70 results about "Visual Basic" patented technology

The invention discloses an intelligent evaluation method for real-time learning and teaching effect feedback, and the method comprises the steps: carrying out the preprocessing of collected multi-modal teaching scene data of a target classroom, and obtaining a multi-modal data stream which comprises the aligned target video data, target audio data and a timestamp; pre-training the visual basic model based on the ViT architecture by adopting a plurality of education scene data sets to obtain a first model, carrying out alignment fusion on the first model and a preset variational automatic encoder structure to obtain a preset target model, and carrying out dimension reduction processing on the extracted spatial-temporal features and feature vectors by utilizing the model to obtain target features; and determining a classroom atmosphere index, a student participation index and a teaching interaction effect index based on teaching behavior characteristics, student behavior characteristics and teaching interaction behavior characteristics identified from the target characteristics, and synchronously generating a real-time teaching quality evaluation report. According to the method, the classroom information can be accurately and comprehensively analyzed, and a real-time and objective evaluation result is synchronously generated.

The invention belongs to the technical field of image recognition, and discloses a few-sample defect detection method based on a prototype prompt fine-tuning visual basic model SAM. Prototype prior information about an image to be segmented is extracted from a small number of example image-mask pairs, so that prior guidance of human beings is replaced; for defect data, a representative foreground prompt point, a high-confidence background prompt point (sparse prompt) and a foreground Mask (dense prompt) are extracted from prototype prior to serve as prototype prompt, and a visual basic model SAM is guided to achieve more accurate segmentation. A visual basic model SAM is finely adjusted by using prototype prompt and a small amount of defect data, a prompt encoder and an image encoder of the SAM are frozen, an LoRA bypass is injected into the image encoder, and the SAM is enabled to better adapt to defect data distribution by updating parameters of the LoRA bypass and a lightweight mask decoder.

The invention discloses a remote sensing image open vocabulary segmentation method and system based on vision-language interaction. According to the method, the advantages of a multi-modal large language model and a semantic segmentation network based on a visual basic model are cooperatively utilized, language-pixel two-way mapping is taken as a core, five types of marks of images, texts, categories, objects and segmentation are introduced as carriers of cross-modal information, and three types of cross-modal fine-grained information interaction modules are taken as bridges, so that the cross-modal information interaction is realized. Bidirectional mapping and alignment of fine-grained information of the multi-modal large language model and the semantic segmentation network are realized, the open vocabulary segmentation capability of the semantic segmentation model is improved, and the method can adapt to different remote sensing scenes and category definitions. The method has the following advantages: the method has high performance, strong generalization ability and good expansibility, can provide any category of semantic segmentation maps for unlabeled target domain images based on instructions, and has high application value in the aspects of urban planning, map making, disaster response and the like.

The invention discloses a three-dimensional dynamic scene graph construction method based on 3D Gauss, and belongs to the field of computer body intelligence. The implementation method comprises the following steps of: realizing object perception and semantic feature extraction of open vocabularies by utilizing a visual basic model; a 3D Gaussian scene with high fidelity and continuous object semantics is constructed through multi-view multi-dimension optimization of 3D Gaussian representation; constructing a multi-level three-dimensional scene graph, extracting spatial levels and semantic relationships among objects by using 3D spatial positions and semantic tags of instance objects existing in a semantic Gaussian graph, and constructing a multi-level spatial semantic topology to accurately represent an environment layout; according to the method for realizing local updating for the Gaussian scene graph based on the environmental structural similarity, environmental change detection is carried out through real-time RGB-D observation and the structural similarity between high-quality rendering views of the Gaussian scene graph, and corresponding local updating is carried out by using rapid training and differentiable rendering of 3D Gaussian representation. And the capability of adapting to a complex dynamic environment of the 3D Gaussian scene graph is improved.

