10693 results about "Digitization" patented technology

A computing system is disclosed for transforming document data into schema-conformant structured outputs. The system obtains document data comprising multi-format structured documents and classifies each document by type and class using vector-based modeling and structural feature analysis. An extraction configuration is selected for each document, the configuration comprising machine-executable instructions for parsing based on semantic and layout characteristics. The system extracts semantic data using structured inference, transforms the semantic data into schema-conformant outputs, and validates the outputs using temporal and domain-specific constraints. Validated structured data may be used for downstream processing, visualizations, or optimization based on performance metrics.

The invention relates to the technical field of deep learning, and discloses an MES digital collaborative management method and system based on deep learning, and the method comprises the steps: collecting multi-source heterogeneous data, and constructing a dimensionless multi-dimensional data set; analyzing a dynamic association relationship among the data items through node feature embedding and edge relationship learning, and generating a production line operation dependency relationship graph; performing collaborative anomaly detection and root cause positioning in combination with the dynamic association weight between the data items to obtain causal association data between the abnormal event and the data parameters; constructing a digital twinborn simulation environment to simulate an influence path of heterogeneous data parameter intervention production disturbance on collaborative anomaly so as to evaluate an anomaly influence result; constructing an iterative scheduling model according to a deep learning framework, and iteratively generating a multi-objective optimized collaborative scheduling strategy to realize dynamic configuration and exception prevention among heterogeneous data; therefore, collaborative optimization management of production operation parameter configuration and abnormity prevention of multi-source heterogeneous data in large-scale production with high dimension, high complexity and dynamic change can be realized.

The invention relates to the technical field of engineering progress management, in particular to an engineering construction digital project management method and system, and the method comprises the steps: database building, real-time data collection, and model building: generating a visual construction progress model; progress deviation judgment: calculating progress deviation and performing judgment; deviation analysis: calculating a resource gap of an affected process, and generating a resource allocation priority list; resource adjustment: pushing an adjustment instruction to the construction terminal according to the priority list; simulation: simulating the adjusted construction progress, and if the deviation is not eliminated, executing a redistribution step; redistribution: executing the deviation analysis step again; and optimization: optimizing subsequent project progress plan generation logic. The system comprises a database building module, a real-time data acquisition module, a model building module, a progress deviation judgment module, a deviation analysis module, a simulation module, a redistribution module and an optimization module. The method and the device have the effect of facilitating fine management of the engineering project.

The invention discloses an intelligent geometric reasoning and semantic understanding method based on a three-dimensional large language model, which comprises the following steps of: acquiring point cloud data of a building component through three-dimensional scanning equipment, associating text information, and constructing a multi-modal three-dimensional large language model comprising a geometric perception coding module, a context semantic understanding module and a parameter efficient fine tuning module; a cross-modal contrast loss and task instruction fine tuning strategy is adopted in model training, and finally semantic recognition, attribute completion and historical background analysis results of the building components are output. The method is suitable for building heritage digital protection, intelligent building process monitoring and three-dimensional digital archive management, component function recognition precision and cultural semantic mining capability in a complex scene can be improved, and real-time semantic updating and interactive response of a dynamic construction environment are supported.

A system for AI-supported leak detection and localization in water distribution infrastructures, consisting of: a large number of distributed sensor nodes mounted along a water pipe, each sensor node comprising the following: a pressure sensor configured to measure local hydraulic pressure fluctuations within the pipeline with a resolution of at least 0.01 bar; a flow sensor configured to measure the volume flow within the pipeline with an accuracy of at least ±0.5% of the measured value; an acoustic sensor configured to detect vibration signatures caused by leaks in a frequency range between 50 Hz and 20 kHz; an embedded microcontroller with integrated analog-to-digital conversion circuitry for digitizing sensor outputs; a wireless communication module configured to transmit time-synchronized sensor data to a cloud-based processing platform; and a local energy subsystem with a rechargeable battery and an optional circuit for generating photovoltaic energy; the cloud-based processing platform includes an artificial intelligence engine that comprises the following: a data acquisition module configured to receive and decode the transmitted sensor data and to perform time alignment; a supervised learning module that is trained on historical data of flagged leaks and non-leaks to classify incoming sensor patterns; an unsupervised learning module configured to detect anomalies by modeling normal operating baselines of the pipeline; and a topology-aware localization module configured to determine leak coordinates using the topology of the pipeline network, modeling the propagation of hydraulic waves, and estimating the arrival time difference from multi-node acoustic detections; and wherein the system is configured to provide real-time leak alerts and georeferenced visualization via a remote monitoring interface.

