2122results about How to "High resolution" patented technology

The invention discloses a photovoltaic cleaning robot based on automatic path planning and an operation method thereof, relates to the technical field of robots, and solves the problem of cleaning blind spots in an existing cleaning robot and the endless loop risk of a path planning algorithm. According to the invention, the surface information of the photovoltaic panel is accurately captured through the multispectral acquisition module, a high-resolution and high-definition comprehensive image is generated through the data fusion processing module, then dirt is accurately identified on different scales by using a multi-scale pyramid structure and an adaptive feature fusion mechanism, and an attention mechanism and environmental perception data are combined, so that the real-time performance of the system is improved. The recognition threshold is dynamically adjusted, and misrecognition and missing detection are reduced; and finally, enabling the robot to autonomously learn in a complex environment and optimize an obstacle avoidance strategy through a dynamic obstacle avoidance algorithm based on deep reinforcement learning. The intelligent level and the cleaning effect of an existing photovoltaic cleaning robot are greatly improved.

The invention discloses an industrial product quality detection method and system based on machine vision, and relates to the technical field of quality detection.During operation of the system, through an industrial camera and an optimized light source layout, high-resolution image acquisition is performed on electronic components in real time by adopting multi-angle shooting, a series of optimization processing is performed on the acquired images, and the quality of the electronic components is detected. The method comprises the following steps: extracting shape, texture and color features according to geometric characteristics of an electronic component to form key parameters for judging defects, identifying potential defects of the electronic component by using a deep learning model algorithm, calculating to obtain a quality evaluation index Q, and calculating the quality evaluation index Q according to calculation results of feature extraction and defect detection. And comprehensively evaluating the quality of each component, comparing the quality evaluation index Q with a first preset threshold value M and a second preset threshold value N to obtain a quality evaluation grade, feeding unqualified detection information back to a production line, and removing unqualified components.

The invention provides an intelligent garden environment monitoring data analysis method and system. Sensors are deployed to collect temperature, humidity and other data in real time to form a data set. Analyzing an environment parameter change mode of each region by adopting a time sequence clustering algorithm, and determining characteristics and trends; and carrying out dimension reduction on the data by using a multi-dimensional scale transformation algorithm to a three-dimensional space for visualization, and generating an environment state map. And evaluating the plant fitness in combination with a plant physiological ecology knowledge base, and generating a scoring matrix to guide the preliminary layout scheme. And dynamically adjusting the layout by applying reinforcement learning, selecting an optimal scheme, and predicting a future environmental evolution path based on the optimal scheme and historical data. An automatic control system framework is constructed, a self-adaptive control algorithm is designed, intelligent regulation and control of plant layout are realized, garden management efficiency is improved, and ecological protection and sustainable development are promoted. Therefore, full-chain intelligent management from environment monitoring to plant layout optimization is realized, and the intelligent level of garden environment monitoring and management is remarkably improved.

The invention discloses a landslide monitoring method based on remote sensing data, and the method comprises the steps: obtaining a high-resolution optical remote sensing image and a synthetic aperture radar image of a research region, carrying out the registration processing of the optical and radar remote sensing images, and obtaining a remote sensing image with an accurate spatial position and an accurate geometric shape; generating a landslide area fusion image with high spectral resolution; the matched high-resolution DEM data is obtained; based on the matched high-resolution DEM data, gradient parameters of the research area are calculated, and a gradient threshold value is determined according to gradient distribution characteristics and prior knowledge; extracting slope direction parameters of the research area by adopting a slope direction algorithm based on grids and vectors, and screening out a landslide-prone area in combination with the slope threshold value; obtaining a landslide risk distribution map; and obtaining the occurrence possibility and danger level of the landslide, and obtaining the landslide condition according to the occurrence possibility and danger level of the landslide. According to the invention, precise identification and dynamic monitoring of landslide disasters are realized.

