16results about How to "Fast modeling" patented technology

The invention discloses a large model three-dimensional modeling system and method based on artificial intelligence, particularly relates to the technical field of three-dimensional modeling, and comprises a large model three-dimensional modeling data acquisition module, a large model three-dimensional modeling data analysis module, a large model three-dimensional modeling data evaluation module and a large model three-dimensional modeling comprehensive analysis module. And a large model three-dimensional modeling man-machine interaction module. The large-model three-dimensional modeling data acquisition module is used for acquiring performance data for analyzing large-model three-dimensional modeling based on artificial intelligence to obtain various parameters for evaluating performance indexes of large-model three-dimensional modeling, and outputting the various parameters to the large-model three-dimensional modeling data analysis module; according to the method, the modeling efficiency parameters are analyzed, an index is obtained to measure the efficiency of the three-dimensional modeling process, various indexes in the modeling process are monitored in real time, an administrator can adjust a modeling strategy and optimize the modeling process according to the indexes, the modeling speed is increased, and resources are optimized.

The present invention relates to a method and device for modeling sedimentary phases based on a generative adversarial network with a single training image. The method constructs a sedimentary phase simulation model with a multi-level generative adversarial network of a pyramid structure, obtains a single real sedimentary phase training image, and generates a multi-scale hierarchical training image based on the training image to train each level of the generative adversarial network. During the training process, the error between the output image of the generator in each layer of the network and the preset condition is judged, and the output image is corrected when the error threshold is exceeded. Finally, the sedimentary phase simulation model with complete training is used to simulate the sedimentary phase based on the given river channel training image and sedimentary phase generation conditions. The present invention reduces the acquisition cost of training data, has a fast modeling speed and high efficiency, and can perform random modeling of sedimentary phases and simulate the uncertainty of sedimentary phases on the premise of meeting given conditions.

The invention discloses a five-degree-of-freedom electromagnetic positioning system and method, and the system comprises a transmitting unit which comprises a plurality of single-axis transmitting coils and is used for generating a magnetic field in a three-dimensional space; the receiving unit is used for collecting field intensity information in a three-dimensional space; the control and data processing unit is used for inputting the field intensity information collected by the receiving unit into a pose initial value prediction network and outputting initial pose information of the receiving unit; inputting the initial attitude information into a coil magnetic field modeling network to obtain theoretical field intensity; and the iterative optimization module acquires a field intensity difference according to the theoretical field intensity and the acquired field intensity information, updates the attitude information according to the field intensity difference, and returns to execute the step of acquiring the theoretical field intensity until a preset iteration termination condition is reached, thereby acquiring the final attitude information. The method can reduce the complexity of a traditional electromagnetic positioning algorithm, improves the positioning precision and speed, can be widely applied to the fields of auxiliary medical treatment, man-machine interaction and motion tracking, and achieves a multi-degree-of-freedom electromagnetic positioning function.

The application discloses a tokamak discharge modeling system based on a bidirectional long short-term memory neural network, which is composed of multiple modules, and the modules are low-coupling functional units.The modules at least include a data transfer module, a Batch data access input module, a training self-defined parameter module, a bidirectional long short-term memory neural network modeling module and a data visualization module.The whole system architecture is mainly divided into two paths, namely data training and data modeling.Using the visualization technology, the tokamak discharge experiment personnel can refer to the discharge modeling results and visualize the model training process.The application can one-key type in the experiment proposal stage to model the whole process of the tokamak discharge curve in advance, so that the experiment personnel and the proposal design personnel can check the validity and rationality of the proposal.The application can also be used for assisting data discovery after the experiment.

The present invention discloses an intelligent design method for building stairs based on Grasshopper platform. The method only requires the building staircase designer to fill in the known parameters of the target building stairs in an Excel table and set the global adjustable parameters in the Rhino software. Then, a battery pack created in a Grasshopper plug-in can be used to generate equal run suggestion schemes and unequal run suggestion schemes as comprehensive design schemes according to whether a net height JG under the ladder beam is set. The method also generates a splicing section construction drawing of each floor of the target building stairs, an overall section construction drawing, a three-dimensional model of the building stairs and a plan construction drawing of each floor according to the comprehensive design scheme. The method has the advantages of fast modeling speed, high efficiency, high accuracy, good integrity and strong ease of use.

