1623 results about "Data preparation" patented technology

The invention relates to an intelligent industrial equipment state monitoring device and method, and the method comprises the steps: installing a vibration sensor, an acoustic sensor and an image sensor at key parts of industrial equipment, and comprehensively collecting the operation data of the equipment. In the data preprocessing stage, vibration and acoustic signals are filtered and denoised, and image data are denoised and enhanced. Extracting multi-dimensional characteristic parameters from three aspects of vibration, acoustics and images; a random forest algorithm is adopted for model training, and the method comprises the steps of data preparation, segmentation, decision tree construction, voting mechanism, majority voting, model verification and optimization, testing and the like. In the model identification stage, the real-time features are input into the trained model, and the equipment state is determined through decision tree prediction and voting summarization. And when an abnormal state is detected, setting a multi-level alarm response according to the probability, and generating a maintenance suggestion. According to the method, the whole-process intelligent monitoring of the industrial equipment is realized, and the fault identification accuracy and the maintenance efficiency are improved.

The invention belongs to the technical field of intelligent traffic, and particularly relates to a commuting regular bus operation line intelligent early warning system, which comprises a data acquisition module, a data communication module, a processing analysis module, an early warning output module and a feedback module, and a commuting regular bus operation line intelligent early warning method comprises a risk prediction model training process, the specific process is as follows: step 1, a data preparation stage; 2, a model construction stage; 3, a model training stage; 4, a reinforcement learning optimization stage; 5, a verification and deployment stage; and step 6, a closed loop iteration stage. According to the invention, the limitation of a single data source can be broken through, the early warning precision is improved, and accidents such as sudden traffic congestion, vehicle faults, driver misoperation and dangerous behaviors of passengers can be timely handled; a processing feedback continuous iteration model is combined to adapt to a complex traffic scene; emergency resources are automatically matched based on the early warning level, and the operation loss is reduced.

The invention discloses a building elevator detection, diagnosis and decision-making method based on a graph retrieval enhanced agent. The method comprises the steps that 1, elevator detection data are prepared and processed; step 2), knowledge extraction; step 3), knowledge fusion; step 4), visualization and optimization of the knowledge graph; 5) performing graph retrieval enhancement generation; step 6), diagnosing a decision-making agent; according to the method, triple information can be extracted from structural data, text data, visual data and other multi-modal data in the elevator detection field by guiding a multi-modal large model through an elevator detection technical specification, and an elevator detection visual target entity and a text named entity are automatically aligned based on a pre-trained vision-language model; the multi-modal knowledge graph in the field of elevator detection is accurately and efficiently generated, and building elevator detection intelligent diagnosis is carried out on the basis of the multi-modal knowledge graph and the fusion graph retrieval enhancement technology.

A method based on a multi-task learning model comprises the following steps: acquiring and processing histopathological image data of gastritis through a data preparation and preprocessing step; feature extraction is performed by using a self-supervised learning pre-trained model, and image blocks are coded into high-dimensional feature vectors; and constructing a multi-task learning model, learning feature representation through a full connection layer module and an attention layer module, and outputting a classification result of each task. And carrying out model training and optimization by using an optimizer, adding multitask loss through a loss function, and dynamically adjusting the model performance. The trained model can automatically detect and grade gastritis, atrophy, acute activity, intestinal metaplasia and other pathological indexes, outputs a standardized evaluation result, and assists in pathological diagnosis. According to the method, a multi-task deep learning framework based on self-supervised learning pre-training is constructed, a traditional single-task modeling mode is broken through, deep learning framework design is driven through pathological index association, and accuracy and clinical practicability of non-neoplastic disease assessment of the stomach are remarkably improved.

The invention provides a lightweight real-time semantic segmentation method for a three-dimensional Gaussian scene, and belongs to the field of computer vision and three-dimensional scene understanding. The process comprises the following steps: S1, data preparation: acquiring multi-modal information data including point cloud, images and poses, and preprocessing the data; s2, carrying out two-dimensional semantic segmentation, and generating a pixel-level semantic segmentation map and a semantic tag for the image by utilizing PP-LiteSeg; s3, performing three-dimensional scene modeling and semantic projection, fusing multi-modal information to complete three-dimensional Gaussian scene reconstruction, and projecting two-dimensional semantics to a Gaussian function to form preliminary Gaussian function semantic features; and S4, semantic optimization and segmentation output: considering image difference, semantic difference and smoothness, establishing a loss function, optimizing semantic features and outputting a segmentation result. According to the method, efficient, real-time and accurate three-dimensional Gaussian semantic segmentation work can be completed, and the method is suitable for scene understanding tasks of light-weight and resource-limited equipment.

