3489 results about "Geometric modeling" patented technology

The invention discloses an electromechanical system fault pre-diagnosis method and system based on digital twinning. The method comprises the following steps of obtaining multi-source data in an electromechanical system operation process; preprocessing the acquired multi-source data, wherein the preprocessing comprises data cleaning, normalization processing and feature extraction; and on the basis of the preprocessed multi-source data, an electromechanical system design drawing, a three-dimensional geometric model, material attributes and a kinetic equation are fused, and a digital twin model is constructed. According to the invention, through a digital twin model dynamic calibration and prediction algorithm, early abnormity of the equipment is identified in advance, the fault probability and the residual life are output, and non-planned shutdown is reduced; by constructing a cross-physical domain fault feature system and fusing model simulation and actual measurement data, the potential fault identification accuracy is improved, and the missed diagnosis rate is reduced; by calibrating parameters of the digital twin model in real time, the method adapts to nonlinear changes of equipment, ensures high-fidelity mapping of the model, and improves fault prediction precision.

The invention relates to the technical field of target detection, in particular to a robot three-dimensional environment sensing method and device based on deep visual learning, and the method comprises the steps: collecting visual data based on a sensing system carried by a robot, carrying out the synchronous processing, extracting the spatial structure characteristics of a synchronous visual data stream and a preliminary semantic segmentation map, and carrying out the recognition of a target image; combining the spatial structure features with the preliminary semantic segmentation map to generate a geometric semantic feature map; extracting and optimizing local, regional and global features of the geometric semantic feature map, and performing three-dimensional modeling processing according to a multi-scale feature tensor to generate a three-dimensional geometric model; and carrying out fusion optimization on the structure information of the three-dimensional geometric model and the geometric semantic feature map, and carrying out verification processing based on a verification framework to obtain three-dimensional environment perception data. Through deep visual learning and visual data fusion, the defects of insufficient accuracy and limited deep semantic understanding ability in complex three-dimensional environment perception in the prior art are solved.

A dynamic optimization system for energy consumption of a refrigeration house based on digital twinning is characterized by comprising a data acquisition module used for acquiring basic structure data of the refrigeration house, technical parameters of a refrigeration system, real-time operation data and historical operation data, preprocessing the data and then outputting a standardized multi-dimensional real-time data stream; the model construction module is used for constructing a 3D geometric model, a thermodynamic transfer model and a refrigeration system mathematical model according to the multi-dimensional real-time data flow, performing machine learning calibration on model parameters through historical operation data, and performing fusion to construct a refrigeration house digital twin model; the prediction analysis module is used for predicting future energy consumption demand and load change according to the refrigeration house digital twin model and the real-time operation data, and outputting an energy consumption prediction result and a load analysis report; a strategy generation module; an execution feedback module; and a learning optimization module. Overall energy consumption of the refrigeration house is reduced, energy utilization efficiency is remarkably improved, and goods storage safety is guaranteed.

The invention discloses a territorial space planning optimization method and system based on three-dimensional modeling, and relates to the technical field of three-dimensional modeling, and the method comprises the steps: obtaining multi-source territorial space data, constructing a three-dimensional space data set, carrying out the three-dimensional geometric modeling, and generating a territorial space three-dimensional model; carrying out multi-dimensional space analysis on the land space three-dimensional model, obtaining a space conflict feature set, carrying out planning constraint, and formulating a space optimization suggestion; executing the space optimization suggestion to perform three-dimensional dynamic modeling, generating a multi-time sequence planning simulation result to perform space planning evaluation, generating a land space planning score, performing planning compensation based on the backtracking space optimization suggestion, updating the space optimization suggestion, and obtaining a space planning optimization scheme. The technical problem that in the prior art, land space planning depends on two-dimensional data, the space conflict and the dynamic evolution process are difficult to comprehensively recognize, and the planning scheme is insufficient in scientificity is solved, and the technical effect of improving the space conflict recognition precision and the planning decision scientificity is achieved.

