699 results about "Digital mapping" patented technology

The invention discloses a traffic supervision system applied to an intelligent street lamp and an intelligent supervision method thereof, relates to the technical field of intelligent traffic, and solves the problems that an existing intelligent street lamp system lacks a physical-digital mapping relation, edge computing resource allocation is low in efficiency and cloud computing delay is high. According to the scheme, on the basis of multi-sensor data fusion, space-time reference unification is carried out by adopting an atomic clock and a GNSS, and a dynamic causal graph is constructed through a graph neural network, so that abnormal event detection is optimized; an improved Jaccard space-time similarity algorithm is adopted to optimize calculation task allocation, an edge calculation cluster is constructed based on 5G-V2X, and high-risk region identification and traffic flow prediction are carried out; a LiFi or 5G-UWB communication medium is adaptively selected through a multi-modal fusion reinforcement learning algorithm, and efficient early warning information synchronization is realized; according to the method, the multi-source data fusion value and the early warning precision are remarkably improved, the computing power resource utilization rate is optimized, and the instruction real-time performance and the system self-adaptive capability in a complex environment are enhanced.

The invention relates to the technical field of intelligent building data processing, and provides a BIM-based bridge collision detection method, which is used for solving the problem of collision early warning lag caused by space-time dislocation of a static model and real-time monitoring data in bridge construction. A comprehensive point cloud data set with a unified space-time reference is generated through multi-source data dynamic fusion and a quantum optimization algorithm, a self-evolutionary dynamic BIM model is constructed, and digital mapping of millimeter-level precision of a construction scene is achieved. A collision risk prediction model is constructed based on causal reasoning and multi-physics field coupling, a multi-dimensional relationship among construction parameters, environment variables and structural response is associated, and a linkage effect caused by mechanical motion is predicted. According to the method, the time dimension limitation of a traditional static model is broken through, full-process closed-loop control of perception-analysis-decision-verification is formed, the real-time performance of collision early warning and the controllability of construction safety are remarkably improved, and the method is suitable for high-precision construction scenes such as complex bridge rotation and cantilever casting.

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 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.

A transmission tower deformation monitoring device based on a phased array radar technology relates to the field of irregular contour metering, and comprises a map construction module used for constructing a regional digital map; the boundary determination module is used for near-field region boundaries; the signal acquisition module is used for acquiring a near-field echo signal of a near-field region through a narrow beam and acquiring a far-field echo signal of a far-field region through a wide beam; the calibration table construction module is used for constructing a signal intensity comparison table; the data processing module is used for determining effective monitoring data; the error calculation module is used for performing error calculation on the effective monitoring data to obtain a phase error and an amplitude error; and the parameter determination module is used for determining iron tower deformation parameters. According to the invention, the accuracy of transmission tower deformation monitoring can be improved.

The invention relates to the technical field of unmanned aerial vehicle flight attitude control, in particular to an unmanned aerial vehicle flight attitude adjustment method and system based on reinforcement learning. Comprising the following steps: constructing a dynamic environment grid map based on a digital map and a real-time semantic segmentation result, and generating an initial track by introducing a space-time constraint fast search random tree algorithm; collecting flight state information, environment perception information and image definition indexes of the unmanned aerial vehicle, inputting the flight state information, the environment perception information and the image definition indexes into a space-time attention encoder, and generating a semantic-fused state tensor; performing importance distribution on the state tensor through an information entropy weighting mechanism to obtain a weighted state vector; inputting into a Meta-SAC model with meta-learning ability, and outputting a target flight reference pose and an LQR controller dynamic gain coefficient; the control module drives the LQR controller to generate a flight control instruction based on the information; and a reward function is constructed based on the image quality and the energy consumption efficiency, and a reinforcement learning strategy is fed back in real time.

The invention discloses an energy management method of an electric bicycle system and the electric bicycle system.The method comprises the steps that battery parameters, motor parameters and whole vehicle kinetic parameters of an electric bicycle are obtained, and digital mapping processing is carried out to obtain a digital twin system; acquiring operation parameters of the battery, the motor and the whole vehicle, and performing distributed feature extraction processing to obtain feature data; training the bionic optimization model fusing the genetic algorithm and the ant colony algorithm to obtain a scenarized energy optimization strategy; performing hierarchical regulation and control processing on the control system to obtain a hierarchical control instruction sequence; and performing dynamic regulation and control processing on energy distribution to obtain a real-time energy optimization control instruction. According to the technical scheme of the invention, intelligent prediction and dynamic optimization distribution of energy demands are realized.

