18results about How to "Integrated precision" patented technology

The invention provides a vehicle collision risk early warning method, system and device and a medium, and belongs to the technical field of vehicle safety control. The method comprises the steps that surrounding environment information of a vehicle is collected in real time through a vehicle built-in sensor and a GPS module; a multi-sensor fusion device technology is used to carry out comprehensive processing on surrounding environment information of a vehicle, and an environment perception model is generated after data integration; based on the environment perception model, target identification is carried out by using a computer vision technology based on a convolutional neural network so as to identify travelers, vehicles, traffic signs and obstacles; performing behavior prediction based on a deep learning model to predict a behavior track of a front vehicle or pedestrian; evaluating a potential collision risk in the current scene in combination with the target recognition result and the behavior prediction result; when a potential collision risk is detected, an early warning signal is sent to a driver, and when the driver does not take corresponding measures, active safety measures are automatically triggered to avoid collision or reduce accident damage.

The invention discloses a tooth three-dimensional digital model registration and tooth neck line extraction method based on oral scanning and CBCT, and relates to the field of tooth three-dimensional digital model registration. Comprising the following steps: respectively acquiring three-dimensional oral images of oral scanning and CBCT (cone beam computed tomography), and automatically segmenting three-dimensional structures of dental crowns and teeth by utilizing pre-trained V-Net and ToothNet image segmentation models. Alignment and registration of two modal images are realized through a feature extraction network, an information exchange network and a rigid transformation network. The feature extraction network extracts local and global features by using far-point sampling and a dynamic graph convolutional neural network, the self-attention and cross-attention module performs feature enhancement and information exchange between the same mode and different modes, and based on a registration result, a tooth neck line is further extracted and optimized. According to the method, the tooth three-dimensional digital model can be quickly and accurately registered, a high-precision tooth neck line extraction result is provided, the manpower and time cost of a medical institution can be reduced, and the medical efficiency is improved.

The invention discloses a book resource sharing and management platform based on cloud computing, relates to the technical field of book resource management, and realizes scientific quantification and durability dynamic analysis of book loss by combining a plurality of algorithms in a data processing module and a cloud computing analysis module. The data processing module constructs an environmental influence factor EHF and an annual aging factor AF through data cleaning and normalization processing, and effectively evaluates the long-term influence of humidity and time on books. Meanwhile, texture and color feature extraction is performed on the image data based on a visual algorithm, and a cover loss score CS and an internal paper wear score IS are generated. And the cloud computing and analyzing module dynamically computes a single borrowing loss rate Ds, an accumulated loss value Dt and a durability score Sr by using the comprehensive loss data set. The module generates a maintenance prompt through the evaluation of the dynamic loss rate and the long-term loss trend, and preliminarily evaluates the use condition of the book.

The invention relates to the technical field of data analysis, in particular to a multi-dimensional data fusion and intelligent analysis system for a smart park, which is characterized in that after various types of data are artificially divided into time sequence features, spatial features and attribute features, a controller respectively performs similarity calculation on the three features and then performs fusion; the time sequence features and the spatial features can cover most of data, the attribute features are used for capturing data which are not suitable for time and space distribution, and regular paths are introduced into the time sequence, so that unified path planning can be utilized to avoid data reference errors caused by path asynchronization and further avoid fusion failures; fusion is carried out by fully considering multiple features such as time, space and attributes of the data, information loss or wrong fusion caused by data isomerism is avoided, so that the accuracy of data fusion is improved, the capture capability of the data fusion method on complex data relationships is higher, and the data fusion efficiency is improved. And the fused data can more truly reflect the actual operation state of the park.

