40results about How to "Strong characteristic" patented technology

The invention provides a pipeline welding real-time feedback control method and system based on machine vision. According to the method, multi-modal data such as a three-dimensional point cloud, a temperature field and a welding seam image are synchronously collected, and key characteristics such as a pose deviation gradient, a cooling rate curve and a welding seam size sequence are extracted after time sequence alignment, filtering and correction fusion. And a CNN and LSTM mixed adaptive deep learning model is utilized to analyze the welding quality state in real time, and sub-optimal parameters are dynamically optimized based on a reinforcement learning strategy, or a three-level response mechanism including heat reduction input, expert knowledge base matching and precise repair is triggered to process defect early warning. The system has an incremental learning capability and can be self-optimized along with a control period. The full-flow intelligent monitoring, real-time feedback and self-adaptive accurate control of the welding process are achieved, and the welding quality and the automation level are remarkably improved.

This invention relates to a method for extracting natural gas hydrates and the perfluorocarbon extractant used therein. The perfluorocarbon extractant comprises a viscosity modifier, an interface modifier, and a solvent; the viscosity modifier includes perfluorotributylamine; and the interface modifier includes perfluoropolyether and / or perfluorobutyric acid. The perfluorocarbon extractant of this invention exhibits high solubility for methane, strong hydrophobic properties, and stable physicochemical properties. During natural gas hydrate extraction, it allows methane released after hydrate decomposition to preferentially enter the extractant phase in a dissolved state and migrate within the reservoir, thereby achieving single-phase liquid extraction of methane. This technical approach can weaken or even eliminate the Jamin effect and seepage blockage caused by gas-liquid two-phase mixing, reduce reservoir flow resistance, and improve the extraction efficiency and continuous gas production capacity of natural gas hydrates in silty clay reservoirs.

The application provides a high-nickel ternary material modification method and a high-nickel positive electrode material with a nitrogen-oxygen coating layer, and relates to the field of lithium ion batteries. In the high-nickel ternary material, surface lattice oxygen is gradually replaced by nitrogen atoms in a high-temperature long-time calcination process in a nitrogen-oxygen mixed atmosphere, so that a nitrogen-oxygen layer is formed on the surface, which is beneficial to the delocalization of electrons, thereby endowing the material with excellent electronic conductivity; meanwhile, the presence of the nitrogen-oxygen layer inhibits the transformation of the surface layer structure into a rock salt phase structure, which is beneficial to realizing efficient ion transport. The formation of the nitrogen-oxygen layer on the surface of the high-nickel ternary material can simultaneously improve the electronic and ionic conduction of the surface of the high-nickel positive electrode material, inhibit the interface side reaction, relieve the volume change, and thereby improve the cycle stability of the battery.

This utility model discloses a conical bidirectional mixing head for cement mixing pile construction, comprising a hollow outer rod, within which a hollow inner rod is nested. A vertical main shaft is fixedly connected to the end of the inner rod, which serves as the mixing shaft and is a hollow shaft with a closed conical end. The inner rod is coaxially fixedly connected to the top of the vertical main shaft, and their inner cavities are interconnected. Straight blades and a grouting pipe communicating with the inner cavity of the vertical main shaft are vertically mounted on the vertical main shaft. The straight blades are the main mixing blades. A positioning sleeve is fitted onto the vertical main shaft. The outer rod and the positioning sleeve are fixedly connected through the free end of a C-shaped mixing blade, which is a secondary mixing blade. This mixing head achieves bidirectional rotation while improving and optimizing its structural form, resulting in stronger mixing capacity and anti-drilling characteristics, which is beneficial to the stability and uniformity of pile formation and can improve construction efficiency to a certain extent.

The invention provides a non-contact vital sign detection system, which relates to the technical field of non-contact detection and comprises an ultra-wideband radar module, an infrared temperature measurement module, a core processing unit and an output unit. The ultra wide band radar module is used for transmitting nanosecond narrow pulse detection waves to a monitoring space and receiving echo signals; the infrared temperature measurement module is used for collecting an infrared radiation signal sent by a monitoring target; the core processing unit is electrically connected with the ultra-wideband radar module and the infrared temperature measurement module, and is configured to process the echo signal and the infrared radiation signal so as to calculate and output quantitative vital sign data and spatial trajectory information of a monitored target; and the output unit is connected with the core processing unit and is used for outputting quantitative vital sign data and spatial trajectory information. According to the invention, high-precision, quantitative and non-contact synchronous monitoring of multi-target vital signs in a complex daily scene can be realized, so that the limitation that the prior art is affected by clothes shielding, human body movement and distance change is overcome.