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

The invention provides an iron tailing-based geopolymer electromagnetic wave attenuation performance optimization control method and system, and relates to the technical field of electromagnetic wave attenuation, and the method comprises the steps: 1, setting a synthesis condition parameter set of an iron tailing-based geopolymer in a simulation environment, comprising virtual matching of raw materials, particle size distribution, concentration gradient of an alkali activator and curing temperature-time coupling parameters, and a forming process is deduced through an amorphous phase topological configuration and a pore network evolution rule of aluminosilicate gel in a geopolymer microstructure; and step 2, constructing an interface reaction kinetics process based on the synthesis condition parameter set, and generating an enhanced interface digital mapping system with periodically arranged aluminum oxide and silicon oxide regular tetrahedrons through virtual iteration of mineral admixture ratio and forming process parameter boundary conditions. According to the method, a strengthened interface is constructed through simulation synthesis, a loss layer database is established, and microstructure regulation and control and multi-band electromagnetic wave efficient attenuation are achieved.

The invention relates to the field of unmanned ship technology control, in particular to an unmanned ship formation path planning method and system based on cloud control. Comprising the following steps: acquiring initial position information and target position information of each unmanned ship in an unmanned ship formation and environment information in a navigation area through a cloud platform; constructing a digital map of the navigation area through the cloud platform; planning a plurality of candidate paths for each unmanned ship through a digital map; calculating a cost function value of each candidate path through a preset cost model, and screening out an initial optimal planning path; the position information of each unmanned ship and the environment information in the navigation area are received in real time through the cloud platform, and the optimal planning path is updated. According to the invention, the position information of all unmanned ships in the formation and the environment data of the navigation area are acquired in a centralized manner through the cloud platform, a unified digital map is constructed and updated in real time, and the limitation of local sensing of a single ship in distributed control is broken through.

A computing device for generating a customized digital map includes one or more memories to store instructions and one or more processors to execute the instructions to perform operations, the operations including: receiving an input from a user relating to customizing features associated with a location viewable on a digital map; in response to receiving the input, implementing a generative machine-learned model to generate the customized digital map which depicts the location with one or more customized features which are generated via the generative machine-learned model based on the input; and providing the customized digital map for presentation via a display device.

The invention relates to the technical field of geometric modeling of three-dimensional models, in particular to a three-dimensional assembly path planning method and system for a solar wing structure. The method comprises the following steps: acquiring three-dimensional assembly data of the solar wing, and constructing an assembly reachable atlas according to the three-dimensional assembly data of the solar wing to obtain assembly reachable atlas data; path evasion type behavior planning is carried out according to the assembly reachable atlas data, and assembly path data is obtained; performing optimization scheduling on the assembly path data to obtain assembly path optimization data; performing interference resolution reconstruction on the assembly path optimization data to obtain assembly path reconstruction data; and performing digital twinning scene generation according to the assembly path reconstruction data to obtain assembly path digital twinning data. According to the method, the whole-process digital mapping from action behavior modeling, assembly time sequence control to interactive linkage is realized, and the verifiability, the interactivity and the assessment capability before deployment of the assembly process are remarkably improved.

The invention particularly relates to a robot outdoor large-scale scene autonomous navigation method based on an open digital map, which comprises the following steps: extracting a road network structure, constructing a road network topological graph, and modeling a road network space by using an R tree; associating a starting point with a target point according to the road network topological graph of the R-tree structure, and performing global path planning based on a Dijkstra algorithm to obtain path points from the starting point to the target point; acquiring laser radar sensor point clouds, and generating a local voxel map by using the point clouds after the ground points are filtered out; performing local target point mapping by combining the global path line segment and a local voxel map to generate a local target point; performing local front-end path search based on a JPS algorithm to obtain a local front-end path from the current position of the robot to a local target point; a safety corridor is constructed according to the local front-end path, and trajectory optimization is carried out; and tracking the optimized trajectory, and planning the speed by considering the nonlinear model of the robot and the actual motion state. The method provided by the invention can realize outdoor large-range long-distance autonomous navigation.

The invention discloses an intelligent control and optimization system and method for the whole process of a photovoltaic power station. The method comprises the following steps: constructing a component-level high-fidelity digital twin through multi-source heterogeneous data fusion and an attention enhancement deep learning model; injecting annotated historical fault data, carrying out joint training by combining a feature embedding layer and an adversarial generative network, and generating a digital mapping body with fault evolution deduction capability; a three-layer architecture is introduced, reinforcement learning and a multi-objective optimization algorithm are fused, and autonomous decisions of power station level power output maximization, equipment loss minimization and maintenance cost optimization are realized in a virtual simulation environment constructed by a digital mapping body. According to the method, accurate dynamic mapping of the physical entity and the virtual space, fault pre-judgment and deduction and closed-loop control of global autonomous optimization are realized, the intelligent level and the full-life-cycle economy of the photovoltaic power station are improved, and the problems of insufficient fidelity, lack of fault deduction capability and low global optimization efficiency in the prior art are solved.

