14results about How to "Accurate feature extraction" patented technology

The invention discloses a COD (Chemical Oxygen Demand) prediction method based on a two-channel convolution time sequence mechanism, which comprises the following steps of: acquiring COD original data for preprocessing to obtain COD input characteristics; constructing a prediction model, and importing data into a dual-channel convolution module; importing the first double-channel convolution output feature vector and the second double-channel convolution output feature vector into a double attention memory network to obtain a time sequence dependence feature; the time sequence dependence characteristics are imported into an output module, and predicted COD output is obtained; training the model, and in the training process, optimizing hyper-parameters of the model based on an improved pelican algorithm; and importing real-time COD original data into the trained model, and obtaining real-time COD output. According to the method, a high-efficiency effluent COD content prediction model of the sewage treatment plant is constructed by creatively fusing the two-channel convolution and the double-attention memory network.

The invention belongs to the technical field of image analysis, and particularly relates to a mulberry growth period identification method and system based on image analysis, and the method comprises the steps: obtaining image data of each growth period of a mulberry, preprocessing the image data, and inputting the preprocessed image data into a feature extraction module; performing feature extraction on the image data in each growth period, and establishing a feature comparison table of the images in each growth period of the mulberry trees; mulberry image data of each mulberry planting area to be identified is collected in real time, and after preprocessing, the mulberry image data is input into the feature extraction module for real-time image feature extraction; inputting the real-time image features into a feature comparison module, calling a mulberry growth period image feature comparison table, and matching the real-time image features with the mulberry growth period image feature comparison table; obtaining the mulberry growth period of each mulberry planting area to be identified according to the matching result of the real-time image feature and the mulberry growth period image feature comparison table; a preset mulberry growth period management measure table is called, management measures of the mulberries in the mulberry planting areas are obtained, mulberry planting management is facilitated, and the economic benefits of mulberry planting are improved.

The invention discloses a clock travel time accuracy automatic detection method based on machine vision, and relates to the technical field of machine vision. The method comprises the steps of capturing an image and a video stream of a target clock in real time through a high-resolution camera, performing preprocessing, extracting clock dial and pointer characteristics through Hough transform and an image segmentation technology, calculating a pointer angle, matching the pointer angle with a standard dial image, and obtaining a static error index; the method comprises the following steps of: dynamically tracking a pointer motion track through an optical flow method and a Kalman filter, analyzing the influence of environmental factors on a travel time error, obtaining a dynamic error index, and finally, comprehensively analyzing a static error and a dynamic error, calculating the travel time error index of the clock, and comparing the travel time error index with an error threshold value, so as to judge the travel time accuracy of the clock. The method can automatically and accurately detect the timekeeper travel time error, improves the detection efficiency, reduces personal errors, and comprehensively analyzes the influence of environmental factors on the timekeeper travel time precision.

The present application provides an image processing method and system for rehabilitation training motion analysis, which relates to the field of image processing technology. By establishing a sample motion image set and annotating it, a feature recognition channel is established to perform rehabilitation motion fitting, and a time-series rehabilitation motion fitting result is established; a joint image capture of the user's rehabilitation motion is performed to establish a joint time-series image set including local images and global images; contour extraction is performed on the global image and the local image, and feature recognition is performed on the contour extraction result using the feature recognition channel to establish a feature recognition result; a user motion time-series set is constructed based on the corresponding image time sequence; and image annotation is performed on the user motion time-series set using the time-series rehabilitation motion fitting result. The present application solves the technical problem that the prior art cannot effectively distinguish and process image information from different perspectives and different parts when performing image recognition, resulting in low accuracy in rehabilitation training motion analysis, and improves the accuracy and efficiency of rehabilitation motion analysis.