The invention relates to a digital resource sharing method and system based on a pedigree mapping relation, and belongs to the technical field of enterprise digital resource management, and the resource sharing method comprises the steps: obtaining multi-source heterogeneous data from an internal distributed system of an enterprise through a preset interface protocol; performing standardization processing on the multi-source heterogeneous data to generate a structured resource pool; a graph dictionary module is constructed, the resources in the structured resource pool are subjected to association topology analysis, and a resource relation topological graph is output; establishing a pedigree relationship between resources and business scenes based on the three-dimensional mapping model, respectively generating and fusing a business process demand map, a production stage demand map and a product capability matching map, and constructing a visual digital resource capability platform; and outputting the target resource identifier and the associated path in response to a resource calling instruction input by a user. Integration and sharing of internal and external multi-source heterogeneous data of an enterprise can be realized, the problem of data islands is solved, and meanwhile, the intelligent degree of a resource management system is improved.

The invention relates to a digital integrated quality management system based on multi-source data fusion, and belongs to the technical field of industrial internet and quality management. A data acquisition layer of the system obtains real-time and static multi-source heterogeneous data through a multi-source adapter; the data processing layer is used for cleaning, converting and standardizing the acquired data; the intelligent analysis layer performs deep analysis and prediction on the data by using an adaptive quality prediction model, an anomaly detection module and a root cause analysis engine; the application service layer displays a quality trend and an anomaly detection result through a visual billboard, and provides credible tracing and collaborative decision-making functions; and the feedback closed layer adjusts system processing logic according to the decision support data to form closed-loop quality control. According to the method, real-time fusion and efficient utilization of multi-source data are realized through a dynamic routing technology, an adaptive quality prediction model and a block chain evidence storage mechanism, and the intelligent level and decision-making efficiency of quality management are remarkably improved.

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

The invention provides a service data security protection method and system for digital enterprise management, and the method comprises the steps: obtaining an access request sequence initiated by a target user at a service operation terminal, generating a dynamic access control strategy vector according to a historical behavior track associated with an access operation identifier, and carrying out the dynamic access control strategy vector; analyzing the dynamic access control strategy vector through a strategy decoder, and generating a real-time access permission instruction; based on the real-time access permission instruction, performing homomorphic encryption mapping on a sensitive data segment in the request context information to generate a ciphertext transmission channel, and performing security parameter synchronization on the ciphertext transmission channel and the cross-department conjoint analysis model; and calling a federated learning framework to carry out multi-modal feature fusion on the real-time operation flow of the service operation terminal, generating an abnormal operation confidence score, triggering a cross-level interception protocol when the abnormal operation confidence score exceeds a dynamic threshold, and rolling back the current session state to a security baseline version. According to the invention, the accuracy and response timeliness of anomaly detection can be improved.

The invention discloses an intelligent monitoring management method and system based on archive digitization, and relates to the technical field of data management, and the method comprises the steps: collecting and preprocessing multi-source archive data, employing a multi-mode BERT model to carry out the feature fusion of different data sources, and generating a unified semantic representation; semantic labeling is performed on archive data through a multi-label classification model, a semantic graph of archive content is constructed by using a graph database, an association relationship between archives is represented, a semantic index tree is constructed based on the semantic graph, and rapid positioning and calling of the archive content are optimized; and recording the change of each file version, positioning the change position based on a semantic index tree, identifying the semantic change of the file through a semantic difference comparison algorithm, recording hash, carrying out granularity division on the file content through the semantic boundary of each level of node in the index tree, and generating a user access strategy. According to the invention, dynamic perception and risk early warning of user behaviors are realized, and the intellectualization and safety of the archive management system are effectively improved.

