81 results about "Visual reconstruction" patented technology

A multimodal intelligent agent system for dynamic environmental monitoring and user-centered support, consisting of: a multimodal sensor module configured to continuously acquire environmental and behavioral data from multiple input modalities, including at least one visual sensor, at least one acoustic sensor, at least one environmental conditions sensor, and at least one proximity or motion detection sensor, each generating modality-specific data streams representing visual images, audio waveforms, physical environmental parameters, and motion signatures within a monitored environment; a data preprocessing and fusion subsystem that is operationally coupled with the multimodal sensor module and configured to normalize, temporally align, and transform the modality-specific data streams into high-dimensional feature embeddings using a variety of encoders, wherein the visual encoder uses convolutional or vision transformer architectures, the audio encoder uses a spectral-temporal feature extractor, and the sensor encoder transforms raw analog data into context vectors suitable for multimodal alignment; a multimodal processing unit consisting of a transformer-based large language model (LLM) trained on paired multimodal datasets and configured to perform semantic fusion, context abstraction, and inference across the aforementioned aligned multimodal feature embeddings to generate a contextual understanding of environmental and behavioral states; an adaptive agent controller coupled to the multimodal inference processing unit and configured to instantiate, manage, and terminate a variety of task-specific intelligent agents, each agent being a software unit configured to perform a specialized function selected from meeting summarization, behavioral analysis, misplaced object detection, or environmental anomaly identification, with the agents dynamically interacting with the inference engine to retrieve contextually relevant multimodal embeddings for task execution; a personalization and adaptive learning subsystem consisting of a user preference database and a neural memory structure configured to update and refine model parameters based on user-specific interaction history, thereby enabling personalized output generation, prioritization of recommendations, and long-term behavioral adaptation; and An output generation interface is operationally connected to the adaptive agent controller and configured to produce multimodal output in textual, visual, and auditory form. The interface is capable of displaying human-readable summaries, notifications, and visual reconstructions of identified entities or environmental states.

The invention provides an oil and gas pipeline defect three-dimensional contour determination method and device. Acquiring a three-axis magnetic flux leakage detection signal of a to-be-detected target oil and gas pipeline and corresponding space coordinate information of the three-axis magnetic flux leakage detection signal; constructing multi-channel input data according to the three-axis magnetic flux leakage detection signal and the space coordinate information; determining a defect contour prediction result of the target oil and gas pipeline according to the multi-channel input data by using a pre-trained defect contour inversion model; wherein the pre-trained defect contour inversion model comprises a multi-axis feature extraction and fusion module, a feature coding module and a multi-task decoding module; the defect contour prediction result is used for indicating whether the target oil and gas pipeline has defects or not, and the defect contour prediction result is further used for indicating the three-dimensional contour shape of the defects under the condition that the target oil and gas pipeline has the defects. Therefore, high-precision visual reconstruction of the defect position and the three-dimensional form of the oil and gas pipeline is realized, and the accuracy of defect identification and the reliability of evaluation are improved.

The invention relates to the technical field of robot visual perception and motion control, and discloses a motion control method and device based on physical reference, equipment and a medium, and the method comprises the steps: obtaining instruction information, a multi-view image and movable assembly pose information; processing the multi-view image according to the instruction information to generate target segmentation information; generating a scale normalization point cloud and a model estimation baseline; determining a physical reference baseline and generating a scale calibration factor; converting the scale normalization point cloud into a physical space point cloud by using a scale calibration factor; extracting a three-dimensional relative position of the target object relative to the movable component in combination with the target segmentation information; an action instruction is generated based on the multi-modal input. According to the method, physical scale alignment of the point cloud is realized through physical reference baseline calibration, so that a visual reconstruction result has real space significance, an accurate action instruction is generated, and the robot space understanding and operation precision is improved.

