858 results about "Reconstruction algorithm" patented technology

The invention discloses an integrated power distribution cabinet energy efficiency dynamic optimization method based on multi-modal data fusion, and relates to the technical field of intelligent power grids. The problems of failure of an energy efficiency optimization model and poor long-term operation stability caused by data time sequence misalignment and error accumulation of a multi-source sensor in the prior art are solved. According to the scheme, the time offset is dynamically corrected through the adaptive time sequence deviation prediction model; compensating missing data by adopting a non-uniform time step reconstruction algorithm and Kalman filtering; detecting an error drift trend through an exponentially weighted moving average model, updating a feature weight, and inhibiting long-term error accumulation; constructing a self-adaptive time sequence attention fusion network model, and fusing physical constraints and a data driving mechanism to generate an optimization decision; bayesian optimization is utilized to quantify parameter uncertainty, closed-loop feedback execution data is carried out, and model parameters are dynamically updated; according to the invention, the precision, long-term stability and equipment safety of energy efficiency optimization of the power distribution cabinet are remarkably improved, and efficient and reliable operation under multi-physics field coupling constraint is ensured.

The invention relates to the technical field of hydraulic engineering safety monitoring, and particularly discloses a hydraulic engineering potential safety hazard assessment and prediction system and method based on image recognition. A multi-scale convolutional neural network is combined with a three-dimensional point cloud registration technology to extract surface visual feature parameters, and adaptive time-frequency analysis and a wavelet packet reconstruction algorithm are used to extract physical feature parameters of internal concealment defects; constructing a dual machine learning framework, eliminating environmental interference through a deep residual network, analyzing a causal relationship between features based on a gating cycle unit, and screening a key feature parameter set; a Gaussian process regression model of an adaptive kernel function is used for dynamic risk prediction, risk abrupt change points are identified in combination with multi-scale wavelet transform, and finally a safety state score and a grading early warning signal are generated through a fuzzy comprehensive evaluation algorithm.

The invention relates to the technical field of rock mechanics, and discloses a rock three-dimensional reconstruction method and system based on multi-source information fusion, and the method comprises the steps: obtaining and preprocessing data, carrying out the spatial feature learning of a fusion feature vector through a 3D-CNN network, and constructing a three-dimensional voxel model of rock microscopic damage; converting the fused image data into a point cloud model of the underground cavern surrounding rock structure by adopting a three-dimensional reconstruction algorithm based on point cloud, and constructing a digital twin framework of the underground cavern surrounding rock structure based on an implicit surface reconstruction algorithm; feature parameters output by the three-dimensional voxel model and the digital twinning framework are used as input, and the optimal supporting opportunity and supporting parameters are output through an LSTM-CNN fusion model; in the underground engineering construction process, surrounding rock deformation data are collected in real time, and a supporting scheme is adjusted in real time through a depth deterministic strategy gradient algorithm; according to the method, the scientificity and timeliness of support design under complex geological conditions can be improved.

The invention discloses an unmanned aerial vehicle-mechanical arm system cooperative control method for precise spraying. According to the method, through a depth camera and a point cloud reconstruction algorithm, three-dimensional modeling of a target surface and semantic recognition of a spraying area are achieved, and a spraying path and spraying posture data are generated. Based on spraying task requirements, a spraying film thickness experience estimation model is established, and path point-level spraying parameters are obtained. Load mass change, mechanical arm mass center change and spraying reaction force of each path point are calculated, and a coupling dynamic model is constructed in combination with the system state. And dynamic compensation control input is generated by using an adaptive sliding mode control algorithm. After spraying is completed, the actual spraying effect is collected through image and point cloud detection, color, film thickness and texture features are extracted and compared with task requirements, error feedback is generated, and control parameters are adjusted online. According to the method, the problems of dynamic change and error compensation in the spraying process are effectively solved, and the method is suitable for a high-precision spraying task in a complex environment.