The invention discloses a precipitation estimation method and system based on territorial rainfall multi-data fusion and application, and relates to the technical field of weather forecast, by fusing radar, satellite and ground observation data, the accuracy and reliability of precipitation prediction are remarkably improved, the fusion of multi-source data can make up for the deficiency of a single data source, and the prediction accuracy and reliability are improved. Through space-time registration and a weighted average fusion algorithm, a macroscopic rainfall background is provided by introducing territorial rainfall data, and radar, satellite and ground observation rainfall data are combined, so that rainfall estimation is more comprehensive, ground observation stations are supplemented and fused, effective data of the stations are increased, the real time of rainfall can be reflected more accurately, and the rainfall estimation accuracy is improved. In addition, the fusion of multi-source data can make up for the deficiency of a single data source, and the advantages of different data sources can be effectively integrated through the space-time registration and weighted average fusion algorithm.

The invention relates to the technical field of camera adjustment, in particular to a camera adaptive adjustment method and system and a storage medium. The method comprises the following steps: acquiring a multi-source environment sensing data set and an initial environment image; performing environment feature recognition on the initial environment image according to the multi-source environment sensing data set to obtain a scene environment feature map; performing semantic scene segmentation on the scene environment feature map to obtain a semantic scene macroscopic feature map; performing time sequence attention target tracking on the scene environment feature map to obtain a target vehicle motion feature map; performing feature constraint fusion on the semantic scene macroscopic feature map and the target vehicle motion feature map to obtain a target vehicle-scene motion semantic map; and performing parameter adjustment on the camera according to the target vehicle-scene motion semantic map to obtain a camera parameter adjustment strategy set. According to the invention, the performance and the image quality of the camera in a changeable environment are improved.

The invention discloses a seabed sediment classification method and product based on seabed surface sediment acoustic characteristics, and relates to the field of seabed acoustic detection. The method comprises the following steps: firstly, acquiring actual measurement data of seabed sound reflection of a measurement area in a marine environment, and performing inversion to obtain seabed surface sediment characteristic parameters; by analyzing the uncertainty and correlation of the characteristic parameters of the seabed surface sediments, acoustic characteristic parameters of the seabed surface sediments are screened out; constructing a deep neural network model based on the acoustic characteristic parameters of the seabed surface sediments, wherein the deep neural network model comprises a seabed acoustic-physical information neural network model and a sediment attribute prediction neural network model; expanding the training sample set according to the actual measurement data of seabed sound reflection and the acoustic characteristic parameters of the seabed surface sediments, and training and testing the deep neural network model; the trained seabed sediment classification model is adopted to perform seabed sediment classification of the measurement area, fine classification of the seabed sediment can be realized, and classification continuity and classification precision are improved.

The invention provides an image rendering method and device, electronic equipment and a storage medium, and the method comprises the steps: calculating a distance between a virtual image obtaining device and a to-be-rendered object through determining the to-be-rendered object in a to-be-rendered scene, and determining the distance as a first loading factor; determining a rendering picture pixel proportion of each to-be-rendered object based on preset view cone size information, determining the rendering picture pixel proportion as a second loading factor, generating a rendering loading priority sequence according to the first loading factor, and determining a model level resource file and a texture level resource file according to the second loading factor, loading the resources to a video memory for rendering according to the sequence; the priority is determined by calculating the distance between the camera and the to-be-rendered object, it is ensured that a high-quality model and texture of a close-range object are loaded preferentially, the instantaneity of the rendering loading process is improved, texture and model grade division is conducted through the pixel proportion of the to-be-rendered object in the picture, and the rendering efficiency is improved. The occupation of a video memory is reduced, and the overall rendering performance of the system is improved.

The invention discloses a bridge pile foundation stress nondestructive testing method and system, the system comprises a sensor arrangement module, a data acquisition module, a data transmission module, a data processing module, a data analysis module, a result display and early warning module, a calibration module, a verification module and the like, sensors are various in type and high in precision, data acquisition multichannel synchronization is realized, and transmission is stable and reliable. Various technologies are used for data processing to construct a model, and a result is visually displayed and pre-warned. The system is further provided with an environment monitoring and structure health assessment module, comprehensive and accurate detection is guaranteed through an innovative formula and method, and efficient operation of the system is guaranteed through cooperative work of all modules. Through multi-module cooperation and innovative design, bridge pile foundation stress high-precision detection, environmental factor analysis and structure health assessment are achieved. Early warning can be carried out in time, bridge safety is guaranteed, a scientific basis is provided for maintenance management, and the reliability and safety of bridge operation are improved.

