10results about How to "Effective alignment" patented technology

The invention discloses a feeding mechanism of an automatic cold header for machining piston pins. The feeding mechanism comprises a main machine (1), a feeding mechanism body (20) is arranged on the front portion of the main machine (1), the feeding mechanism is connected with an iron wire frame (2) through an iron wire, and a discharging mechanism (3) is arranged on one side of the main machine (1). The feeding mechanism body comprises a base (4), fixed wheels (5) are arranged on one side of the base (4), and moving wheels (6) are arranged on the other side of the base (4). Side bases (7) are arranged on two sides of the base (4), sliding bases (8) are arranged on the inner sides of the side bases (7), and the contact surfaces of the sliding bases (8) and the side bases (7) are in sliding fit. The moving wheels (6) are arranged on the upper sides of the sliding bases (8), a threaded barrel (9) is fixed below the sliding bases (8), a threaded column (10) is arranged in the threaded barrel (9), a bearing pedestal (11) is arranged at one end of the threaded column (10), a rotating ring (12) is fixed to the other end of the threaded column (10), the rotating ring (12) is provided with a circle of small holes (13), and a rotating handle (14) is arranged in one small hole (13) in an embedded mode.

The invention belongs to the technical field of bearing lubrication state monitoring, and particularly relates to a bearing lubrication state real-time monitoring method based on hydrodynamic model correction, and the method comprises the steps: obtaining an original vibration acceleration signal and operation state data, carrying out the variational mode decomposition of the original vibration acceleration signal, and extracting a cavitation feature component; calculating a cavitation kinetic energy impact index according to the extracted cavitation characteristic component and the operation state data; dynamically correcting the equivalent fluid density of the lubricating oil according to the cavitation kinetic energy impact index and the contact area pressure of the bearing estimated by the running state data; and solving the minimum oil film thickness of a lubricating oil film by utilizing the corrected equivalent fluid density, and determining a lubrication safety margin index by combining the temperature rise rate of the bearing. According to the invention, the defect of monitoring result distortion caused by single-phase fluid hypothesis is overcome, and the accuracy of bearing lubrication state monitoring and early risk early warning is improved.

The utility model discloses a combined connection structure of a battery box, which comprises splicing seats clamped at two ends of a battery cell, two adjacent sides of each splicing seat are respectively provided with a first key strip, and the other two adjacent sides of each splicing seat are respectively provided with a first key slot matched with the first key strip; the adjacent splicing seats are mutually clamped through first key strips and first key grooves, the frame assembly comprises a first L-shaped frame body and a second L-shaped frame body, second key strips matched with the first key grooves are formed in the inner wall of the first L-shaped frame body, and second key grooves matched with the first key strips are formed in the inner wall of the second L-shaped frame body; the end parts of the first L-shaped frame body and the second L-shaped frame body are mutually clamped and locked; according to the lithium battery support structure, the problem that the upper ends of the battery cells are difficult to align and clamp in the holes of the support when the upper support is mounted and clamped due to the fact that the upper support and the lower support of the lithium battery support structure in the prior art are mostly of an integrated structure and the number of the battery cells is large is solved.

The application provides an infrared and visible light image fusion method based on text semantic consistency guidance, relates to the technical field of multi-modal image fusion, and comprises the following steps: fine-grained text semantics of infrared and visible light images are generated respectively and mapped to a unified embedding space; a cross-modal attention mechanism is used to bidirectionally compensate and enhance the text semantics, and a unified text semantic prior is constructed; a structure-intensity decoupled double-branch encoder is used to extract structure texture features of visible light and intensity saliency features of infrared respectively; under the guidance of the text semantic prior, explicit semantic consistency constraints and implicit semantic distribution consistency constraints are used to align the double-modal visual features in a shared semantic space; and finally, the text semantic prior is used as a global modulation signal to adaptively weight and fuse the aligned features and decode to generate a fusion image. The application effectively solves the problems of insufficient semantic modeling and poor fusion result consistency of existing methods.

