441 results about "Spatial structure" patented technology

The invention discloses a distribution line unmanned aerial vehicle edge end real-time visual identification and target detection method and system based on lightweight AI. The method is based on a YOLOv8 architecture, and constructs a complete lightweight detection framework comprising a feature extractor, an enhancement module and a simplified detection head by introducing a space structure maintaining assembly, a separated large kernel convolution and a weighted reconstruction feature pyramid. A cross-dimension semantic relation model is constructed by utilizing a combined attention structure, multi-level feature fusion is realized by reconstructing a feature pyramid network, model training is performed by adopting a hybrid optimization function and a dynamic sample adjustment mechanism, and the model is deployed on a mobile computing platform after being optimized by a hierarchical knowledge transfer method. According to the method, the detection speed is remarkably increased while high precision is kept, and real-time identification and anomaly analysis of the power line element are effectively realized.

The invention provides a digital twin three-dimensional model construction method based on a generative large model, and the method comprises the steps: obtaining multi-source monitoring data of a power distribution network, and processing the multi-source monitoring data into a training data set; the method comprises the following steps: mapping multi-source monitoring data into a multi-scale tensor subspace through tensor wavelet structured transformation, adaptively extracting spatial features through a learnable wavelet kernel, and keeping the structural continuity of a physical field in combination with a geometric prior regular term; constructing and training a generative adversarial network through a training data set; inputting and analyzing the physical parameter vector of the target scene, and if the topological similarity score is lower than a preset threshold value, adjusting noise vector regeneration; and if yes, outputting a three-dimensional model tensor and importing the three-dimensional model tensor into a digital twin platform, and driving real-time physical field visualization. According to the method, characteristics of a multi-scale space structure and a nonlinear physical field can be reserved, the physical rationality and generalization ability of the generated model are remarkably improved, and depth identification of topological attributes (such as hole connectivity and surface defects) and local geometric defects of the three-dimensional model is realized.

The invention discloses a rolling bearing vibration signal multi-mode fault classification method. The method comprises the following steps: collecting a vibration time sequence signal of a rolling bearing; the vibration time sequence signals are input into a time sequence branch network and a space branch network in parallel, and the time sequence branch network extracts time sequence dependence characteristics of the signals through a one-dimensional convolutional neural network and a bidirectional gating circulation unit; the spatial branch network converts the vibration time sequence signal into a Markov transform field image, and extracts spatial structure features of the image by using a two-dimensional convolutional neural network and a window Transform-based visual network; performing bidirectional interaction and weighted fusion on the time sequence features and the spatial features through a cross-modal attention mechanism to obtain fusion features; and inputting the fusion features into a classifier, and outputting a fault classification result of the rolling bearing. According to the method, the problems that a traditional single-mode fault diagnosis model is insufficient in adaptability to complex working conditions, multi-mode feature fusion is insufficient, and the generalization ability is weak due to model structure redundancy are solved.

The invention provides a mine slope risk assessment method and system based on multi-source data space-time fusion, and relates to the technical field of mine monitoring, and the method comprises the steps: obtaining geological and engineering data, and constructing a basic model representing a slope three-dimensional space structure; collecting multi-source heterogeneous monitoring data on a mine slope field, and aligning and fusing the multi-source heterogeneous monitoring data with the basic model in a space-time dimension to generate a continuous dynamic parameter field; and setting a data fusion model, and taking the basic model and the dynamic parameter field as input to obtain a four-dimensional space-time fusion risk field. According to the method, the basic model representing the three-dimensional space structure of the slope is constructed, and a unified space-time bearing base is provided for all heterogeneous monitoring data, so that originally isolated data such as GNSS displacement, deep displacement, blasting vibration and underground water level can be fused in a unified coordinate system; the problem of data splitting in the prior art is fundamentally solved, and integral and integrated expression of slope engineering and geological conditions is formed.