The invention relates to the technical field of nondestructive testing, and discloses a bimetal composite pipe three-dimensional reconstruction method and system based on multi-source data fusion. The method comprises the following steps: acquiring magnetic flux leakage signals and thickness data of the bimetal composite pipe in a high-pressure environment, and preprocessing the magnetic flux leakage signals and the thickness data to obtain a clean multi-source signal data set; performing time domain and frequency domain feature alignment on the data set to generate a fusion data matrix; extracting a preliminary defect feature set through multi-layer convolution processing; performing classification training on the defect features to obtain a defect type classification result containing confidence scores; if the crack exists, depth fitting is carried out to quantify the crack depth; if the preset risk threshold value is exceeded, generating a three-dimensional defect distribution model and evaluating connectivity; and finally, outputting a quantitative evaluation report of the pipeline risk level. According to the method, efficient fusion of multi-source data, intelligent identification of defect types and three-dimensional visual reconstruction are realized, and the accuracy and evaluation efficiency of pipeline defect detection are remarkably improved.

The invention relates to the technical field of remote sensing, and discloses a forest land health state analysis method and system based on multi-source remote sensing image analysis. The system comprises a multi-source remote sensing acquisition module, a feature extraction and fusion module, a health assessment module, a traceability analysis module, a strategy matching module, a visual reconstruction module, an early warning decision module and an execution feedback module, and constructs a multi-modal forest land observation data set by fusing multi-source remote sensing data of multispectrum, hyperspectrum, radar and thermal infrared. The comprehensive extraction of multi-dimensional information such as vegetation coverage, canopy biochemical characteristics, under-forest structures and surface thermal environments is realized, the limitation of single data source analysis is overcome, and the comprehensiveness and accuracy of forest land health condition evaluation are improved; through dynamic comparison of multi-stage remote sensing images and health risk level mapping, early identification and early warning of forest growth abnormity, degeneration trend and pest and disease risk are realized.

The invention belongs to the technical field of 3D visual reconstruction, and particularly provides a 3DGS segmentation method and system based on boundary adaptive Gaussian splitting, and aims to solve the problem of incomplete structure caused by directly deleting boundary Gaussian in the prior art by accurately positioning and segmenting the boundary Gaussian through a gradient consistency index through a boundary adaptive Gaussian segmentation technology. The boundary mBIoT is improved by 1.3%; meanwhile, a semantic-vision joint alternating optimization mechanism is innovatively proposed, continuous mask labels and alpha are utilized for synthetic rendering, and semantic loss and vision loss are combined for alternating optimization, so that compared with a traditional scheme singly depending on mask or feature optimization, the PSNR is improved by 0.29 dB, the texture definition is improved by 15%, and the segmentation precision and the vision quality are both considered; besides, through small-scale fuzzy Gaussian recognition and a targeted optimization strategy, mIoU is only reduced by 1.1% when the mask error rate reaches 20%, robustness to error masks is remarkably enhanced, and the bottleneck that an existing method is sensitive to pre-training mask errors is broken through.

The invention discloses an underwater image de-scattering method, system and device for scattering field decoupling in the technical field of underwater optical imaging and computer vision, and the method comprises the steps: calculating an intensity graph and a scene linear polarization degree graph according to an obtained multi-scale underwater image; according to the intensity image, performing image signal decomposition by using a dynamically constrained background light separation model to obtain a background scattering component and a target feature component; according to the scene linear polarization degree map and the background scattering component, calculating fusion transmissivity according to a non-linear optical transmission equation; carrying out noise suppression processing on the fusion transmissivity by adopting a double-constraint optimization architecture to obtain the fusion transmissivity subjected to noise suppression processing; and performing image reconstruction through a reverse optical propagation model according to the fusion transmissivity and the target feature component subjected to noise suppression processing to obtain a scattering-removed underwater image. The method effectively breaks through the dependence of a traditional method on the uniform hypothesis of a scattered field, and provides high-precision visual restoration support for an underwater detection task.

