1672 results about "Digital mockup" patented technology

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 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 discloses a low-altitude aircraft take-off and landing platform site selection optimization method. The method comprises the steps that real-time dynamic data and static GIS data including urban traffic flow data, meteorological data, landform data, environment data and POI data are acquired; constructing an urban three-dimensional digital model according to the static GIS data, and constructing a digital twin model according to the three-dimensional digital model and the real-time dynamic data; constructing environment constraint conditions and safety constraint conditions of candidate take-off and landing platform positions in the digital twin model; performing multi-objective optimization on the digital twin model through a particle swarm optimization algorithm, and generating an optimal candidate take-off and landing platform site selection scheme set meeting environment constraint conditions and safety constraint conditions; and dynamically updating the digital twin model according to real-time data feedback, and dynamically adjusting the site selection scheme of the take-off and landing platform. According to the method, the virtual city model is constructed through the digital twin, an accurate simulation environment and real-time feedback are provided for site selection optimization, and the accuracy and feasibility of a site selection scheme are improved.

Methods and systems for a document review process are provided. The method includes receiving an input digital model representation comprising at least one externally-accessible model endpoint for generating a digital artifact. Then, generating a document splice comprising access to multiple document subunits, with at least one document subunit written in a natural language and comprising the digital artifact; the access to each document subunit is provided through an externally-accessible document endpoint for the subunit. Then, generating a document by combining the document subunits, and generating a view associated with the document, based on an user authorization result including selective access rights to the document subunits. The view comprises access to the digital model representation, the digital artifact, each document subunit, and the document. Finally, receiving a user input and updating, via one of the externally-accessible document endpoints, the document splice based on the user input.

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 technical field of big data, and discloses an urban rail transit emergency processing method and system based on a visual large model, and the method comprises the steps: collecting visual data, sound data and equipment operation data at a platform and a carriage of urban rail transit, and carrying out the preprocessing and timestamp synchronization; performing dynamic frame difference processing on continuous video frames of the visual data to generate a two-dimensional motion heat map, and performing space-time aggregation; constructing a three-dimensional digital model based on engineering drawings of the platform and the carriage, and establishing a mapping relation between the two-dimensional motion heat map and the three-dimensional digital model; performing abnormal behavior detection on a mapping result, performing multi-modal fusion analysis in combination with sound data and equipment operation data, and determining the severity of an abnormal event; emergency response strategies are formulated according to severity levels of the abnormal events, a control instruction is sent to the rail transit equipment through an industrial protocol interface, and abnormal event information is pushed to a control center, inspection personnel and passenger terminals.

The invention relates to the technical field of bridge construction, solves the technical problem that progress or quality data cannot be updated in real time due to difficulty in direct synchronous data feedback between a construction site and a management system, and particularly relates to a BIM-based intelligent two-dimensional code bridge construction quality and progress management and control system. The geometric information, the construction progress and the quality standard of the bridge structure are recorded in detail, and a unique two-dimensional code is generated for each construction node. A constructor obtains real-time node information by scanning the two-dimensional code, updates the construction progress and uploads quality detection data, and analyzes and feeds back the construction data in real time, so that efficient management and control of the construction progress and quality are ensured. The system overcomes the defects in the prior art, has the characteristics of timely information updating, accurate data, automatic management, high traceability and the like, and can remarkably improve the management efficiency and the quality control level of bridge construction.

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 provides a high-efficiency ship automatic control system, and the system comprises a comprehensive control platform which carries out the unified integration of subsystems, sets a target function, and carries out the data sharing, collaborative optimization and global control among the subsystems. The intelligent sensing network module is used for collecting historical data and real-time data; the self-learning control module is used for constructing a control strategy generation model; the ship digital model module is used for constructing a digital model for simulating ship navigation so as to test different ship control strategies; the decision execution module is used for generating and executing a control instruction; and the safety monitoring module detects abnormal behaviors of the ship and gives an alarm. According to the invention, a plurality of subsystem modules can be integrated in a unified manner, data sharing, collaborative optimization and global control are realized, an optimal control strategy can be provided, the cost and risk of actual navigation are reduced, and the control precision, response speed and operation efficiency of a ship are improved.

The invention discloses a construction site intelligent safety management system and method based on a BIM technology, and relates to the technical field of constructional engineering, and the method comprises the following steps: S100, building a three-dimensional digital model of a construction site by using BIM software, and collecting construction site data by deploying a video camera, personnel positioning equipment and an environment sensor; s200, preprocessing the acquired data, and performing potential safety hazard identification and environment detection on the construction site; s300, fusing and displaying the generated BIM three-dimensional digital model and the processed data through spatial alignment, data mapping and dynamic updating; s400, calculating the probability of occurrence of potential safety hazards on the construction site and a comprehensive decision score; and S500, carrying out quantitative assessment on the potential safety hazards of the construction site by constructing a risk assessment model, sorting the calculated risk scores of the potential safety hazards, and determining the priority processing sequence of the potential safety hazards of the construction site, so that intelligent safety management of the construction site is realized.