The invention relates to the technical field of digital twinning control, and discloses a composite structure prefabricated segment digital twinning-adaptive assembly correction method and system, and the method comprises the steps: reading BIM geometric model data, and constructing an assembly reference coordinate system and a digital twinning geometry of a prefabricated segment; composite data are collected in real time, and end tooth groove boundary feature point cloud is extracted; matching the actually measured point cloud with the digital twinborn geometry through a dynamic point cloud registration technology, calculating a six-degree-of-freedom pose error vector, and generating a predicted total pose error vector in combination with a pre-constructed pose drift prediction model; based on the error vector, a mechanical fine adjustment jack is driven to execute position and posture adjustment until the position and posture are converged, and then tooth groove precise meshing and mechanical locking are completed; according to the method, through the synergistic effect of the dynamic mapping of the digital twinborn model and the self-adaptive correction algorithm, high-precision dynamic correction of the multi-combination structure segment assembly process is achieved on the premise that an original mechanical connection structure is not changed.

The invention discloses a robot 3D visual guide grabbing method, which belongs to the field of robot application, and comprises the following steps of: comprehensively scanning a working environment by using a high-resolution 3D visual sensor to obtain point cloud data of a scene; carrying out object identification on the processed point cloud data by using a deep learning algorithm; a model capable of recognizing a target object is trained, the model can distinguish objects of different types, shapes, colors and textures, point cloud data are obtained through omnibearing scanning of a high-resolution 3D vision sensor, professional preprocessing is carried out, and the working environment can be accurately perceived. The deep learning algorithm is combined with multiple segmentation methods, objects can be accurately recognized and segmented under the complex background and shielding, meanwhile, geometric models of the objects and the environment are created through a specific algorithm, a comprehensive environment map is formed, an accurate basis is provided for follow-up operation, and object characteristics, gripper capacity and multiple grabbing strategies are comprehensively considered.

The invention discloses a virtual water gauge water level identification method based on a multi-mask matching segmentation network. The method comprises the steps that S1, a real-time image of a target water body area is acquired through a preset position camera; S2, a virtual water gauge geometric model is constructed; s3, extracting a region with high confidence as a candidate region of the segmentation model; s4, designing a new training data structure optimization method to help the model better understand and utilize information of different levels; s5, respectively carrying out maximum matching on the rows and the columns, finding out an optimized matching path with the minimum total cost, and obtaining a finer segmentation result; s6, identifying a water body boundary in the high-precision water body segmentation map through a Canny edge detection algorithm, and extracting a waterline position; and S7, calculating an actual water level value, and for the water body with dynamic movement, smoothing the water level reading by adopting an interval time sampling moving average method. The defects of a traditional physical water gauge and an existing image processing technology are overcome, and an innovative solution is provided for water level monitoring.

The invention relates to the technical field of electrical design and intelligent optimization calculation, and discloses a capacitor structure design optimization method and system and a storage medium. The method comprises the following steps: carrying out modeling processing on capacitor structure parameters, and dividing a parameter space to obtain an initial model set; obtaining a multi-physical field simulation model according to the geometric model and the material attributes; obtaining performance indexes such as capacitance value, heat distribution and stress based on the simulation model; setting a target function and constraint conditions, and operating an optimization algorithm to obtain an optimal solution set; performing feedback control processing on an optimization result, and analyzing a convergence path to obtain a structure parameter; according to the invention, the execution efficiency of capacitor structure design optimization is improved, and the consistency and stability of the optimization process and the performance prediction result are improved.

The embodiment of the invention discloses a numerical calculation method for water outlet icebreaking of a navigation body and related equipment, and the method comprises the steps: generating a floating ice field geometric model according to a working condition, and constructing a navigation body model of the navigation body; grid division is carried out on the floating ice field geometric model and the navigation body model, and a water outlet icebreaking calculation model is obtained; fluid-solid coupling relation definition is conducted on the effluent icebreaking calculation model, and an optimized effluent icebreaking calculation model is obtained; the optimized water outlet icebreaking calculation model is used for simulating the water outlet icebreaking conditions of the navigation body at different moments, and water outlet icebreaking numerical values of the navigation body at different moments are obtained. The fluid-solid coupling relation definition can more accurately describe the interaction between fluid and solid, and in the water outlet icebreaking process, collision and extrusion between a navigation body and an ice layer and subsequent icebreaking behaviors all relate to a complex fluid-solid coupling phenomenon, so that the fluid-solid coupling relation definition is carried out on the water outlet icebreaking calculation model, and the calculation accuracy is improved. Therefore, an accurate value of water outlet icebreaking of the navigation body is obtained.