According to the geologic body occurrence environment visualization reconstruction method and system applied to the underground metal mine continuous mining process, an underground rock body environment holographic sensing method with multi-scale, multi-physical-quantity and multi-time-resolution information is fused, and a three-dimensional occurrence environment model capable of dynamically evolving in real time is constructed; and accurate guidance and risk prevention and control of the continuous mining process are realized by virtue of a visualization and intelligent feedback mechanism, so that powerful technical support is provided for safe, efficient and intelligent mining of the deep metal mine.

The invention relates to the crossing field of mineralogy and geochemistry, in particular to a mineral and element relation analysis method for gold-bearing minerals based on image matching and data fusion and application of the mineral and element relation analysis method. Comprising the steps of ore sample processing, gold-bearing mineral image calibration through AMICS, trace element scanning through fixed-point EPMA and EDS, image-level matching and spatial data fusion and gold-bearing mineral-element relation analysis. According to the method, the AMICS mineral map and the EPMA element information are fused, relation analysis of trace elements in the gold ore and visual reconstruction of a mineral-element corresponding mechanism can be achieved, and effective data and image models are provided for microcosmic metallogenic information requirements in digital mine construction.

The invention discloses a metamorphic rock P-T-t trajectory reconstruction method and system based on multi-source data fusion, and relates to the field of metamorphic rock P-T-t trajectory reconstruction.The method comprises the steps that electronic probe microcell map data and internal standard sample data of a metamorphic rock sample are obtained, and multi-stage data correction is conducted; carrying out mineral phase identification and chemical component quantitative interpretation to generate a mineral classification map, an element concentration distribution map and a total rock main component data set; thermodynamic phase equilibrium simulation is carried out, a P-T view profile map is generated, and a P-T track is extracted; performing microcell in-situ chronological analysis on selected minerals in the mineral classification map to obtain chronological data; and carrying out space-time coupling on the P-T trajectory and chronology data, constructing a P-T-t three-dimensional trajectory model of the metamorphic rock, and outputting a visual reconstruction report. According to the method, automation and standardization of the whole process of metamorphic rock P-T-t trajectory from data acquisition and processing to model reconstruction are realized, and the precision, efficiency and reliability of trajectory analysis are improved.

The invention relates to the technical field of robot automatic polishing, in particular to a vision-guided robot polishing path planning method and system, and the method comprises the steps: obtaining a workpiece surface point cloud in real time through a three-dimensional vision sensor, generating a trajectory seed point with adaptive density in combination with curvature, a normal vector and a defect semantic tag, and carrying out the positioning of a workpiece surface point; and a polishing quality evaluation function is constructed based on force sense feedback and a visual reconstruction result, so that path online correction is realized. According to the method, the polishing path and the tool posture can be dynamically adjusted, high-precision differentiation processing is achieved on the free-form surface, the edge and the defect area, and the polishing consistency and efficiency are improved.

The invention discloses a gallium oxide substrate surface grinding morphology prediction method and system, and relates to the technical field of nondestructive testing, and the method comprises the steps: collecting a surface fluorescence distribution image, matching the surface fluorescence distribution image with a pre-constructed fluorescence-crack mapping database, and generating a two-dimensional crack depth distribution cloud picture; marking a risk area coordinate set in the two-dimensional crack depth distribution cloud picture based on a preset crack depth threshold value; inputting the risk area coordinate set into a Bessel beam chromatography scanner for multi-angle transmission type scanning, synchronously collecting scattering images generated by scanning, and fusing the scattering images to generate a subsurface damage three-dimensional model; and performing data registration and weight superposition on the two-dimensional crack depth distribution cloud picture and the subsurface damage three-dimensional model, and outputting a morphology prediction report and grinding process optimization parameters. The subsurface damage three-dimensional model is constructed through the Bessel beam tomography scanning and multi-angle scattering image fusion technology, and high-resolution visual reconstruction of microcracks and damage distribution in the gallium oxide substrate is achieved.