The present invention relates to a fully automatic venous puncture identification integrated robot, comprising a puncture module, an image acquisition module and a positioning platform; the puncture module comprises a puncture module housing, a y-axis rotation unit, an x-axis rotation unit, a z-axis rotation unit and a puncture head component; the y-axis rotation unit comprises a y-axis motor and a y-axis bearing; the x-axis rotation unit comprises an x-axis rotating shaft and an x-axis housing; the z-axis rotation unit comprises a z-axis motor, a z-axis rotating shaft, a z-axis bearing, a worm and a worm gear; the image acquisition module comprises an ultrasonic probe, an image linear motor and a near-infrared camera, and the ultrasonic probe is fixed on the front active end of the image linear motor through a probe bracket to perform up and down linear motion. Compared with the prior art, the present invention greatly reduces the overall volume of the device through the three-dimensional staggered design of each module; realizes precise control of the puncture head component; and realizes fully automated precise control.

The present invention provides a fast self-built behavior modal recognition system, which belongs to the field of behavior modal recognition. The system comprises: a behavior modal data acquisition and processing module, which is used to acquire human behavior modal data by using a mobile device, and perform feature engineering processing on the acquired data, thereby obtaining behavior modal features; a behavior modal recognition model establishment module, which establishes a behavior modal recognition model based on the behavior modal features by using a fast self-built method; and a behavior modal recognition module, which inputs the behavior modal features to be tested into the behavior modal recognition model, thereby obtaining the category of the behavior modal features to be tested. The present invention makes up for the problems of the existing behavior modal recognition system in terms of slow construction speed, low recognition accuracy and insufficient lightweight.

The invention provides a scene modeling method, device and equipment, a computer readable storage medium and a product. The method comprises the following steps: acquiring a plurality of original 2D pictures in a target video; detecting a 2D feature point of each original 2D picture, and determining a matched picture of each original 2D picture; for each original 2D picture and the matched picture thereof, performing 2D feature point matching and pose calculation to obtain a camera pose corresponding to each original 2D picture and a 3D feature point corresponding to the matched 2D feature point on each original 2D picture; respectively rendering the 3D feature points corresponding to each original 2D picture to obtain a rendered 2D picture corresponding to each original 2D picture; training a loss function to obtain a scene model of the scene; according to the embodiment of the invention, scene modeling can be completed through the target video capable of covering each angle of the scene, and the image of any view angle can be freely output; the modeling method is simple in process, high in modeling speed and good in new view angle synthesis quality effect.

The invention relates to the technical field of three-dimensional model optimization, and discloses a three-dimensional model optimization method based on an indoor transformer substation monitoring video, which comprises the following steps of: establishing a transformer substation model library in which a model is not subjected to mapping processing; arranging a three-dimensional model; acquiring an equipment model in the three-dimensional model, and acquiring a live-action picture of equipment of the same model through a monitoring video; performing mapping processing on a model part of the three-dimensional model by adopting a live-action picture; and completing the construction. Compared with the prior art, the method has the advantages that the modeling speed is higher, local details of all inspection point positions can be covered basically, the inspection point positions can be consistent with real scenes basically, the modeling quality is high, and the modeling efficiency is high. Moreover, the model is constructed by adopting the original monitoring equipment, and a panoramic camera and a laser radar are not needed, so that the construction cost is low.

The invention relates to a submarine pipeline reel pipe type laying GUI plug-in automatic modeling method. The method comprises the following steps: 1) establishing a GUI plug-in; 2) inputting module parameters into the GUI plug-in; and 3) calculating output, and completing modeling. According to the method, the PYTHON language and the GUI plug-in are combined, automatic rapid modeling of the reel pipe type laying process of the submarine pipeline is achieved, only about 10 seconds are needed for automatic modeling of each model, and the modeling speed is greatly increased.

The present invention relates to the technical field of engineering monitoring, and in particular to a method and device for detecting internal defects of a pipeline by stereoscopic visual imaging. The method comprises the following steps: obtaining a depth image of the interior of a pipeline to be detected; determining an initial frame image in the depth image, and selecting a key frame image located after a time series of the initial frame image according to a preset angle condition and / or mileage condition; constructing a three-dimensional model of the interior of the pipeline to be detected; extracting point cloud data from all the depth images, performing cylinder fitting and segmentation processing on all the point cloud data, and determining a cylinder segment of a non-cylindrical surface according to a preset cylinder determination threshold; bringing the position information and image information of the defect target into the three-dimensional model for reconstruction to obtain a three-dimensional model with defect information. The method can quickly construct an internal model of a pipeline with defect information.