The invention belongs to the technical field of air traffic control and conduction, and particularly relates to an intelligent question answering system method for air traffic control and conduction business knowledge. According to the method and the system, the refined and context-aware information retrieval capability of the air traffic control communication service knowledge and the expert-level air traffic control communication service knowledge question and answer capability are provided, the knowledge management and application efficiency is effectively improved, and powerful support is provided for digital transformation of related services. Comprising the following steps: S1, a document content extraction and storage process; s2, a content retrieval and generation process; s3, content slice optimization is carried out through extraction and similarity calculation of adjacent paragraphs or contents, complete contents are in the same slice, and the content retrieval efficiency is improved; according to the method, the label library and the business rule library are continuously iteratively optimized, so that the provided service is always in the optimal state.

The invention provides a flood control project virtual simulation and risk rehearsal method based on digital twinning, and the method comprises the following steps: obtaining drainage basin multi-source data collected by a space-air-ground integrated monitoring network, the multi-source data comprising a satellite remote sensing image, an unmanned aerial vehicle LiDAR point cloud and ground sensor monitoring data; inputting the multi-source data into a pre-constructed digital twin model, wherein the model comprises a spatio-temporal data fusion layer, a physical process modeling layer, a multi-scale simulation deduction layer and a risk assessment decision-making layer which are connected in sequence; the spatio-temporal data fusion layer is used for performing spatio-temporal registration and feature fusion on input multi-source data, and constructing a total element digital backplane; through the space-air-ground integrated monitoring network and the spatio-temporal data fusion technology, high-precision digital mapping of basin total elements is realized. Compared with a traditional single data source, time-space consistency of flood routing simulation is remarkably improved through multi-source data fusion, and prediction errors of key physical processes such as river channel scouring are greatly reduced.

The embodiment of the invention relates to the technical field of computers, in particular to an energy conservation and emission reduction scheme generation method and system based on a digital factory, and the method comprises the steps: obtaining real-time energy consumption data streams and emission monitoring signals of a plurality of pieces of production equipment in the digital factory; extracting dynamic energy consumption characteristics from the real-time energy consumption data stream, and identifying periodic emission parameters from the emission monitoring signal; a target energy consumption prediction algorithm is called to process the dynamic energy consumption characteristics and the periodic emission parameters, and energy consumption prediction results and emission intensity distribution of multiple time periods are generated; and generating an equipment-level optimization regulation and control scheme and a production line-level scheduling strategy according to the energy consumption prediction result and the emission intensity distribution in combination with preset energy conservation and emission reduction constraint conditions. Therefore, collaborative optimization of equipment-level real-time regulation and control and production line-level flexible scheduling can be realized.

The invention relates to the technical field of building model design, in particular to a building three-dimensional model lightweight design method and system based on artificial intelligence, and the method comprises the steps: extracting geometric-semantic features of a building model through a multi-scale curvature filtering and semantic segmentation network, constructing a fusion feature vector matrix, and obtaining a fusion feature vector matrix; by utilizing an integrated graph convolutional network and a self-adaptive neural simplification network of a double-branch attention mechanism, differential resampling is executed based on vertex importance weight, a simplified intermediate model is generated, a surface microstructure is recovered by means of a generative adversarial network, grid holes are corrected by combining Delou inner triangulation, and a surface microstructure is obtained. A non-uniform rational B-spline curved surface is adopted to reconstruct a key decoration component and output a lightweight model, so that the problems of insufficient geometric feature retention and semantic information splitting in the traditional technology are solved, intelligent, efficient and lightweight of a building three-dimensional model is realized, visual fidelity is ensured while data compression is performed, and the construction quality is improved. And the digital management requirement of the whole life cycle of the building is met.

The invention discloses a double-engine government affair question and answer method based on large model fine tuning and RAG retrieval, belongs to the field of government affair digitization and natural language processing, and combines large model language understanding generation ability, retrieval enhancement generation technology and a structured reasoning mode. The defects of a traditional government affair question and answer method in the aspects of dynamic policy response, complex semantic understanding and compliance control are overcome. Government affair field knowledge is adapted through large-model fine adjustment, and high-precision and timeliness answering of government affair consultation is realized in combination with vector retrieval and a dynamic updating mechanism. The core innovation of the method lies in deep fusion of a double-engine architecture and dynamic knowledge management, the accuracy and response efficiency of government affair questions and answers are improved on the premise of ensuring policy compliance, and the method is suitable for intelligent upgrading of scenes such as government affair service halls and online consultation platforms.

