382results about How to "Improve fidelity" patented technology

The invention belongs to the technical field of a computer system based on a specific calculation model, relates to a virtual and real dynamic interaction-based digital twin bearing fault diagnosis method, and aims to solve the problem that a bearing system in large rotary mechanical equipment is large in fault hidden danger and avoid serious consequences caused by faults. A generally used bearing is in a high-precision standard, so that system signal data under bearing fault operation is not easy to collect in a factory, and the unbalanced data sample distribution directly leads to finiteness of fault diagnosis in a real scene only based on a big data driving mode classification method, so that the system signal data cannot be easily collected in the factory. The method has good scene working condition self-adaption, can be applied to fault diagnosis of a bearing system in different industrial scenes, fully excavates limited industrial data information and rich physical priori knowledge, and ensures safe and reliable operation of an important rotating mechanical system through a digital twinning technology.

The invention belongs to the field of artificial intelligence, medical treatment and finance, and relates to a speech synthesis method, which comprises the following steps: performing phoneme conversion and context embedding on a text data set, obtaining a phoneme sequence and a text context vector, extracting reference speech features of a reference audio sample, inputting a phoneme-level potential diffusion model, and generating phoneme-level prior potential features; inputting the phoneme-level prior potential features into a phoneme-level variational self-encoding model to obtain reconstructed speech features; extracting a target voice feature of the target audio sample; performing alignment and up-sampling on the target voice feature and a phoneme sequence to obtain a target audio frame-level feature; and training based on the reconstructed speech features and the target audio frame-level features to obtain a conditional control speech synthesis model, and synthesizing the to-be-converted text speech. The invention further provides a speech synthesis device, computer equipment and a storage medium. In addition, the invention also relates to a block chain technology, and the to-be-converted text can be stored in a block chain. The speaker fidelity and the text understandability are improved.

The invention relates to the technical field of remote sensing image processing, and particularly discloses a remote sensing hyperspectral image super-resolution fusion algorithm and system based on quaternion, and the method comprises the steps: employing a panchromatic image quaternion feature extraction module, a hyperspectral image quaternion feature extraction module and a self-attention fusion module, constructing a double-U-Net structure network based on a multi-scale residual link and a jump link; and inputting a panchromatic image and a low-resolution hyperspectral image, and calculating to obtain a high-spatial-resolution hyperspectral image. According to the method, a plurality of spectral channels of a hyperspectral image and high-resolution spatial information of a panchromatic image are embedded into four components of a quaternion, and spectral and spatial characteristics are modeled uniformly by means of algebra of the quaternion; and then feature extraction, fusion and reconstruction are carried out through a deep learning network, and finally a fusion image with both high spectral fidelity and high spatial resolution is generated, so that the spatial resolution of the hyperspectral image can be significantly improved, and the spectral consistency of the hyperspectral image can be maintained at the same time.

The invention discloses a CFD-based liquefied natural gas after-condenser structure optimization method and device. The method comprises the steps that an initial model is established based on an assembly drawing, and geometric pretreatment is completed; extracting a fluid channel, segmenting and selecting a computational domain, and carrying out local encryption on a key region; generating a polyhedral grid by using a grid division tool, and completing grid independence verification; importing the polyhedral mesh into a solver, and carrying out model setting; boundary and wall surface conditions are set, and solver parameters are configured; setting an initial flow field through a hybrid initialization method, and establishing a flow monitoring window and a temperature monitoring window to adjust simulation until convergence; analyzing the temperature cloud picture and the flow vector diagram, and adjusting the structure of the baffle plate; the tower diameter and the height of the filler layer are adjusted according to a physical quantity field change curve of the filler layer, and the performance of the recondenser is continuously improved. The problem of how to deeply study the flow heat transfer rule in the after-condenser and obtain the flow field distribution characteristics is solved, so that the working performance of the after-condenser is improved.

The invention belongs to the technical field of calibration simulation, and particularly discloses a cable partial discharge calibration simulation system based on a programmable control technology, and the system comprises a signal parameter setting module which can set standard signal parameters through a signal generator, and covers the frequency, the amplitude, the pulse width and the repetition frequency. The signal coupling transmission module enables the signal generator to generate a signal and couple the signal to a cable. The environment parameter acquisition module is responsible for acquiring environment temperature, humidity, magnetic field intensity and electric field intensity of the signal generator and the cable at each monitoring point. The environmental parameter correction module monitors signal parameters of each monitoring point of the cable in real time according to the collected environmental data and analyzes temperature, humidity, magnetic field and electric field correction indexes. And the signal parameter correction module corrects and analyzes the cable signal parameters accordingly. And the deviation degree abnormity judgment module analyzes the deviation degree index based on the standard and the corrected signal parameter, and judges whether the deviation degree index is abnormal or not for calibration.