The invention provides a rockburst intelligent early warning method based on cross-modal fusion of while-drilling sensing data and micro-seismic monitoring data, and belongs to the technical field of tunnel and underground engineering safety and disaster prevention and control. The rockburst intelligent early warning method comprises the steps that while-drilling parameters are acquired in real time, and drilling three-dimensional track coordinates are recorded; microseismic event signals induced by drilling disturbance are collected in real time, and seismic source parameters are inverted; preprocessing and time-space alignment are carried out on the collected while-drilling parameters and the microseismic event signals, and an aligned multi-modal feature sequence is generated; inputting the aligned multi-modal feature sequence into a trained rockburst risk prediction model, and outputting a rockburst risk grade probability distributed along the axis of each drilling track; and performing spatial interpolation on the rockburst risk level probability distributed along the axis of each drilling track, generating a three-dimensional rockburst risk probability field in front of the tunnel face, performing three-dimensional visual reconstruction on the three-dimensional rockburst risk probability field, performing risk level judgment according to a three-dimensional risk cloud picture in front of the tunnel face, and automatically issuing a corresponding early warning signal.

The invention discloses a visual dense reconstruction method based on pure geometric Gaussian splashing, and relates to the field of visual reconstruction, and the method comprises the steps: distributing an initial Gaussian element for each obtained initial point cloud, and forming an initial Gaussian model; generating a training set based on the acquired RGB images; determining single-view, multi-view and key point consistency loss on the basis of the training set and camera parameters in combination with a pure geometric Gaussian splash technology which only retains geometric parameters to represent a scene, and determining training loss on the basis of the loss; training the initial Gaussian model based on the training loss to obtain a pure geometric Gaussian model; and obtaining rendering depths under all views by adopting a pure geometric Gaussian model, and obtaining a visual dense reconstruction result through TSDF fusion in combination with RGB images and camera parameters corresponding to the views. According to the method, the convergence speed of the reconstruction geometry can be accelerated under the condition of reducing the computing resource and hardware performance requirements, so that the higher geometric accuracy can be achieved in a shorter time.

The invention belongs to the technical field of clothing defect detection, and particularly relates to a suspension type clothing defect detection method, which comprises the following steps of: 1, synchronously acquiring multi-view images of clothing in a suspension state through a plurality of groups of industrial cameras which are annularly arranged; step 2, reconstructing a 3D point cloud model of the garment based on the multi-view image, and calculating a fabric tensile deformation rate; step 3, executing deformation compensation: registering each view angle image to a standard plane template by using thin plate spline transformation (TPS), and eliminating geometric distortion; the millimeter-level three-dimensional reconstruction of the hanging clothes is realized through a multi-view stereoscopic vision and coding mark point system, and the vision reconstruction error is obviously reduced. And a dynamic TPS registration technology (control point density self-adaptive deformation rate) is combined, so that the geometric distortion compensation success rate is obviously improved. The synchronously calculated fabric tensile deformation rate eta provides accurate physical parameter support for subsequent processing, and solves the core pain point of deformation interference in flexible textile detection.

The invention relates to the technical field of coal mine safety production monitoring and intelligent early warning, and discloses a coal mine hidden danger recognition and intelligent early warning system based on multi-modal feature fusion, which comprises a multi-modal sensing data acquisition module, an edge calculation preprocessing module, a central analysis server and a linkage alarm execution module, the central analysis server integrates a three-dimensional flow field construction unit, a feature extraction unit, a cross-modal fusion unit and a hidden danger decision-making unit. The method comprises the following steps: constructing a three-dimensional limited flow field by using monocular depth estimation and an optical flow field, and inverting gas concentration into a virtual gas concentration distribution diagram; performing cross-modal depth correlation on visible light, infrared, audio and gas distribution characteristics by using a Transform network; and the hidden danger decision-making unit identifies categories based on the fused features and generates a visual heat map. According to the method, visual reconstruction of invisible gas and deep complementation of heterogeneous data are realized, the problem of high false alarm rate of single-mode monitoring is solved, and the accuracy and response speed of coal mine hidden danger early warning are improved.