The invention discloses a water quality time sequence prediction method of an SSA-VMD-LSTM-XGBoost hybrid model, and belongs to the technical field of water quality monitoring and prediction. Comprising the following steps: (1) data preparation and preprocessing; (2) optimizing the water quality time sequence decomposition of the VMD based on SSA: optimizing a penalty factor and a modal number of the VMD by adopting a sparrow search algorithm (SSA), and decomposing the water quality time sequence into a plurality of sub-components with high stability and low complexity by utilizing the optimized VMD; (3) construction and training of an LSTM-XGBoost hybrid prediction model: constructing a hybrid prediction model fusing long-short term memory (LSTM) and extreme gradient boost (XGBoost), inputting a high-frequency component into the LSTM model, inputting a low-frequency component into the XGBoost model, and finally performing superposition and integration on prediction results of the models; and (4) multi-component prediction result integration and performance verification. According to the method, adaptive optimization of VMD parameters is realized through SSA, the feature extraction and time sequence modeling capability is improved by combining the advantages of LSTM and XGBoost, and the prediction precision and stability of the water quality time sequence are effectively improved.

The invention relates to the technical field of traffic safety, in particular to a beforehand prevention and control strategy generation method for traffic safety risks of a highway network in a mountainous area. Comprising the following steps: risk diagnosis and data preparation: collecting road basic information, historical traffic flow data, meteorological data and accident-prone point data of a mountainous area expressway network; constructing a risk assessment model: selecting road alignment, traffic flow, weather and environmental toughness indexes, determining index weights by adopting an improved analytic hierarchy process, constructing the risk assessment model through a fuzzy comprehensive evaluation method, and calculating the traffic safety risk level of each road section; a prevention and control strategy is generated; and strategy verification and iteration. According to the method, the total factor evaluation model is constructed by integrating the road network topological structure, the traffic flow characteristics and the meteorological sensitive section data, so that the problem of one-sided risk identification caused by single factor analysis in the prior art is solved, and systematic description of the mountainous area highway composite risk scene is realized.

The invention belongs to the technical field of geological engineering and environmental science, and relates to a rock freeze-thaw damage prediction method based on deep learning, which comprises the steps of data preparation, data preprocessing, data set generation, model construction, model training, model verification and prediction output. According to the method, the deep learning model is applied, the rock freeze-thaw damage rule is automatically extracted by using the deep learning model, and the large parameter quantity is achieved; adopting a missing value processing method, and marking a missing value through a two-dimensional vector; for sample distinguishing of different sources, hot independent coding is adopted; in the aspect of a mask mechanism, the model focuses on real data. According to the rock freeze-thaw damage prediction method, a deep learning model can be constructed, the model not only improves the prediction precision, but also enhances the robustness and generalization ability of the model, multiple defects of a traditional model can be effectively overcome, the output of the model can be used for evaluation and engineering design of rock freeze-thaw damage, and the method is suitable for large-scale popularization and application. Scientific basis and decision support are provided, and the method has a wide application prospect.

The invention discloses a big language model evaluation system and method, and belongs to the technical field of big language model evaluation. The system comprises a data preparation module, a basic performance evaluation module, an efficiency evaluation module, a robustness and multi-mode evaluation module, a distributed task scheduling module and a feedback optimization module. Through the system, the technical effect of multi-dimensionally, efficiently and automatically evaluating the large language model is achieved.