The invention discloses a surgical instrument segmentation method based on a double prior guidance network, and the method specifically comprises the steps: constructing a hybrid encoder composed of a visual basic model and a state space model, and carrying out the coding processing of an input surgical endoscope image; the visual basic model is an SAM2-Hira model; the state space model is a MambaVision model; an adapter layer composed of a standard adapter and a Mama enhancement adapter is arranged between the coding layer and the decoding layer, and coding features are processed; setting a multi-context guide decoder, and performing multi-scale feature recovery and mask segmentation; a hybrid encoder, a Mama enhancement adapter and a multi-context guidance decoder form a double-priori guidance network DPG-Net, and high-precision surgical instrument segmentation is realized by using the DPG-Net. Accurate visual support is provided for the surgical robot, and the accuracy and safety of surgical operation are greatly improved.

The invention discloses a double-branch change detection method and system based on a visual basic model, and belongs to the technical field of remote sensing image detection. According to the method, a double-branch change detection network model based on a visual basic model is provided, and the frozen visual basic model FastSAM is used as an encoder, so that the feature extraction capability of the network model is greatly improved. In order to realize migration of extracted features of a visual basic model to the field of remote sensing images, a change detection adapter module is designed in the method, and effective conversion from universal visual features to remote sensing image features is realized through fine tuning of a small number of model parameters. In addition, the method also provides a double-branch change detection module, a single-temporal semantic feature decoder and a double-temporal change feature decoder are respectively utilized, and through mining of single-temporal remote sensing image semantic information and fusion of double-temporal multi-scale features, the feature discrimination capability is further improved, so that the detection performance of the model is improved.

The invention belongs to the technical field of ejectors, and particularly relates to an ejector structure design calculation method. The method solves the problem that the ejection performance of the ejector is poor due to structural parameter selection under the normal production operation working condition, and the specific technical scheme is as follows: firstly, determining an ejector design formula and an initial working condition, and constructing an initial ejector model; secondly, analyzing model performance, and determining an injection coefficient calculation formula; then setting a design variable value range, taking any parameter in the range to form a group of design variable matrixes, constructing a new ejector model, and determining an ejection coefficient; and finally, repeating the process until the optimal design variable matrix of the ejector is obtained. According to the method, the Visual Basic editor is used for compiling the macro code to obtain the initial structure parameter and the optimal injection coefficient, the injection coefficient obtained by changing the structure parameter is subjected to structure optimization, and the size parameter enabling the injection performance of the ejector to be better is selected, so that the reasonable structure parameter of the ejector is obtained, and the performance of the ejector is maximized.

The invention discloses a zero sample object instance segmentation system and method in a robot environment, and belongs to the technical field of robot visual perception, and the method comprises the steps: firstly carrying out the Vibriis color mapping preprocessing of a depth image, and then generating an initial object mask candidate through an SAM (Segment Anything Model) model; meanwhile, a pre-trained Vision Transform is used as a feature description model of a scene to process the image, an attention map of the last layer is extracted, and a feature weighting mechanism is constructed based on the information entropy of the attention map; a similarity matrix of the image blocks and the background blocks is calculated to remove non-object masks, a K-Medoids clustering algorithm is adopted for each remaining object mask candidate to obtain representative sampling points, and finally the points serve as prompt information to be input into an SAM model to obtain an accurate object instance segmentation result. According to the method, the zero sample generalization ability of the visual basic model is fully utilized, accurate segmentation of the unseen object can be realized without additional training, and the method has good universality and practical value.

The invention discloses a road environment semantic segmentation method based on cross-modal difference modulation and a visual basic model, and the method is based on a DINOv3 visual basic model of a frozen weight, and introduces a complementary difference fusion module and a bidirectional context flow alignment module through the design of double-flow frozen coding and difference perception interaction. According to the method, a cross attention weight is generated by calculating a local high-frequency difference, so that transverse anti-noise complementation of RGB texture information and a Depth geometric structure is realized, and single-mode noise pollution is effectively inhibited; the two-way cross-scale alignment mechanism of top-down space mask guidance and bottom-up channel feature feedback is constructed by the two-way cross-scale alignment mechanism, and the scale barrier of deep and shallow layer features is broken. Therefore, joint feature representation with deep synergy of semantics and structures is obtained, inhibition of the model to environmental noise and reservation of tiny object details are balanced in a complex road scene, and pixel-level semantic segmentation with high generalization and high precision is realized.