The invention discloses a semantic-guided lightweight three-dimensional reconstruction method based on three-dimensional Gaussian, and the method comprises the steps: inputting a multi-view two-dimensional image, and extracting two-dimensional semantic features; projecting the two-dimensional semantic features to the three-dimensional Gaussian primitives; learning three-dimensional semantic distribution in a knowledge distillation mode, and predicting three-dimensional semantic embedding features; dividing a large scene into a plurality of sub-regions, and performing divide-and-conquer three-dimensional Gaussian optimization and semantic learning; performing semantic-guided lightweight modeling on the three-dimensional Gaussian primitives in each sub-region; executing sub-scene fusion, and performing quantitative compression on the three-dimensional data in the fused scene; and a rasterization renderer is used to carry out efficient real-time rendering under any visual angle, and a lightweight large-scene three-dimensional reconstruction result is output. According to the method provided by the invention, the real-time rendering performance of a complex scene can be remarkably improved, the model volume is effectively compressed, meanwhile, the model training process is accelerated, and the method is suitable for efficient three-dimensional visual reconstruction tasks.

The invention relates to the technical field of power supply areas, in particular to a dense power supply area visual reconstruction method based on laser radar guidance, and the method comprises the steps: combining laser radar equipment and binocular image collection equipment, and carrying out the synchronous data collection of a target area, so as to obtain laser radar point cloud data and binocular image data; performing external parameter calibration on the laser radar and the binocular camera, solving a rotation matrix and a translation vector, and establishing a unified world coordinate system; projecting the laser radar point cloud to a binocular image coordinate system to obtain an image sparse reference depth; carrying out depth correction and restoration on a binocular reconstruction result by using the laser radar point cloud; constructing a joint optimization model comprising a binocular constraint term and a laser radar constraint term, and optimizing the depth map; according to the method, a three-dimensional point cloud or model with high precision and density is generated, the three-dimensional point cloud or model is used for measuring and identifying the power supply area equipment, and reliable technical support is provided for accurate measurement and identification of the power supply area equipment by guiding a repair and joint optimization scheme.

The invention discloses a virtual interaction training method and device for a visual reconstruction brain-computer interface, and the method comprises the steps: generating a virtual reality scene, and extracting an importance representation related to a current task in the virtual reality scene; key visual information is extracted, and the key visual information is mapped to the spatial position of the cortex electrode array through a preset visual field-cortex topological mapping model; converting the mapped key visual information into space-time stimulation parameters of the cortical electrode based on a pre-acquired individualized perception function constraint; collecting behavior data of task execution of the user under the spatio-temporal stimulation parameters to calculate a behavior error comprehensive score; and dynamically adjusting difficulty parameters of the task according to the behavior error comprehensive score so as to complete closed-loop adaptive training. The problems of single training scene, subjective parameter adjustment, system closed-loop deficiency and insufficient evaluation standards are solved, and personalized and quantitative evaluation, safety and high efficiency of visual reconstruction brain-computer interface postoperative rehabilitation training are realized.

Disclosed are a model training method based on visual reconstruction technology, a device, and a cluster, relating to the technical field of visual reconstruction. The method comprises: acquiring a residual between a first image and a second image generated by an initial model on the basis of first photographing parameters; and using the residual to train the initial model to obtain a trained initial model (e.g., an image generation model). The residual between the first image and the second image can faithfully reflect information of first content and first interference information in the first image, thereby increasing the possibility of the image generation model accurately identifying interference information in images, and thus ensuring the production of high-quality images.