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 discloses a water pump station operation adjusting device and method, and relates to the technical field of pump station operation adjusting, a first prediction result is output through operation data of a water pump station, and whether the first prediction result needs to be corrected or not is judged based on environment characteristics, equipment state data and water quality data to obtain inlet and outlet valve adjusting parameters; constructing a water pump station digital model, receiving a simulation regulation feedback result, and optimizing inlet and outlet valve regulation parameters by utilizing the simulation regulation feedback result; an inlet and outlet valve adjusting stability threshold value is obtained, relation feedback verification is conducted through the inlet and outlet valve adjusting stability threshold value, and whether a protection mechanism is triggered or not is judged through the simulation adjusting feedback result and the relation feedback verification result; and when the protection mechanism is triggered, the dynamic current stabilizer is started, and the current stabilization parameters of the dynamic current stabilizer are adjusted. Intelligent accurate control, active protection and energy efficiency optimization are realized, the system stability is improved, and the operation and maintenance cost is reduced.

The invention relates to a digital twinning-based production line simulation method and related equipment, and the method comprises the following steps: carrying out the historical operation data mining of a target production line, and obtaining the historical time sequence operation data; performing dynamic logic simulation construction on the target production line based on the mapping space digital topological structure and the historical time sequence operation data to obtain a production line operation logic model; analyzing whether an abnormal position exists in the production line operation logic model or not through a preset anomaly detection algorithm, and if yes, labeling the abnormal position to obtain an abnormal labeling position; wherein the abnormal labeling position is a logic conflict point position and a non-optimal configuration area; and performing flow reconstruction on the production line operation logic model based on the abnormal labeling position to obtain a target virtual three-dimensional model, thereby solving the technical problems that the mapping relation between the physical entity and the digital model of the production line is not accurate enough and the actual operation state of the production line is difficult to reflect truly.

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 discloses a digital twinning system and method for displaying a ship spraying robot flow field through element universe, and belongs to the technical field of intelligent equipment. The digital twinning system comprises a physical entity layer, a data and tool layer, a digital twinning layer and a virtual application layer. The working method comprises the steps that actual parameters of the ship spraying robot are collected, and entity mapping is carried out; building of a virtual physical model of the ship spraying robot is completed; establishing a digital model of the spraying flow field of the ship spraying robot; building a virtual flow field simulation model based on a digital model; checking of ship spraying simulation results is completed through the simulation model, and simulation parameters are optimized; and completing entity improvement and reconstruction of the ship spraying robot. The visual display method for the spraying flow field of the ship spraying robot is constructed for the spraying operation scene in the dock by adopting a virtual simulation method based on the universe and the digital twinning technology. And the working efficiency is improved while the labor cost is reduced.

An intelligent control method of an evaporative cooling air conditioning system based on deep reinforcement learning belongs to the technical field of refrigeration equipment regulation and control, and comprises the following steps: acquiring a space element of a refrigeration area, and converting the space element into a digital model, so as to generate a functional block division strategy of the refrigeration area based on the digital model. Based on a functional block division strategy, acquiring sensor data on each functional block fusion node, and preprocessing the sensor data to obtain a multi-dimensional feature data set; and fusing the multi-dimensional feature data set into a performance index to output a performance evaluation vector of each functional block. And based on the performance evaluation vector, constructing an experience playback pool fusion global state vector so as to carry out offline training on a DDPG algorithm and obtain an intelligent control strategy network. And the real-time performance index of the refrigeration area is input into an intelligent control strategy network to be processed, and a control instruction of the evaporative cooling air conditioning system is output. And controlling the valve opening and the fan frequency of each functional block in response to the control instruction.

The invention discloses a 3D printing parameter optimization method and system based on artificial intelligence, and the method comprises the steps: scanning a printing object, determining the size and shape of the printing object, and obtaining a digital model of the printing object; the three-dimensional digital model of the printing object is divided into a plurality of layered sheets according to a preset rule, and a single-layer printing path, corresponding to each sheet, in the printing process is obtained; constructing a dynamic error analysis model, and calculating a geometric error index of the current slice; when any index exceeds a threshold value, triggering a parameter optimization process; inputting the real-time data and the error indexes into a pre-trained artificial intelligence optimization model to obtain a parameter adjustment scheme; actual forming data are obtained through a three-dimensional scanner, accumulative errors are calculated, and weight parameters of the artificial intelligence optimization model are updated. The method has the advantages that the real-time printing data is compared with ideal data, the printing parameters are dynamically adjusted through the artificial intelligence optimization model, the path is corrected according to errors, and the precision and quality of the printing process are improved.

In order to overcome the defects in the prior art, the invention provides an urban gas pipeline risk assessment method based on a knowledge graph, and the method comprises the following steps: 1, collecting historical information of gas pipeline accidents, including detailed information of accident causes, types and consequences, and collecting images, videos and text reports of accident sites, needed information is identified and extracted, then multi-modal fusion is carried out to obtain a comprehensive feature vector, and an urban gas pipeline-risk source knowledge graph is established. And step 2, constructing an urban gas pipeline digital model. And step 3, acquiring related information of the running state of the urban gas pipeline in real time, and inputting the information into the urban gas pipeline-risk source knowledge graph for risk assessment. And for risk points which may form risks, marking and prompting the risk points on the urban gas pipeline digital model, and / or forming alarm information. Through the advanced knowledge graph technology, early warning and real-time response to gas pipeline accidents are achieved, and the overall emergency management efficiency and scientificity are 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.