The invention discloses a high-end space light illumination control system with scene adaptability, and relates to the technical field of intelligent light control, and the high-end space light illumination control system is characterized in that spatial structure geometric information and surface material distribution are acquired, and an illumination path simulation model is constructed; in combination with a preset illumination demand template, identifying a position with illumination deviation; performing illumination coverage simulation on each position point based on the lamp type and the light emitting angle, calculating an illumination coverage contribution value, and constructing a reference matrix; according to a matrix result, lamp model selection and angle adjustment are carried out, partition control operation is executed, and dynamic optimization adjustment of the brightness, the angle and the color temperature is achieved; closed-loop correction is completed through illumination feedback; according to the invention, the adaptive ability of illumination control to a complex space structure and a dynamic scene is improved, higher control precision and energy efficiency are achieved, and the method is suitable for intelligent illumination systems in scenes such as exhibition display, commercial retail and high-end residence.

The invention discloses a manifold perception-based large model navigation method guided by a historical topological graph, and relates to a computer vision technology. The method aims at solving the challenges that in the navigation process, long-distance reasoning experience is insufficient, instruction fragments and dynamic visual observation are difficult to align, and large model reasoning is prone to illusion interference. Firstly, a large model based on an encoder-decoder structure is used for supplementing historical information coding for a visual observation sequence, and therefore global topological information guidance is provided for long-distance reasoning. And secondly, in order to effectively solve the problem that large model reasoning is subjected to illusion interference, significant space-time differences in a visual observation sequence are mined by using a multi-curvature manifold, so that the large model can accurately describe the current environment and make a decision according to a visual reference object in thinking. Besides, in order to strengthen the perception capability of the large model to the space structure and establish a graph self-attention mechanism, the node distance embedded in the constructed historical topological graph is combined with the visual similarity so as to model the space relationship between the nodes.

The invention discloses a metal tube surface defect detection method based on an improved RT-DETR (Reverse Transcription-DETR), which is used for carrying out multi-dimensional improvement on an RT-DETR model aiming at the problems that the surface defects of the metal tube are diversified in form, different in scale and complex in background. A backbone network of RT-DETR is modified by using a C3k2 module in a backbone network of yov11, a cylinder position coding module CPE is introduced to model a spatial structure of a cylindrical surface of a metal pipe, and a GLAF module integrated with local, medium and global attention mechanisms is adopted to perform multi-stage feature extraction. Secondly, in a feature fusion layer, a cylinder damage perception attention interaction module AIFIDML is used for replacing a standard Transform encoder, and the context perception ability of the model to defects is enhanced; and finally, in the neck network Neck, multi-level feature integration is optimized by adopting a multi-scale adaptive double-path feature fusion module MSADF, and the detection capability of tiny and large-scale defects is improved. According to the method, high-precision and high-efficiency end-to-end detection on the surface defects of the metal tube is realized.

The invention discloses an indoor scene image generation method and device, equipment and a storage medium. The method comprises the following steps: firstly, acquiring a to-be-processed image with furniture and information input by a user, and performing furniture elimination processing on the image to obtain an empty room image; and then generating a cue word for image generation based on a preset cue word template in combination with user input information. Meanwhile, extracting a depth map and a semantic segmentation map of the empty room image, and inputting the depth map and the semantic segmentation map into a pre-trained depth map control model and a pre-trained semantic segmentation control model for feature coding to obtain corresponding depth control features and semantic segmentation control features. And performing parameter adjustment on the large-scale graph generation model subjected to parameter fine adjustment by using the two types of control features to obtain a target graph generation model. And finally, inputting the empty room image and the cue word into the target image generation model to obtain a target image. According to the method, the spatial structure stability and the personalized design requirement of the user on the indoor layout can be considered.

The invention provides a dynamic planning method and system for a low-altitude route of an unmanned aerial vehicle, and relates to the technical field of intelligent planning, and the method comprises the steps: receiving a new delivery point instruction, obtaining a three-dimensional coordinate, carrying out the task integration of a new delivery point and an existing delivery point, defining a geographic range through three objects, namely a communication base station, a wireless tower and a permanent landmark building, and carrying out the task integration. Performing space structure division on the geographic range, and disassembling the geographic range into uniformly distributed three-dimensional voxel grids; and generating an adjustment value according to the coordinate interval of the three-dimensional voxel grid, including the delivery point density, the linear distance with the landmark building and other structural attributes, and adjusting the task integration result by using the adjustment value to obtain the adjusted task integration result of all delivery points. According to the invention, safe, efficient and accurate route planning of a multi-delivery-point dynamic newly-added scene in a complex low-altitude environment is realized.