The invention relates to the technical field of nursing scheme intelligent recommendation systems, in particular to a cardiology patient personalized nursing scheme intelligent recommendation system optimization method, which comprises a dynamic rare case recognition engine for accurately capturing and recognizing cardiology rare cases by using medical data and text data, and comprises a data preparation module, a data retrieval module, a data processing module and a data processing module, the candidate sample classification and recognition module is used for recognizing whether rare cases exist or not, and the problems existing in the prior art are effectively solved by introducing innovation modules such as a dynamic rare case recognition engine, a knowledge graph builder and a rare case simulation generator. According to the invention, continuous learning and updating can be realized, rare cases can be accurately identified, and customized nursing suggestions can be provided based on individual features of patients. Meanwhile, the decision-making process of the system has good interpretability, decision-making basis can be provided for doctors, and usability and credibility of the system in clinical practice are greatly improved.

The invention relates to the technical field of laser surface modification, and discloses a water turbine laser cladding process parameter intelligent optimization system and method, and the system comprises a data preparation and preprocessing module which is used for obtaining and preprocessing water turbine laser cladding process parameters and corresponding cladding layer quality index data; a physical information guided multi-task prediction network construction and training module; a causal inference analysis module; and a causal perception multi-objective optimization algorithm module. By constructing a multi-task prediction network guided by physical information, the internal physical law of the laser cladding process is fused into a deep learning model, and the accuracy and reliability of predicting a plurality of key quality indexes of the laser cladding layer of the water turbine are remarkably improved. Compared with a traditional pure data driving model, the method can generate a prediction result more conforming to an actual cladding mechanism by means of guidance of physical constraints even under the condition of limited experimental data, and lays a solid data foundation for subsequent process optimization.

The invention provides an SQL statement analysis method and device, equipment and a storage medium, and relates to the technical field of databases. According to the specific scheme, the method comprises the steps that SQL statements and metadata thereof are obtained, wherein the metadata comprises table names, column names and predicates of the SQL statements; target statistical information corresponding to the metadata is obtained from a preset statistical information base, and the target statistical information comprises table statistical information, column statistical information and index statistical information. And loading the statistical information to a test environment database to obtain a simulation database corresponding to the SQL statement. And based on the statistical information, determining an execution plan of the SQL statement, and executing the SQL statement in the simulation database according to the execution plan to obtain a statement analysis result. The method can effectively solve the problem of analysis distortion caused by the distribution difference of test and production environment data, and can obtain a more accurate SQL analysis result without complex data preparation.

The invention discloses a laser radar point cloud real-time three-dimensional target detection method suitable for an unmanned vehicle. The method comprises the following steps: preparing a target detection data set; model training; updating parameters; simulation data preparation; performing model reasoning and result evaluation based on simulation data; model deployment and real vehicle data preparation; performing model reasoning and data storage based on real vehicle data; uploading and marking the data; and incremental learning and parameter updating. The three-dimensional target detection model provided by the invention specifically comprises a preprocessing module; a backbone network module; a neck network module; a feature selection module; and a detection head module. According to the three-dimensional target detection model provided by the invention, the point cloud data is converted into various aerial view images, and richer spatial information is mined; a two-dimensional distance sensing sparse convolutional layer is adopted to perform feature extraction, and multi-view feature fusion is realized through a double-branch network; a multi-scale feature pyramid network and a feature selection module are combined, so that the detection precision of a long-distance target and a small target is effectively improved; by constructing an efficient neural network architecture and combining training, simulation testing and real data incremental learning strategies, the robustness, precision and adaptive capacity of a target detection model are effectively improved, and powerful perception support is provided for an unmanned driving system.

The invention provides a data backup method based on a large model, and the method comprises the steps: S1, building a data model, and carrying out the data understanding and analysis and model training and optimization; s2, making a backup strategy: performing intelligent analysis on a current to-be-backed-up data set by utilizing the trained large model; dynamically adjusting and optimizing a data backup strategy according to an analysis result of the large model, respectively adopting incremental backup or full backup or differential backup, and reasonably distributing backup resources and priorities according to importance and recovery requirements of data; s3, data backup execution: executing data backup operation according to the backup strategy formulated in S2; and S4, backup management and optimization: managing and optimizing the backup data regularly.