This invention discloses a method for identifying the deterioration of subgrades of long-service highways based on a shear modulus inversion model. It utilizes various sensors embedded in the subgrade to acquire multi-source monitoring data of the target road segment, including environmental parameters, mechanical response parameters, physical state parameters, and spatial location information, and performs preprocessing. By constructing hysteresis features and removing recursive features, an in-situ subgrade shear modulus inversion model is established, transforming the multi-source monitoring data into a shear modulus distribution field that considers the temporal changes in the subgrade's state and includes spatial information. Based on the inverted spatial distribution field of the shear modulus, spatial variability indices such as the coefficient of variation and Moran's index are calculated, and the subgrade deterioration state is identified based on its evolution trend, outputting the deterioration identification result. This invention provides a scientific basis for maintenance decisions.

The application discloses a kind of slope displacement monitoring method, equipment and medium based on quantum single photon, belong to displacement monitoring technical field.The application is by emitting rectangular light pulse according to cycle, utilizes single-photon detector to complete echo detection and constructs photon counting time sequence, carries out wavelet denoising and reconstruction processing to it, extracts main peak position shift, energy change and form distortion matches multi-class feature vector, then carries out feature fusion and solution by multi-source feature fusion BP neural network, finally outputs high-precision slope displacement result.The application combines quantum single-photon detection technology with wavelet denoising, neural network fusion algorithm, effectively suppresses noise interference, improves displacement feature extraction accuracy, and is suitable for high-precision, long-distance real-time monitoring of slope micro-deformation.

The embodiment of the invention provides a single-channel electroencephalogram sleep analysis method and related equipment, and belongs to the technical field of electroencephalogram signal processing and sleep health monitoring. The method comprises the following steps: carrying out preprocessing and time sequence enhancement on a single-channel electroencephalogram signal; extracting local and global features by using a double-branch feature encoder; feature distribution alignment between a source domain and a target domain is realized through a double-contrast adversarial learning module in combination with cross-view time sequence contrast learning and conditional adversarial alignment; dynamically adjusting a target domain sample weight based on information gain through a learnable reweighting module so as to correct label distribution offset; and finally, high-precision sleep staging or OSAHS event detection is realized by utilizing a classifier. According to the method, the generalization ability and robustness of the model in a cross-subject scene are remarkably improved, accurate analysis can be achieved only through single-channel electroencephalogram signals, the hardware cost and the use threshold are reduced, and the method is suitable for scenes such as family sleep monitoring and telemedicine screening.

The invention relates to the technical field of preparation of cleansing products, in particular to a formula and a preparation method of skin-refreshing cleansing oil, and the skin-refreshing cleansing oil is characterized in that fatty acid ester components and polyoxyethylene ether surfactants in a system are balanced by adjusting the proportion of cetyl alcohol ethyl hexanoate to PEG-8 diisostearate; after the proportion is adjusted, the dosage of sorbitol polyether-30 tetraoleate is increased to 7%, and the strength of an interfacial film is improved; and tocopherol is added as an antioxidant substance, so that the risk of emulsification collapse caused by oxidation stability is reduced. According to the skin-refreshing cleansing oil formula and the preparation method, the strength of an interfacial film is enhanced by increasing the dosage of sorbitol polyether-30 tetraoleate, oxidation is inhibited by adding tocopherol, the content of squalane is dynamically adjusted by combining the proportion of polar / non-polar components, emulsification collapse and layering are effectively avoided, and it is ensured that the product is kept in a stable state in the storage and use process.

The present application belongs to the field of fingerprint, and particularly relates to a method for detecting medicinal stems of Dendrobium candidum from Sichuan origin. The specific technical scheme is as follows: a method for constructing a Dendrobium fingerprint, comprising the following steps: mixing a Dendrobium powder to be tested with methanol, performing ultrasonic treatment, taking out, cooling, and filtering; evaporating the filtrate under reduced pressure, dissolving the residue with methanol, filtering, shaking, and obtaining a test solution; taking the test solution, performing high performance liquid chromatography detection, and recording a fingerprint; and the chromatography detection has the following chromatography conditions: a C 18 reverse-phase chromatography column; a column temperature of 30 DEG C; a detection wavelength of 270 nm; and a total flow rate of 0.8 mL / min. The method provided by the present application is simple to operate, has complete characteristic components, and has a stable test solution, high precision, and good reproducibility.