The invention provides a distribution network power transmission line insulator damage detection method based on a YOLO algorithm, and the method comprises the steps: collecting a plurality of insulator defect images, and carrying out the data enhancement of the images; performing image detail enhancement on an insulator defect area in the image by using a super-resolution reconstruction algorithm; constructing a composite loss function to guide an image detail enhancement process; constructing an insulator defect detection model based on a YOLOv8 framework; constructing a multi-task joint loss function as an insulator defect detection model training target; and a real-time feedback mechanism is introduced, a weighted combination loss function is constructed, gradient updating optimization is executed, and the detection capability of the insulator defect detection model is improved. The method has high detection precision and robustness, can effectively improve the efficiency and accuracy of fault detection, significantly reduces the burden and cost of manual inspection, improves the safety and reliability of a power system, and provides powerful support for the operation and maintenance of power equipment.

The invention provides a high-precision intelligent welding system adapting to complex working conditions, and relates to the technical field of laser welding. The high-precision intelligent welding system adapting to the complex working conditions comprises the steps of S1, based on a high-resolution visual sensor and a laser scanner, S2, based on preprocessed point cloud data, S3, based on a workpiece three-dimensional model, S4, based on an optimized welding path and a material attribute database, S5, based on a dynamic welding parameter set, S6, based on a real-time welding track, and S7, based on multi-sensor monitoring data. S8, based on a defect detection report; and S9, based on a repaired welding result and historical welding data. According to the invention, the geometric data of the workpiece are collected through the high-resolution visual sensor and the laser scanner, the filtering algorithm is applied to remove noise to generate high-quality point cloud data, and a subsequent processing foundation is laid; workpiece features are identified by using a point cloud processing algorithm and a machine learning method based on the point cloud data, and an accurate digital model is generated through a three-dimensional reconstruction algorithm.

The invention relates to the technical field of three-dimensional imaging, in particular to a four-eye structured light stereoscopic vision imaging method, which comprises the following steps of: arranging four cameras and synchronously acquiring multi-view image data with structured light stripes; carrying out image preprocessing and stripe code identification on the obtained four-view-angle structured light image data, and extracting structured light stripe center line positions and corresponding space projection information under each view angle; generating three-dimensional point cloud data of the high-precision target sample by using parallax calculation and a three-dimensional reconstruction algorithm, and completing spatial registration and filtering optimization of point cloud; fusing the point cloud and the light intensity data by combining the reflection intensity information of the multi-view structured light stripes to generate a composite data set containing space and spectral information; and constructing a dense parallax field and executing three-dimensional consistency verification, and generating a high-fidelity three-dimensional reconstruction model with a complete topological relation and sparse shielding compensation capability. According to the invention, the problems of insufficient view angle coverage, shielding area information loss, difficult edge structure matching and the like of traditional stereoscopic vision imaging can be solved.

The invention discloses a cross-platform virtual-real fusion scene construction method and system based on AI space calculation, and relates to the technical field of artificial intelligence and space calculation, and the method comprises the steps: carrying out the multi-scale feature fusion based on a received cross-modal conversion instruction set, and generating an initial image sequence; carrying out implicit field coding on a target object by combining a three-dimensional reconstruction algorithm to obtain an initial parameterized model; performing space-time alignment on the multi-view video stream, loading a digital scene asset package in combination with physical sensing data and a preset spatial index structure, and establishing a bidirectional data channel between a virtual scene and a physical sensor; performing rendering and illumination parameter adjustment on the initial parameterized model to obtain an optimized parameter model; performing differential coding processing on the optimization parameter model to obtain a target virtual-real scene fusion model; and distributing the target virtual-real scene fusion model to a preset terminal. The invention provides a virtual-real fusion construction method for end-to-end collaborative optimization, which is suitable for cross-platform live broadcast or dynamic interaction scenes.

The invention discloses a multi-source data fusion accounting method for a full-life-cycle carbon footprint, and belongs to the technical field of carbon footprint accounting, and the method specifically comprises the steps: inputting a purchase order number set of a target product, and automatically associating a logistics fuel consumption record, a production process energy consumption log and a supplier indirect emission declaration form according to the purchase order number; constructing a dynamic traceability tree taking a purchase order as a root node, and generating a trunk path through matching of an order material code and a workpiece batch number of a process energy consumption log; when the difference between the logistics real-time energy consumption data and the supplier declaration form exceeds a set threshold value, activating a dispute data branch reconstruction algorithm, and creating parallel branches to temporarily replace dispute data segments while keeping trunk continuity; finally, outputting a full-life-cycle carbon footprint data graph with dispute marks, and reserving original association paths of all data sources; according to the invention, reliable support is provided for accurately tracing the full-life-cycle carbon emission of the product and improving the carbon footprint accounting quality.