The invention discloses an automatic defect detection method and system based on cross-resolution and multi-level knowledge distillation. The method comprises the following steps: step 1, acquiring an image of an intelligent manufacturing product to be detected; 2, constructing a teacher-student network distillation model for intelligent manufacturing defect detection; 3, training the teacher network model in the step 2 by using the data set obtained in the step 1; 4, training a student network model in the teacher-student network distillation model in a cross-resolution and multi-level manner; and step 5, utilizing the trained student network model to carry out defect detection on a to-be-detected intelligent manufacturing product image. A cross-resolution and multi-level knowledge distillation framework is provided, and a collaborative distillation mechanism of local details and a spatial structure is designed; a morphological self-adaptive semantic distillation method is provided, and the method can be better suitable for dynamic and real-time defect detection of intelligent manufacturing products.

The invention provides an intelligent interpretation method for a dual-temporal remote sensing image, which is characterized in that a boundary constraint change detection model BCnet based on a visual basic model is constructed by the method, the universal semantic representation potential of the visual basic model is fully excavated, and a difference detail enhancement module and a multi-scale edge enhancement module are introduced, so that the visual basic model can be fully interpreted. The problem that high-frequency information is lost in the direct migration process of the visual basic model is solved, and fine description of tiny changes and complex textures is achieved. On the basis, an edge feature constraint strategy is designed, an edge feature aggregation module is combined, boundary supervision signals are introduced into a feature space and a prediction space at the same time, and the continuity and geometric integrity of a change detection result in the space structure are enhanced. Experimental results prove that the BCnet has the advantages of high robustness and precision in high-difficulty scenes such as dense building groups and complex edges.

The invention provides a multi-mode fusion switch cabinet partial discharge on-line monitoring system and method, and the system comprises a space setting module which employs a preset space analysis algorithm, obtains an installation point position set according to the space structure of a switch cabinet, and carries out the installation of the installation point position set; the data acquisition module is used for acquiring data acquired by the ultrahigh frequency sensor, the ultrasonic sensor and the temperature sensor, preprocessing the data and then sending the data to the signal processing module; carrying out power frequency noise suppression on the ultrahigh frequency signal and the ultrasonic signal through a self-adaptive filter circuit, and extracting partial discharge characteristics from the filtered signal by adopting a wavelet transform algorithm; and the AI model fusing the convolutional neural network and the long-short-term memory network performs fusion analysis on the partial discharge characteristics and the temperature sensor data, realizes partial discharge mode identification and insulation degradation trend prediction, and outputs early warning information. According to the technical scheme provided by the invention, high-precision and anti-interference discharge detection can be realized.

The invention relates to the technical field of multi-point defocusing, in particular to an optical stable photochromic multi-point defocusing myopia control lens prescription generation method, which comprises the following steps of: based on eyes of a wearer, scanning cornea by using a non-contact optical device, extracting iris and pupil characteristics, monitoring an adjustment reaction, and performing three-dimensional reconstruction and multi-direction light path tracking analysis to obtain a myopia control lens prescription. And the distribution configuration of the annular belt is optimized, and the overlapping area is adjusted by combining the photochromic requirement, so that the fusion function partition structure is formed. Through collection of multi-source visual parameters and combination of spatial modeling and focusing path analysis, cut-through association of individual visual behaviors and structural attributes in lens customization is realized, functional requirements of different areas and photochromic response are dynamically fused through hierarchical adjustment of the spatial structure of an intervention area, and the visual performance of the lens is improved. And the material and microstructure data are synchronously output to a manufacturing link, so that the collaborative matching capability of the visual area partition is enhanced, and the requirements of a wearer on visual comfort and optical reaction are met.