This invention provides a directional underwater acoustic sensor based on an asymmetric structure and its directional calculation method, relating to the fields of biomimetic underwater acoustic sensing and microelectromechanical systems (MEMS). The sensor comprises: a biomimetic nerve fiber strut with its top end fixed to the sound pressure-sensitive region of a waterproof asymmetric elliptical tympanic membrane, and its bottom end connected to the outer reflective film of a fully sealed fiber optic Fabry-Perot interferometer cavity; based on the mechanical vibration generated by the waterproof asymmetric elliptical tympanic membrane under the excitation of water sound pressure, the vibration is transmitted axially through the biomimetic nerve fiber strut, resulting in the synchronous displacement of the outer reflective film, thereby changing the physical cavity length of the fully sealed fiber optic Fabry-Perot interferometer cavity. This invention solves the technical bottleneck of traditional multi-node arrays in miniaturized scenarios by integrating the monoauricular directional mechanism of the giant wax moth with a miniature fiber optic hydrophone architecture.

The method for sensing the temperature of urban solid waste incinerators based on a self-attention hybrid ensemble network belongs to the field of industrial process modeling and intelligent sensing. This method addresses the highly nonlinear, multi-condition variations, and complex physical-chemical reaction mechanisms in the MSWI process. Combining an ensemble learning framework and improved neural network structure, it introduces moving block bootstrap (MBB) sampling based on maximum likelihood estimation (MLE), a self-attention mechanism, and a hybrid ensemble strategy fusing AdaBoost and Bagging to achieve accurate modeling and dynamic prediction of the Fourier transform (FT). This results in good modeling accuracy, generalization ability, and engineering adaptability. Experimental verification under multiple benchmark problems and actual MSWI conditions demonstrates that the proposed FT sensing method exhibits superior performance in both prediction accuracy and robustness, possessing significant engineering application potential and widespread value.

The present application relates to a monoclonal antibody of yeast phase protein HSP30 of Marnifia and its application, and belongs to the technical field of biological medicine. The yeast phase protein HSP30 of Marnifia is a protein up-regulated after Marnifia is cultured at 37 DEG C or infected in a human body state, and can be used for early diagnosis of Marnifia infection. Based on the expression of the recombinant protein of the yeast phase protein HSP30 of Marnifia, a mouse monoclonal antibody specifically combined with the yeast phase protein HSP30 of Marnifia is screened, and is combined and optimized to form a Marnifia detection kit. Compared with the traditional Marnifia detection method, the antibody takes the HSP30 with strong species specificity as a target, can effectively capture the yeast phase protein released by Marnifia in serum, is more convenient for capturing complete fungi, has higher sensitivity, and has no aspergillus cross reaction; compared with the traditional fungal culture diagnosis mode, it is more rapid and simple, and greatly improves the detection efficiency and the detection sensitivity.

The invention belongs to the technical field of medical imaging, and discloses a gallium-based radioactive contrast agent with glycosylation-modified maltotriose covalently linked with a chelating group as well as a preparation method and application of the gallium-based radioactive contrast agent. The preparation method comprises the following steps: firstly, modifying a cyclic polydentate ligand, carrying out nucleophilic substitution, then connecting an upper half flexible chain or loop chain, and then carrying out glycosylation on acetyl protected maltotriose to connect a sugar part half flexible chain or loop chain; the preparation method comprises the following steps: preparing a polydentate ligand, carrying out reductive amination or urea condensation connection on aldehyde groups or aldehyde groups on half-chain end groups on two sides to form a whole, adding methanol and a sodium methoxide solution, removing acetyl of sugar, adding trifluoroacetic acid to remove protective groups on the polydentate ligand, and finally adding 68Ga for chelation to obtain a final product. And maltotriose is adopted as a guiding group, so that higher bacterial specificity and permeability are achieved, and the distinguishing degree in diagnosis of bacterial infection is higher.