The invention belongs to the technical field of medical imaging and digital twinning, and discloses a thoracic cavity whole organ system level lung four-dimensional EIT detection digital motif and a library building method, and the method comprises the following steps: S1) segmenting multiple organs such as double lungs, heart, sternum, rib and thoracic vertebra central axis skeleton, main bronchus and esophagus based on an individual CT image, and building a three-dimensional geometric model integrated with an EIT electrode array; s2) simulating breathing dynamic deformation by using a solid mechanical model; s3) establishing a coupling relationship between deformation and conductivity, and generating dynamic three-dimensional conductivity distribution; s4) solving an electromagnetic field positive problem, and generating a virtual EIT voltage database synchronous with respiration; the invention also covers pathological model integration and data enhancement strategies. According to the method, a high-fidelity, large-scale and diversified EIT virtual data set can be generated, core data support is provided for training and verifying an AI intelligent reconstruction algorithm, and a model foundation is laid for developing a lung function dynamic digital twinning system.

The invention provides a nuclear power safety report data extraction method and system based on multi-modal feature fusion, and belongs to the technical field of data processing, and the method comprises the steps: obtaining a nuclear power safety report document; carrying out OCR (Optical Character Recognition) and paragraph segmentation on the report document to obtain first text data; performing text semantic understanding on the first text data by adopting a dynamic template matching and semantic driving extraction mechanism to obtain a text feature vector; segmenting pages of the report document and identifying fonts and fonts to generate first format data; adopting a cross-page table reconstruction algorithm to detect table cells of the first format data and splicing a cross-page table to obtain format feature vectors; performing feature alignment on the text feature vector and the format feature vector, and inputting the text feature vector and the format feature vector into a large pre-training language model to obtain a semantic vector; and fusing the text feature vector, the format feature vector and the semantic vector to form a composite expression unit, and intelligently extracting structured data based on the composite expression unit. The semantic entity and structural relationship in the nuclear power report can be effectively identified.

The invention discloses a naked-eye 3D display system based on a light field reconstruction algorithm, particularly relates to the technical field of naked-eye 3D, and comprises a controlled scattering medium light field acquisition environment, scattering feature enhanced light field data analysis, a hierarchical depth optimization engine and dynamic wavefront modulation display control. According to the invention, through the controlled scattering medium environment design, a scattering signal which is regarded as interference in the traditional technology is converted into a depth characteristic carrier, the data acquisition quality in a complex optical environment is obviously improved, richer and more reliable basic data are provided for light field reconstruction, and the data acquisition efficiency is improved. An innovative hierarchical deep optimization engine is combined with a multi-scale confidence evaluation and discontinuous region processing method, so that the problem of edge blur in a shielding boundary and a geometric structure complex scene is effectively solved, and the geometric consistency and detail fidelity of a reconstructed light field are greatly improved.

The invention discloses a self-adaptive grid dynamic encryption method for underground water pollution migration simulation, which belongs to the technical field of underground water pollution migration simulation, and comprises the following steps of: forming 0.05-meter super-resolution grids at the periphery of a dispersed pollution source through a local grid dynamic encryption technology triggered by a pollution source intensity gradient threshold; the pollutant frontal surface tracking error is greatly reduced, and the high-precision pollutant migration simulation capability is realized. Moreover, based on an intelligent grid reconstruction algorithm of a flux threshold, the number of calculation units is reduced by 62%, the time consumed by single simulation is shortened to 3.3 hours, and the overall mass conservation error is kept to be smaller than 1% while the calculation efficiency is improved. Meanwhile, for a heterogeneous aquifer, through a pollution flux and stratum permeability coefficient dual-drive encryption strategy, the solute transport prediction precision of the model at the boundary of a clay lenticular body is greatly improved, and the problems that in the prior art, the simulation precision of the periphery of a pollution source is insufficient, and the calculation efficiency is low due to global encryption are effectively solved.