The embodiment of the invention provides a three-dimensional Gaussian model updating method and device and computing equipment, and the three-dimensional Gaussian model updating method comprises the steps: obtaining a three-dimensional Gaussian model constructed based on a three-dimensional scene, and a reconstructed image and an original image of the three-dimensional Gaussian model at a target visual angle, the three-dimensional Gaussian model is constructed based on three-dimensional Gaussian points of the three-dimensional scene; based on the original image and the reconstructed image, determining a first loss of the current updating period, and based on the number of Gaussian points in the three-dimensional Gaussian model, determining a second loss of the current updating period, the first loss representing the fitting degree of the reconstructed image and the three-dimensional scene, and the second loss representing the fitting degree of the reconstructed image and the three-dimensional scene; the number of the Gaussian points is determined based on pruning of the three-dimensional Gaussian points of the three-dimensional Gaussian model in the previous updating period; and updating the three-dimensional Gaussian model based on the first loss and the second loss. The accuracy of visual reconstruction in the updating process of the three-dimensional Gaussian model is ensured, and performance loss and computing resource consumption caused by three-dimensional Gaussian point redundancy are avoided.

The invention discloses a visual reconstruction adjustment method and system, and the scheme comprises the steps: carrying out the stimulation threshold testing of all electrodes in an electrode array one by one, and obtaining the threshold range of each electrode through combining with the first feedback information of a tester for the stimulation threshold testing; performing a stimulation matching test based on the threshold range of each electrode, and adjusting and determining a mapping relationship between the stimulation parameter of each electrode and the visual perception data in combination with second feedback information of the tester for the stimulation matching test; and determining model parameters of the visual reconstruction model based on the mapping relation. According to the scheme, the mapping relation between the stimulation parameters and the visual perception data is adjusted and determined in combination with the real feedback of the testers, stimulation for the testers is more personalized and accurate, the perception difference between different testers can be better quantified for the same test process of the electrodes, and the test efficiency is improved. And the adaptation effect of the visual reconstruction model and a tester is improved.

The invention relates to the technical field of wind power plant monitoring and management, and particularly discloses a virtual-real fusion real-time reconstruction method and system for a wind power plant profile. Comprising five stages: firstly, realizing task adaptive allocation through a heterogeneous edge-cloud cooperative computing node cluster; secondly, performing semantic paragraph division and value marking on the wind power plant based on terrain, wake flow and power gradient features; thirdly, calculating a semantic fracture index to judge the profile stability, and generating a reconstruction instruction when needed; then, differential visual reconstruction is executed according to the value label, and dual-path robustness verification is carried out in parallel; and finally, performing closed-loop optimization on system parameters in combination with interactive feedback and a verification result. According to the method, the whole process from data acquisition, semantic understanding, intelligent reconstruction to continuous optimization is realized, and the real-time performance, the accuracy and the self-adaptive capability of visual analysis of the wind power plant are improved.

This invention discloses a method and system for three-dimensional phenotypic analysis of potatoes based on computer vision. The method includes: first, constructing an acquisition system with multiple high-resolution RGB cameras in a triangular layout and a uniform light source, including a motion turntable; after distortion correction via checkerboard calibration, simultaneously acquiring multi-view images of potatoes; next, generating a dense depth map through sparse point cloud reconstruction combined with an improved multi-view stereo vision reconstruction mechanism including dynamic adaptive aggregation range and curvature-weighted matching cost calculation; registering and fusing to obtain three-dimensional point cloud data containing color and texture; then extracting basic parameters such as length, volume, and RGB features; and using an improved point cloud segmentation model including curvature-sensitive sampling, geometric attention modules, and a hybrid loss function to segment buds and extract parameters such as number and depth. This provides technical support for potato breeding and intelligent sorting.

This invention discloses a visual reconstruction method and system for remote sensing interpretation based on a generative diffusion model. The method includes: acquiring high-resolution and low-resolution remote sensing image data; adding different levels of Gaussian noise to the training data using a forward stochastic differential equation until pure Gaussian noise data is obtained; training a noise conditional scoring network to predict the scores corresponding to these noisy data; adding noise to the low-resolution image using a forward stochastic differential equation to finally obtain pure Gaussian noise; using a trained neural network to guide the random noise to gradually converge and generate a super-resolution remote sensing image; rapidly identifying land cover types on the generated remote sensing image; and delineating land cover patches on the original remote sensing image and assigning patch information based on the identified land cover categories. This invention achieves a super-resolution effect from low resolution without changing the land cover types and patch boundaries, thereby reducing interpretation costs and improving interpretation efficiency.