The invention discloses a speed reducer gear box lightweight design method based on topological optimization, and relates to the technical field of gear box optimization. A processing end establishes a three-dimensional parameterized geometric model, performs finite element mesh generation on the geometric model, optimizes material distribution of the geometric model based on a topological optimization algorithm, and optimizes the material distribution of the geometric model; a topological optimization result is parameterized into geometric features, a random generation tool is used for generating a plurality of initial variable sets, a genetic algorithm is used for outputting an optimal variable set, a geometric model is reconstructed according to the topological optimization result and a genetic algorithm result, and after dynamic-static simulation is conducted on the reconstructed geometric model, the optimal variable set is obtained. And evaluating whether the performance of the reconstructed geometric model reaches the standard or not, and if not, optimizing genetic algorithm parameters according to an evaluation result and then cyclically reconstructing the geometric model until the performance of the reconstructed geometric model reaches the standard. The design system intelligently optimizes a local high-stress area, so that the lightweight requirement is met, and meanwhile, the strength and rigidity balance of the structure is ensured.

The invention discloses a BIM model automatic generation method and system based on point cloud data, and belongs to the field of building information modelling, and the transmission method comprises the steps: obtaining original point cloud data, and employing a filtering algorithm based on point cloud density adaptive adjustment to carry out the preprocessing of the point cloud data; constructing a voxel octree structure for the preprocessed point cloud data, and performing semantic classification on the point cloud; geometric modeling is carried out based on the segmented point cloud subsets, and a fitting algorithm is adopted to carry out shape completion on a point cloud area; semantic annotation is carried out on the components subjected to geometric reconstruction, a corresponding relation between component types and spatial attributes is constructed, fusion features based on a point feature histogram and a local curvature are adopted, and classification is carried out; a standard BIM component family is converted, and a three-dimensional BIM model is constructed through the mapping relation. According to the method, the voxel octree data structure and the deep semantic segmentation neural network model are combined, division and semantic recognition are performed on the point cloud data, the intelligent degree of the model is improved, and manual intervention is reduced.

The invention provides a logistics robot control method and system based on artificial intelligence. After the surface material type, the three-dimensional geometric contour and the surface stress distribution data of a to-be-sorted package are obtained through the logistics robot, the hardness grade and the friction coefficient interval of the package are determined, the geometric model of the package is reconstructed, the minimum external cube size is calculated, and the fragile area is recognized. The adsorption or clamping mode and the unfolding angle are adjusted according to the information, the contact point and track of the grabbing action are optimized, and meanwhile a sorting strategy is generated by considering dynamic factors such as the flow speed of a sorting line. In the execution process, action parameters and decision weights are updated through an artificial intelligence algorithm according to the actual effect, and the sorting strategy is continuously optimized. According to the technical scheme provided by the invention, the control efficiency and precision of the logistics robot can be improved.

The invention discloses an airspace engine 3D printing deformation compensation method. The method comprises the following steps that S1, a three-dimensional CAD model is constructed, and a printing layer set is generated; s2, obtaining residual stress tensor fields of the current printing layer and the historical printing layer; s3, constructing a stress shadow mapping tensor; s4, inputting the stress shadow mapping tensor and the current printing layer parameter into the reverse stress propagation neural network model, and outputting a deformation prediction vector field; s5, performing geometric compensation processing on the geometric area of the printing layer according to the deformation prediction vector field; s6, generating a printing code according to the compensated geometric model; and S7, carrying out error comparison on the actual deformation vector field and the overall prediction deformation vector field, if the error is greater than a preset tolerance threshold value, feeding back error information to the step S5, and repeatedly executing the steps S5 to S7 until the error vector field does not exceed the tolerance threshold value. According to the method, residual stress modeling and the reverse stress propagation neural network are fused, and 3D printing deformation compensation of the airspace engine is achieved.

The invention provides a virtual-real fusion method for digital twinning operation of a cell drug factory in combination with the Internet of Things, which comprises the following steps: constructing geometric models of a culture medium storage tank and a bioreactor through three-dimensional modeling software, mapping and associating historical process parameters of a culture medium supply system and temperature, pH value and dissolved oxygen concentration monitored in real time with the geometric models, and obtaining a virtual-real fusion model of the culture medium storage tank and the bioreactor; constructing a digital twinborn model of the reactor; analyzing historical production data through a machine learning algorithm, extracting a quantitative relationship among key parameters of a cell metabolism rate, a glucose concentration in a culture medium, a pH value and a reactor temperature, establishing a cell growth prediction model, and predicting a growth trend and a metabolism demand of cells in a target time period; a virtual-real mapping mechanism of a culture medium supply system is integrated with an intelligent manufacturing execution system, so that the culture medium supply process and the whole pharmaceutical production process are subjected to collaborative optimization control, and the working procedures of the culture medium production process are automatically dispatched according to a production plan and cell growth requirements.