The invention discloses a face super-resolution method and system based on a dual guide diffusion model, and the method comprises the steps: collecting low-resolution non-front face images and high-resolution front face images, and constructing a training data pair; after the low-resolution non-front face image is preliminarily restored, a rough front face image is obtained; mapping a face image of a pixel space to an implicit space, enabling a diffusion model to calculate in the implicit space, pre-training an unconditional diffusion model, taking a training result as an initialization parameter of the diffusion model, and freezing an encoder of a denoising network; extracting face priori features from the rough front face image, and capturing spatial and semantic correlation between the face priori features and de-noising features through a hybrid cross attention mechanism; face identity coding information is extracted, and a denoising network is embedded; and initializing a Gaussian noise map, carrying out continuous iterative denoising by using the trained diffusion model, mapping a denoising result in an implicit space to a pixel space, and finally reconstructing a high-resolution front face image.

The invention discloses a method and a model for generating a quantum state set, and the method comprises the following steps: adding depolarization noise to any target quantum state through a non-unitary forward process, and converting the target quantum state into a completely mixed state; and de-noising the complete mixed state by using a trainable reverse process, and recovering the complete mixed state to the target quantum state. According to the method, more stable convergence performance is achieved based on the convex optimization method, the pure state and the mixed state can be effectively generated, excellent fidelity is achieved, and the huge potential in the aspect of coping with complex quantum generation tasks is shown.

The invention discloses a vehicle-mounted VR back-row entertainment method, and relates to the field of VR, and the method comprises the steps: processing image data transmitted by a controller through a DCT plate negative reflection plate lens, and generating aerial imaging data; reflecting a virtual image corresponding to the aerial imaging data by using a plane mirror to generate real image data; performing voice recognition processing on the input voice of the user to obtain voice control instruction data; acquiring image data of user gestures through an infrared sensor, and performing image recognition to obtain gesture control instruction data; generating interaction control data according to the three-dimensional coordinates in the real image data, the voice control instruction and the gesture control instruction; aiming at low image imaging precision in a vehicle-mounted VR entertainment system in the prior art, high-quality aerial imaging is generated by using a DCT plate negative reflection plate lens and a plane mirror, multi-modal interaction and the like are realized by adopting voice recognition and gesture recognition, and a vivid imaging effect is provided.

The invention provides a method for converting a multi-stage non-iodine contrast CT image into a coronary artery CTA image, and the method comprises the steps: obtaining an NCCT volume and a CTA volume which are matched, carrying out the data preprocessing, and constructing a training data set; respectively constructing an NCCT encoder-decoder network and a CTA encoder-decoder network, and carrying out pre-training based on the training data set; a mark reconstruction network is constructed and trained, a masked low-resolution CTA mark is reconstructed under the condition of an NCCT token obtained by a trained encoder, and then a low-resolution CTA volume is reconstructed by a trained decoder; a super-resolution network is constructed and trained, and a high-resolution CTA image is generated from the low-resolution CTA image; a high resolution CTA volume is generated from the low resolution NCCT volume using the trained multi-stage model. The method has the beneficial effects that the problem of posterior crash is avoided, the decoding speed during reasoning is higher, and the training process is more stable; in the low-resolution stage, the model can learn multi-scale context information, and in the high-resolution stage, the model focuses on recovering details, more fine structures are reserved in final output, and clinical requirements can be met.

The invention discloses a three-dimensional reconstruction method based on an unmanned aerial vehicle and edge calculation, and relates to the field of unmanned aerial vehicle technology, edge calculation and three-dimensional scene reconstruction. The method comprises the following steps: an unmanned aerial vehicle carries a monocular camera to collect a standard-definition video stream from a target scene; the edge computing device receives and processes the standard-definition video stream in real time to generate preliminary low-precision three-dimensional reconstruction; the edge computing device identifies a defect area by analyzing a low-precision reconstruction result, and optimizes the flight path of the unmanned aerial vehicle; the unmanned aerial vehicle collects a high-definition video stream from the target scene according to the optimized path; and the edge computing device performs off-line high-precision three-dimensional reconstruction on the high-definition video stream so as to improve the accuracy and detail expression capability of a reconstructed scene. Through cooperative work of the unmanned aerial vehicle and the edge calculation end, real-time data processing, feedback and decision making in the collection process and high-precision three-dimensional reconstruction result storage in the off-line stage are achieved, and the real-time performance and accuracy of three-dimensional reconstruction are effectively improved.