The invention relates to a sparse view angle pose-free scene reconstruction method and system based on 3DGS, belongs to the field of computer vision and three-dimensional reconstruction, and solves the problems of easy failure and poor geometric consistency in a sparse view angle or weak texture environment due to dependence on accurate camera pose priori. Constructing a double-flow sensing module containing semantic flow and geometric flow, extracting semantic features by using a visual basic model, and extracting an explicit geometric corresponding relation by using a dense feature matching network, so as to regress relative camera pose under pose-free priori; a geometric guidance depth refinement module combining a potential diffusion model architecture and Pluecker ray coding is introduced, and scale fuzziness of monocular depth estimation is eliminated through a depth residual prediction mechanism; and based on the micronizable Gaussian rasterization, performing end-to-end optimization by using a self-supervised loss function including rendering consistency, reprojection and epipolar geometric constraint. According to the method, high-fidelity three-dimensional reconstruction is realized without supervision of external parameter true values, and geometric stability and rendering quality in a complex scene are improved.

The invention relates to the technical field of visual position recognition, and particularly discloses a multi-modal visual position recognition reordering method and system based on guidance, and the method comprises the steps: obtaining a query image, and retrieving a plurality of candidate images based on a pre-trained visual basic model and the query image; constructing a composite multi-modal prompt object, wherein the composite multi-modal prompt object comprises an image pair formed by the query image and the current candidate image, and an instruction text used for guiding a multi-modal large language model to perform visual comparison; outputting a structured similarity judgment result, wherein the result comprises a quantitative similarity score; and sorting based on the similarity scores corresponding to all the candidate images, and determining the candidate image with the highest score as an optimal matching result. Through combination of guiding type prompt engineering and structured output, an intermediate text generation link is avoided fundamentally, and the calculation efficiency is improved while the fidelity of all original visual information is reserved.

The invention belongs to the technical field of computer vision, and particularly discloses a monocular 3D object detection method for realizing depth enhancement based on a visual basic model, electronic equipment and a readable storage medium, and the method comprises the steps: S1, building a data set: employing a monocular camera to collect a pavement scene, and obtaining an RGB image in the pavement scene; s2, image preprocessing: preprocessing the RGB image for subsequent feature extraction and depth estimation; s3, performing feature extraction by adopting a dual-backbone network: performing visual semantic feature extraction on the preprocessed RGB image by using DINOv2; performing depth feature extraction on the preprocessed RGB image by using a DPT head; s4, generation of depth perception query points: inputting the visual semantic features and the depth features into a DETR network to generate the depth perception query points; and S5, target detection output: using an MLP-based detection head to obtain information of the category, the size, the center point position, the depth, the 3D size and the direction of the object.

The invention relates to the technical field of model parameter fine tuning, and discloses an efficient parameter fine tuning method for a visual basic model of a dense prediction task, which comprises the following steps: introducing a dynamic parameter generation module for the visual basic model, and parallelly adding an HOM module on a multi-head attention module and a feedforward neural network of each transformer layer of the visual basic model; each super network in the dynamic parameter generation module generates a convolution kernel parameter for the corresponding convolution layer; when fine tuning training is carried out on the visual basic model, parameters of the visual basic model are kept frozen, and parameters of the HOM module and the dynamic parameter generation module are trained through dense prediction task training images. According to the method, resources consumed when the visual basic model is applied to the dense prediction task are effectively reduced, and the cost and the training time of a video memory are reduced.