A visual recreation system of visually generating a recreation of a medical incident associated with a patient. The system includes a memory and a processor. The memory includes instructions that, when carried out by the processor, cause the processor to receive, from one or more sensors, a location information and a time information of the medical incident. The instructions further cause the processor to generate a visual simulation of the medical incident including an avatar of the patient and a visual reconstruction of a medical environment based on the location information and the time information of the medical incident.

The invention provides an architectural space information model strong generalization generation method and system integrating a feature matrix. The system comprises a data extraction module, a design task translation module, a visual reconstruction module and a question and answer interaction module. According to the method and the system, a representation normal form of building space parameters can be constructed in combination with an architect perspective, building geometric parameters are translated into a mathematical matrix, automatic and rapid coding of the parameters is realized, and the understanding ability of a large model for building space is improved; and as a data level front-end technology, the method supports the application of a large model in the aspects of building simulation model automatic construction, building design knowledge questions and answers, building effect picture rendering and the like.

This invention relates to the field of intelligent inspection and 3D visual reconstruction technology for automotive parts, specifically a method for 3D image reconstruction of lightweight internal high-pressure molded parts for automobiles. The method includes: acquiring a sequence of structured light images, multispectral reflectance texture images, and a priori simulation model of the target object, and completing spatial coordinate registration; dynamically allocating edge extraction weights based on the local curvature gradient of the 2D image to generate a non-uniform density 3D point cloud; constructing a surface reconstruction energy function using surface reflectance variation data as a penalty term, fitting the point cloud to the 3D surface, and generating a target 3D mesh model; fusing 3D geometric features, 2D texture features, and the priori simulation model, and outputting the wall thickness reduction rate and residual stress distribution via a graph neural network; generating and overlaying a risk heat map, and outputting the evaluation decision results. This invention extends from geometric reconstruction to risk semantic reconstruction, improving the comprehensiveness and reliability of online inspection of internal high-pressure molded parts.

The invention discloses a visual reconstruction implantation system based on optical communication and a control method. The implantation system comprises an external device and an implantation device, the external device is used for generating a stimulation instruction according to the visual information, embedding a near-infrared light signal, then sending the near-infrared light signal to the implantation device, and receiving a feedback light signal from the implantation device; the implantation device comprises a nerve interface module which is used for carrying out electrical stimulation on optic nerve tissue according to a stimulation instruction in the near-infrared light signal and collecting an electrophysiological signal; and the optical communication and energy supply module is used for receiving the near-infrared light signal, supplying power to the implanted device, modulating the electrophysiological signal into a feedback light signal and then transmitting the feedback light signal to an external device. Reliable issuing of a high-channel stimulation instruction and real-time feedback of a neural electrophysiological signal are realized through a near-infrared light bidirectional time division multiplexing link, and the resolution and stability of visual reconstruction are remarkably improved.

This application discloses a model training method based on visual reconstruction supervision, comprising: constructing a video anomaly detection model to be trained, the model including a global-local temporal modeling module, a feature reconstruction module, and one or more visual classification branch modules; obtaining an anomaly video training set, and performing weakly supervised training on the video anomaly detection model to be trained using the anomaly video training set to obtain a trained target detection model; wherein, the global-local temporal modeling module is used to obtain target visual features corresponding to each target input video in the anomaly video training set, and the feature reconstruction module and the visual classification branch module are used to perform visual reconstruction supervision based on each target visual feature during the weakly supervised training process. Implementing this application embodiment can fully utilize the original visual signals of the video training set during the model training process, which is beneficial to improving the weakly supervised model's ability to understand fine-grained visual features and its temporal modeling ability.