This invention relates to the fields of biomedical imaging and spatial metabolomics, specifically to a method and system for registering spatial metabolomics images with HE-stained images. The method includes the following steps: acquiring two adjacent frozen sections of the same tissue sample; performing image preprocessing using the frozen sections; obtaining the extraction and matching results of key point features in the images; performing rigid image registration and obtaining a rigid registration result; performing non-rigid image registration and obtaining a non-rigid registration result; and obtaining the spatial location information of the spatial metabolic imaging using the non-rigid registration result. This invention, by combining advanced image processing technology and machine learning algorithms, automatically registers metabolic imaging images with HE-stained images, significantly improving registration accuracy and efficiency. It effectively solves the problems of large errors, low efficiency, and strong operational dependence in existing manual registration methods, meeting the needs of high-throughput, high-precision spatial metabolomics analysis and supporting large-scale data processing.

The application discloses a large language model training method based on mortise and tenon building drawing and text alignment, relates to the technical field of model training, and comprises the following steps: acquiring a mortise and tenon structure library and a conventional structure library; acquiring structure characteristic parameters of mortise and tenon structures by using a structure disassembly analysis method; acquiring structure conventional parameters of the mortise and tenon structures by using a conventional correlation method; constructing a mortise and tenon text training method, and training a large language model by using the mortise and tenon text training method; in the aspect of mortise and tenon recognition and discrimination, the application lacks an analysis method for point feature and geometric position constraint of mortise and tenon structures, cannot accurately recognize different mortise and tenon structures and judge the positions of the mortise and tenon structures, and thus problems of recognition confusion and text annotation errors and omissions exist when recognizing mortise and tenon in drawings.

The invention provides a model training method, a voice processing method and related equipment. The method comprises the following steps: acquiring a sample audio and a text annotation and an audio annotation corresponding to the sample audio; performing first training processing on the language processing model according to the sample audio and the audio annotation to obtain a first audio processing model; and performing second training processing on the first audio processing model according to the sample audio and the text label to obtain a target audio processing model. According to the embodiment of the invention, the stability and accuracy of a model processing result can be improved.

This invention provides a traffic situation perception method, device, electronic device, and storage medium, belonging to the field of intelligent transportation technology. The method includes: acquiring multi-dimensional heterogeneous data, including human-dimensional data, vehicle-dimensional data, road-dimensional data, and cloud-dimensional data; acquiring the target scene categories corresponding to the multi-dimensional heterogeneous data and determining the scene priorities corresponding to the target scene categories; dynamically configuring perception resources according to the scene priorities to obtain configured perception resources, which are used to characterize sensor sampling frequency, sensor cooperation mode, and redundant device on / off status; and performing traffic situation perception based on the configured perception resources to obtain traffic situation perception results. The above solution can achieve real-time and accurate assessment of traffic situations, providing lane-level refined information for traffic management departments, supporting accurate decision-making, and effectively alleviating road network congestion and accident risks.

This invention relates to a feature extraction model training and feature extraction method and apparatus, comprising: inputting a training image set into an initial model; extracting several local features from the training images by a local feature extraction layer; enhancing the several local features using an attention mechanism by a feature enhancement layer to obtain several enhanced local features; a global feature extraction layer aggregating features based on the several enhanced local features to obtain global features; and a classifier obtaining predicted classification parameters for the training images based on the global features. The model parameters of the initial model are adjusted according to the label parameters and predicted classification parameters of the training images to obtain a trained classification model. A feature extraction model for extracting global features is constructed based on the local feature extraction layer, feature enhancement layer, and global feature extraction layer in the classification model. This provides an end-to-end global feature extraction scheme, reducing algorithm complexity.

This invention relates to the field of network security technology and discloses a vulnerability graph alignment method based on text and graph embedding interaction, comprising the following steps: obtaining publicly available vulnerability data from the National Information Security Vulnerability Database and the National Information Security Vulnerability Sharing Platform; constructing a dataset for vulnerability entity alignment experiments; constructing a graph embedding representation model QuatAE based on the QuatE model; and introducing a new relation r. a Serving as a "connecting bridge" between two knowledge graphs, this vulnerability graph alignment method, based on text and graph embedding interaction, fully considers the characteristics of vulnerability graphs. It uses a unified model to comprehensively consider information at the entity, neighbor, and attribute levels, achieving good alignment results. Besides supporting vulnerability graphs, it is also compatible with entity alignment work for knowledge graphs in other domains because it incorporates information from various levels of the knowledge graph that affect knowledge alignment into the training process, demonstrating strong versatility.