The invention discloses a handheld object three-dimensional reconstruction method based on three-dimensional generation prior and semantic consistency, and the method comprises the steps: obtaining the three-dimensional prior from an input RGB image sequence in combination with a pre-trained multi-modal model and a three-dimensional generation model; performing semantic alignment on the three-dimensional priori and an input image based on feature similarity measurement of a visual basic model, and preliminarily estimating the pose of an object; completing initial three-dimensional reconstruction of the object according to the rough pose by using a neural radiation field; fine adjustment is carried out on the object pose based on the initial reconstruction result; and generating a high-precision three-dimensional reconstruction result through a nerve radiation field by using the optimized pose. The method has the advantages that the inherent problem of pose estimation in a hand-held object scene is solved through semantic consistency constraint; high-precision object three-dimensional reconstruction can be realized only by depending on RGB video data easy to obtain; dependence of a traditional method on complex sensor data or manual annotation is remarkably reduced, and reconstruction efficiency and practicability are effectively improved.

The invention provides an industrial surface defect detection method fusing a large model and domain knowledge, and relates to the technical field of industrial detection, and the method specifically comprises the steps: designing a double-branch knowledge injection network model which comprises a backbone network, a side branch network, a texture encoder, a curvature extraction network and a detection head network, extracting general visual features by using a visual large model in the backbone network, then adding and fusing the general visual features with supplementary features extracted by the bypass network to obtain deep features, extracting texture features by using a texture encoder, and embedding texture domain knowledge into the deep features and the texture features in a cross attention mode to obtain a deep texture domain; optimizing a loss function according to the stress attention map; positioning and classifying defects by using a detection head network; and training and evaluating the double-branch knowledge injection network model. According to the technical scheme, the problems that in the prior art, the detection performance of a visual basic model in an industrial scene is reduced due to field differences, and the small sample generalization ability is weak are solved.

The invention provides a tunneling rock slag real-time identification method based on U-Net-SAM coupling driving, and the method achieves the high-precision quantization of the size and morphological parameters of rock slag through the fusion of a semantic segmentation network (U-net) and a visual basis model (Segment Anything Model, SAM). The method comprises the following steps: firstly, reasoning an input rock slag image by using a U-Net network to generate a rough rock slag identification result; then, obtaining the center-of-mass coordinate of each rock ballast from the rough result as the automatic prompt input of an SAM model, and extracting an initial mask in combination with the zero sample segmentation capability of the SAM; and carrying out post-processing optimization on the initial mask by adopting a morphological noise filtering method and an intersection-to-union ratio threshold optimization overlapped region processing strategy. According to the method, automatic segmentation and size and shape parameter extraction of the tunnel conveyor belt rock ballast image are realized, the boundary identification problem of a traditional method in a dense particle scene is effectively solved, and real-time and reliable rock machine interaction data support is provided for realizing intelligent tunneling parameter regulation and control.

The invention relates to a visual intelligent monitoring method and system for operation of a belt conveyor. The method comprises the following steps: collecting a video stream through visual sensing equipment deployed along a belt conveyor; performing dynamic image quality evaluation and enhancement at an edge computing node, and operating a lightweight deep learning model to detect typical anomalies in real time; meanwhile, uploading the data to a cloud end, extracting high-dimensional features by using a visual basic model, and comparing the high-dimensional features with a dynamic feature distribution model to identify unknown anomalies; when an exception is found, data from the non-visual sensing module in the same time period are automatically called and analyzed to perform collaborative verification; and through a verification result, carrying out fusion decision making and generating final diagnosis and early warning information. The system comprises a visual perception module, an edge computing gateway, a non-visual sensing module and a cloud intelligent analysis platform. According to the invention, omnibearing and high-reliability intelligent monitoring and early warning of the running state of the belt conveyor are realized.