The invention is applicable to the technical field of manufacturing execution systems, and provides a material positioning method and system, the method is applied to a manufacturing execution system with an AR operation terminal, and the method comprises the steps: obtaining a signal of an intelligent electronic tag configured on a material unit through a multi-mode positioning base station network deployed in a material region, real-time position information of the material units is obtained through calculation; constructing and maintaining a digital map which is synchronous with the physical storage environment in real time, and updating the real-time position information of the material unit to the digital map; when a material demand from a manufacturing execution system is received, real-time position information of a needed target material unit is determined in the digital map according to the material demand, and a navigation path to the target material unit is planned; based on the real-time position information of the target material unit and the planned navigation path, visual navigation guidance is provided for an operator through the AR operation terminal. The problem that materials cannot be quickly and accurately positioned and found in the existing production process is solved.

The invention discloses a vehicle fault positioning and rescue scheduling platform and method based on GPS track acquisition, and relates to the field of vehicle fault positioning and rescue scheduling, and the method comprises the steps: uploading vehicle GPS and OBD data obtained by a vehicle-mounted terminal to a cloud database through employing a mobile network technology; according to the vehicle GPS and OBD data in the cloud database, the vehicle fault type is predicted and judged, and the position coordinate information of the vehicle fault is obtained; based on an expert scoring method, processing response levels of different vehicle fault types are set; accessing a high-precision digital map API (Application Program Interface), and marking schedulable rescue points and fixed GPS (Global Positioning System) coordinate data in a digital map; and establishing a vehicle fault rescue scheduling model, and carrying out comprehensive evaluation and scheduling on surrounding schedulable rescue points. The method has the advantages of effectively predicting the state and fault of the vehicle, efficiently responding to the processing of the fault of the vehicle, and preferentially scheduling and screening the schedulable rescue points.

The invention discloses a multi-communication link management platform and method for a wireless ad hoc network, and relates to the technical field of wireless communication, and the platform comprises a situation awareness module which is used for collecting data and generating situation data in combination with a three-dimensional digital map; the link occlusion risk prediction module is used for generating a predictive occlusion index based on the situation data; the multi-link comprehensive efficiency evaluation module is used for receiving the predictive shielding index and calculating and outputting a link comprehensive efficiency score in combination with the operation cost data; and the predictive switching decision and execution module is used for responding to the switching condition and switching the service data flow from the currently activated link to the inactivated link with the highest link comprehensive performance score. According to the method, communication interruption is avoided by actively predicting switching, expensive links are used as required, a multi-target model is adopted, the cost is reduced, and the method can flexibly adapt to service requirements.

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.

The invention relates to the technical field of digital twinning, in particular to a smart park digital twinning construction method and system based on big data, and the method comprises the steps: constructing a multi-source heterogeneous data fusion collection system, deploying an Internet of Things sensor in a smart park to collect data, and carrying out the data preprocessing; constructing a distributed big data storage architecture to store the collected data, and extracting data depth features in combination with machine learning and deep learning algorithms; constructing a park high-precision digital twin three-dimensional model, and accurately associating the extracted data depth features with a model entity; a real-time data monitoring mechanism is set, a message queue and edge calculation are adopted for data transmission and processing, a multi-terminal human-computer interaction interface regular evaluation model is developed, and an optimization algorithm is used for iteratively upgrading the digital twinborn body. Through full-process data driving and model iteration, digital mapping of the smart park is realized, and the management efficiency and decision scientificity of the smart park are improved.

The invention relates to the technical field of 5G communication, in particular to a 5G network signal low-altitude coverage test method and system, and the method comprises the steps: obtaining regional geographic information and base station layout information; based on geographic information and base station layout, constructing a three-dimensional digital map, dividing a dynamic test grid, and determining an initial flight path; according to a multi-source sensor of the unmanned aerial vehicle, in combination with the initial flight path, collecting 5G signal parameters and environmental data of the grid points through a preset frequency; a wavelet transform filtering algorithm is adopted for calibration, abnormal values are removed, and feature vectors are generated; according to the feature vectors, a coverage evaluation model is constructed in combination with a three-dimensional digital map, and a low-altitude coverage blind area and an interference type are identified; and if a low-altitude coverage blind area or abnormal interference is detected, dynamically adjusting the flight path of the unmanned aerial vehicle through an A-star algorithm, testing a target area, and generating a coverage optimization scheme based on a test result. Therefore, the problems of low test efficiency, lagging data processing and analysis and the like in the prior art are solved.