An airspace flow prediction method and device based on an ensemble learning algorithm are provided. The method includes the steps: collecting historical airspace flow data and related spatial structure data, and preprocessing; constructing a GNN model, and calculating an influence degree of each node and an influence degree between the nodes in an airspace network by using the GNN model, the node being any airport or any waypoint; performing, by the GNN model, feature conversion and attention fusion on the influence degree of the node, the influence degree between the nodes and time series data to acquire a fused feature vector; inputting the fused feature vector into an LSTM model to acquire a predicted airspace flow of the node; and applying the predicted airspace flow of the node to manage navigation of traffic in the airspace network.

The application discloses a kind of based on qualitative and quantitative function evaluation's triculture space identification method, triculture space is divided into production, life, ecology 3 single spaces and production-life, production-ecology, life-ecology, production-life-ecology 4 compound spaces.Through calculating qualitative function evaluation value, calculating quantitative function evaluation value, calculating qualitative-quantitative function evaluation value, calculating first and last position index, judging triculture space type four steps are carried out to triculture space identification division, and the space type of correction maladjustment is utilized Coupling coordination degree.The application provides theoretical basis for land space structure and space layout optimization research, guides land space structure and space layout optimization, improves the rationality and scientificity of resource element configuration in land space planning, promotes land space pattern optimization development, with practical significance and good application prospect.

The invention discloses a mine sewage transportation channel multi-parameter collaborative inversion method and system based on dynamic step length adjustment and a storage medium, and the method comprises the steps: collecting geophysical prospecting data reflecting the multi-physical property characteristics of an underground medium in a detection region of a hidden sewage transportation channel of an abandoned mine, and constructing a standardized multi-parameter characteristic data set through position matching; and building a multi-parameter joint inversion framework, establishing a comprehensive objective function fusing data fitting precision, parameter collaborative constraint and numerical stability, and introducing cross gradient constraint to realize physical property parameter model space structure association. An improved gradient descent method is adopted for iterative solution, a first-order iteration information and second-order function feature double-factor coupled dynamic step length adjusting mechanism is constructed, step length is regulated and controlled in a staged mode, invalid abnormal data are removed in combination with clustering analysis, iteration is conducted till convergence conditions are met, and the iteration effect is optimized in a staged mode. And finally, a multi-parameter fusion inversion model fitting actual geological features is obtained. And technical support is provided for detection of the hidden sewage transportation channel of the abandoned mine.

The invention discloses a base station positioning method and system, and relates to the technical field of indoor positioning, and the method comprises the steps: obtaining target scene space structure information and signal propagation shielding characteristics which meet preset space constraints, building a scene shielding model, determining a candidate deployment region, and eliminating a high-attenuation shielding region. Acquiring a base station position Pi and a distance Lij between a label and the base station in the candidate area, and constructing a label position solving equation containing vector and matrix representation; and determining a base station layout avoidance scheme, deploying base stations, re-collecting data, correcting distance data, substituting the corrected distance data into the equation to obtain a label accurate position by combining the condition that the matrix determinant is close to zero and a scene shielding model, and obtaining a label positioning result. The system comprises a scene modeling module, a candidate area determination module, a data acquisition and equation construction module, a base station layout optimization module, a distance data correction module and a label position solving module, and solves the problems of unreasonable layout and large distance measurement error of the existing method.

The invention discloses a target navigation method and device based on active three-dimensional Gaussian spatter and visual language model reasoning, and the method comprises the steps: obtaining an RGB-D image and a pose in an unknown environment, and constructing an incremental three-dimensional Gaussian spatter map as persistent memory through active perception; generating an exploration map based on the constructed 3DGS map, extracting a leading edge point and carrying out space structure adaptive clustering; generating a guidance track, carrying out free viewpoint optimization based on the track, and rendering a leading edge point first-person view angle image containing rich information; constructing a structured visual prompt, combining with a thinking chain prompt, inputting a visual language model to carry out reasoning planning, and selecting an optimal navigation target; in the navigation process, a real-time target detector is used for screening potential targets, and a new view angle is rendered in a 3DGS space through an action decision VLM for target re-verification. The navigation success rate and efficiency are improved.