The invention relates to the technical field of point cloud processing, in particular to a vehicle-mounted laser point cloud semantic segmentation method and system assisted by an SAM2 visual basis model, and the method comprises the steps: constructing a semantic segmentation multi-mode learning model for learning complementary information from different modes through employing an SAM2 model and a point cloud network; a training sample pair is utilized to perform joint training on the multi-modal learning model, and the point cloud network after joint training is used as a point cloud semantic segmentation target model, extracting a target segmentation mask by using an SAM2 model, and constraining the point cloud network to predict the semantic consistency of the point cloud by using a semantic score of the target segmentation mask based on a semantic classifier, so as to integrate image color and texture knowledge into point cloud network training through knowledge distillation in modal interaction; and inputting the to-be-processed vehicle-mounted laser point cloud into the point cloud semantic segmentation target model so as to perform semantic prediction output on the to-be-processed vehicle-mounted laser point cloud. According to the method, the semantic segmentation performance of the point cloud can be improved, and a multi-modal interaction gain effect can be obtained.

The invention belongs to the field of PCBA quality detection, and relates to a small sample PCBA component target detection method and system based on a visual basic model. The method comprises the following steps: acquiring a pre-positioning prompt box from a PCBA image to be detected by using a positioning basic model, and acquiring a single-scale freezing feature of the image by using a classification basic model; expanding the single-scale features into multi-scale features for pre-positioning and classification tasks; pre-positioning prompts are screened and filtered, and query point features are obtained by using a feature alignment query sampling method; and predicting a classification result and a regression result for each query point feature according to the multi-scale positioning feature, the classification feature, the query point feature and the prompt box. According to the method, the high-precision model is quickly and finely adjusted under the condition that training data is scarce, the training cost is reduced, and the requirement for quick switching of production lines in small-batch PCBA production is effectively met.

The application relates to the field of image segmentation technology in computer vision, in particular to a zero-shot image segmentation method and system based on a three-dimensional model of an object. The method comprises the following steps: for any untrained object, a three-dimensional model of the object is rendered into a two-dimensional RGB reference image under a plurality of preset discrete viewing angles by using a graphics rendering engine, the reference image is input into a visual basic model DINOv3, a global feature vector representing semantic information of the object is extracted, and a reference feature library is constructed; a target image to be segmented is input into a visual basic model SAM2, and a plurality of candidate object masks are generated; for each candidate mask, an object image corresponding to the candidate mask is cropped from the target image, and the object image is input into the DINOv3 to extract a semantic feature vector of the candidate object; by calculating the cosine similarity between the candidate object feature vector and each template feature vector in the reference feature library, the top similar degrees with the highest values are selected, and an arithmetic mean value of the similar degrees is calculated, and the mean value is taken as the classification confidence of the candidate object mask; the class label of each candidate object mask is determined according to the confidence, and the class label of the target object and a corresponding pixel-level segmentation mask are output.

The invention discloses a disordered stacking scene long-sequence object grabbing and releasing decision method based on a double-loop strategy, and the method comprises two closed-loop strategies, the inner-loop strategy is responsible for improving the object matching accuracy through active sensing, after a mechanical arm grabs an object, the strategy can sense the object from multiple visual angles through rotating the object, and the object matching accuracy is improved. And the matching confidence of the object and the object in the target scene is increased. The outer ring strategy is responsible for executing grabbing and placing operations, predicting grabbing actions in combination with uncertain object matching and grabbing capability, and determining whether the object is directly placed at a target position or not according to the matching condition of the current object, or temporarily placing the object in a buffer area; according to the method, object matching, inner ring strategy learning and implementation of an inner ring termination mechanism are carried out by utilizing a pre-trained visual basic model, the method is more robust to the influence of perception noise, and the optimality of a task level is considered through reinforcement learning closed-loop optimization, so that a better task success rate and efficiency are achieved.