This invention relates to a method and system for automated generation of TSN network configurations based on a reflective agent. The system comprises a network state perception and intent reflection module, a YANG model configuration parameter and structure decision module, and a YANG model intelligent generation module. The network state perception and intent reflection module is responsible for structurally perceiving network state and resource information. The YANG model configuration parameter and structure decision module integrates multi-source knowledge and contextual information, intelligently generating deployable TSN scheduling parameters and YANG configuration structures based on business needs. The YANG model intelligent generation module automatically generates the corresponding YANG configuration model based on the optimized results, performs syntax and semantic verification, and then distributes it to the target device, achieving automated deployment of network configurations. This invention significantly improves the intelligence level and deployment automation capability of TSN network configuration by introducing reflective reasoning and feedback optimization techniques.

The present invention relates to the field of afforestation configuration technology, and is a afforestation configuration method and system based on geographic data, and the specific method includes: collecting initial geographic data of historical afforestation areas; performing feature selection processing and data dimension reduction processing on the initial geographic data; classifying afforestation design patterns of different afforestation areas to be selected, and evaluating the applicability of afforestation design patterns; updating the alternative afforestation design patterns of afforestation areas under different geographical conditions to the afforestation personnel design platform, and automatically providing suggestions to afforestation personnel. The present invention solves the problem in the prior art that the requirements of afforestation engineering management involve many aspects such as plot segmentation, garden landscaping, basal fertilizer application, shading, support, horizontal belt landscaping, working roads, etc., which leads to a huge workload when the design content is complicated.

The invention discloses an electronic commerce large model construction method and device and a medium, and relates to the technical field of electronic commerce. The method comprises the steps of obtaining historical transaction data, logistics data and unstructured data, and extracting features of the unstructured data through natural language processing; according to the historical transaction data and the logistics data, a sales prediction model and a logistics timeliness prediction model are constructed through Hadoop, and an e-commerce model is established based on an e-commerce industry database and an AI large model; performing cross validation and parameter optimization on the sales prediction model and the logistics timeliness prediction model, and integrating with an electronic commerce model to obtain an electronic commerce large model; and performing micro-servitization on the electronic commerce large model, and configuring a service discovery mechanism to enable micro-services to communicate with each other. According to the method, a comprehensive electronic commerce large model is formed, and comprehensive data analysis and decision support can be provided for enterprises.

The invention discloses a visible light-infrared target detection method based on structured feature extraction and dynamic weight prediction. The method comprises the following steps: carrying out target detection through a visible light-infrared target detection model based on structured feature extraction and dynamic weight prediction; the target detection model comprises a thermal radiation-motion feature extraction module, a multi-granularity visual representation module and a collaborative weight decision module; the thermal radiation-motion feature extraction module is used for capturing thermal radiation information in an infrared image and motion features of a target so as to generate infrared feature representation; the multi-granularity visual representation module is used for performing structured multi-granularity visual feature extraction on the visible light image so as to construct visible light feature representation; and the collaborative weight decision module is used for performing cross integration of infrared feature representation and visible light feature representation through a dynamic weight prediction mechanism. The target detection model provided by the invention has better performance in a visible light-infrared target detection task.

The present invention relates to the technical field of information processing and three-dimensional modeling, and specifically to a method for multi-source marine geological information fusion and three-dimensional visualization modeling, comprising the following steps: S1: collecting marine geological data from multiple sources; S2: preprocessing the collected data; S3: extracting marine geological features from the preprocessed data; S4: multi-dimensionally fusing the features extracted in S3; S5: constructing a marine geological three-dimensional model based on the fused data in S4 using a three-dimensional grid construction algorithm; S6: optimizing the constructed three-dimensional model; S7: visualizing the optimized three-dimensional model to generate an interactive three-dimensional display interface. The present invention improves the accuracy, information integrity and user interactive analysis capabilities of the marine geological model through multi-source data fusion, refined three-dimensional model construction and efficient visualization processing, and provides reliable technical support for geological research and engineering applications.