The invention discloses a quantum state preparation method and device, equipment and a medium. The method comprises the following steps: setting a target quantum state to be prepared; setting a Hamiltonian, wherein the Hamiltonian is a negative value of an outer product of a left vector and a right vector of the target quantum state; constructing a variable component sub-line including training parameters according to the target quantum state; substituting a preset parameter value as a training parameter into the variable component sub-line so as to output a training quantum state evolved by the variable component sub-line; measuring an expected value of the training quantum state, which is evolved by the variable component sub-line, about the Hamiltonian; training a training parameter of the variable component sub-line through a classical optimizer until the training parameter or an expected value is converged to a set threshold value; comparing the training parameter or the expected value with a set threshold value; and outputting the training quantum state evolved by the variable component sub-line under the convergence condition as a to-be-prepared target quantum state. The preparation of any quantum state can be efficiently and accurately realized.

The invention discloses a non-abandoned inheritance display method and system based on artificial intelligence, and particularly relates to the technical field of artificial intelligence. The method comprises the following steps: acquiring dynamic detail time sequence data from a capture system in real time, analyzing track smoothness by using Fourier transform, generating a lossless reference model by using ultrahigh-resolution 3D scanning and image equipment, and performing texture consistency analysis on a difference salient region by combining point cloud registration and a difference heat map to evaluate spatial deviation of a track. Judging the fidelity of dynamic details, comprehensively evaluating the capture accuracy in combination with the smoothness and the fidelity, dividing capture results into accurate capture and inaccurate capture, and archiving the accurately captured data to realize efficient reuse; for data which are not accurately captured, track dynamic complementation is carried out through an autoregression integral moving average model, the accuracy and integrity of non-abandoned dynamic detail capturing are remarkably improved, it is ensured that the display effect is real and coherent, and accurate inheritance and wide propagation of non-abandoned cultural connotation are facilitated.

The invention discloses an integrated fatigue small crack initiation in-situ monitoring device and a detection method, and belongs to the technical field of material mechanical property testing. Comprising a fatigue test system, a digital image correlation test system and an acoustic emission test system, the fatigue test system is used for detecting the fatigue performance of a sample under loading force in situ and comprises a console and an in-situ fatigue testing machine, and two tensile fixtures are symmetrically arranged on the in-situ fatigue testing machine; the digital image correlation test system is used for in-situ detection of time and space trajectories of crack initiation and expansion under fatigue of a sample, and comprises a camera, a bracket and an image acquisition card; the acoustic emission test system is used for in-situ detection of an accumulation process of internal damage of a sample under fatigue, and comprises an acoustic emission sensor, a preamplifier and an acoustic emission acquisition card. By simultaneously capturing and recording the mechanical response, the optical image and the acoustic emission signal of the sample in the fatigue test period, real-time in-situ monitoring of multi-source information such as mechanical, optical and acoustic emission is realized.

The embodiment of the invention discloses a coupling device of superconducting quantum bits, a superconducting quantum chip and a quantum computer. The superconducting quantum bit coupling device comprises a first superconducting quantum bit, a second superconducting quantum bit, a first coupling module, a second coupling module and an adjustable coupling module, the adjustable coupling module comprises two resonance circuits, and the frequency of at least one resonance circuit is adjustable. According to the invention, the coupling strength symbols between the two resonance circuits of the adjustable coupling module and the first superconducting quantum bit are opposite and the coupling strength symbols are the same or similar, and the coupling strength symbols between the two resonance circuits of the adjustable coupling module and the second superconducting quantum bit are the same and the coupling strength symbols are the same or similar. Therefore, when the frequency of the first resonance circuit is the same as or close to the frequency of the second resonance circuit, the coupling between the two superconducting quantum bits is turned off, the coupling turn-off is irrelevant to the frequency of the two superconducting quantum bits, and the two superconducting quantum bits have the characteristic of full-band coupling turn-off.

