17results about How to "Speed ​​up the extraction process" patented technology

The application relates to the technical field of electronic reconnaissance and signal processing, and discloses a radiation source individual fine identification method and system based on multi-modal feature fusion, which comprises the following steps: performing preprocessing on obtained radiation source original signals to obtain signal pulse units; extracting time domain sequences of the signal pulse units and performing multi-domain transformation to generate a first time-frequency image, a second time-frequency image and a bispectrum image; inputting the first time-frequency image, the second time-frequency image, the bispectrum image and the time domain sequence into a multi-modal fusion neural network; extracting corresponding image depth features and comprehensive sequence features through three image processing branches and a sequence processing branch; performing cross-modal fusion on the three image depth features and the comprehensive sequence features through a fusion module to output a radiation source individual identification result. The application improves the identification accuracy of radiation source individuals in a complex electromagnetic environment.

The invention discloses a park weed accurate identification method and system based on an improved YOLOv11 deep convolutional network, and the method comprises the steps: 1, constructing a multi-scene park weed image data set, 2, carrying out the data enhancement and preprocessing, 3, constructing an improved YOLOv11 lightweight network model, 4, designing a self-adaptive loss function, and 5, carrying out the recognition of a multi-scene park weed image data set. Multi-stage model training and optimization are executed; and step 6, weed real-time identification and result output are realized. According to the method, a lightweight attention mechanism and an improved multi-scale feature fusion structure are introduced, the extraction and distinguishing capability of the network on small-scale weed features under a complex background is remarkably enhanced, the false detection and omission ratio is effectively reduced, and the dynamic label distribution strategy and a loss function fusing global context information are adopted, so that the robustness of the network is improved. And the detection robustness and the positioning precision of the model in dense, shielded and form-variable weed scenes are improved.

The invention relates to the technical field of image processing, and discloses a landslide surface crack three-dimensional evolution unmanned aerial vehicle image extraction and quantification method and system, and the method comprises the steps: carrying out the bundle adjustment of an ultrahigh overlapping degree aerial image of a landslide surface, and obtaining an image shooting position, an image attitude parameter and a sparse three-dimensional point cloud; constructing a high-precision digital earth surface model; performing digital mosaic on the aerial image with the ultrahigh overlapping degree to obtain an orthoimage; performing semantic segmentation on the orthoimage to obtain a crack binary segmentation map of the orthoimage so as to identify a main crack and a secondary crack; performing three-dimensional geometric parameter analysis on the main crack and the secondary crack to obtain a crack three-dimensional space attribute parameter; carrying out evolution trajectory tracking on the crack three-dimensional space attribute parameters to obtain evolution data so as to construct a visual map of the landslide surface; according to the invention, the efficiency of extracting and quantifying the three-dimensional evolution unmanned aerial vehicle image of the landslide surface crack can be improved.

The embodiment of the invention provides a training method and device for a trajectory prediction model of an automatic driving vehicle. The method comprises the following steps: acquiring a historical driving data set of a target vehicle; based on the historical driving data set, constructing a target unlabeled data set, a target labeled data set and a preference optimization data set; based on the target unlabeled data, pre-training the initial visual motion model to obtain a pre-trained model; training the pre-training model based on the target labeled data set to obtain a training model; and based on the preference optimization data set, performing direct preference optimization training on the training model to obtain a trajectory prediction model. The technical problem that the accuracy of automatic driving track prediction is low is solved.

This utility model relates to the technical field of beverage extraction equipment, specifically disclosing a pecan beverage extraction device with homogenization assistance. The device includes a vessel body, which is the main equipment for pecan beverage extraction. The upper cover of the vessel body is penetrated by the output end of a drive motor, which is connected to a stirring rod. The bottom end of the stirring rod is rotatably connected to the top end of a support frame, and the bottom end of the support frame is aligned with the bottom opening of the vessel body. In this pecan beverage extraction device with homogenization assistance, the drive motor rotates the stirring rod. The spiral-shaped stirring blades on the outer wall of the stirring rod, while rotating, cause the material to move in a circular motion. The spiral shape generates axial thrust, promoting the liquid to circulate up and down, enhancing the stirring effect. This allows the extract to fully contact the pecan raw material, effectively increasing the extraction rate of water-soluble active substances, improving the overall extraction effect, reducing raw material waste, strengthening the stirring action, and accelerating the extraction process.

This invention discloses an automatic classification model and method for diabetic retinopathy. The automatic classification model includes convolutional input, feature extraction, and classification output. The convolutional input consists of 7×7 convolutions and max pooling, responsible for resizing the input image to extract shallow features. Feature extraction includes four alternately connected dense blocks and three transition layers, mainly responsible for extracting feature information of retinal lesions. The classification output consists of global average pooling and fully connected layers, responsible for outputting the classification result of diabetic retinopathy. This invention designs an attention mechanism (SC) based on retinal lesion features to increase the network's focus on lesion features and fully extract retinal lesion features. Using the lightweight network DenseNet121 as the backbone, the dense layers are improved by connecting the SC module after 3×3 convolutions, increasing the classifier's ability to express high-level semantic features and improving classification accuracy. This invention can achieve automatic classification of diabetic retinopathy levels.