The invention relates to the field of biological information, and discloses a gene library acquisition method for improving single cell amplification genome coverage. Aiming at the problem of insufficient single cell amplification genome coverage, the gene library acquisition method provides a strategy of composite library construction and optimized data integration, and comprises the following steps: constructing at least one original text library and one sub-library with different average fragment lengths for the same single cell sample, and sequencing; performing Barcode resolution and effective single cell identification on the sequencing data through a customizable sequence feature detection method; and further, according to the identification result of the effective single cell, identifying, collecting and merging all sequencing sequence data of the same single cell, generating an integrated sequence data set of the corresponding single cell, and obtaining gene library data of the single cell. The SAG coverage degree of the obtained gene library data is high, and the method has high reliability and application value when being used for genome analysis of the single cell downstream.

The invention relates to the technical field of text processing, and discloses a manuscript generation method and device, equipment and a storage medium. The method comprises the following steps: constructing a template library containing a plurality of template frames and a knowledge base containing a plurality of domain knowledge bases; obtaining use features of a user and each template frame, receiving a free prompt input by the user, and constructing a task feature vector; matching a target template frame based on the first sorting model, and generating a task instruction based on a standard prompt word of the target template frame; if the matching target template frame does not exist, generating a task instruction based on the free prompt word; matching a target knowledge fragment from the knowledge base based on a second sorting model; and forming a large model generation manuscript called by a generation instruction in combination with the target knowledge fragment and the task instruction, correcting the manuscript in response to interaction between a user and the manuscript until a finalized manuscript is generated, and collecting interaction data to perform quality evaluation on the finalized manuscript. According to the method, high-quality, personalized and automatic generation of the manuscripts in the specific field is realized.

The present invention discloses a method for assessing the debris flow susceptibility of a watershed unit based on multi-model Stacking integration, comprising the steps of: constructing a debris flow disaster data set; building a Stacking integrated learning model, including four base models, a feature stacking layer, an attention layer, a mixed feature layer, a feature splicing layer and a meta-model; training the Stacking integrated learning model; predicting the probability of debris flow disasters in the watershed unit to be identified; and assessing the debris flow susceptibility. The present invention can efficiently fuse multi-source data, and creatively splice the mixed probability vector with the original sample, so that the meta-model can simultaneously utilize the intrinsic features of the original data and the prediction information of the base model during the training process, can reduce the interference of irrelevant information and the dependence on the quality of the base model, can more comprehensively capture the key information in the data, and enhance the model's ability to interpret complex data and the prediction accuracy.

The present invention discloses a typhoon wind speed prediction method based on Mamba-ASPP and R-TCN, and the steps are as follows: obtaining a satellite cloud image data set and performing preprocessing; constructing a Mamba-ASPP module for spatial feature extraction, constructing an R-TCN module for temporal feature extraction, obtaining a global spatial feature map of an input image and a temporal feature map splicing, placing an ITSAM module for comprehensive feature processing, and obtaining a predicted value of the typhoon wind speed; integrating the three modules into an initial wind speed prediction model, optimizing and training the initial wind speed prediction model hyperparameters with a seagull algorithm optimized by an adaptive genetic algorithm, and obtaining a target wind speed prediction model; inputting the image data of the previous hour into the target wind speed prediction model, and predicting the typhoon wind speed of the next hour. The method of the present invention effectively improves the efficiency and accuracy of typhoon prediction by integrating feature information of different time and space scales to cope with the complexity of typhoon wind speed changes.

The invention discloses a method and a device for synchronous electric power meter reading, which relates to the technical field of electric power meter reading, and solves the technical problems of imperfect time calibration and updating mechanism of meter reading equipment and lack of systematic multi-dimensional verification and analysis process for periodically collected data. The invention uses a built-in high-precision clock module, calibrates according to quartz crystal pulses every second and continuously interacts with an authoritative time service source, thereby improving the operation and maintenance repair speed and the accuracy of the time dimension of electric power data, and finely controls multiple links of startup and network access to ensure stable network connection, avoids data reporting loss like automatic reconnection of remote office VPN, supports continuous and stable transmission of electric power data to a main station, verifies and classifies collected data in multiple dimensions, provides a solid and reliable data foundation for electric power allocation and cost accounting, and intelligently monitors system upgrades and parameter changes, and performs initialization on demand priority. The whole process improves equipment stability and maintainability, ensures stable operation of meter reading equipment, and reduces failure downtime.