The invention relates to the technical field of image recognition, in particular to a red tide algae recognition method based on a hyperspectral image, and the method comprises the following steps: calculating a gradient value of each pixel in a spatial dimension and a spectral dimension according to an original hyperspectral data cube, setting a diffusion coefficient for diffusion, and obtaining a three-dimensional fidelity hyperspectral data cube. According to the method, the gradient change trends of the spatial dimension and the spectral dimension are extracted at the same time pixel by pixel, and the regional differentiation diffusion operation is matched, so that the expression ability of the alga plaque boundary and the consistency of the internal spectral characteristics are enhanced, and the fidelity of the hyperspectral data in fine-grained region identification is improved; upper convex hull calculation of a spectral reflectivity curve and construction of a continuous spectrum background baseline are introduced, and characterization of pigment absorption morphological characteristics is enhanced; and in combination with the characteristic absorption wave bands of chlorophyll and phycobiliary, the synergistic weighted response to the distribution of the algae pigment is realized, and the sensitivity and selectivity of pigment recognition are improved.

The invention discloses a short-path optical pulse based on a three-energy-level system, a generation method and application. Specific forms of Hamiltonian and Rabian frequency parameters thereof are constructed in a three-level quantum system, and the parameters are optimized to obtain a light pulse which enables an evolution path of a quantum state on a Bloch sphere to be shortest and is high in fidelity. According to the optical pulse obtained by the technical scheme, the quantum state can be changed into a target state from any initial state through a circular evolution path, and logic gate operation is executed. The evolution time of the gate can be effectively shortened through the circular evolution trajectory, and the quantum gate reduces the influence of environmental noise while keeping the robustness of the control error. According to the quantum logic gate, multi-parameter optimization is carried out on the optical pulse waveform, the quantum logic gate is suitable for a rare earth ion Pr < 3 + >: Y2SiO5 quantum bit system device and an inverted-V-shaped three-energy-level system, and the high-fidelity quantum logic gate is obtained.

The invention discloses a narrow pulse ns-level time delay compensation type plane reflection array antenna, and belongs to the technical field of antennas. The array antenna comprises a reflective array antenna and a feed source, and each array element in the reflective array antenna performs corresponding time delay compensation on electromagnetic waves emitted by the feed source according to the distance between the array element and the feed source so as to improve the gain of a reflective wave beam in an expected direction. According to the invention, a time delay type compensation method is adopted to enable the pulse signals fed into the plane reflection antenna array to realize simultaneous, equal-amplitude and same-phase superposition in a far field, so that the time domain pulse distortion phenomenon is avoided, the time delay compensation of electromagnetic waves fed into the plane reflection antenna array can be effectively realized, and the antenna gain and the fidelity of received signals are improved.

The invention belongs to the technical field of ultrasonic positioning diagnosis, and discloses an ultrasonic phased array positioning diagnosis method for a dry-type bushing capacitor core with a microcrack defect, and the ultrasonic detection technology is a nondestructive detection method, and cannot damage a detected insulating material, so that the ultrasonic phased array positioning diagnosis method is suitable for long-term monitoring and preventive maintenance. As the ultrasonic wave has very high frequency and energy, the ultrasonic wave can penetrate through a thicker layer of the insulating material, so that tiny defects and changes can be found. The ultrasonic detection technology can find out the defects of aging, bubbles, impurities, cracks and the like in the insulating material, and can also be applied to other types of electrical equipment, such as cables, transformers and the like, so as to evaluate the insulating properties of the electrical equipment. And taking the discharge signal starting time measured by an electric detection method as a reference to obtain time delay data of all the sensors, and then carrying out next positioning calculation.

The invention proposes a small sample image generation method based on semantic mask reconstruction and category semantic learning, and relates to the technical field of image processing, and the method comprises the steps: S1, constructing a network model comprising a generator and a discriminator based on a LoFGAN network; s2, extracting depth features of the input image by using a generator, and fusing the depth features of the input image into depth semantic features through local feature fusion; s3, semantic mask reconstruction is carried out on the depth semantic features, and a corresponding image is generated through a decoder; s4, sending a real image and a corresponding image generated by the decoder into a discriminator to train the network model; s5, inputting the small sample image into the trained network model to obtain a generated image similar to the input image; according to the method, the extraction precision of the semantic information can be effectively enhanced, the understanding ability of the generation model for a complex semantic scene is improved, and the fidelity and definition of the image are improved.