The invention relates to the technical field of model construction, in particular to a method and a system for establishing a parameterized tunnel model based on cloud computing. The method comprises the following steps: acquiring a tunnel section type, and extracting structural semantics to obtain a section feature description set; constructing a parameter dictionary of the tunnel section based on the section feature description set to obtain a dynamic parameter dictionary data set; and modeling a tunnel section geometric structure based on the dynamic parameter dictionary data set to obtain an initial three-dimensional tunnel geometric model. Through dynamic parameter management, a self-correction mechanism and a Latin hypercube sampling technology, efficient automatic modeling and precise simulation optimization of the tunnel section three-dimensional geometric model are achieved, meanwhile, through unified packaging and authority control, safe and efficient management and collaborative sharing of cloud model resources are achieved, and the method is suitable for large-scale popularization and application. And the intelligent level and the engineering efficiency of tunnel model construction and application are comprehensively improved.

The invention belongs to the technical field of robotics and computer vision, and relates to a multi-source information fusion outdoor inspection scene three-dimensional color geometric model reconstruction method, which comprises the following steps: (1) inertial measurement unit IMU internal reference calibration initialization, (2) laser radar and inertial measurement unit IMU combined external reference calibration, (3) state estimation, (4) using an i-Octree structure to store an updated map, and (5) performing multi-source information fusion. (5) carrying out loopback detection to solve the mileage drift problem; (6) carrying out data fusion of the laser radar and the color camera; and (7) carrying out 3D global positioning. The method has the advantages that the information of the laser radar and the information of the color camera are fused, the one-to-one mapping relation between each space point in the three-dimensional point cloud and a certain pixel point in the color image is established, visual information is more completely described, and the visual information is more accurately and accurately positioned by using a 3D global positioning method. And high-precision reconstruction of the three-dimensional color geometric model is realized.

The invention relates to the technical field of material testing, and discloses a coating performance simulation method based on high and low temperature circulation and prestress loading. The method comprises the following steps: constructing a coating matrix composite geometric model containing a microporous structure by acquiring actually measured working condition data of a pipeline or a part, and quantifying surface roughness and interface curvature characteristics in combination with reverse engineering; a dynamic temperature-pressure coupling equation is generated based on the thermal expansion effect of a high-pressure medium, a micropore optical load is converted into equivalent heat flux density through a ray tracing algorithm, transient boundary conditions of a temperature field and a stress field are associated, a crack initiation path is simulated through an extended finite element method, and a life attenuation curve is predicted in combination with a Paris formula. And material parameters are dynamically corrected based on experimental data, and a multi-parameter collaborative optimization model is generated. According to the method, the problems of interface stress evaluation distortion and life prediction deviation under the combined load are solved, and multi-physics field co-simulation support is provided for coating compression resistance optimization.

The invention discloses an intelligent sound effect processing system for an automobile DSP (Digital Signal Processor) power amplifier, particularly relates to the field of optimization of a vehicle-mounted sound system, is used for solving the problem of insufficient sound effect consistency and intelligent adaptation in a dynamic sound field, and is characterized in that acoustic data are acquired in real time through a microphone array, and a three-dimensional acoustic geometric model is constructed by combining ray tracing and an image source method; sound wave propagation paths and dynamic changes are accurately captured; frequency bands of low-frequency, intermediate-frequency and high-frequency signals are divided, spectrum distribution differences and gain fluctuation centers of all seats are evaluated, and a sound field consistency adjustment coefficient is generated and used for guiding design and optimization of a directional beam forming filter, adjusting amplitudes and phases of all sound channels, forming directional sound beams and suppressing noise interference; therefore, the method can dynamically adapt to the acoustic environment change in the vehicle, improves the sound effect balance and high fidelity, and enhances the auditory experience and satisfaction of passengers.

The invention discloses a PINNs high Reynolds number flow field solving method based on regulatory factor optimization, and the scheme comprises the steps: firstly building a physical model, on one hand, building a geometric model according to the actual condition of a reactor core rod bundle channel, and on the other hand, building a fluid mechanics model containing continuity, momentum conservation and energy conservation equations; a data set is obtained, and experimental data is preprocessed and then integrated with simulated data through experimental measurement and numerical simulation; and then training a physical information neural network, designing a neural network model of specific input and output, introducing a regulatory factor to balance the difference between a physical equation and data, constructing a comprehensive loss function containing internal data points, boundary data points and equation loss, and training the network by using a training data set until convergence. And finally, solving a flow field by using the trained neural network, outputting predicted values of a velocity field, a pressure field and a temperature field, and solving the high Reynolds number flow field of the reactor core.