The invention discloses a double-stage pre-training system for reading of an industrial inspection instrument, and belongs to the technical field of industrial visual inspection and intelligent inspection. The system comprises a structure interpretable parameterized instrument data synthesis module, a mask auto-encoder pre-training module, a multi-task joint pre-training module and an industrial deployment fine tuning module. Constructing a plurality of pointer type instrument structure templates based on a parametric modeling mode, randomizing parameters to generate high-diversity synthetic instrument data, and automatically generating corresponding structure and semantic annotations; performing unsupervised pre-training by using a mask auto-encoder to learn the relationship between the underlying structure features of the instrument image and the global space; then, multi-task joint pre-training is introduced into the shared backbone network, and multi-task collaborative optimization is carried out; in the industrial deployment stage, through multiple task heads and newly added target detection task heads, small sample fine adjustment is carried out in combination with a small amount of field data, and rapid adaptation to a specific industrial environment is realized. The method is suitable for an automatic instrument reading scene in a complex industrial environment.

The invention discloses a primer group and a method for specifically detecting osmanthus fragrans ingredients in food and application of the primer group and the method. A base sequence of a forward primer is shown as SEQ ID NO.1; the base sequence of the reverse primer is as shown in SEQ ID NO. 2; and the base sequence of the fluorescent probe is as shown in SEQ ID NO.3. The method has the advantages that the osmanthus fragrans ingredients are specifically detected, and the detection sensitivity of the osmanthus fragrans ingredients reaches 1 pg / mu L; the detection method has strong specificity and repeatability, and can provide an effective technical means for detection of osmanthus fragrans ingredients in the food ingredient detection process.

This invention relates to the field of molecular biology, specifically to a nested PCR primer set, kit, and application for detecting porcine infectious pleuropneumonia. The kit, developed by establishing a nested PCR amplification primer set, can be used for the in vitro diagnosis of porcine infectious pleuropneumonia. It exhibits high sensitivity, detecting up to 0.355 pg / µL, which is nearly 100 times higher than that of conventional PCR (35.5 pg / µL). It also demonstrates strong specificity, overcoming the problems of low sensitivity and poor specificity associated with conventional PCR. Compared to existing indirect immunofluorescence and serological detection methods, it offers lower cost and a shorter detection cycle.

The application relates to the technical field of direction of arrival estimation, and specifically discloses a cross-platform complex domain feature enhancement coherent super-resolution DOA estimation method, which comprises the following steps: firstly, a complex domain CVSIMO learning model is constructed; sampling data is preprocessed; forward propagation of the CVSIMO learning model is carried out; backward propagation and parameter optimization of the CVSIMO learning model are carried out; data reconstruction and signal separation are carried out; cross-platform super-resolution DOA estimation is carried out; and finally, model simulation experiments are carried out. The cross-platform complex domain feature enhancement coherent super-resolution DOA estimation method can realize feature mining of multiple point source signals, converts a multi-source estimation problem into a single point source DOA estimation problem by using a complex domain neural network model, enhances the features of coherent signals by using the advantages of the complex domain neural network, improves the performance and precision of the super-resolution DOA estimation algorithm, can also realize data separation of multiple coherent signals and cross-platform DOA estimation, and realizes real angle estimation through ingenious feature solving.

The present application relates to the technical field of hair care essential oil, and particularly relates to a hair care essential oil composition containing rosemary leaf oil and mint oil. The hair care essential oil composition contains 75-80 parts of cyclopentasiloxane, 8-11 parts of dimethicone, 8-11 parts of isohexadecane, 0.1-0.5 parts of tocopheryl acetate and 2.6-3.6 parts of plant essential oil by mass. The plant essential oil contains essential oil A and essential oil B. The essential oil A contains rosemary leaf oil and mint oil, and the essential oil B is prepared from pomelo pericarp, hyptis and perilla leaf by a specific process. The preparation process involves intermittent microwave treatment, cellulase enzymolysis, co-water distillation extraction and other processes. The hair care essential oil composition provided in the present application is compounded with specific plant essential oil in a traditional hair care formula, successfully endows the product with good antidandruff effect on the premise of basically maintaining the original softening care effect.