This invention discloses a method for generating emotional data graphics based on natural language understanding, comprising the following steps: collecting emotional text data, brand visual basic element data, and cross-cultural context description data, and processing the collected data; performing natural language understanding on the processed emotional text data; constructing a multi-domain visual semantic tensor flow that evolves over time; constructing a cross-cultural intent field and binding the parameters of the cross-cultural intent field to the multi-domain visual semantic tensor flow; generating a preliminary emotional graphic structure sequence through a generative visual network; dynamically adjusting the preliminary emotional graphic structure sequence; and outputting it to different media scenarios to obtain emotional data graphics. This invention integrates natural language understanding and multimodal generation technologies to achieve emotion-semantic driven graphics generation, possessing the advantages of semantic accuracy, cultural adaptation, and visual coherence.

The application provides a cross-domain small sample wideband signal detection and recognition method based on a visual basic model, and relates to the cross technical field of electromagnetic signal processing and computer vision. The application reduces the interference of background noise in the wideband time-frequency graph on the candidate area by performing double condition screening based on target confidence and aspect ratio features on the preliminary candidate frame, and combining the field of view expansion in the frequency axis direction, and improves the incomplete coverage problem of the slender signal candidate frame. By extracting multiple intermediate layer features and the final layer feature map of the visual basic model for fusion in the channel dimension, the problem that the local texture information in the wideband time-frequency graph is smoothed or covered in the layer-by-layer abstraction process is solved. The parameters of the visual segmentation model and the visual basic model are kept frozen, and only the small sample fine tuning of the front region proposal network is performed, so that the calculation overhead caused by full fine tuning of the visual basic model during cross-domain small sample adaptation is avoided, and the method is suitable for deployment on devices with limited computing resources.

The invention discloses a cross-domain small sample semantic segmentation method and device, equipment and a medium. According to the cross-domain small sample semantic segmentation method, a hierarchical data set is constructed through a controllable style offset synthesis image, and a style feature extraction and weight generation network is trained; a dynamic weight is generated based on the difference between the target domain image style representation and the source domain reference, feature layer level modulation is performed on the visual basis model encoder features, and the feature alignment problem is accurately solved; image semantic information is fused and supported through a memory attention mechanism, so that the problem of small sample semantic sparsity is effectively relieved; meanwhile, the synthetic data accurately controls style offset through a stable diffusion model, and uncontrollability of traditional generative data expansion is avoided. According to the method, the cross-domain small sample semantic segmentation performance is remarkably improved, and the problems of prompt mismatching, feature dislocation and out-of-control data expansion caused by domain offset are solved.

The invention discloses a fine tuning method, a fine tuning system and fine tuning equipment for a visual basic model, and a storage medium, which are corresponding schemes, and the related scheme aims to solve the problems of overlarge GPU video memory consumption, high quantitative perception fine tuning overhead and the like in the fine tuning and deployment process under the condition of limited resources in the existing parameter efficient fine tuning scheme. According to the scheme, through a sub-network adapter quantization perception fine tuning and block-level activation quantizer fine tuning strategy, video memory consumption in a fine tuning stage is remarkably reduced, and meanwhile, the calculation complexity consistent with that of an original backbone network in a reasoning stage is kept; therefore, a feasible and efficient solution is provided for low-resource deployment of the visual basic model in diversified downstream tasks.

The invention provides an information visualization method and system applied to intelligent building carbon emission monitoring, and the method comprises the steps: firstly obtaining an initial carbon emission data set of different functional regions of an intelligent building in a continuous monitoring period, and then carrying out the feature extraction of the initial carbon emission data set; generating a time sequence fluctuation feature set and a spatial distribution correlation feature set, then inputting the time sequence fluctuation feature set and the spatial distribution correlation feature set into a machine learning network, generating a visual basic data set containing a carbon emission evolution trend and a regional difference feature, and according to a visual coding rule, determining the carbon emission evolution trend according to the visual basic data set. According to the method, the characteristic components are mapped into graphical representation parameters, and finally, visual graph data containing a time axis carbon emission fluctuation thermodynamic diagram and a building space carbon emission distribution color block diagram are generated based on the graphical representation parameters, and are presented in real time through an interactive display interface, so that management personnel can quickly and accurately master the carbon emission condition of a building, and the construction efficiency is improved. And abnormal carbon emission regions and evolution trends can be found in time.