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.

A solid structure deformation and damage analysis method based on a quasi-bond finite element method comprises: carrying out geometric modeling on a target structure body, and distribution and subdivision to generate a grid of a traditional finite element method; dividing the target structure body into a finite element region and a quasi-bond region, and calculating a finite element region system stiffness matrix and a quasi-bond region system stiffness matrix to obtain an overall stiffness matrix of the target structure body; setting a boundary condition for the target structure body, applying an external load, and calculating a system force matrix under current load and boundary state; and judging a quasi-bond breakage condition according to a node trial displacement, then calculating a node displacement of the structure body and an equivalent damage parameter at each node, and outputting cloud charts of a displacement field and an equivalent damage field.

The invention discloses a curved surface grid generation and self-adaptive re-division method for fluid simulation. The method comprises the following steps: acquiring CAD fluid model data; processing closed surface, short edge and parameter domain distortion and geometric interference problems of the CAD fluid model to obtain a defect-free continuous geometric model; performing curved surface discretization processing on the defect-free continuous geometric model to generate an initial grid; and performing grid re-division processing on the initial grid to generate an optimized anisotropic curved surface grid. The problems of high geometric defect sensitivity, large isotropic grid redundancy and sudden size change of the tail end of a parameter domain in related technologies of fluid simulation are solved.

The embodiment of the invention discloses a numerical optimization method and device considering bearing capacity of a steel frame and a concrete precast slab, equipment and a medium. The method comprises the following steps: acquiring geometric parameters, material attributes and connection parameters between the steel frame and the concrete precast slab corresponding to the steel frame and the concrete precast slab respectively, constructing a geometric model based on the geometric parameters, performing finite element mesh division, assigning the material attributes and the connection parameters to generate a finite element model, and performing finite element mesh division on the finite element model; static load analysis and dynamic load analysis are conducted on the finite element model to obtain bearing capacity response, continuous optimization is conducted by changing set parameters, material attributes and connection parameters, the influence of different parameters on the bearing capacity is analyzed, an optimal parameter combination is found, the bearing capacity is maximized, and the bearing capacity is improved. Marking stress concentration and weak areas based on the optimal parameter combination; according to the technical scheme, the accuracy of the steel frame-concrete prefabricated slab model can be improved, and the effectiveness and efficiency of parameter optimization on stress concentration and weak areas are improved.

The invention belongs to the field of data processing, and discloses a test method for pedestrian trajectory prediction and emergency braking in an automatic driving platform. Comprising the steps of segmenting a real road environment in advance, obtaining grades of small road segments according to calculation of segmentation coefficients, carrying out resource allocation on the small segments according to the grades, and collecting multi-source heterogeneous data; importance optimization is introduced into a Transform model, possible prediction trajectories of individuals are generated, graph data are constructed by using the possible prediction trajectories, an Autoencoder anomaly detection mechanism is introduced, high-risk trajectories are screened out, and high-risk scenes are output; combining the road geometric model with the high-risk scene to form a scene library, loading the scene in a simulation platform, carrying out automatic driving test, carrying out virtual-real data cross validation in combination with real vehicle test data, and obtaining performance evaluation of an algorithm; pedestrian track prediction and emergency braking in the automatic driving process are achieved.

The invention provides a BIM-based building engineering three-dimensional modeling method and system, and relates to the technical field of building information modeling. The method comprises the following steps: generating a point cloud model through multi-source data acquisition and preprocessing; semantic annotation is carried out on the model, and a structure complexity coefficient and a component association degree parameter are introduced; constructing a three-dimensional topological relation and optimizing the grid model; construction management information is embedded into the model, and dynamic updating and multi-dimensional data integration are achieved; and finally, verification and output are carried out through point cloud and geometric model matching. According to the method, the modeling efficiency and precision are improved, the data integration and dynamic adaptability of the model are enhanced, and the storage performance and the calculation burden in a complex scene are remarkably optimized.