The invention discloses a fragmented cultivated land remote sensing extraction method based on space-time deep learning, and the method comprises the following steps: 1, data preparation: selecting a high-spatial-resolution remote sensing image, and capturing the space-time change of cultivated land in different growth cycles and environment conditions in combination with multi-temporal data of different times and seasons; a second step of model construction: constructing a deep learning model fusing ConvNeXt and a U-Net framework, namely a ConvNeXt-U model; 3, model training is carried out; 4, crushing cultivated land extraction; and 5, analyzing and outputting a result. Through multi-dimensional technical innovation, remarkable performance breakthrough and application value improvement are realized in the field of fragmented cultivated land remote sensing monitoring, and the method is specifically embodied in the following three aspects: 1, high precision and robustness improvement are realized; 2, optimizing calculation efficiency and generalization ability; and 3, dynamic evolution analysis and decision support capability.

The invention discloses an intelligent glasses audio processing method and system. The method comprises the following steps: data preparation: collecting source domain data and target domain data; pre-training the model; converting the audio signal into sequence data; an RNN model is constructed and trained; model migration: migrating the pre-trained RNN model to target domain data, and processing noise in the target domain data by using parameters and features of the pre-trained model; intelligent analysis and processing, wherein noise in target domain data is analyzed and processed by using the migrated RNN model; real-time processing and feedback are carried out, audio signals captured by a microphone of the intelligent glasses are analyzed in real time, and a noise reduction strategy is dynamically adjusted to eliminate background noise; and optimizing noise reduction parameters and strategies according to the feedback. By collecting the source domain data and performing model pre-training, the RNN model can identify and process various known noise types and features and quickly adapt to different noise environments, so that the noise reduction effect is improved, and a user can obtain the optimal hearing experience in different noise environments.

The invention discloses a semantic remote sensing image generation method based on a de-noising diffusion probability model. The method comprises the following steps: preparing data; constructing a de-noising diffusion model, and respectively constructing a forward diffusion structure and a reverse reasoning de-noising structure; in the training process, Gaussian noise is gradually added according to the initial remote sensing influence, and a noise target image is generated; and performing reverse reasoning sampling based on the target noise image and the semantic layout image to generate remote sensing influence. According to the method, through an improved conditional denoising network structure, a noise image is input into an encoder, meanwhile, semantic layout is embedded into the decoder through multi-layer space self-adaptive normalization operation, and the generation process of a semantic remote sensing image is fitted. By introducing a sampling strategy without classifier guidance, the semantic correlation is remarkably enhanced while the fidelity of the generated image is improved.

The invention discloses an information transmission attack event intelligent prediction model training optimization method, which comprises the steps of a data preparation stage, a feature engineering stage, a model selection and construction stage, a model training stage and a model evaluation and optimization stage, and is characterized in that data are collected from multiple channels, noise is cleaned and removed, missing values and abnormal values are processed, and accurate labeling is carried out; then key features are extracted, redundant irrelevant features are removed through selection, standardized transformation is carried out, a proper model is selected based on data features, an integrated learning combination model is adopted, then a training set, a verification set and a test set are divided, a proper optimizer is selected, parameters are adjusted to be over, training is carried out through the training set, and over-fitting is prevented through the verification set; and finally, using index evaluation of accuracy and recall rate, and adjusting hyper-parameters and continuous optimization of the model structure through cross validation. According to the method, data collection, data imbalance processing and data noise interference processing are adopted, data diversity is improved, and interference of data noise is reduced.

The invention relates to the technical field of data preparation pipeline recommendation, in particular to a context semantic perception-oriented data preparation pipeline recommendation method and system. The method comprises the following steps: performing data preprocessing on an obtained automatic pipeline construction data set; aiming at the preprocessed data set, carrying out double-view feature fusion based on a self-attention mechanism; for the preprocessed data set, context semantic information extraction based on a large language model is carried out; based on feature fusion and information extraction, a pipeline recommendation result is obtained through a deep reinforcement learning network; evaluating and optimizing a pipeline recommendation result; and an output pipeline. Through double-view feature fusion based on a self-attention mechanism, end-to-end dynamic integration is carried out on statistical attributes of a data set and a historical pipeline component sequence.

The invention provides a knowledge graph construction and semantic query control method and system of a BIM (Building Information Modeling). Comprising the following steps of BIM model data preparation and attribute extension, IFC data analysis and knowledge graph modeling rule definition, knowledge graph automatic construction tool development, Neo4j graph database storage and management, natural language processing and large model semantic understanding, Cyber query statement generation and IOT interface calling, and query and control of an interaction process. According to the knowledge graph construction and semantic query control method for the BIM model, the BIM model is converted into the knowledge graph, and unified query and control from building space information to semantic level are realized by utilizing a natural language processing technology and large model capability.