The invention relates to the technical field of intelligent detection, and discloses a camera shell detection method and system based on machine vision, and the method comprises the steps: setting a plurality of image collection devices, collecting the image data of a to-be-detected shell, and obtaining a feature vector; splitting the image data chain into a plurality of sub-data chains; performing defect identification on the image data in each sub-data chain, and determining a defect area and a defect depth through contour overlapping degree de-duplication; obtaining surface detection evaluation of the to-be-detected shell according to the defect area and the defect depth; performing constant-temperature heating on the to-be-detected shell, and acquiring an infrared image of the to-be-detected shell to determine internal detection evaluation of the to-be-detected shell; and obtaining an overall defect value of the to-be-detected shell according to the surface detection evaluation and the internal detection evaluation, and performing detection early warning. By fusing the surface and internal detection evaluation values, quality evaluation and early warning are realized, and the problems of high omission ratio and low manual detection efficiency of traditional detection are solved.

The present invention discloses a multimodal image semantic segmentation method and system based on cross-level guided fusion, which utilizes multimodal data of RGB image information and additional modal image information such as depth information, thermal imaging information and spectral information, fuses multimodal information and uses it for image semantic segmentation tasks. The method first converts the additional modal image into the same size and visual range as the RGB image, and then uses a dual-branch encoder based on the Transformer basic block to extract RGB modal and additional modal features, and uses a cross-level guided fusion module to guide the multimodal fusion of high-level features in shallow, middle and high-level features using low-level features, and finally uses a decoder to decode the multi-level fusion features, and outputs the image category pixel by pixel, which solves the problem of poor semantic segmentation performance in complex scenes; the technical solution of the present invention can be applied to complex scenes such as robot intelligent sorting and drone intelligent inspection.

The invention provides an embedded large model decision control method and system fusing multi-modal sensing information, and relates to the field of intelligent decision control, and the method comprises the steps: receiving multi-modal data of a space non-cooperative target, carrying out the preprocessing and feature extraction of the multi-modal data, and obtaining a multi-modal sensing information fusion model; deep semantic fusion is realized through unified semantic space projection and a dynamic confidence weighting strategy, and a unified environment representation vector is output; constructing an input representation fusing a time sequence context, domain knowledge Prompt and a sample example, performing target intention recognition and risk level evaluation in stages through a lightweight deployed large model, and generating a structured decision strategy; a structured decision strategy is converted into a spacecraft motion instruction, and a target control sequence is solved through a designed simplified MPC controller. According to the method, the non-cooperative target sensing precision and the data utilization efficiency can be improved, the autonomous intelligent decision-making capability of the terminal is enhanced, and safe and efficient capture and on-orbit service tasks are supported.

The application relates to the technical field of intelligent packaging production line automatic control and multi-station collaborative regulation, and discloses an intelligent packaging production line automatic control method and system, the method comprises the following steps: collecting visual and force feedback data, fusing and preprocessing to obtain multi-station integration information, identifying an abnormal mode, determining product switching, and generating a parameter adjustment scheme; issuing a regulation instruction to correct an execution parameter and analyzing a collaborative deviation; collecting updated data to analyze a deviation value and iteratively optimizing the parameter; executing action coordination and monitoring packaging completeness to obtain an adaptive stable state of the production line. The method realizes multi-station working condition collaborative sensing, intelligent adaptation of product switching and dynamic correction of parameters, solves the problems of response lagging, parameter regulation rigidity and insufficient packaging quality stability of a traditional packaging production line, and improves the operation efficiency and finished product qualification rate of the intelligent packaging production line.