The invention discloses a quantum state preparation method and device for any real vector, a medium and equipment, and relates to the technical field of quantum computing, and the quantum state preparation method for any real vector comprises the steps: carrying out the normalization processing of a to-be-coded first real vector; judging whether a positive integer n which satisfies 2n = N exists or not; if it is determined that no positive integer n satisfies 2n = N, the value of the positive integer n is determined, so that the positive integer n satisfies a condition 2n-1lt; nlt; 2n; expanding the first real vector based on the values of the positive integers n and N to obtain a second real vector; constructing a quantum circuit based on the second real vector; executing a quantum circuit, and performing quantum state coding on the second real vector to obtain an initial quantum state coding result; and obtaining the quantum state coding result of the first real vector based on the initial quantum state coding result, thereby improving efficiency and accuracy.

The invention relates to an intraoperative hypotension prediction method and device based on deep learning, and belongs to the technical field of medical big data processing. The method comprises the following steps: performing intra-operative arterial pressure waveform denoising by a wavelet threshold method; when hypotension prediction is carried out on an intra-operative patient, the probability of occurrence of hypotension after a period of time is predicted in combination with an intra-operative respiration index besides using conventional patient own population statistical characteristics and denoised intra-operative arterial pressure waveforms as input characteristics of an intra-operative hypotension prediction model; sHAP value calculation is carried out on features which have important influences on the prediction result of the intraoperative hypotension prediction model, the contribution of the features to the prediction result is quantified, and reference is provided for clinic in a visual sorting mode.

The invention provides a three-dimensional scene digital fusion method for intelligent test internal and external field environment characteristics, and belongs to the technical field of three-dimensional scene digital fusion. According to the method, multi-source data is normalized through a data fusion algorithm, so that the data source of three-dimensional live-action construction is more sufficient and more comprehensive, the coverage integrity of a combat scene is improved, the surveying and mapping precision and modeling details are improved, the labor cost is further reduced, and the method is of great significance to three-dimensional live-action construction of internal and external fields.

The invention provides an airborne infrared image super-resolution reconstruction system based on degradation perception. The airborne infrared image super-resolution reconstruction system comprises a degradation representation learning framework, a residual state space block and a cross-modal feature integration framework. The method comprises the following steps: firstly, extracting degradation information from an infrared image through a self-supervised learning framework; then, the spatial dependence of the respective features of the visible light image and the infrared image is captured through a feature extraction method based on a spatial model; and finally, the visible light is fully utilized to carry out feature supplement on the infrared image, and an unmanned aerial vehicle infrared super-resolution result with a more real visual perception effect is generated.

The invention belongs to the technical field of numerical simulation and damage intelligent detection, and relates to an aircraft surface structure damage infrared feature simulation and sample generation method. According to the method, through the design of the virtual interface layer and the secondary homogenization, the magnitude order improvement of the full-size transient thermal simulation efficiency is realized while the physical precision is ensured, and batch simulation of a large number of damaged working conditions becomes possible. The physical effect of a sensor of the infrared camera is simulated through high fidelity, so that a simulation image is highly consistent with real external field data in the aspects of noise, texture and other characteristics, and the'simulation-reality 'domain difference is effectively reduced. By directly associating the simulation process with the preset damage parameters, automatic, accurate and zero-error generation of the truth value information is realized, and the problems of high manual labeling cost and poor consistency are fundamentally solved. According to the method, an efficient and reliable data generation way is provided for training a high-precision and high-robustness external field intelligent detection model, and the method has a definite engineering practical value.

The invention belongs to the technical field of molecular biology, and particularly discloses heat-resistant MMLV reverse transcriptase and application thereof. According to the heat-resistant MMLV reverse transcriptase provided by the invention, T246K mutation is introduced into a DNA polymerase active structural domain of a wild type MMLV reverse transcriptase, so that the heat stability and reverse transcription activity of the enzyme are remarkably improved. E286R, E302K and / or L435R mutations are further introduced into a thumb domain and a connecting structural domain, and the heat resistance of the enzyme can be improved by cooperating with T246K mutations. Meanwhile, D524G, E562Q and / or D583N mutations are introduced into an RNase H active structural domain, so that the RNase H activity of the enzyme can be reduced or removed. Compared with a commercial enzyme, the heat-resistant MMLV reverse transcriptase has the optimal reaction temperature higher than 50 DEG C, is suitable for high-temperature reverse transcription reaction, and has wide application prospects and higher economic value in the fields of molecular diagnosis and medical examination.