The invention belongs to the technical field of ocean temperature data identification, and discloses a thermocline identification method and system based on hyperbolic tangent function fitting. The method comprises the steps of data preparation and preprocessing; selecting a section range; fitting a curve; and determining a thermocline upper bound and a thermocline lower bound through a rolling function. According to the method, the hyperbolic tangent function is used for fitting the S-shaped characteristic curve of the temperature-depth profile, and the structure of the ocean thermocline is accurately described. Compared with a traditional Sigmoid function, the hyperbolic tangent function is better in performance when fitting a steep change area, and rapid temperature change of a thermocline central area can be caught more finely. According to the method, fitting parameters (amplitude, steep degree, central position and vertical offset) are optimized by adopting a trust region reflection algorithm, so that a fitting result is ensured to have higher precision and robustness, and the method is suitable for complex ocean profile data.

The present invention relates to the field of image processing technology, and discloses a method for automatic segmentation and recognition of ultrasound images based on deep learning, including the following steps: S1, data preparation and preprocessing, S2, self-supervised learning pre-training, S3, transfer learning and incremental learning, S4, multi-task joint learning, S5, image quality enhancement and super-resolution reconstruction: S6, multi-modal information fusion, information fusion by combining modal images, S7, model training and optimization, increasing data diversity by using data enhancement technology. By fusing ultrasound images with modal image information, the segmentation accuracy and lesion recognition ability of ultrasound images are improved, and different modal images provide complementary features, showing significant advantages in the recognition of complex lesions, complex locations, complex morphology and early lesions. At the same time, by fusing images of different modalities through a deep learning model, image quality can be improved and diagnostic accuracy can be improved.

The invention relates to a method for size measurement and appearance detection of an O-shaped ring. The size measurement of the O-shaped ring is carried out through the modes of image preprocessing, region-of-interest segmentation, edge extraction, central point determination and the like. And then, appearance detection is carried out, edge type defect detection and internal defect detection are respectively carried out, and then result fusion and screening are carried out. Therefore, high-precision measurement can be achieved, the device is suitable for O-shaped rings of different types, existing defects can be found in time, and detection of edge defects such as burrs and deckle edges is met. Moreover, a deep learning algorithm is adopted for internal defect detection, and internal defects such as glue shortage, impurities, damage and fracture can be accurately recognized through the steps of data preparation, model training, model optimization and the like.

The invention discloses a BIM (Building Information Modeling) full information analysis and conversion method based on a light database, which comprises the following steps: S1, preparing BIM data, traversing a design model in a source file, and establishing a linear design model structure tree; s2, BIM data analysis: traversing elements in all design models in the linear design model structure tree, and analyzing a full information data structure, including geometry, attribute and material information, of the elements; s3, BIM data conversion is carried out, the element information obtained through analysis is classified and converted into database objects capable of being recognized by a light database, and the database objects adopt a full-information data structure and are written into a user-defined database file. According to the method, full information conversion of BIM data can be achieved, it is guaranteed that element geometry, attribute and material information in the model are not lost, high data conversion and storage efficiency and element-level updating synchronization are achieved, the effects of data seamless circulation and shared application are achieved, and the data delivery and application requirements for various scenes in the infrastructure field are met.

The invention discloses a microcirculation blood vessel automatic segmentation and quantification method based on enhanced feature fusion. The method comprises the following steps: step 1, data preparation and preprocessing; 2, designing an enhanced feature fusion network model; step 3, automatically segmenting microcirculation blood vessels; and 4, quantifying the diameter of the blood vessel based on the segmentation result. According to the enhanced feature fusion network model provided by the invention, accurate segmentation of microvessels is realized through fusion and enhancement of multi-scale features. The model shows good generalization ability on diversified clinical image data through effective data enhancement, and can adapt to image data with different illumination conditions, contrast ratios and resolutions. Therefore, the model has high practicability and reliability in practical application. According to the method, the diameter of the blood vessel is quantified through the segmentation result, the diameter of the microvessel can be accurately measured, and powerful support is provided for evaluation of the clinical microcirculation function.