The application belongs to the technical field of power secondary equipment modeling, and discloses a power secondary equipment visual modeling and configuration system and method based on external electrical signal labeling, equipment and medium, to solve the problems of large manual workload, poor configuration consistency, missing function model and non-uniform engineering model in traditional processes. The system comprises: a geometric modeling module for establishing a geometric model; a PLP construction module for organizing physical terminals Pin, ports Port and logic layers L to form physical logic ports PLP; an external electrical signal import module for receiving external signals and labeling to Port; a semantic binding module for binding electrical signal semantics to PLP; a Function organization module for combining multiple PLPs to form a function object Function; and a model export module for exporting a structured file. The application realizes the standardization and semanticization of equipment modeling, and improves the design efficiency and data consistency.

This invention discloses a method for constructing a structured light dataset based on a digital twin model of a fringe projection system. A digital twin model of the system is constructed in computer graphics software based on the hardware and calibration parameters of a real fringe projection system. Then, a node tree is used to simulate the surface properties of the object under test and the system's ambient light source is set. The parameters are adjusted to diversify the target surface properties and ambient light. Fringe images are then imported, and automated projection and data acquisition of the digital twin model are achieved through programming. Finally, fringe images and corresponding depth images that meet the user's quantity requirements are generated, completing the construction of the structured light dataset. This invention solves the problems of complex fringe projection data acquisition processes, high dataset production costs, and limited data samples in real-world scenarios, achieving rapid and high-precision construction of fringe projection system datasets.

The present invention provides a method for reconstructing and repairing a motion-recovery structure of a tunnel, comprising: calculating a camera intrinsic parameter matrix and a lens distortion coefficient through camera images; preprocessing the first image and extracting sparse feature points; adding a new image, preprocessing, extracting sparse feature points, and matching them with the sparse feature points of the first image; calculating the transformation matrix between images through the result of feature point matching; extracting dense feature points from the new image, and triangulating the spatial coordinates of the dense feature points based on the transformation matrix; generating a three-dimensional point cloud model according to the spatial coordinates of the dense feature points; and repairing and improving the three-dimensional point cloud model in the form of a generated surface to obtain a complete entity three-dimensional model. The invention can realize three-dimensional reconstruction of an on-site tunnel through multiple tunnel images, thereby solving the technical problems of slow tunnel modeling, inaccuracy, and inability to complete real-time modeling in the prior art of tunnel three-dimensional visualization technology.

This invention relates to a dual-scale finite element analysis method and system for steel-concrete composite bridges, belonging to the field of civil engineering technology. The method includes the following steps: S1, establishing the length division of large-scale (macro-element) and small-scale (fine-element) regions in the steel-concrete composite bridge model, clarifying the focus of local stress analysis; S2, establishing components in both large and small scale regions and selecting element types for each component, simplifying the dual-scale model; S3, defining the material constitutive models of both large and small scale components to achieve consistency in material scale between macro-element and fine-element models; S4, assembling the spatial positions of each component and connecting the large and small scale regions to achieve consistency in deformation between the macro and fine models; S5, defining the internal contacts of each component and completing mesh generation; S6, applying actual loads and arranging actual boundary conditions to perform dual-scale finite element analysis. The technical solution of this invention has advantages such as high accuracy, high computational efficiency, and fast modeling speed.

The invention belongs to the technical field of natural language processing, and particularly relates to a BERT-based space science task demand classification method, system and product. The method comprises the following steps: inputting a space science task demand text into a pre-established and trained classification model to obtain estimation probabilities of the demand text in different categories, and selecting the category with the highest estimation probability to realize classification; the categories comprise a scientific target, a performance demand, a load demand and an engineering demand; the classification model adopts a BERT model, and a complex structure and non-sequence content of a language are learned by using an attention mechanism, a full-connection neural network layer and position coding. According to the method, the SysML demand model for natural language processing and space science task demonstration modeling is combined, a basis is provided for automatically constructing the SysML demand model, the MBSE modeling speed is increased, the system design efficiency is improved, and the design cost is reduced.