This application relates to a method, system, and storage medium for extracting structured data from literature. The method includes: acquiring information about the literature to be processed and information about the domain of interest; generating intent recognition routing results by calling a corresponding domain intent recognition model based on the information about the domain of interest; constructing prompt words based on the information about the domain of interest and the intent recognition routing results, and extracting first target literature information from the information about the literature to be processed; constructing a corresponding domain similarity function based on the intent recognition routing results, and generating second target literature information by removing similar literature from the first target literature information; and outputting experimental information, experimental knowledge graphs, and source relationships in a structured form based on the second target literature information. This application determines the intent recognition routing results through domain intent recognition, adaptively selects the extraction strategy and domain similarity function, and combines multimodal parsing and block processing to achieve high-completeness extraction, cross-domain adaptive fusion, and traceable structured output of experimental information from cross-domain literature.

The invention relates to the technical field of computer vision and agricultural automation, and particularly discloses a method for grading the quality of ziziphus mauritiana based on an improved YOLO model so as to solve the problems that a traditional grading method is low in efficiency, poor in precision and difficult to deal with complex backgrounds and small damage. According to the method, a BCW-YOLO model is provided by constructing a multi-angle Maoyesian jujube image data set and performing data enhancement, a bidirectional feature pyramid network (BiFPN) is integrated on the basis of YOLOv8 to improve the feature fusion capability, a context conversion attention mechanism (COT) is introduced to enhance detail capture, and a WIoUv3 loss function is adopted to optimize the positioning performance. According to the scheme, the model is remarkably superior to a traditional model in the aspects of accuracy, recall rate, mAP and other indexes, the model is particularly excellent in performance in complex background and small damage detection, an effective scheme is provided for achieving automatic and accurate grading of the ziziphus mauritiana, and the method has wide application prospects.

The utility model discloses a kind of stem cell exosome's extraction device with repairing skin and anti-aging, it is related to stem cell exosome extraction technical field.The utility model includes dilution tank, dilution tank top is fixed with quantitative feeding assembly, flush type tank cleaning assembly is arranged in the rear side of dilution tank, filter tank is arranged in the side of dilution tank, a part of flush type tank cleaning assembly is respectively located in dilution tank inside and filter tank inside, and fixed with discharge pipe and blowdown pipe in the side of filter tank, discharge pipe is located above blowdown pipe.The utility model can quantitatively feed exosome and diluent by quantitative feeding assembly, and it is convenient to reasonably configure reagent, and exosome can be diluted to suitable concentration, reduce the interaction between exosome, avoid aggregation to form precipitate, flush simultaneously to dilution tank and filter tank by flush type tank cleaning assembly, accelerate entire exosome extraction process.

The utility model belongs to the technical field of electronic atomized liquid processing, and particularly relates to a healthy electronic atomized liquid processing device based on an alcohol extraction method, which comprises an extraction tank, the outer surface of the bottom of the extraction tank is fixedly connected with a stirring mechanism, one side of the top end of the extraction tank is provided with a feed port, and the center of the bottom end of the extraction tank is fixedly sleeved with a discharge pipe. The extraction tank comprises a stirring mechanism for stirring raw materials, a spraying mechanism for spraying a solvent and a filtering mechanism for removing impurities; one side of the discharging pipe is fixedly sleeved with an inclined cylinder, and the inclined cylinder is obliquely arranged; in the using process, through the arrangement of the filtering mechanism, solid impurities in a mixed solution can be accurately intercepted, an extracting solution and the impurities can be effectively separated, the purity of the extracting solution is ensured, the impurities are discharged through the impurity outlet pipe, frequent manual intervention is not needed, manpower is saved, the impurities can be continuously and efficiently treated, and continuous and stable operation of the device is guaranteed.

The invention discloses a batch statistics and analysis method, device and equipment for emission data of a hybrid electric vehicle and a storage medium, and relates to the technical field of vehicle emission, and the method comprises the steps: obtaining vehicle model information, an emission type, a test site, a test report file and a data statistics template inputted by a user; determining a target test template according to the vehicle type information, the test site and the emission type; based on the target test template, reading the test report file to obtain test data; and obtaining a data statistics file based on the data statistics template and the test data. A preset test template is matched through a test site input by a user and an emission type, and automatic analysis of reports in different formats is achieved; and the analyzed test data is automatically written into a statistical file according to a standardized format in combination with a data statistical template selected by a user, so that the efficiency and accuracy of data extraction and summarization are remarkably improved.

