the error value of each measurement point, and outputting a comprehensive evaluation report. According to the invention, the precision part detection efficiency and precision are improved, and the production quality control and later problem traceability of the precision part are enhanced.

The invention relates to the technical field of safety production management, and discloses a safety production hidden danger closed-loop management and control supervision system and a risk assessment early warning method, and the system comprises an environment modeling module which is used for building a digital model of a production unit and calculating a systematic instability coefficient based on historical data; the risk assessment module is used for quantifying the initial risk potential energy in combination with the coefficient and the hidden danger inherent attribute; the risk evolution module is used for dynamically updating risk potential energy according to a time and space coupling rule; the closed-loop verification module is used for judging the actual effectiveness of the intervention measure by comparing the management entropy before and after the intervention after the intervention measure is implemented; and the self-adaptive feedback module is used for automatically updating the systematic instability coefficient in the environment model according to the judgment result. According to the method, risk quantification, dynamic early warning, effect verification and model self-optimization are integrated, a complete closed-loop management process is formed, and the scientificity and foresight of safety production management are remarkably improved.

The invention discloses an optimization design method for improving the environmental performance of a building site, and relates to the technical field of site design. The method comprises the following steps: S1, collecting and preprocessing basic information of a building site; s2, building a multi-dimensional digital model of the building site; s3, site environment performance simulation and index quantification are carried out; s4, environment performance parameters are intelligently optimized; s5, verifying and finally determining an optimization scheme; and S6, optimizing effect tracking and dynamic adjustment. According to the method, adjustable variables of a building candidate model serve as genes, binary crossover and polynomial variation are simulated through iteration of 100-200 sets of initial populations, and a multivariable combination space is automatically traversed; according to the method, planning specifications and a cost threshold value are converted into algorithm constraints, it is ensured that an output scheme is compliant and economical, 3-5 groups of non-inferior solution schemes are screened out in 3-5 rounds of iteration, standardized values of environmental performance parameters after optimization are all larger than or equal to 0.7, local optimum is avoided, and optimization efficiency and scheme comprehensiveness are greatly improved.

The invention discloses a method, a system, equipment and a medium for constructing an integrated digital model of a full-motion analog machine, and belongs to the field of analog machines. The method is suitable for constructing the digital model based on the unstructured heterogeneous data, and obtains the structured text information set from the document through the named entity recognition and relation extraction technology by obtaining the three-dimensional geometric model and the unstructured technical document of the full-motion simulator. And a three-dimensional model is combined to jointly construct a heterogeneous knowledge graph containing two types of nodes representing technical entities and three-dimensional parts. And link prediction is performed on the graph by using a graph neural network, so that a target edge representing deep implicit association between the text information and the three-dimensional part can be inferred. The target edges are updated to the atlas, and all complete technical information associated with the parts is bound to the corresponding three-dimensional geometric model, so that an all-moving simulator integrated digital model with complete information is generated, and the digital model construction efficiency and the model information association degree can be improved.

The invention discloses a three-dimensional reconstruction method and system based on deep learning, and belongs to the technical field of computer vision and deep learning. An existing neural implicit method is poor in reconstruction result in a weak texture area and a non-Lambert surface. Traditional geometric supervision depends on noise data, resulting in incomplete surface; in order to solve the problems of low global and local feature fusion efficiency in the optimization process and the like, the multi-view image of a target object is obtained by using a camera; obtaining camera parameters and sparse point clouds corresponding to the multi-view image by using an SfM model; building a three-dimensional reconstruction model based on the neural radiation field, and obtaining a reconstruction result; and evaluating a reconstruction result, and comprehensively evaluating the reconstruction result through the performance indexes. According to the invention, the problems of fuzzy reconstruction and poor precision of the target object in the weak texture area are solved; the problem of data annotation is solved, data acquisition is simple, and extra effective supervision does not need to be introduced; the cultural relic heritage with the weak texture area can be converted into a digital model permanently stored in a computer.