The invention discloses an establishment method of a rock multi-field coupling model and the model thereof, and belongs to the technical field of engineering geological survey. Through comprehensive application of multi-scale modeling, a nondestructive testing technology, a machine learning algorithm and intelligent optimization, the problems that existing multi-physical field coupling analysis is not deep enough, the precision of an observation method is poor, and a device is prone to being damaged are solved, the advantages of various observations are combined, the building and analysis precision of a rock multi-field coupling model is improved, and the rock multi-field coupling analysis precision is improved. The method has the advantages that the method is simple and convenient to operate, the intelligence and automation level of the model is enhanced, microstructure data and macroscopic response are combined, deeper understanding of rock behaviors is provided, meanwhile, more accurate and efficient decision support can be provided for rock engineering design and construction, and the method is worthy of popularization and application. And a novel analysis and simulation method is provided for the field of rock mechanics research. Through the comprehensive scheme, behaviors of rocks under complex geological conditions can be better predicted and managed, so that the safety and economical efficiency of engineering are improved.

The invention provides a potential diffusion model-based building planning image generation method and system. The method comprises the steps of data preprocessing, potential diffusion model structure improvement, input condition optimization, model training, image generation and the like. The relevance between text description and architectural styles is enhanced by preprocessing data sets of architectural design images and urban planning images and introducing channel crossing and space crossing attention mechanisms. Meanwhile, an architectural style embedding vector, an architectural description text embedding vector and a geometric information embedding vector are combined as additional input conditions to ensure that the generated image contains complete information. In a model training process, a multi-scale strategy and depth separable convolution are adopted, so that the recognition capability and training efficiency of the model to a large scene are improved. The system comprises a data preprocessing module, a model optimization module, a model training module, an image generation module and the like, and can generate a high-resolution building image conforming to design specifications. The method and system can be widely applied to the fields of building design and urban planning, and design efficiency and accuracy are improved.

The invention provides an auxiliary dental implant generation method based on a diffusion model, and relates to the technical field of stomatology, and the method comprises the steps: obtaining historical patient oral cavity CBCT image data, carrying out the preprocessing of the historical patient oral cavity CBCT image data, obtaining a CBCT image data set, training a multi-task segmentation network through the CBCT image data set, and obtaining a CBCT image data set; obtaining an intra-oral tissue segmentation result in the segmentation network; respectively training a detection network from three dimensions of a cross section, a coronal plane and a sagittal plane by using an intraoral tissue segmentation result; obtaining detection results in three directions of a cross section, a coronal plane and a sagittal plane by using a detection network; fusing the detection results in the three directions of the cross section, the coronal plane and the sagittal plane, constructing a three-dimensional bounding box by using a majority voting algorithm, and obtaining an edentulous region; the intraoral segmentation result and the edentulous area serve as prompt information, the network is guided through the iteration process to generate the post-implantation effect, the implantation effect obtained through the method is high in accuracy, and a more accurate auxiliary tool can be provided for stomatology.

The invention discloses a cardiac interventional operation scene reconstruction method based on 3D ultrasonic imaging rendering. The method comprises the following steps: collecting a preoperative computed tomography or magnetic resonance imaging image as prior information; the real-time 3D ultrasonic image sequence is preprocessed; the method comprises the following steps of: performing voxelization on an image by combining prior information, converting image data into voxel representation in a three-dimensional space, and creating an initial 3D Gaussian function set; parameters of the initial Gaussian function are optimized through an iterative optimization algorithm, so that the difference between the reconstructed image and the actual image is minimized; in combination with interventional catheter shape constraint, adaptive density control of a 3D Gaussian function is alternately carried out; and rendering tiles one by one by adopting a Gaussian sputtering technology based on the tiles to realize real-time three-dimensional scene reconstruction and display. According to the method, prior information and interventional catheter shape constraints are combined, various structures in an operation scene are represented and simulated by using a 3D Gaussian function, and efficient rendering is realized through a tile-based sputtering technology.