The invention relates to the technical field of petroleum drilling engineering, and particularly discloses an analysis and prediction system based on petroleum drilling and production data, which realizes virtual reconstruction of a physical drilling system by constructing a digital mapping body, and adopts a multi-source sensor network to collect drill string vortex frequency and wellbore temperature field gradient data. Forming a time sequence feature set through time sequence alignment and fusion processing; performing differential transformation and modal decomposition on the feature set to extract transient response feature vectors and generate a risk distribution diagram; establishing a parameter-risk correlation model, and decoupling mechanical vibration and thermal stress interference by solving a physical equation to obtain a dynamic risk index; the risk indexes and the process parameters are mapped to a three-dimensional grid to construct a multi-dimensional feature space, a feature fusion grid is adopted to achieve information aggregation and reconstruction, and comprehensive risk assessment indexes are generated; a dynamic early warning threshold value is established according to the evaluation indexes, and drilling parameters are optimized in real time through closed-loop control.

The invention relates to the field of digital mapping, and discloses a landslide susceptibility mapping method and device and a medium, and the method comprises the steps: obtaining multi-source remote sensing data and historical landslide data, and making a disaster-inducing factor data set and a landslide sample data set; inputting all landslide sample data sets into an AGNES clustering algorithm and a VIIQ feature optimization strategy module to obtain an optimal feature combination; dividing the optimized data features into a training set and a test set, inputting the training set and the test set into a machine learning classifier and a deep learning classifier, and performing model precision evaluation to obtain a preliminary landslide susceptibility map; inputting the landslide susceptibility map into the Blending heterogeneous integrated model to obtain a final landslide susceptibility mapping result; the integrated model is deeply explained in combination with an SHAP algorithm, and a landslide susceptibility result graph is optimized; according to the method, the landslide susceptibility can be predicted more effectively, and a scientific basis is provided for risk assessment, early warning and prevention of geological disasters.

The invention relates to the technical field of industrial and mining town planning, in particular to an industrial and mining town planning layout optimization method and system based on multi-agent reinforcement learning, and the method comprises the steps: obtaining geographic information system (GIS) data, population distribution data and functional area planning data of a target industrial and mining town; based on GIS data, constructing a digital map model of the industrial and mining town; dividing the industrial and mining town into a plurality of functional areas according to the population distribution data and the functional area planning data, and abstracting each functional area into an intelligent agent; constructing a multi-agent reinforcement learning environment based on a plurality of agents; a multi-agent reinforcement learning algorithm is utilized to optimize the planning layout of the industrial and mining town; the optimized mining town planning layout scheme is generated, a multi-agent reinforcement learning framework is adopted, and multiple planning targets such as economic benefits, environmental protection and social fairness can be considered at the same time. Each functional area serves as an intelligent agent, and overall optimization under a complex target is achieved through mutual cooperation and competition.

The invention discloses a high-precision intelligent distance measurement positioning device for a heading machine, and belongs to the technical field of heading positioning. An elastic base station network comprises a plurality of groups of miniature base stations arranged on the side wall and the top plate of a roadway; the multi-source sensing module fuses multi-source data through the data acquisition and processing device to realize continuous dynamic positioning in the working process of the heading machine, the target card provides an absolute distance reference, the inertial navigation unit calculates the pose of the heading machine in real time through a built-in MEMS gyroscope and an accelerometer, the geological radar scans the stratum structure in the heading direction in real time, and the real-time positioning of the heading machine is realized. And the laser radar scans a roadway contour by emitting a laser beam to perform point cloud matching with a preset three-dimensional digital map. The probability of tunneling deviation caused by settlement or displacement is reduced by arranging the vertical crossing positioning network and dynamically correcting the positioning reference, the working environment and self state information of the tunneling machine can be comprehensively sensed from different dimensions, and the actual distance and position relation between the tunneling machine and objects around a roadway can be judged in an auxiliary mode.