The invention discloses a grid runoff forecasting method based on a space-time diagram convolutional network, and particularly relates to the technical field of runoff forecasting. The method comprises the following steps: firstly, performing grid division on a target drainage basin based on a landform and hydrological response unit, and constructing a space map structure; combining historical rainfall, runoff and remote sensing meteorological data to construct a space-time input feature sequence; extracting a spatial dependency relationship by using a graph convolutional network, extracting a time evolution feature by combining a long-short term memory network, and generating a node representation fused with the spatial-temporal feature; outputting a runoff prediction result of multiple time steps through a full-connection neural network; for a heavy rainfall event, input characteristics and a space structure can be updated in real time, dynamic completion and local graph structure reconstruction of missing data are realized, and finally a gridding runoff distribution graph in a future time period is generated. The method has high precision, high timeliness and high adaptability, and is suitable for runoff intelligent prediction in complex terrains and sudden heavy rainfall scenes.

The invention discloses a self-supervised monocular depth estimation method based on an enhanced multi-scale pose network, and relates to the field of computer vision. The problems that in an existing self-supervision monocular depth estimation method, the pose network structure is simple, the time sequence modeling capacity is insufficient, and geometric constraints are missing are solved. According to the method, a self-supervision joint training framework composed of a depth estimation sub-network and an enhanced pose estimation sub-network is constructed; wherein the pose estimation sub-network extracts multi-scale spatial structure features through a layer-by-layer feature fusion encoder, and adopts a context fusion decoder based on time sequence attention to model a motion dependency relationship between continuous frames; meanwhile, a self-supervised pose consistency loss function is introduced, geometric continuity of a camera track is enhanced through forward and reverse transformation consistency constraint and closed-loop geometric constraint, and collaborative optimization of depth prediction and pose estimation is realized. The method is also suitable for the application fields of automatic driving, robot perception, augmented reality and the like.

The invention discloses a porosity real-time prediction method and system based on alkane gas and carbon isotope characteristics thereof and a storage medium, relates to the technical field of oil and gas reservoir development, and aims to solve the problem of insufficient precision in a heterogeneous reservoir due to neglect of dynamic characteristics of alkane gas carbon isotope in an existing method. The method is characterized by comprising the following steps: S100, collecting alkane gas concentration and carbon isotope data thereof in real time, and performing time-frequency decomposition on the data to obtain characteristic data on different time and frequency scales; s200, performing multi-scale feature extraction on the data to obtain multi-scale features; s300, performing feature fusion on the multi-scale features; s400, constructing a GNN network model, introducing an adaptive attention mechanism, residual connection, jump aggregation and time sequence dynamic fusion, constructing a graph structure by taking a reservoir unit as a node, and mapping a fusion feature to a graph node attribute; and S500, constructing a reservoir space structure based on the GNN network model, and combining physical constraint optimization to realize real-time prediction of the porosity.

The invention discloses a carbon fiber prepreg yarn defect detection method based on dual-coding feature fusion and a spatial attention mechanism, and belongs to the technical field of intelligent visual detection and deep learning application. In order to solve the problems of insufficient feature extraction, insufficient cross-scale fusion capability and poor adaptability to complex texture backgrounds in the existing carbon fiber prepreg yarn surface defect detection, the invention provides a dual-encoder trunk structure composed of a lightweight OfficientNet branch and a large kernel convolution UniRepLKNet branch. The method is used for realizing synchronous modeling of shallow texture and deep semantic features. Dynamic aggregation of different scale features is completed through a multi-stage feature fusion module (MAFFM), and a space attention enhancement network (SAE-FPN) is introduced into a feature pyramid to improve the significance expression and space structure consistency of a defect area. According to the method, the problem that a traditional detection algorithm is insufficient in recognition precision of small-size, low-contrast and complex background defects is effectively solved, the detection accuracy of the model is improved, and compared with an existing network, the feature expression ability and robustness have certain advancement.