The invention discloses an ultrasonic welding quality online detection system and method, and belongs to the technical field of laser non-contact measurement. According to the invention, the laser vibration measurement probe is rigidly coupled to the welding head to construct a follow-up-common-optical-path measurement unit, so that synchronous acquisition of vibration signals with zero mass loading is realized; further extracting an attenuation section after welding is finished from the signal, and inverting an equivalent modal damping ratio representing an interface energy dissipation state based on an exponential attenuation model; meanwhile, multi-dimensional physical indexes such as an energy transfer efficiency index Itrans and an input stability index Iin are calculated, and in combination with an energy absorption state index, collaborative fusion is carried out through a weighted fusion formula to obtain a comprehensive quality score; and finally, realizing quality attenuation early warning based on time sequence trend analysis of a scoring sequence. On-line sensing of the deep dynamic state in the welding process is achieved, the early defect recognition sensitivity and the system anti-interference capacity are remarkably improved, and the predictive maintenance capacity of quality degradation is achieved.

The invention discloses a conditional diffusion model magnetic resonance image generation method based on artificial intelligence. The method comprises the following steps: acquiring a magnetic resonance 2D image and a corresponding label; constructing a conditional diffusion model training set; constructing a conditional diffusion model; constructing and generating a loss function; performing end-to-end training on the conditional diffusion model; and respectively inputting the random Gaussian noise and the image label into the trained conditional diffusion model, and outputting an image. The method makes full use of the advantage of gradual diffusion generation of the conditional diffusion model, improves the precision and fidelity of the generation effect, enables the output image to be used for expanding the magnetic resonance data set of the selected organism part, can make full use of parameters of different initialized Gaussian noises and different generation time steps, and improves the generation efficiency. Under the condition that the generation precision and fidelity are guaranteed, the diversity of output images is improved, mode collapse is avoided, and the method has practical application value; the generated image does not need to be annotated, the method can be directly used for an image segmentation task, and the sample number of a segmentation data set is increased.

The invention belongs to the technical field of cutting machining, and relates to a real-time cutting force calculation method and device based on cutting simulation. Boolean operation is conducted on the tool and the Dexcel model of the to-be-machined workpiece, the cutting length of each Dexcel segment of the to-be-machined workpiece is obtained, and the cutting volume of the tool location point of the tool is obtained through calculation based on the cutting lengths of all the Dexcel segments of the to-be-machined workpiece; each cutter tooth is divided into M cutting edge infinitesimal elements, and the lag angle of the cutter at the axial cutting depth h is calculated; calculating the radial contact angle at each cutting edge infinitesimal element on each cutter tooth and the cutting thickness on the to-be-processed workpiece; calculating tangential, radial and axial cutting force infinitesimal elements acting on each cutting edge infinitesimal element; performing coordinate transformation on the tangential, radial and axial cutting force infinitesimal elements to obtain cutting force components of the cutting edge infinitesimal elements on x, y and z axes; and integrating the cutting edge components of all the cutting edge infinitesimal elements to obtain the instantaneous cutting force acting on the cutter.

The invention discloses a satellite image super-resolution method based on deep learning, which belongs to the field of image processing. The present invention builds an extensible deep super-resolution network, performs 4 times super-resolution on remote sensing satellite images, and can well reconstruct the characteristics of transmission line tower insulators, with the characteristics of high fidelity and fast processing speed; in order to improve the ability of the super-resolution network to learn the characteristics of remote sensing satellite images, further, a data augmentation method is adopted, and the weight normalization layer replaces the batch normalization layer to more accurately learn the characteristics of remote sensing satellite images; further, a jump-connected pixel reorganization and amplification module is introduced, which speeds up the calculation speed without a series of additional convolutions, and is well applied to power grid remote sensing image processing. It can be used for subsequent transmission line inspection, transmission line tower identification, insulator detection and other purposes.

The present invention provides a micro-nano structure transfer preparation method and application thereof, and relates to the field of optical devices. Specifically, the micro-nano structure transfer preparation method provided by the present invention is a micro-nano structure stripping process for transferring an exposed glue structure to a conventional or unconventional substrate and developing the exposed glue structure. The transfer preparation method can be completed by two methods, specifically, after preparing an auxiliary film, a glue structure, and a protective layer on a substrate, an intermediate dielectric layer is used to sequentially remove the excess film layer structure, and finally a conventional or unconventional substrate is used to bond the glue structure and remove the intermediate dielectric layer. The method provided by the present invention can be used to prepare meta-materials or micro-nano structures on unconventional substrates such as curved substrates and flexible substrates, and realize their applications in the fields of micro-nano optical devices, flexible devices, and integrated optoelectronic devices.