The invention relates to the technical field of intelligent power grids, in particular to a power distribution network battery digital dynamic management system based on digital twinning. Comprising a data acquisition unit; the digital twinborn modeling unit is used for constructing a battery-power grid-environment multi-dimensional dynamic twinborn body and realizing virtual-real bidirectional mapping and adaptive updating by combining a multi-physics field coupling model and a long-short-term memory network time sequence prediction algorithm; a dynamic optimization unit; and executing the feedback unit. Through a distributed heterogeneous sensing network of a data acquisition unit, multi-dimensional operation data of a battery pack and a key node of a power distribution network are acquired, and a high-fidelity data set containing four-dimensional labels of a battery state, a power grid parameter, time and a position is generated in combination with a spatial-temporal feature extraction technology; the deep fusion of the full life cycle state of the battery and the global operation data of the power distribution network is realized, and the comprehensive data support covering the global is provided for the optimization decision.

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

The invention relates to the technical field of data processing, and discloses a preoperative risk comprehensive assessment method and system for breast cancer and a storage medium. The method comprises the following steps: collecting clinical information and carrying out digital processing; segmenting the multi-modal image by using deep learning; analyzing a pathological section based on the image features; carrying out multi-level fusion modeling to obtain a risk heat map; performing digital twinborn simulation in combination with a knowledge graph; and dynamically processing the follow-up data to form a risk adjustment mechanism. According to the method, accurate quantification and individualized treatment decision making of the preoperative risk of the breast cancer patient are realized, and the prediction accuracy is improved.

The invention provides a user behavior data mining method and system applied to digital enterprise management, and the method comprises the steps: collecting the multi-dimensional behavior data of a target user in a business operation interface, carrying out the multi-modal data analysis of the multi-dimensional behavior data, generating a behavior track feature set with time sequence relevance, and carrying out the mining of the behavior track feature set; training an adaptive time sequence analysis model based on the behavior trajectory feature set, capturing a long and short term dependency relationship in a user behavior mode by the time sequence analysis model through a dynamic window division strategy, generating a potential loss risk prediction index, and constructing an interaction process parameter matrix according to the potential loss risk prediction index; and calling the optimized interaction process parameter matrix to drive a service operation interface to reconstruct, generating an interaction interface adaptive to the current user behavior mode, and iteratively updating the time sequence analysis model through an incremental feedback mechanism in a preset verification period. According to the invention, the comprehensiveness and accuracy of user behavior pattern mining can be improved.

The embodiment of the invention provides a BIM-based hydraulic engineering digital management method and system. The method comprises the following steps: performing multi-dimensional monitoring system deployment on a to-be-monitored area to obtain multi-dimensional hydrological data; constructing a first BIM based on the topographic data of the to-be-monitored area, the real-time work area image, the hydraulic engineering construction information of the design stage and the equipment deployment information; fusing the multi-dimensional hydrological data, the water area change data, the construction progress data and the operation and maintenance monitoring data into the first BIM, and constructing a second BIM including a full life cycle; and through the equipment characteristic curve and the water conservancy project physical model, in combination with the multi-dimensional hydrological data and the water area change data, predicting a water conservancy project structure change trend and potential risk factors in the second BIM, fusing the water conservancy project structure change trend and the potential risk factors into the second BIM, and displaying an implementation effect and improvement suggestions in real time in the second BIM. A user is assisted to realize hydraulic engineering digital management, and the engineering management efficiency is improved.

The invention discloses an intelligent port crane operation monitoring method and system, and relates to the technical field of crane systems.The system comprises a data acquisition unit, an edge computing node, a cloud processor and an early warning monitoring unit, through deep fusion and efficient treatment of multi-source data, a dynamic twinborn data pool architecture is established, and the dynamic twinborn data pool architecture is established; cross-data-type space-time alignment is realized through multi-modal coding, and the feature fusion accuracy is improved; the dynamic twin data pool is intelligently analyzed, and the crane operation path is optimized in real time, so that the track of the crane operation path is dynamically adjusted, and the working efficiency is improved; through a digital twin model and integration of a multi-modal physical engine, the operation physical state deviation of the crane is evaluated, an operation plan is corrected in real time, and the operation precision is improved; video streams are deeply analyzed, virtual-real comparison is carried out, and model prediction video deviation is monitored, so that the analogue simulation precision is improved; by establishing a closed-loop early warning mechanism, full-process digital management and control are realized, and intelligent upgrading of a port is promoted.