The invention relates to the technical field of moisture prediction, in particular to a 3D reconstruction and illumination adaptive coal surface moisture prediction method, which comprises the following steps of: planning an unmanned aerial vehicle route to collect an image, reconstructing coal pile surface space structure information, forming a local triangular grid, dividing an illumination continuous area, establishing a space corresponding relation between the illumination area and a hyperspectral pixel, and predicting the moisture content of a coal pile. And geometric registration and reflection transition correction are executed, depression points and shoulder positions of a reflection curve are detected in the identification interval, moisture absorption characteristics are constructed, a moisture migration link is established in the slope direction, and a coal surface moisture prediction result is obtained. According to the method, a spatial structure and illumination distribution are coupled, a dynamic correction relation is established to unify reflection information, image registration and spectrum reconstruction are combined to strengthen geometric consistency, a moisture migration trend is extracted by utilizing depression point and slope correlation, link and accumulation distribution is formed, and a corrected reflection characteristic represents a spectrum attenuation rule. The spatial continuity and numerical stability of moisture identification are ensured, and the water content grade division is coherent and comparable.

The invention discloses a digital surface model (DSM)-guided hyperspectral and LiDAR combined unmixing method, relates to the field of multi-modal image processing, and aims to solve the problems of end member confusion and insufficient space structure maintenance caused by spectrum similarity in hyperspectral unmixing. According to the method, a hyperspectral image and LiDAR data of the same area are obtained, a LiDAR elevation map is expanded into a multiband profile through an attribute configuration file method, and a digital surface model (DSM) is generated. Constructing a spectrum and space double-branch auto-encoder, respectively extracting spectrum and space features and carrying out fusion mapping, obtaining an abundance matrix by using a normalized exponential function, and reconstructing a hyperspectral image; a double-branch adaptive mixed channel attention mechanism is designed in a spectrum branch, and a space attention mechanism is introduced in a space branch. In the training process, a self-defined loss function is formed by combining DSM-guided structure entropy regularization, spectral angular distance and root-mean-square error, and the space continuity and boundary retention of the abundance graph are improved. The method is suitable for hyperspectral unmixing and multi-modal remote sensing fine identification under complex terrains.

The invention relates to the technical field of image processing, and discloses a man-made forest remote sensing image segmentation method, electronic equipment and a computer readable storage medium, the method introduces MMF, performs joint modeling on vegetation indexes such as NDVI, EVI and the like and RGB images so as to improve the ability of a model to distinguish a man-made forest from other ground features, and introduces KAN-HGC so as to improve the efficiency of the model to distinguish the man-made forest from other ground features. A graph structure is adaptively constructed by combining a space structure and feature similarity, so that the recognition capability of the model on a boundary region is improved, and GIGA is introduced, and key region features are enhanced through multi-dimensional attention, so that background interference is effectively inhibited, and the recognition accuracy of the model in a complex scene is improved; besides, an FReSCO optimizer is provided, a boundary enhancement and spectrum adjustment mechanism is introduced in training, and the adaptability and cross-scene generalization ability of the model to spectrum differences are improved.

The invention relates to a pure vision autonomous navigation method for a mobile robot based on ground structure line guidance. The method comprises the following steps: binocular-inertia joint perception and state estimation; carrying out ground area constraint and two-dimensional line segment extraction; performing line segment screening and geometric fitting based on motion direction constraint; carrying out three-dimensional parametric modeling and category discrimination on multiple types of ground structure lines; calculating a pose error of a relative structure line and constructing a reference trajectory; performing closed-loop motion control based on the Lapunov stability; carrying out key position judgment and motion decision making based on an image template; compared with the prior art, by introducing a visual guidance mechanism based on a ground structure line and fully utilizing natural ground structure characteristics such as a space structure boundary line, stable autonomous navigation of the mobile robot is realized without depending on artificial markers such as a high-precision map, GNSS (Global Navigation Satellite System) positioning, a two-dimensional code and a magnetic stripe; the dependence of the system on complex sensors and environment modification is effectively reduced, and the deployment and maintenance cost is remarkably reduced; meanwhile, through unified modeling of multi-type structural line features, the adaptive capacity and robustness of the navigation method in various application scenes such as warehouse logistics, park inspection, in-plant transportation and semi-structured roads are improved, high navigation stability and reliability can be kept in tasks such as fixed route inspection, lane keeping and back-and-forth running, and the navigation method is suitable for large-scale popularization and application. And the method has good engineering practical value and popularization and application prospects.