The invention provides a bone image superposition presentation method and system based on an augmented reality technology, and relates to the technical field of intelligent medical systems.The method comprises the steps that fracture image data for a fracture area of a patient are acquired, and the fracture image data comprise a CT image, an X-ray image and a light field image; analyzing a light model corresponding to the light field image; the light model provides propagation information of light in a fracture area space of a patient; determining multi-modal fusion features corresponding to the fracture image data based on a light model and a feature fusion network; the feature fusion network adopts a convolutional neural network; reconstructing a three-dimensional model of the fracture part based on the multi-modal fusion features; and aligning and calibrating the reconstructed three-dimensional model of the fracture part and the fracture area of the patient in the actual operation scene. Therefore, the augmented reality technology, the light model and the feature fusion network are comprehensively applied, the three-dimensional model of the fracture part is accurately overlaid and presented in real time in a real operation scene space, and the accuracy and efficiency of the operation can be remarkably improved.

The invention discloses an external bias pixel structure of a light receiving stage circuit in a dynamic vision sensor pixel. The external bias pixel structure comprises a photodiode, a logarithmic tube, an amplifier tube, a switch tube and a bias tube, the bias tube, the switch tube and the amplifier tube are sequentially connected in series, a source electrode of the bias tube is connected with a power supply, a drain electrode of the bias tube is connected with a drain electrode of the switch tube, a source electrode of the switch tube is connected with a drain electrode of the amplifier tube, and a source electrode of the amplifier tube is grounded; the grid electrode of the bias tube is in signal connection with Vbias, the grid electrode of the switch tube is in signal connection with Vcpr, the source electrode of the switch tube and the drain electrode of the amplifier tube are connected with the grid electrode of the logarithmic tube, the drain electrode of the logarithmic tube is connected with a power supply, the source electrode of the logarithmic tube is connected with the negative electrode of the photodiode, and the positive electrode of the photodiode is grounded. And a grid electrode of the amplifier tube is also connected between the source electrode of the logarithmic tube and the negative electrode of the photodiode.

The invention relates to a concrete surface crack detection method and system based on a machine learning algorithm, and the method comprises the steps: obtaining concrete wave velocity, frequency, amplitude, strength, elastic modulus, temperature and linear expansion coefficient, analyzing characteristic parameters, building a mathematical relation model, fitting data through a machine learning algorithm, determining model parameters, and calculating the concrete surface crack detection result. Relevant parameters of to-be-detected concrete are input into the multi-parameter digital quantity plate model, the model calculates potential risks and predicted values of cracks, the multi-parameter digital quantity plate model is applied to real-time detection of the concrete structure to achieve quality evaluation and crack prediction, the concrete structure is monitored regularly, and model data are updated; the system comprises a data acquisition module, a data preprocessing module, a feature extraction module, a crack measurement module, a multi-parameter digital quantity board model establishment module, a multi-parameter comprehensive analysis module, a real-time monitoring and early warning module, a database management module and a user interface and interaction module.

The invention provides a comprehensive energy multi-element load short-term prediction method based on secondary decoupling, and the method comprises the steps: collecting a multi-element load data set, reconstructing a winter data set and a summer data set in the multi-element load data set, and carrying out the primary decoupling; decomposing the reconstructed winter data set and summer data set; dividing the decomposed data into a periodic term and a trend term so as to perform secondary decoupling; screening the trend items, and predicting the screened trend items to obtain a trend item prediction result; and performing multivariate load prediction on the periodic term to obtain a periodic term prediction result, and adding the trend term prediction result and the periodic term prediction result to obtain a final prediction result. According to the method, the coupling of the multi-element load piece can be effectively captured, accurate decomposition of nonlinear and non-stationary signals can be realized, the anti-noise capability and the time frequency resolution are improved, and the balance is considered while the prediction accuracy is effectively improved.