The invention provides an automatic assembling method and system for an inductor magnetic core. Wherein continuous images of the surface of the magnetic core are captured through multi-band rapid scanning, and geometric deformation characteristics are extracted; meanwhile, a distributed optical fiber sensing array is adopted to collect pressure data of the contact surface, and a real-time distribution diagram is generated through a three-dimensional field reconstruction algorithm. And then carrying out space coordinate alignment on the edge contour offset of geometric deformation and the pressure gradient, and constructing a comprehensive quality index. And based on the dynamic deviation of the index, a contact angle compensation value and an assembly speed optimization value are solved in real time, and a parameter adjustment instruction is generated. And finally, through parallel calculation of instruction data, a pressure tolerance threshold value and a deformation compensation parameter are synchronously updated, dynamic balance of deformation correction and pressure control in the assembly process is realized, and a self-adaptive closed-loop precision control system is formed. According to the technical scheme, through multi-dimensional sensing data fusion and real-time parameter adjustment, dynamic matching of deformation and stress states in the magnetic core assembling process is achieved.

The invention provides an automatic processing method for real-time observation data of an ocean station, and belongs to the technical field of ocean observation data processing. A wavelet packet decomposition multi-scale noise separation algorithm is established to distinguish environmental noise and real signals, dual-sensor redundancy configuration is combined with Bayesian inference to identify sensor drift, and a calibration coefficient is updated in real time through a recursive least square method. A one-dimensional time sequence is mapped to a high-dimensional phase space by utilizing a phase space reconstruction algorithm to realize high-precision prediction of a chaotic signal, a hierarchical data storage architecture is established, and a data migration strategy is iteratively optimized through a hierarchical correlation degree function; the technical problem that time synchronization signals are difficult to reconstruct accurately according to asynchronous sampling data of multiple sensors of an ocean station is solved.

Disclosed in the present invention are a wind field correction method and system applied to a single-station remote sensing device. The method comprises the following specific steps: S1, acquiring wind field data by means of a data information acquisition apparatus, and obtaining radial wind speed information of a current acquisition point by means of a data processing module; S2, on the basis of the hypothesis of an uniform wind field, using a classical wind speed reconstruction algorithm for inversion to obtain a three-dimensional wind speed vector of the acquisition point; S3, establishing an error model; and S4, on the basis of an error factor β calculated by means of the error model, performing data correction on the three-dimensional wind speed vector of the acquisition point obtained by means of inversion in S2. Compared with the prior art, the present invention provides a high-precision correction method, which can be operated on low-end hardware, and can also be directly installed and operated on hardware of a measurement device, so that measurement results can be corrected in real time, no additional complexity can be brought to users, and costs are reduced; and the present invention is suitable for solving complex system problems when accurate boundary conditions cannot be obtained.

The invention relates to the technical field of massage, in particular to an intelligent abdominal acupoint massage system and method based on multi-modal sensing, and the system comprises a multi-modal sensing module which comprises a 3D depth camera, an infrared thermal imager, a myoelectricity sensor and a millimeter wave radar; the intelligent decision-making module is used for updating a massage strategy in real time by applying a Poisson reconstruction algorithm, a multi-modal fusion positioning algorithm and a dynamic coordinate mapping method; the execution device comprises an end effector and is composed of a bionic massage head for executing massage, a six-dimensional force sensor for feeding back a pressure value in real time and an infrared temperature control module; the three-degree-of-freedom mechanical arm is composed of xyz translation compensation and a rotating joint and is used for controlling the end effector to move; the power module is used for supplying power to the system. The device has the advantages that accurate acupoint tracking and positioning can be provided for a user, safe, controllable, autonomously adjustable massage force is provided, and different massage schemes are set according to individuation of the physique of the user.