The present invention discloses a method for grasping and releasing objects in a long sequence of cluttered stacking scenes based on a dual-loop strategy, which includes two closed-loop strategies. The inner-loop strategy is responsible for improving the accuracy of object matching through active perception. When the robot grasps the object, the strategy will perceive it from multiple perspectives by rotating the object to increase the confidence of the object matching with the object in the target scene. The outer-loop strategy is responsible for executing the grasping and placing operations, combining the uncertain object matching and grasping ability to predict the grasping action, and deciding whether to place it directly at the target position or temporarily place the object in the buffer according to the matching situation of the current object. The method uses a pre-trained visual basic model for object matching, inner-loop strategy learning and inner-loop termination mechanism. The method is more robust to the influence of perception noise, and considers the optimality of the task level through reinforcement learning closed-loop optimization, thereby achieving better task success rate and efficiency.

The embodiments of the present application provide a point cloud network training method, a point cloud data processing method and a device, which relate to the fields of unmanned driving and intelligent transportation technology. The method uses a point cloud network to obtain three-dimensional point cloud features of lidar point cloud data, uses an image encoder to obtain two-dimensional image features of multi-view images, uses a visual basic model to mask and divide the multi-view images to obtain multiple object regions, uses a text encoder to generate text features corresponding to each object region, performs image-text-point cloud contrast learning based on text features, two-dimensional region pixel features corresponding to each object region, and three-dimensional region point cloud features, generates semantic region contrast loss values, and trains the point cloud network. Multimodal data is used for joint training, and region-level text features are used to achieve fine-grained cross-modal feature alignment. The self-supervised contrast learning framework based on image-text-point cloud is used to enhance the representation ability of the point cloud network and improve the accuracy of downstream three-dimensional point cloud understanding.

The application discloses an agent method and system for continuous evolution of robot operation skills, comprising a skill sharing semantic rendering module, a skill sharing representation distillation module and a skill specific evolution planner. In view of long-term challenges such as 3D scene representation and human task learning faced by robots when adapting to new sequence tasks in actual scenes, the application can continuously learn new 3D scene semantics and robot operation skills from skill sharing and skill specific attributes. The skill sharing semantic rendering module and the skill sharing representation distillation module effectively learn 3D scene semantics by means of neural radiance fields and visual basic models, solve the problem of 3D scene representation neglect, and use the skill specific evolution planner to decouple skill knowledge through latent and low-rank space, and continuously embed new skill specific knowledge. The application also designs a robot operation benchmark, and experiments show that the method significantly improves the performance in robot operation tasks.

The invention discloses a digital design and performance prediction method for the whole forging and rolling process of a wheel with a complex structure, and belongs to the technical field of metal plastic forming and digital simulation. In order to overcome the defects of an existing method, an integrated forming module is designed; the invention provides a parametric modeling method, based on Visual Basic and SolidWorks secondary development, wheel parameters in an Access database are accessed, and three-dimensional modeling and assembling of all molds and blanks are automatically completed; a structure performance prediction method is provided, a parameterized model is automatically imported into finite element software, a microstructure model and a cellular automaton are integrated through secondary development, macro-micro coupling calculation is carried out, and microstructure distribution after the wheel is formed is predicted. According to the method, full-process digitization and automation from wheel design parameter input to final structure performance prediction are achieved, the research and development efficiency and the forming quality prediction precision of the complex-structure wheel are remarkably improved, and a core technical support is provided for optimal manufacturing of rail transit key components.