The invention provides a high-resolution positioning method for noise source imaging. The high-resolution positioning method comprises the following steps: designing a sensor array layout and selecting sensors; a multi-channel acquisition synchronous acquisition sensor and records signals to obtain acquisition data; performing signal processing on the acquired data by using adaptive filtering, machine learning calibration, time-frequency analysis and intelligent beam forming technologies to obtain standard data; based on the standard data, performing high-resolution direction estimation by using an optimized multi-signal classification algorithm, and removing a sidelobe effect by using an optimized CLEAN algorithm to complete processing of the standard data; and based on the processed standard data, reconstructing a spatial distribution image of the noise source by using an inversion technology, and dynamically adjusting the resolution of the reconstructed spatial distribution image by using an adaptive algorithm to realize high-resolution positioning of noise source imaging. According to the invention, high-resolution positioning can be realized, the reliability and precision of noise detection are improved, and noise control and management are more effective.

The invention relates to an artificial intelligence-based interactive digital human generation method, which comprises the following steps of defining basic features and generation requirements of a digital human through a condition vector, generating a potential space representation by using an encoder, generating a preliminary image by using a decoder, and adjusting the image in combination with a style migration technology to adapt to a target environment, and improving the resolution and details by using a super-resolution network, and finally generating a static digital human image. And collecting multi-modal data, and integrating features through a fusion function to generate a unified representation. Based on conditional vectors, random noise, and multi-modal unified features, expressions and speech representations conforming to emotions or states are generated. And a basis is provided for expression and voice response of the digital human by identifying the emotion of the user. Real-time response is generated through a reaction function, and more accurate and intelligent interaction is achieved by combining reinforcement learning optimization behaviors and response strategies. A high-quality personalized digital human image can be generated, and intelligent response is realized through multi-modal interaction and emotion perception.

The invention relates to the technical field of boiler temperature measurement, in particular to a waste heat boiler three-dimensional temperature field construction method and system and a medium. The method comprises the following steps: performing optical fiber sensor grid deployment on the waste heat boiler to obtain a distributed temperature acquisition network; sampling the distributed temperature acquisition network to obtain a boiler real-time temperature sequence; performing data preprocessing on the boiler real-time temperature sequence to obtain a boiler initial temperature field; based on the initial temperature field of the boiler, fluid dynamic model parameter construction is conducted on the waste heat boiler, and a boiler fluid characteristic parameter set is obtained; performing multi-physical field coupling on the boiler fluid characteristic parameter set to obtain a boiler theoretical temperature field; acoustic sensor array arrangement and data acquisition are carried out on the waste heat boiler, and acoustic spectrum acquisition data are obtained. By fusing various temperature field data, respective advantages and limitations can be complemented, and comprehensive and accurate temperature information of the waste heat boiler is obtained.

The invention discloses a plant disease and insect pest detection device and method based on unmanned aerial vehicle high-altitude texture imaging, and belongs to the technical field of intelligent agriculture. The plant disease and insect pest detection device comprises an unmanned aerial vehicle body, a holder, a high-altitude texture camera and a small host; the high-altitude texture camera is arranged below the holder, the holder is arranged below the small host, the small host is arranged below the unmanned aerial vehicle main body, the unmanned aerial vehicle main body is provided with a front high-definition camera, a battery and an illumination sensor, and the high-altitude texture camera is wirelessly provided with a handle and a flight remote controller through Bluetooth; according to the plant disease and insect pest detection method, high-precision recognition of disease and insect pests is achieved on the basis of high-altitude texture feature fusion in combination with an environment perception driven data enhancement module, a self-adaptive multi-scale image convolution feature extraction and fusion module and a disease and insect pest area attention fusion module. The technology effectively supports precise detection of diseases and insect pests in agricultural production, and provides a scientific basis for agricultural management and pesticide application.