The invention provides a substation equipment multi-view point cloud data fusion three-dimensional modeling method and system. The method comprises the steps of obtaining original point cloud data under multiple views and synchronously updating an equipment surface feature topological relation; setting a registration reference surface matched with the geometric structure of the equipment according to the spatial distribution characteristics of the point cloud; carrying out rigid alignment by adopting a point cloud registration algorithm based on a feature weight; and calling a curvature constraint-based three-dimensional reconstruction algorithm through the registration error parameters to generate a high-precision grid model. Wherein the registration reference plane setting needs to judge the point cloud coverage integrity and the overlapping area condition, and when the condition is not met, the collection view angle is adjusted or the point cloud data is complemented; the registration condition is judged through a curvature consistency index; the three-dimensional reconstruction process comprises voxel down-sampling, curved surface function construction and grid optimization, and the final model needs to be subjected to electrical safety verification. According to the method, high-precision three-dimensional modeling of the substation equipment can be realized, and the geometric feature reduction degree and the electrical safety verification reliability are improved.

The invention provides a real-time positioning and puncture track control method and system in a breast duct endoscope operation, and relates to the technical field of medical instruments, and the method comprises the following steps: establishing a real-time coordinate system by collecting an endoscopic image sequence and electromagnetic position data, obtaining lumen three-dimensional depth information by using a light field reconstruction algorithm, and adjusting and calculating stress field distribution by using a grid form. And determining an optimal track based on the puncture safety coefficient, and adjusting compensation parameters in real time by using the optical fiber strain sensing array. Accurate positioning and track control of the puncture instrument in the breast duct endoscope operation are achieved, and operation safety and accuracy are improved.

The invention provides an aero-engine blade damage detection method based on an improved ultrasonic sparse reconstruction algorithm, and relates to the technical field of aero-engine damage detection positioning, and the method comprises the steps: S1, setting a sensor array and a detection region of a to-be-detected blade, and obtaining an actual measurement damage reflection signal dictionary through a sensor; s2, wave number distribution of a detection area is determined according to a variable thickness structure Lamb wave propagation model, signal reconstruction is carried out, and a theoretical damage reflection signal dictionary is obtained; s3, an aero-engine blade damage detection model is constructed, an improved sparse reconstruction algorithm is adopted to solve and optimize, and damage judgment is carried out; and S4, according to the pixel value distribution condition of the aero-engine blade detection area, generating an aero-engine blade damage detection image, and positioning a damage position. According to the method, the reflected wave amplitude information can be obtained from the array sensor signals, and the reflected wave amplitude information is compared with the reconstructed theoretical reflected signals, so that the damage condition of the aero-engine blade is identified and positioned.

The invention belongs to the technical field of image processing, and mainly relates to a teenager scoliosis detection method based on three-dimensional imaging, and the method comprises the steps: obtaining multi-modal data such as a back depth map, temperature distribution, an attitude angle and a plantar pressure matrix of a detected person through a depth camera, an infrared imaging device, an I MU sensor and a plantar pressure distribution sensor; the cloud platform generates a three-dimensional point cloud and obtains a back three-dimensional curved surface model by adopting a Poisson surface reconstruction algorithm, overall attitude correction is carried out by combining an attitude angle and pressure gravity center shift, and a thermal correction model of local deformation is constructed based on a thermodynamic diagram; automatically identifying spine segment mark points through a deep learning algorithm, fitting a spine center line, calculating a Cobb angle, judging the scoliosis degree and outputting an analysis report; according to the method, X-ray imaging is not needed, the detection safety is high, attitude, mechanical and thermal information can be fused, artificial attitude interference is reduced, and the method is suitable for early screening and dynamic reexamination of teenagers.

The invention discloses a narrow space spraying trajectory planning method based on reinforcement learning, and the method comprises the steps: obtaining environment data through a LiDAR camera and a TOF camera, and obtaining a LiDAR point cloud and a TOF point cloud; carrying out registration fusion on the LiDAR point cloud and the TOF point cloud, and carrying out normal vector unification processing on each point of the fused point cloud; reconstructing by introducing a Poisson equation improved by shielding perception weight, and eliminating noise through Laplacian smoothing processing to obtain a target point cloud set; designing a state space, an action space and a reward function of reinforcement learning according to the planning of the spraying path trajectory, and determining a spraying path trajectory planning strategy; and based on the target point cloud set, adopting a spraying path trajectory planning strategy to carry out spraying trajectory planning. Sensor data fusion is realized through an improved iterative nearest point algorithm, and a shielding area is repaired in cooperation with a Poisson reconstruction algorithm, so that accurate correspondence between input parameters and output film thickness can be realized, and the control precision of the spraying thickness is remarkably improved.