The invention discloses a bolt pre-tightening force monitoring system and method based on ultrasonic waves. The system comprises an ultrasonic probe, a data acquisition and processing analyzer and the like, a probe array is uniformly mounted on the surface of a bolt, an improved depth residual network is arranged in the data acquisition and processing analyzer and can process a flight time difference signal, and an audible and visual alarm can trigger a three-level alarm according to pre-tightening force deviation. The data remote transmission device provides data for the digital twin system, the display unit displays a pre-tightening force distribution atlas in real time, and a digital twin system platform generates a three-dimensional visual dynamic model and the like. According to the method, bolt pre-tightening force monitoring is achieved through the system. The system is high in measurement precision, convenient to install, capable of achieving real-time early warning and intelligent operation and maintenance, and suitable for various industrial fields.

The present invention discloses an intelligent customer service method for adaptively adjusting interaction strategies using reinforcement learning, comprising the following steps: S1, obtaining natural language text input by a user, generating a multidimensional state vector; S2, applying a real-time dynamic clustering algorithm to group users, and updating clustering results; S3, generating an action space of a specific state, and combining a rule-based strategy with a learning-based strategy to enrich the action space; S4, aggregating neighbor node features through a graph convolutional network, and extracting local features; S5, applying a graph attention network to weight neighbor node features, and dynamically assigning attention weights; S6, using a convoluted ladder-type deep exploration network to optimize interaction strategies and optimize action selection; S7, combining online learning with an experience playback mechanism, continuously updating interaction strategies, and responding quickly to dynamically changing user needs. The present invention combines reinforcement learning, graph neural networks, and sentiment analysis technology to achieve adaptive adjustment and optimization of intelligent customer service interaction strategies.

The invention discloses a feature extraction method based on a directional space and a multi-scale feature extraction network and a detection system, relates to the technical field of cloth flaw detection, and solves the problem of low feature extraction precision when feature extraction is performed on fine flaws with different shapes in the prior art. The method comprises the steps that a textile cloth image is acquired, feature extraction is conducted on the textile cloth image through a directional space and multi-scale feature extraction module, and the directional space and multi-scale feature extraction module carries out feature extraction on the textile cloth image; the directional space and multi-scale feature extraction module adopts a multi-branch parallel gradient stacking mode to arrange an information flow of network internal feature extraction, and a re-parameterization convolution module is adopted as a branch feature extraction component in the directional space and multi-scale feature extraction module, so that spatial features of warp and weft flaws can be extracted more effectively; therefore, the feature extraction is more accurate, and the defect features from the global to the local and from the small scale to the large scale can be captured.

This invention relates to a method for NAION prediction and acute phase differentiation based on deformable convolution and multi-site OCTA, belonging to the field of ophthalmic disease diagnosis and medical image processing technology. This invention acquires OCTA images of ocular samples and extracts data from key sites. Using the OCTA deformable convolution feature extraction module as the core feature extraction backbone network, its optimal performance is verified through single-site experiments, and a multi-site joint diagnostic framework is constructed. Radiomics features are introduced, and deep fusion of deformable convolution features and radiomics features is achieved through a bidirectional cyclic feature interaction module. Finally, a hybrid expert module is added to the three-site joint model to decouple NAION prediction from the acute phase differentiation task of NAION / ON, improving diagnostic accuracy. This invention effectively solves the problems of difficult accurate differentiation between the acute phases of NAION and ON and insufficient early prediction of NAION, providing reliable technical support for ophthalmic clinical diagnosis.

The present invention provides a surface morphology reconstruction method and system integrating machine learning and spatial frequency analysis, including: step 1: using the analytical formula of the spatial frequency method to generate a large number of two-dimensional arrays representing rough surfaces; step 2: by analyzing the height distribution and frequency energy proportion of the generated surface, combined with the machine learning method, extracting the characteristic variables of the analytical formula; step 3: combining the test or simulation method to establish the relationship between the characteristic parameters and the surface performance, which is used for performance prediction and surface reconstruction in specific scenarios. The present invention can realize the surface morphology feature extraction based on the spatial frequency analysis method, reconstruct the rough surface suitable for current carrying performance prediction, and thus improve the practical significance of the surface reconstruction method in performance prediction.