The invention provides a black soil cultivated land organic carbon degradation diagnosis method based on geographic big data and AI, and the method comprises the steps: constructing a black soil area cultivated land organic carbon degradation potential influence factor data set and a diagnosis model, judging the spatial correlation of potential influence factors, training the diagnosis model based on the data set, and obtaining a diagnosis result of the black soil area cultivated land organic carbon degradation. And performing black soil area cultivated land organic carbon degradation influence factor identification based on the trained diagnosis model, and performing organic carbon degradation influence factor action mechanism analysis based on an identification result. According to the method, by utilizing high resolution and high timeliness of geographic space big data and cloud computing, multiple types of potential factors such as climate, soil, terrain and agricultural activities which influence organic carbon change of the cultivated land in the black soil area can be quickly calculated on a large-area scale, and manpower, material resources and time costs caused by large-area data collection and analysis are avoided; by using an artificial intelligence algorithm, the main control factor of organic carbon degradation of the cultivated land in the black soil area is accurately identified from multiple potential factors, and the current degradation situation can be objectively evaluated.

A non-contact three-dimensional displacement sensor disclosed by the present invention comprises a mechanical probe and a detection circuit, the mechanical probe part comprises a differential fixed block and an end cross beam, and two parallel inner side surfaces of the differential fixed block are fixedly provided with a first differential coil and a second differential coil respectively. A first target conductor located under the first differential coil is fixedly arranged on the top face of the suspension end of the cross beam, and a second target conductor located over the second differential coil is fixedly arranged on the bottom face of the suspension end of the cross beam. A pair of receiving coils Lx is fixedly arranged on the top face of the first target conductor or / and the bottom face of the second target conductor in the horizontal x direction, a pair of receiving coils Ly is fixedly arranged in the horizontal y direction, and the two pairs of receiving coils form a structure shaped like a Chinese character'jing '. The non-contact three-dimensional displacement sensor provided by the invention has better quasi-static resolution and displacement sensitivity in three directions, realizes decoupling in all directions, and has excellent performance in indexes such as linearity, measuring range and the like.

The invention relates to the technical field of charging load prediction, in particular to an electric vehicle charging load prediction method and system combining sequence decomposition and feature extraction and a storage medium. Comprising the steps of performing variational mode decomposition on time sequence load data to obtain a plurality of intrinsic mode functions; performing zero padding expansion on each intrinsic mode function, and then converting the intrinsic mode function to a frequency domain; enhancing the frequency domain data through a multi-layer perceptron based on complex domain calculation; constructing a prediction model based on a complex field attention module and a residual module, inputting the enhanced spectrum data into the prediction model, extracting a data characteristic frequency combination through the complex field attention module and the residual module, and carrying out weighted summation on all intrinsic mode functions as the output of the prediction model; and calculating a difference value between real data and prediction data according to a mean square error of a time sequence and an average absolute value error of a frequency spectrum to obtain time domain and frequency domain mixed loss, and updating prediction model parameters. According to the invention, the problem of large prediction error under the influence of random load fluctuation is solved.

The invention discloses a rolling bearing fault diagnosis method based on a WSETCNNLBKALSSVM, and the method comprises the steps: S1, carrying out the modal decomposition of a rolling bearing vibration signal collected by a sensor through employing the high time-frequency resolution characteristic of wavelet synchronous extraction transformation, obtaining a decomposed time-frequency image, and carrying out the lossless compression processing of the time-frequency image; s2, dividing the time-frequency image data after lossless compression processing into a training set and a test set according to a set proportion; s3, inputting the generated time-frequency image data in the training set into a two-dimensional convolutional neural network, and performing adaptive fault feature extraction; and S4, taking an output result of a full connection layer of the two-dimensional convolutional neural network as an input of a least square support vector machine, optimizing hyper-parameters of the LSSVM by adopting an improved black-wing optimization algorithm, and completing classification of fault types through the LSSVM. The rolling bearing fault diagnosis method has higher classification precision and diagnosis accuracy in rolling bearing fault diagnosis.