The invention discloses a multispectral and hyperspectral imaging proportion balance optimization method based on satellite remote sensing, and relates to the technical field of balance optimization, and the method comprises the following steps: collecting a first image data set which comprises a low-resolution multispectral image and a hyperspectral image; preprocessing the first image data set, screening out a low-resolution multispectral image from the preprocessed images, improving the spatial resolution of the multispectral image by adopting a super-resolution reconstruction algorithm, and generating a reconstructed multispectral image; fusing the reconstructed multispectral image and the preprocessed hyperspectral image, and performing optimization processing on a fusion result to generate an image with an optimal resolution; and extracting second image data based on the optimal resolution image, and establishing a proportion balance optimization model. According to the method, the data processing time and the imaging cost are accurately calculated, and the image fusion quality evaluation is combined, so that the optimal allocation of the imaging ratio under limited resources is realized.

The invention discloses a low-illumination scene image three-dimensional reconstruction method and device based on a 3D Gaussian ball, and electronic equipment. The method comprises the following steps: 1) obtaining a low-illumination scene image; 2) initializing information of a 3D Gaussian ball in a 3D space according to camera parameters and a volume rendering technology to obtain an initial position, an initial radius and an initial color of the 3D Gaussian ball, and writing the initialized 3D Gaussian ball information into the enhanced image; 3) constructing an illumination adjustment network based on the convolutional neural network fused with the attention mechanism; 4) inputting the enhanced image obtained in the step 2) into an illumination adjustment network, and predicting an illumination weight value of each 3D Gaussian ball by using the illumination adjustment network; and 5) based on the initial information of the 3D Gaussian ball and the illumination weight value, performing three-dimensional scene reconstruction by using a three-dimensional reconstruction algorithm to obtain a three-dimensional scene reconstruction view Cnor under normal illumination. According to the invention, the illumination can be dynamically adjusted through the illumination adjusting network, and the real restoration of the low-illumination scene is realized.

The invention discloses a cable accessory nondestructive testing method based on image recognition, and relates to the technical field of data encryption, and the method comprises the steps: extracting a photon signal from an anti-noise coding control template, generating an anti-interference partial discharge light spot image in combination with a compressed sensing reconstruction algorithm, and carrying out the alignment fusion of the anti-interference partial discharge light spot image and LiDAR point cloud data of a cable accessory, generating spatial registration feature data; based on the neural radiation field network, constructing a three-dimensional radiation field model, inputting spatial registration feature data, generating time-space domain deformation field data and partial discharge hot spot distribution data, and generating a defect candidate region by using a spatial weighted fusion algorithm; a deep residual network is used as a framework, a transfer learning strategy is combined, a cable accessory defect model is constructed, a defect candidate area is input, and a cable accessory health state report is generated. According to the method, the three-dimensional radiation field model is constructed by using the neural radiation field network as a framework, accurate positioning and characterization of defects are realized, the detection reliability is improved, and the method is suitable for micro-defect identification.

The invention provides a cabinet three-dimensional temperature field monitoring method and device, and the method comprises the steps: arranging four temperature sensors at the positions of left and right columns at the rear part of a cabinet and the left and right middle positions of the rear side of the top of the cabinet respectively, and forming vertical and horizontal coordinate distribution points covering a key heat distribution region of the cabinet; collecting temperature data of the four temperature sensors at preset coordinate positions, and inputting the temperature data into a three-dimensional temperature field reconstruction algorithm model; processing the temperature data by adopting a bilinear interpolation method, and calculating the temperature value of any position in the cabinet; and according to a hot air convection rule, introducing height direction temperature gradient correction into the three-dimensional temperature field reconstruction algorithm model so as to eliminate the influence of heat convection on a temperature monitoring result. The three-dimensional temperature distribution in the cabinet can be accurately reflected, the temperature monitoring precision and real-time performance are improved, and the installation and maintenance cost is reduced.