The application discloses a kind of phthalocyanine copper / inverted pyramid perovskite heterojunction photodetector and preparation method, comprising the following steps: 1) etching out the silicon piece with pyramid surface microstructure, as the base of growing perovskite single crystal;2) preparation perovskite solution, utilize silicon piece and glass piece as the frame of growth area restriction, utilize seed growth method, space limited method and inverse temperature crystallization method combined growth method preparation surface pyramid perovskite single crystal;3) utilize thermal evaporation in perovskite single crystal surface evaporation phthalocyanine copper film formation heterojunction, then utilize conductive carbon paste as upper and lower electrode preparation photodetector.The photodetector prepared by the application is stable at normal temperature and pressure, and has better photoelectric response relative to the control group device in a high humidity environment, and the device has good long-time humidity stability, which has guiding significance for the future high-performance photodetection field in high humidity environment such as sea, ship and island.

The invention relates to the technical field of intelligent traffic video analysis and law enforcement assistance, and particularly provides a traffic violation detection and linkage reporting method and system based on YOLOv13. The method comprises the following steps: accessing a video stream and preprocessing; a SegNextAttention attention and SDI (Serial Digital Interface) feature interaction module is introduced to improve the YOLOv13 network; inputting the image sequence into the improved YOLOv13, and outputting a target bounding box, a violation category and confidence; obtaining stability judgment through target association and time sequence verification, triggering violation events according to a rule base, and performing duplicate removal; and generating an evidence packet containing a panorama and a target cutting picture, binding geographic coordinates for uploading, performing hierarchical alarm on a linkage peripheral, and monitoring the state of the equipment for remote configuration at the same time. According to the method, the problems of false detection, missing detection, category jitter and insufficient engineering closed loop in a complex scene are solved, and the traffic violation discovery and disposal efficiency is improved.

This invention relates to the field of optical fiber geometric reconstruction technology, specifically to a one-dimensional reconstruction analysis method for optical fiber ring stress, comprising: reading and processing finite metadata of the optical fiber ring to obtain Cartesian coordinate system node information; converting the Cartesian coordinate system node information to the cylindrical coordinate system; determining the forward and reverse optical fiber paths to which each node belongs; calibrating the length of each node based on the optical fiber winding process parameters and the optical fiber path to obtain the one-dimensional unfolded length of each node; interpolating based on the one-dimensional unfolded length to obtain a discrete point stress value sequence; conducting an actual optical fiber ring experiment based on the finite metadata to obtain a polarization coupling curve; comparing the polarization coupling curve with the discrete point stress value sequence to obtain the one-dimensional reconstruction accuracy result; this invention can improve the accuracy of optical fiber ring stress analysis results along the optical path.

The invention discloses an earth and rockfill dam ERT (electrical resistivity tomography) image leakage feature identification method and system based on improved YOLOv8, and belongs to the technical field of disaster prevention and reduction of water conservancy projects. The method comprises the following steps: acquiring earth and rockfill dam resistivity tomography original data, performing inversion imaging and standardization processing, and constructing an initial resistivity tomography image data set; expanding the resistivity tomography image data set by adopting a data augmentation operation; carrying out labeling and data set division on the leakage area; an instance segmentation neural network model of the improved YOLOv8 architecture is constructed, and a data set is used for training; inputting a to-be-detected ERT image into the trained model, and outputting a bounding box, confidence and a segmentation mask of a leakage area; and finally, post-processing is carried out, and an improved linear growth model is utilized to evaluate leakage characteristics. The method effectively improves the recognition precision and robustness of the leakage features in the ERT image, and achieves the automation and intelligentization of leakage detection.

The application discloses a bearing fault detection method, system, medium and electronic equipment, and the method comprises the following steps: collecting one-dimensional time series vibration signals of a rolling bearing to be diagnosed under a running state; reconstructing the one-dimensional time series vibration signals into a phase space trajectory matrix representing system dynamics trajectories; converting the phase space trajectory matrix into a two-dimensional gray-scale image; inputting the two-dimensional gray-scale image into a preset deep image denoising convolutional neural network to output a feature image corresponding to the two-dimensional gray-scale image after noise reduction; inputting the feature image after noise reduction into a preset deep convolutional classification neural network for feature extraction and classification, and outputting a fault state category corresponding to the rolling bearing to be diagnosed. Therefore, by adopting the embodiments of the application, the robustness and accuracy of bearing fault diagnosis can be significantly enhanced, and high-reliability fault state determination is realized.

This invention provides a polyp image segmentation method based on depth guidance and a triple decoder. Using RGB images as input, the method expands the channels of the depth image, extracts its multi-layer features, and performs layer-by-layer feature fusion. This effectively utilizes the spatial structural characteristics of polyps in the depth image, enhances multi-scale semantic and spatial location information, and helps the network extract the polyp region more completely. A triple decoder architecture is designed to extract fine-grained detail features and deep semantic features of the polyps. The combined decoder integrates the advantages of both types of features, improving multi-scale feature extraction and fusion capabilities. A high- and low-resolution dual-branch feature extraction strategy is adopted to adapt to high-resolution input. Combined with the advantages of a pre-trained backbone network, this method reduces model computational complexity while ensuring no loss of polyp detail information, thus balancing segmentation performance and operational efficiency.