The invention relates to the technical field of image display, in particular to a metal metallographic structure image classification display method and system. The method comprises the following steps: preprocessing a metal metallographic structure image, calculating and evaluating an image feature index Tzls, quantifying the overall quality of the metal metallographic structure image, automatically screening low-quality and repeated images, and reducing stored and processed invalid data; classifying the metal metallographic structure images by using two characteristics of structural factors and defect factors of the metal metallographic structure; the metal metallographic structure image is classified and graded by calculating and evaluating a structure characteristic index Jtzs and a defect characteristic index Qtzs; and all grade division results are transmitted to the image classification display module, so that timely and accurate data transmission is ensured, a user searches and filters according to a structure grade or a defect grade, a required image is quickly found, and the working efficiency is improved.

The invention relates to a spatio-temporal trajectory data character representation and feature extraction method, and belongs to the technical field of trajectory data processing. Through the steps of preprocessing, standard vectorization, unit circle character processing and feature extraction, efficient characterization and depth feature mining of spatio-temporal trajectory data are realized. According to the method, data missing is effectively filled, data processing efficiency is improved, data interpretability is enhanced through character representation, meanwhile, the feature extraction process is optimized, calculation complexity is reduced, and data analysis precision is improved. The method is suitable for various spatio-temporal trajectory data, has wide applicability and high flexibility, is easy to implement, and provides a novel and effective technical means for the fields of trajectory data analysis, clustering, pattern recognition and the like.

The invention relates to the technical field of power transformers, and provides a transformer voiceprint detection method and system based on edge intelligence and deep learning, and the method comprises the steps: S1, collecting an original voiceprint signal, and carrying out the preprocessing and voice enhancement, and obtaining a voiceprint signal; s2, carrying out real-time analysis and feature extraction on the voiceprint signal by the edge computing node by utilizing a preliminary analysis deep learning model, and judging whether voiceprint abnormity exists or not; s3, when the edge computing node judges that abnormity exists, the voiceprint signal and related feature data are uploaded to a cloud big data platform; s4, collecting massive voiceprint data, constructing and continuously updating a standard voiceprint library, and optimizing to generate a deep analysis model; s5, issuing the optimized depth analysis model or model parameters to an edge computing node, and updating the preliminary analysis model; and S6, identifying the type and severity of the defect, carrying out fault positioning, and triggering an early warning and decision instruction. According to the invention, the voiceprint of the transformer can be well detected.

A difference guided multi-directional complementary remote sensing land use classification mapping method comprises the following steps: S1, acquiring a public remote sensing image dataset and performing cropping; S2, constructing a multi-class pixel-level classification model for land use classification and mapping, and training the model by using the cropped remote sensing image dataset; S3, processing a remote sensing image to be classified and mapped by using the trained multi-class pixel-level classification model to obtain a land use classification map; the model adjusts the attention distribution of aggregated features by using difference information to enhance the difference between classes with similar textures; meanwhile, four sub-bands obtained by wavelet decomposition are used to extract edge features in different directions, and texture information is used to position edge details to retain the contour and boundary information of small-area ground objects. The present application can perform pixel-level classification on multi-class ground objects in a remote sensing image and generate a land use classification map.

The present invention relates to the technical field of blood oxygen saturation, and specifically to a method for detecting local brain tissue blood oxygen saturation, comprising the following steps: S1, based on the original spectral data of the brain tissue, using high-pass and low-pass filter methods to perform signal preprocessing, remove low-frequency and high-frequency noise in the signal, and use discrete Fourier transform and wavelet transform to perform frequency domain analysis and filtering processing to generate preprocessed spectral data. Based on the original spectral data of the brain tissue, the present invention uses high-pass and low-pass filters to perform signal preprocessing, effectively removes low-frequency and high-frequency noise, and uses discrete Fourier transform and wavelet transform to perform frequency domain analysis and filtering processing to generate high-quality preprocessed spectral data.