The invention discloses a long-time-sequence remote sensing crop classification method. The method comprises the following steps: extracting spectral features of a to-be-detected region in different periods; constructing an NDVI phenological characteristic curve of the typical crops; judging the crop category based on the similarity between the curves, and generating a crop classification map of 10 meters of the to-be-detected region; performing upscaling and stratified sampling processing on the crop classification result of the sentinel No.2 satellite, and constructing training and verification samples of Landsat satellite crop classification; carrying out space-time fusion on the Landsat multiband spectral image, and constructing crop supervision classification input based on a characteristic spectrum of the synthesized image and a derived vegetation index; and constructing a crop classifier of a Landsat satellite based on the random forest model, and generating a crop classification map of the to-be-detected region. According to the method, crop classification is carried out by fully utilizing the advantages of high resolution of the sentinel No.2 satellite and long sequence of the Landsat satellite, and long-sequence crop classification drawing of the to-be-detected area can be realized.

The invention relates to the technical field of gene engineering, and discloses a KASP marker related to thousand seed weight of wheat and application of the KASP marker. The SNP locus is located at the 33397938bp position of the wheat 1A chromosome, and the polymorphism is C / T. Through phenotypic difference and exon capture analysis, the SNP site closely related to the thousand seed weight is determined, and a specific KASP primer combination is designed, so that rapid and accurate identification of the wheat thousand seed weight is realized. The application of the technology has significant significance in accelerating high-yield breeding of wheat. According to the method, the selection efficiency is improved, the breeding period is shortened, and wheat varieties with ideal thousand seed weight can be quickly identified and cultivated. By using the KASP primer combination, an effective tool is provided for molecular marker-assisted selection of wheat yield-related traits, and the overall yield and quality of crops can be improved.

The invention discloses a personalized insurance scheme customization method and device, equipment and a storage medium, and belongs to the technical field of artificial intelligence. The method comprises the steps of obtaining a to-be-processed text and a to-be-processed image; capturing high-resolution details of the image to be processed; capturing low-resolution details of the image to be processed; performing feature fusion on the image high-resolution features and the image low-resolution features to obtain image fusion features; performing text semantic recognition on the to-be-processed text to obtain text semantic features; carrying out feature fusion on the image fusion features and the text semantic features to obtain a multi-modal feature sequence; and importing the multi-modal feature sequence into a pre-trained insurance scheme generation model, and outputting a personalized insurance scheme. The invention further relates to the technical field of block chains, and the to-be-processed data is stored on the block chain node. According to the method, the information complementarity between the image and the text is enhanced through multi-modal feature fusion, and the accuracy and personalized matching degree of insurance scheme generation are effectively improved.

The invention discloses an urban low-altitude wind field monitoring method and system, and relates to the field of urban environment forecasting, and the method comprises the steps: building a four-dimensional variation assimilation system based on a four-dimensional variation target extensive function formula by taking a wind field state variable of a target urban region as a state variable in the four-dimensional variation target extensive function formula; inputting the wind field observation data of each observation point in the target city area into a four-dimensional variational assimilation system for initial field assimilation to obtain assimilated wind field data; taking the assimilated wind field data as initial meteorological field data, performing wind field simulation calculation by using a preset WRF-LES mode, and obtaining wind field forecast data in the target urban area; real-time monitoring data of each observation point in a target city area is assimilated into an initial and boundary meteorological condition input field of a preset WRF-LES mode through a four-dimensional variational assimilation system, so that wind field information in the target city area is reflected, and city low-altitude wind field forecast data can be acquired in near real time with high resolution.

The invention belongs to the technical field of aging state evaluation, and discloses a high-voltage dry-type bushing insulation aging state evaluation method based on frequency domain characteristics. According to the method, the FDS is adopted, and the aging state of the insulating material is evaluated according to the change of the complex capacitance, the complex dielectric constant, the dielectric loss factor and the like of a medium along with the frequency under a low-voltage sine alternating electric field. The FDS shows remarkable superiority in a plurality of aspects. The method comprises the following steps: firstly, measuring a dielectric constant and dielectric loss of an insulating medium in a wide frequency range by an FDS method so as to obtain response characteristics of the insulating material under different frequencies; according to the method, the frequency domain dielectric spectroscopy FDS is combined with the advantages of wide frequency range measurement, frequency domain feature recognition, comprehensive reflection of electrical characteristics and the like, so that accurate evaluation of the insulation aging state of the high-voltage dry-type bushing is realized. The safety and the stability of a power system can be improved, and faults and accidents caused by insulation aging can be reduced.