The invention relates to the technical field of textile resource recycling and green chemistry, and discloses a method for green selective dissolution of polyester and recovery of high-value wool fibers. A hydrophobic deep-eutectic solvent system constructed based on menthol and benzoic acid is prepared, the weak acidity and hydrophobic characteristics of the hydrophobic deep-eutectic solvent system and the specific affinity of the hydrophobic deep-eutectic solvent system to polyester are utilized, polyester can be rapidly and directionally depolymerized under specific conditions, meanwhile, wool fibers are effectively protected, and the structural integrity of the wool fibers is reserved to the maximum extent. The method provides a solution for green and efficient separation and bi-component high-valued recovery of the polyester / wool blended fabric, and has industrial application value and environmental protection significance.

This invention relates to the field of visual inspection technology and discloses a paper roll ridge detection system and method. The invention includes: irradiating the surface of the paper roll with a first laser to obtain a first wavelength image, and alternately irradiating the surface of the paper roll with a second laser to obtain a second wavelength image; performing geometric registration on the first wavelength image and the second wavelength image to obtain a first registered image and a second registered image; performing scattering intensity difference operation on the first registered image and the second registered image to obtain a ridge enhancement feature map; and performing cross-attention recognition on the ridge enhancement feature map to obtain a ridge position mask and height prediction results. This invention can automatically optimize detection parameters for different types of paper, solving the problem of poor adaptability caused by fixed threshold settings in existing technologies, and has higher detection accuracy and robustness compared to single-task methods.

The invention discloses an electromyographic signal and gesture intention mapping model, and belongs to the field of artificial intelligence and biological signal processing. According to the method, for double-arm sign language recognition, synchronous electromyographic signals of a left forearm and a right forearm when a user executes a preset sign language gesture are collected, and a training data set is constructed; a double-flow fusion neural network model based on an attention mechanism is constructed to serve as a mapping model, an encoder part of the double-flow fusion neural network model comprises two long and short-term memory network branches for processing left and right arm signals respectively, and a decoder part of the double-flow fusion neural network model comprises a fusion attention layer used for dynamically calculating and fusing weights of double-arm features to generate a joint context vector. And finally mapping to a gesture intention category. The model effectively captures the space-time cooperation relation of the muscle activities of the two arms in the gestures of the two hands, and high-precision and robust mapping from original electromyographic signals to complex gesture intentions is achieved. The invention also protects the training method of the model and the application of the model in a sign language recognition system.

This invention relates to the field of machine learning, providing a method, system, and electronic device for rock damage identification. The method includes: acquiring multi-stage damage data of a rock under test, wherein the multi-stage damage data is acquired through a piezoelectric sensing element disposed on or inside the surface of the rock under test; constructing an original dataset based on the multi-stage damage data; and inputting the original dataset into a pre-constructed convolutional neural network model to obtain the damage identification result of the rock under test. This invention addresses the shortcomings of related technologies, such as the easy loss of key damage details like local spectral deformation and peak shift, enabling real-time, non-destructive, and continuous monitoring of rock damage. It automatically extracts damage features and accurately classifies damage at each cycle stage from initial loading to final failure, providing reliable technical support for early warning of geotechnical engineering disasters.

The application relates to the field of biological medicine, and specifically discloses a high-affinity T cell receptor for recognizing Smith antigen peptide and application thereof. The T cell receptor comprises a TCR alpha chain and a beta chain variable domain with specific sequences, the variable domain contains specific CDR3 sequences for targeting a Smith antigen peptide-RVLGLVLLRGENLVS-HLA-DRB115:01 complex, and a specific site cysteine mutation is introduced into the constant region of the alpha chain and the beta chain, so that the KD value of the target complex can reach the nanomolar level. A bispecific complex and a pharmaceutical composition based on the T cell receptor can be used for preparing a drug for treating systemic lupus erythematosus or lupus nephritis, and have the advantages of high target recognition specificity, high binding affinity and good structural stability; the matching preparation method steps are controllable and reproducible, can be adapted to large-scale production, and provide a new molecular tool for precise treatment of related autoimmune diseases.

The present invention relates to the technical field of automated maintenance operations in power systems. Specifically, it relates to a complete set of operation systems and methods for power verification and grounding wire hanging and removal based on an unmanned aerial vehicle (UAV). The method includes: determining whether the transmission wire is charged, and generating a permission signal after confirming that there is no electricity; under the trigger of the permission signal, controlling the UAV to perform the operation of hanging or removing the grounding wire, and identifying and discriminating the state of the grounding wire device during the operation; monitoring the operation process in the steps of hanging and removing the grounding wire in real time, and transmitting the operation status and associated operation site images to the ground terminal. Through the present invention, a systematic process integrating power verification, state identification, and operation monitoring is achieved, realizing full-process automation and intelligence from power verification to hanging and removing operations and then to process monitoring, improving operation safety and efficiency.