The invention discloses an intelligent warehouse optimization management platform based on digital twinning and space-time prediction, which relates to the technical field of intelligent warehouse management and comprises a digital twinning model construction module, a space-time prediction module, an inventory optimization management module and a user interaction module. The digital twinborn model construction module realizes digital mapping of a physical warehousing system by constructing a warehousing space three-dimensional model and associating warehousing sensing data, and the space-time prediction module constructs a prediction model to obtain inventory prediction information in a future time period, and performs inventory risk assessment and early warning according to the inventory prediction information; the inventory optimization management module generates an inventory strategy and an optimized storage position according to the prediction result, the storage cost and the cargo demand, and formulates a warehouse-in and warehouse-out task scheduling scheme; and the user interaction module visually presents the warehouse management data and receives a user interaction operation instruction to realize man-machine collaborative management, and the platform reduces the vacancy rate of the warehouse space and the interruption risk of the supply chain, and reduces the invalid carrying energy consumption.

The invention relates to the technical field of data processing, and discloses an intelligent supply chain optimization management method and system based on digital AI analysis. The method comprises the steps of collecting supply chain data to construct a resource library; analyzing a sales mode and K-means + + clustering customer features by using LSTM, and predicting demands; inventory parameters and supplier scores are calculated, and a collaborative data set is formed; commodity association is analyzed through a knowledge graph, and a commodity selection decision is generated; resource allocation is carried out by applying multi-objective optimization, and cross-channel inventory collaboration is realized; and an integral rule and a merchant enabling scheme are constructed based on the transaction data, and a digital asset closed-loop mechanism is formed. According to the intelligent supply chain optimization management method based on AI analysis, global visualization, dynamic scheduling and accurate allocation of inventory resources are realized, the technical problem of cross-channel inventory collaborative allocation is solved, and the operation efficiency and the resource utilization rate of the whole supply chain are remarkably improved.

The invention discloses a privatized AI question and answer method, system and device and a medium, and relates to the technical field of artificial intelligence, and the method comprises the steps: collecting a file archive data set, and constructing a local digital knowledge base; selecting an AI semantic large model architecture; performing annotation enhancement on a local digital knowledge base by using an AI semantic large model architecture, and constructing a knowledge base AI question and answer model; performing permission marking on the local digital knowledge base based on a user access control rule, and establishing a knowledge base retrieval mechanism; and integrating and fusing the knowledge base retrieval mechanism and the knowledge base AI question-answering model to generate a retrieval enhanced AI question-answering model, performing semantic question-answering retrieval on the request question information, and outputting a user request question-answering result. The technical problem that in the prior art, data security, intelligent question answering and information retrieval efficiency are insufficient is solved, and the technical effect of improving intelligence and data access control of the question answering system is achieved.

The invention relates to an electrical load prediction and optimization regulation and control method and system for high-energy-consumption equipment, and solves the problems of inaccurate load prediction, single regulation and control means and difficulty in dynamic adaptation of the high-energy-consumption equipment, and the method comprises the steps: collecting multi-source data of the high-energy-consumption equipment in real time, constructing a dynamic equipment collaborative causal graph after preprocessing, and extracting key constraints; inputting the data and the constraints into the dynamic digital sample model to obtain a system state simulation result; based on the result, a multi-objective optimization regulation and control strategy is generated and executed by using a meta-learning + reinforcement learning decision framework; and collecting actual data comparison deviation, starting hierarchical federated learning when a threshold value is exceeded, grouping and aggregating similar experiences according to a causal graph topology, and dynamically calibrating model parameters and a decision framework. The method has the following effects that accurate load prediction and multi-target cooperative regulation and control of the high-energy-consumption equipment are achieved, working condition changes are dynamically adapted, the cost is reduced, and continuous production and the service life of the equipment are guaranteed.