The invention belongs to the technical field of material structure optimization, and discloses an enhanced structure design method and system for a three-period minimal curved surface, and the method comprises the steps: modeling a geometric model of the three-period minimal curved surface, and exporting the model; expanding a design domain of the geometric model to obtain a corresponding expanded design space of the three-period minimal curved surface; a design space file is converted into an analysis data structure, a general expression mode is constructed for the three-period minimum curved surface, an equal geometric element stiffness matrix is solved by using a Gaussian integral method and the elastic performance of each point in a design domain and assembled into a global stiffness matrix, and the structural response of the model is solved; constructing a local density distribution function in the Bayesian unit, and assembling all LDDFs to obtain a global density distribution function for presenting structural topology; and constructing a stiffness maximization topological optimization mathematical model for the TPMS, calculating an objective function and analyzing sensitivity, and carrying out iterative updating on design variables in the optimization model based on an optimal criterion method to obtain a reinforced structure configuration.

The invention discloses a dynamic path planning method and system for a multi-axis collaborative welding robot, and belongs to the field of robot welding. The method comprises the steps that basic data of a welded workpiece is collected, and a welding basic database is constructed; constructing a geometric model of the welding seam path according to the data in the welding basic database, and parameterizing the geometric model of the welding seam path; on the basis of the parameter result, an initial track is generated between two adjacent weld joint sections of the welding path, time parameterization is conducted on the initial track, and a time-dependent time parameterization spinor track is obtained; and constructing a spinor mapping model based on inverse kinematics and a spinor theory to convert the time parameterized spinor trajectory into a target spinor. The welding quality is improved.

The invention provides a flexible electronic device multi-physics field coupling performance simulation analysis method, and relates to the technical field of flexible electronics. Firstly, a geometric model of the flexible electronic device is constructed; the geometric model comprises a three-layer assembly structure of an electronic chip, an adhesive layer and a flexible PCB layer, and an interconnection structure; setting electromagnetic-thermal-mechanical field coupling parameters of the geometric model; then, grid division is conducted on the geometric model through a finite element method, and the grid division precision of the area where the interconnection structure is located is higher than that of other areas; building a multi-physics field coupling model according to the geometric model and set coupling parameters; applying load and boundary conditions to the model, wherein the load and boundary conditions comprise a current source, a heat generation load, a convection heat dissipation boundary condition and a mechanical constraint condition; and simulating the working state of the flexible electronic device under the action of multi-physics field coupling in the multi-physics field coupling model to obtain a simulation result. According to the method, the performance and the failure mechanism of the flexible electronic device under the multi-physics field coupling effect can be effectively predicted.

The invention discloses a concrete 3D printing path optimization method and system based on reinforcement learning, and the method comprises the following steps: representing a to-be-printed geometric model as a graphic structure composed of nodes and edges, each node being a specific position in a three-dimensional space on a printing surface, and the edge representing a feasible moving path of a printing head between the nodes; performing dynamic optimization on the movement of the spray head through a Q-learning algorithm, performing iterative calculation on a Q value, and completing dynamic adjustment on a printing path on the basis of an instant reward to obtain an optimal printing path with the minimum movement distance, the minimum sharp turn frequency and the minimum start-stop frequency; the optimal printing path data is converted into a standard G code, and the 3D printer is controlled to execute the optimal printing path based on the standard G code; by combining the solving process of the optimal Q value in the Q-learning algorithm and the construction requirements of concrete 3D printing path planning, the accurate optimal printing path planning can be efficiently completed through the self-learning-self-evolution algorithm advantages of the reinforcement learning model.

The invention provides a spacecraft full-cycle monitoring and operation and maintenance decision-making system based on digital twinning, and belongs to the field of spacecraft full-life-cycle intelligent operation and maintenance. Structural data and environmental data are monitored in real time through a multi-source sensor network deployed on a spacecraft structure; fusing physical entity monitoring data and damage evolution rule virtual data in a twin database, constructing a structure damage resistance and mechanical property evaluation model, and executing geometric modeling, physical attribute analysis, behavior response simulation and damage evolution rule modeling; based on a simulation result of the virtual model, life prediction and reusability evaluation are carried out, and an operation and maintenance decision instruction is generated; and feeding back the operation and maintenance decision instruction to the physical entity to execute control, and updating state data in the twin database to perform iterative optimization. According to the method, full-scene coverage from design demonstration to scrap recovery is supported, the problem of stage splitting in the traditional technology is solved, and a closed-loop optimization mechanism is formed through real-time interaction between the physical entity and the virtual model.