The invention relates to the technical field of parking resource scheduling, in particular to an intelligent parking resource scheduling system based on multi-source data analysis, which realizes full-scale digital mapping from vehicle individual behaviors and parking lot flow fields to a regional traffic network by constructing microscopic-mesoscopic-macroscopic three-level parking twin bodies and fusing dynamic and static multi-source data. Through scale conversion and data interaction among the three-level model, dynamic characteristics of parking resources in space and time dimensions are accurately captured, a high-fidelity and fine-grained digital twinborn base is provided for scheduling decision making, splitting of local optimization and global collaboration in traditional scheduling is broken through, the accuracy and foresight of resource allocation are supported, and the scheduling efficiency is improved. Based on dynamic construction of an algorithm strategy library and twinborn simulation evaluation, the scheme takes a multi-dimensional scheduling value as a core to realize closed-loop optimization of algorithm combination-effect rehearsal-strategy optimization-dynamic adjustment.

Examples of the presently disclosed technology provide computerized systems and methods that automatically update digital maps in response to detected traffic sign updates in a rapid and efficient manner. Examples achieve these advantages by cascading traffic sign updates through multi-layered digital maps using specific methodologies tailored to different types of traffic sign updates. By providing rapid and accurate digital map updates, examples can improve performance for autonomous driving systems that rely on digital maps for determining vehicle navigation paths—thereby improving traffic safety and efficiency.

The invention discloses an operation and maintenance prediction and self-repairing method, system and device and a storage medium, and the method comprises the following steps: collecting structured, semi-structured and non-structured data, and carrying out the analysis and format conversion of the collected data, and obtaining the preprocessed data; based on the preprocessed data, constructing a dynamic knowledge graph containing entities, relationships, attributes and timestamps; a depth map neural network model is applied to analyze the dynamic knowledge graph, node features are aggregated, key fault nodes are identified, and potential faults are predicted; when the potential fault is predicted, a reinforcement learning algorithm is applied, and a repair strategy is generated according to the operation and maintenance cost, the service influence and the repair duration; and executing the restoration strategy, and feeding back an execution result and performance index data in the restoration process to update the dynamic knowledge graph and depth graph neural network model. Panoramic digital mapping of the operation and maintenance system is realized, and the fault prediction accuracy and the self-repairing success rate are improved.

The invention relates to the technical field of digital twinning, in particular to a distributed photovoltaic power generation system optimization control method based on digital twinning, and the method comprises the steps: constructing a photovoltaic module-level digital twinning body through multi-source data fusion, generating a digital mapping body through the training of historical fault data, and achieving the simulation of the operation state of a photovoltaic module; a multi-agent cluster is introduced, a multi-target optimization architecture framework coordinated with the multi-agent cluster is constructed, and balance and collaborative optimization among multiple targets are achieved through power generation efficiency optimization, equipment life management, power distribution coordination and other agents; and based on the candidate strategy after multi-target collaborative optimization, the improved grey wolf algorithm is used for further optimization processing, an optimal scheme is obtained, system optimization operation is realized, and power generation benefits and stability are improved.

The invention discloses a complex system fault twinborn deduction method based on virtual-real dynamic fusion, and the method comprises the steps: firstly designing a preprocessing scheme of multi-modal data fusion, and carrying out the spatial-temporal feature unification and standardized state vector construction of heterogeneous sensing data; secondly, establishing a real-to-virtual digital mapping model, deducing a multilevel system state based on a dynamic Bayesian network, and introducing a weighted adaptive gradient correction mechanism to improve the fitting precision of the model; and finally, proposing a fault evolution evaluation method of reflecting real from virtual, and realizing hierarchical identification and early warning triggering of the system operation state. By introducing a digital twinning thought, establishing a virtual model synchronously evolved with an entity system, and constructing a whole-process twinning deduction framework of perception, modeling and evaluation, the method can significantly improve the accuracy, response efficiency and robustness of fault prediction of a complex system.

The invention relates to an automatic printing calibration method and system combining vision and AI and a storage medium, a multi-mode sensing unit integrated on a printing head assembly synchronously collects two-dimensional images, three-dimensional shapes and working spacing data of a printing area in real time, and data fusion is carried out by using a model based on a neural radiation field; generating a high-fidelity multi-dimensional digital mapping; carrying out defect identification and positioning by adopting a pre-trained convolutional neural network CNN model, and predicting an optimal adjustment parameter according to the defect information and the historical sequence through a long short-term memory network LSTM model; the edge calculation and control unit is combined with a material characteristic database to generate a hierarchical calibration instruction in a millisecond level, adopts fuzzy PID self-optimization control to dynamically set PID control parameters, and is combined with feed-forward compensation of the inertial measurement unit to perform accurate, smooth and robust control on an execution mechanism, so that the accuracy of calibration is improved. And full-automatic, high-precision and real-time online calibration of the printing process in a complex industrial environment is realized.