The invention belongs to the field of data analysis, and discloses a data analysis method for engineering consultation digital intelligent management, which comprises the following steps: mapping multi-project heterogeneous data to a unified three-dimensional space-time grid through space-time grid division to form a grid mapping data set; detecting data format and attribute conflicts based on neighborhood correlation analysis, and distributing calibration data through dynamic weight; missing values and abnormal fluctuations are processed by using a time sequence autoregression model and a smoothing technology, and a complete data set with continuous time is constructed by combining linear interpolation and multi-dimensional data fusion; key indexes are extracted to construct a digital mirror image containing spatial positions, time sequences and attribute association, real-time data are synchronized, and outlier grid points are corrected; and multi-scene operation conditions are constructed based on the mirror image data, and a dynamic adjustment scheme is realized. According to the method, heterogeneous data are integrated through space-time grids, digital twinning and multi-scene simulation technologies are combined, and full-period management and risk intelligent decision-making of engineering data are achieved.

The invention discloses an e-government affair platform management method and system based on cloud data, and relates to the technical field of data processing, and the method comprises the steps: collecting multi-source data, carrying out the preprocessing, extracting keywords, carrying out the multi-scale time coding and multi-scale space coding of the data, and storing the data in a database; constructing a government affair knowledge graph based on the database, mining association relationships of department services and analyzing affair handling habits of users; generating a government affair examination and approval path based on the knowledge graph, optimizing the government affair examination and approval path through reinforcement learning, dynamically adjusting the examination and approval process, performing a simulation test by using a digital twinning technology, and verifying the user satisfaction after the examination and approval process is optimized; and storing the government affair data in a database and performing authority management. According to the method, the electronic government affair data processing capability can be improved, the examination and approval efficiency is improved, the verifiability of an optimization strategy is enhanced, and technical support is provided for digital and intelligent transformation of government affair services.

The invention discloses a digital intelligent switch cabinet state comprehensive sensing system based on AI, and the system comprises a multi-source data collection module, a data preprocessing and synchronization module, an edge calculation feature extraction module, an AI intelligent fusion recognition module, an expert rule diagnosis module, and a cloud comprehensive evaluation and decision module. Various types of sensors are deployed to respectively acquire environmental parameters, electrical parameters and partial discharge signals generated in the operation process of the switch cabinet to form an original multi-modal data stream. The method has the advantages that the recognition precision and response speed of the complex operation state of the switch cabinet are improved, hidden faults under multi-modal data mismatch can be effectively found, and the misjudgment and missed judgment risks are reduced. Meanwhile, a closed-loop diagnosis system is constructed, intelligent evaluation and interpretable feedback of fault types, positions and trends are achieved, scientificity and reliability of operation and maintenance decisions are enhanced, and the method is suitable for intelligent upgrading of an electric power system.

A digital auxiliary repair method for an ancient building. The method comprises: point cloud data acquisition, point cloud model establishment, point cloud model optimization and model printing, which involve: scanning and photographing a damaged ancient building by using a three-dimensional scanner and an industrial camera, so as to obtain three-dimensional point cloud data of the ancient building; performing preprocessing, such as data registration, data denoising, data simplification and data segmentation, on the point cloud data to obtain data for establishing a three-dimensional model; optimizing the three-dimensional model; and by means of a 3D printer, obtaining a mold for a damage position of the ancient building, and on the basis of requirements, using the mold to perform molding to obtain a wooden or brick-mixed workpiece.

The invention relates to the technical field of cultural heritage digital protection, in particular to a super-volume historic building three-dimensional simulation modeling method based on multi-source heterogeneous data, and the method comprises the steps: firstly collecting node multi-source heterogeneous data such as laser point cloud, images, structural mechanical parameters and historical repair records, and then carrying out node feature enhancement through a node feature enhancement module; using an improved generative adversarial network to strengthen node edge features, adopting an adaptive threshold segmentation algorithm to extract surface texture features, converting mechanics and size data into a three-dimensional constraint condition parameter matrix, then using a topological relation verification algorithm, using a graph neural network to traverse and verify a component connection relation, and obtaining a three-dimensional confrontation model; and a re-calibration mechanism is triggered when the deviation exceeds the limit, the weight is adjusted based on a Bayesian optimization algorithm, fusion verification is carried out again, finally, hierarchical grid division is adopted to construct high-precision sub-models, and the sub-models are spliced into an integral three-dimensional model, so that the model precision and reliability are improved, and reliable digital support is provided for ancient building protection.