The application discloses a terminal building layout optimization method based on a space structure auxiliary line, equipment and medium, relates to the technical field of computer-aided design, and the method is classified by acquiring a terminal building design scheme drawing file, obtains terminal building multi-source data, provides a standardized data basis for subsequent technical implementation, and guarantees the accuracy of the whole process; the space structure line high-precision mapping technology is adopted, the terminal building physical elements are converted into digital structured constraints, the deviation between the physical and digital nodes is less than or equal to 1 m, and the problem that the deviation between the traditional flow lines and the actual trajectories is large is solved; the layout distribution multi-dimensional evaluation index is adopted, the single index limitation is broken through, the extreme value interference is avoided, more accurate and reliable data basis is provided for subsequent evaluation; finally, the dynamic iterative optimization mechanism is combined, the short board index in the initial optimization scheme is oriented and optimized, and thus the optimal technical scheme is output, the whole process data driving is realized, and the application range is wide.

The invention discloses a multi-source radio signal simulation method and system, electronic equipment and a storage medium, and the method comprises the steps: building a three-dimensional electromagnetic field model for a target scene; dynamically generating a multi-protocol signal based on the three-dimensional electromagnetic field model; establishing a time-varying interference model based on the multi-protocol signal; building a hardware-in-the-loop verification platform based on the time-varying interference model so as to construct a dynamic signal simulation engine; and simulating and outputting a multi-source radio signal by using the dynamic signal model engine. According to the method, three-dimensional electromagnetic field modeling is carried out on a target scene, signal propagation characteristics in a complex space structure can be accurately described, concurrent communication behaviors of a multi-system terminal can be simulated at the same time by dynamically generating a multi-protocol signal mechanism, a time-varying interference model is established to quantify a signal shielding effect, high-precision indoor signal environment simulation is achieved, and the method is suitable for large-scale popularization and application. The problem of signal distortion existing in a traditional static modeling mode is effectively solved.

The invention discloses a single-master-node collaborative navigation algorithm constrained by space-time kinematics information, which comprises the following steps of: S1, constraining positioning of slave nodes by using a time sequence of an inertial trajectory of a master node, and constructing a collaborative positioning optimization objective function; s2, correcting an elevation error by utilizing elevation information, determining solving targets as an optimal rotation matrix and an optimal translation matrix, and introducing spatial relative position constraints between a master node and slave nodes; s3, solving an optimal rotation matrix and an optimal translation matrix by adopting an SVD (Singular Value Decomposition) method; according to the method, through the space-time continuity characteristic of motion of the unmanned aerial vehicle, fusion modeling is carried out by using the trajectory characteristics and the space structure, and high-precision positioning estimation of slave nodes is realized on the basis of keeping the lightweight design of the system.

The application discloses an iris recognition method and device, system and storage medium, and performs preprocessing such as polar coordinate unfolding on an input original iris image in sequence; multi-level image features are extracted through an inverse residual module; subsequently, a global depth convolution GDConv operator is introduced to replace a global average pooling layer, so that spatial structure distribution features of iris textures are retained under the premise of extremely low calculation complexity; finally, an ArcFace loss function is used to optimize the distribution of features in a hyperspherical space, and the class separability of the features is enhanced by introducing an additive angle interval. By using the technical scheme of the application, the technical problems that spatial topological information is lost due to a global average pooling GAP operation in iris feature extraction by using an existing lightweight convolutional neural network, and the feature discrimination is reduced due to network lightweight are solved.