The invention provides an urban special-shaped building monomer reconstruction method based on inclination model data, and the method comprises the steps: carrying out the segmentation and recognition of a monomer building based on deep learning, European clustering and a graph cutting method, fitting a feature plane of the monomer building based on a point cloud plane fitting algorithm, recognizing a complex special-shaped building according to the normal vector gradient distribution of the feature plane, and carrying out the reconstruction of the complex special-shaped building. According to the method, the three-dimensional Mesh model of the special-shaped building is simplified, textures are filled and the like, reconstruction of a complex special-shaped building model is achieved, and the problems that an existing mainstream urban building monomer reconstruction algorithm is only suitable for common regular buildings, and automatic monomer segmentation and three-dimensional structured reconstruction of the special-shaped building cannot be achieved are solved. And the automation rate and the efficiency of reconstruction of the special-shaped building in the urban scene are obviously improved.

The invention relates to the technical field of pelvic floor muscle training, in particular to a pelvic floor muscle recovery training system. The system comprises a physiological data acquisition module, a feature extraction and analysis module, a model training and optimization module, a personalized training plan module, a dynamic training adjustment module, a three-dimensional mapping and modeling module, a biomechanical simulation module and an intelligent state recognition module. According to the invention, through fusion of a convolutional neural network and a time sequence analysis method, key physiological features are extracted and analyzed, a personalized training plan module is combined with an intelligent algorithm, a training scheme is customized for a user, specific requirements are matched, and a dynamic training adjustment module monitors and adjusts training parameters in real time. The three-dimensional mapping and modeling module provides a muscle model by using an imaging technology and a three-dimensional reconstruction algorithm, the biomechanical simulation module simulates the mechanical response of muscles through a finite element analysis and computer simulation technology, and the intelligent state recognition module analyzes the muscle state by using a support vector machine technology.

The present application relates to a geographic mosaicking method and apparatus for video images, and a computer device and a storage medium. The method comprises: acquiring real-time live streaming images of a real-world scene that are collected by a plurality of unmanned aerial vehicles, and GNSS information of the plurality of unmanned aerial vehicles; using a visual SLAM algorithm to perform input frame tracking on the real-time live streaming images, and performing pose estimation on successfully tracked input frames by combining visual trajectories and the GNSS information, so as to generate georeferenced camera poses; using the camera poses and a surface reconstruction algorithm to perform densification processing on the input frames, so as to generate a depth map of the real-world scene, and mapping the depth map into dense three-dimensional point clouds, so as to construct a three-dimensional surface model of the real-world scene; and using the camera pose and the three-dimensional surface model to perform orthorectification on the input frames, and integrating the orthorectified input frames into a global mosaicked map, so as to generate a global image of the real-world scene. By means of the embodiments of the present application, a real-time picture of a real-world scene can be quickly acquired, thereby providing robust support for a rapid emergency response in a real-world scene.

The invention discloses a crop irrigation optimization method and device based on digital twinning and three-dimensional reconstruction. The method comprises the following steps: collecting crop growth data and video data of environmental parameters in real time through a multi-mode sensing network; performing frame extraction on the video data to obtain a multi-view image sequence, and generating a crop three-dimensional point cloud model based on a multi-angle reconstruction algorithm; semantic segmentation is carried out on the three-dimensional point cloud of the crop, phenotypic features of the crop are extracted, and environmental data are fused to construct a crop growth digital twinborn body; establishing a soil humidity prediction model, inputting time sequence environment data and crop phenotypic characteristic data, and outputting a soil humidity prediction value in future time; finally, irrigation strategy simulation is executed in the digital twin platform, the optimal irrigation amount is determined through a minimization cost function, an optimal irrigation instruction is issued to a physical irrigation actuator, and closed-loop control is achieved. Through three innovations of three-dimensional phenotype extraction, environment coupling modeling and dynamic strategy simulation, the problems of response lag and extensive decision of a traditional method are solved.