Knowledge graph guide and entity controlled techniques for generating synthetic doctor-patient conversations. In one particular aspect, a method is provided that includes obtaining an original dataset containing textual dialogue associated with a plurality of individual doctor-patient conversations for training a machine learning model, constructing input data by using named entity recognition to capture and categorize named medical entities present in the dialogue, generating prepared input data by arranging the input data in an annotated turn-by-turn conversation format using an input data preparation algorithm having various control parameters, training the machine learning model using the prepared input data, utilizing a knowledge graph to identify a plurality of symptoms mapped to a randomly selected disease, and causing the trained machine learning model to generate a synthetic doctor-patient conversation by inputting the plurality of symptoms to the machine learning model as a first control parameter of a conversation generation control algorithm.

The invention discloses a steel wire rope derailment early warning method and system for a container crane pulley block, and the method comprises the steps: collecting real-time images of the pulley block and a steel wire rope, and achieving the automatic monitoring of the operation state of the steel wire rope through feature point detection and geometric transformation estimation; and by utilizing the structural knowledge of the pulley block, accurate derailment detection is carried out under the condition of a small number of samples or even almost no samples, so that the cost of data preparation and model training is reduced. The method is easy to control, a user only needs to manually specify masks of a pulley area, a cross beam area and a steel wire rope area and mark key structure feature points, the system can automatically extract features and perform subsequent automatic detection, and the method has good user friendliness and adaptability.

The invention discloses an aircraft aerodynamic characteristic intelligent reconstruction method based on multi-source data fusion, and relates to the field of intelligent fluid mechanics prediction.The aircraft aerodynamic characteristic intelligent reconstruction method comprises the steps that aircraft multi-source aerodynamic characteristic data are prepared, and an aircraft flow characteristic intelligent dimension reduction model is established; the method comprises the following steps: preparing a data set of an aircraft aerodynamic characteristic intelligent prediction model for multi-source data fusion, establishing the aircraft aerodynamic characteristic intelligent prediction model for multi-source data fusion, carrying out iterative training on the aircraft aerodynamic characteristic intelligent prediction model, and carrying out verification by adopting a corresponding verification data set. And the weight model with the optimal performance is selected to predict the subsequent to-be-tested working condition. According to the aircraft aerodynamic characteristic intelligent reconstruction method based on multi-source data fusion provided by the invention, efficient fusion of multi-source data information can be realized, so that a high-precision aircraft aerodynamic characteristic intelligent reconstruction prediction model is constructed.

The present application relates to the technical field of electric digital data processing, and provides a data preparation method, system, and device for AIGC-based interaction analysis, and a medium. The method comprises: by means of a pre-trained text label recognition model, determining a label sequence corresponding to an interaction statement having experienced first word segmentation processing; on the basis of a preset expression construction rule, determining a label combination sequence corresponding to the label sequence and a conditional expression corresponding to the label combination sequence; by means of a preconfigured database candidate text dictionary and a preset matching rule, determining a strictly matched keyword and a fuzzily matched keyword corresponding to the interaction statement having experienced second word segmentation processing; on the basis of the strictly matched keyword and a similar candidate text corresponding to the fuzzily matched keyword, determining a word segmentation result corresponding to the second word segmentation processing; on the basis of a plurality of preset SQL statement rule matching templates, generating an interaction SQL query statement, so as to determine, on the basis of the interaction SQL query statement, interaction response information corresponding to the interaction statement.

The invention relates to the technical field of artificial intelligence, and discloses a multi-modal model training fine-tuning optimization method based on artificial intelligence, which comprises the following specific steps: preparing and preprocessing data, collecting a large-scale multi-modal data set, constructing an image text data set containing one million high-quality images and text description accurately corresponding to the images, and performing fine-tuning optimization on the image text data set. According to the method, data are subjected to comprehensive and meticulous cleaning operation, noise data and mistakenly labeled data in the data are strictly removed, so that the accuracy and reliability of the data are ensured, and the effective utilization rate of the data can be increased by at least 20% through data cleaning; wherein the image data is screened, blurred, low-resolution and unclear-content images are removed, it is ensured that each image can clearly convey specific visual information, and text description, grammar error checking, spelling error and semantic ambiguity parts are corrected or deleted.