The invention relates to the field of high polymer materials, in particular to acrylic air foam with high buffering performance and water resistance and a preparation method of the acrylic air foam, and the acrylic air foam is prepared from the following raw materials in parts by weight: 100 parts of acrylate copolymer prepolymer; 0.5 to 2 parts of a cross-linking agent; 0.5 to 3 parts of a foam structure stabilizer; 2-8 parts of a chemical foaming agent; 0.1 to 1 part of a photoinitiator; wherein the acrylate copolymer prepolymer is a sizing agent with certain viscosity formed by prepolymerizing various acrylate monomers under the action of the photoinitiator, the 24-hour water absorption rate of the foam can be lower than 0.5%, the static water contact angle is larger than 110 degrees, and the foam is far superior to traditional acrylic foam; and the long-term waterproof reliability in a harsh environment is ensured.

This invention discloses a groundwater model parameter estimation method combining deep learning and local set updating, comprising: constructing a groundwater numerical model; constructing a prior parameter sample set; obtaining the corresponding model output set; acquiring observation data; determining the number and proportion of local sets; randomly selecting local center samples and calculating similarity; forming a local prior sample set; updating all local prior sample sets one by one to a local posterior sample set; randomly selecting posterior samples to obtain the overall posterior sample set; replacing the prior sample set with the posterior sample set, and iteratively outputting the parameter estimation results. By combining a deep learning update mechanism with a local set update strategy, this invention effectively addresses the challenges of high-dimensionality, strong nonlinearity, non-Gaussian parameter distribution, and the "different parameters, same effect" problem in groundwater model parameter estimation, significantly improving the computational efficiency and accuracy of data assimilation algorithms in complex groundwater system parameter estimation tasks.

The application discloses a discharge detection device and method for a general high-low voltage switch cabinet for mines, and belongs to the technical field of switch cabinet detection. The device comprises a modularized sensor assembly, a signal acquisition unit, an edge computing node unit and an upper monitoring platform, and adopts a UHF sensor, an ultrasonic sensor and a transient ground voltage sensor for cooperative detection. The method comprises multi-source signal synchronous acquisition, adaptive denoising based on a sparrow search algorithm optimization, double-domain feature extraction, discharge type identification based on an attention-enhanced convolutional neural network, and multi-sensor information fusion and hierarchical early warning based on evidence theory. The application compensates for the blind area of a single detection method through cooperative work of multiple types of sensors, improves the denoising effect through adaptive parameter optimization, enhances the recognition accuracy through double-domain features and an attention mechanism, and improves the diagnosis reliability through multi-sensor fusion, so that accurate detection and intelligent diagnosis of local discharge of a mine switch cabinet are realized.

The invention discloses a behavior recognition method and device based on a video text model and a storage medium, and belongs to the technical field of artificial intelligence. The method comprises the following steps: acquiring video data to be identified, and performing frame extraction on the video data to obtain multiple video frames; inputting the multiple video frames into a preset video text model to obtain a behavior recognition result of the target object in the video data; wherein the video text model comprises an intra-frame attention module, a cross-frame attention module and a video text feature fusion module. According to the method, the time sequence relation between the video frames can be captured, the information between different frames can be integrated, semantic information is provided for behavior recognition, a video text model can comprehensively judge the behavior action of the target object from the two aspects of vision and semantics, and therefore the accuracy of behavior action recognition in the open set video is improved.

The application discloses a UI element dynamic positioning method and system based on deep learning, which comprises the following steps: collecting screenshot, element type and position coordinate data of a UI interface, enhancing data through rotation, scaling and noise injection to obtain standard UI interface data; extracting global features of a UI image by using a pre-trained CNN model, strengthening feature weights of buttons and input boxes, outputting boundary box coordinates of a target element through a full connection layer, and establishing a deep learning model; optimizing hyperparameters of the deep learning model by using a PSO particle swarm algorithm to obtain a target deep learning model; and inputting the standard UI interface data into the target deep learning model for prediction to output boundary box coordinates of a UI element, thereby significantly improving the accuracy of UI element positioning.