617 results about "Data preparation" patented technology

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 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 provides a data weaving platform-oriented self-service data preparation method, which comprises the following steps of: accessing original data and associated structured metadata of a target data source into a data weaving platform to generate standardized accessed original data and associated structured metadata; based on a semantic model of the data weaving platform, performing classified semantic annotation on the associated structured metadata, and positioning a transfer node of the associated structured metadata to generate a data blood relationship record; performing bidirectional quality verification on the data consanguinity record according to semantics and path continuity, and matching with a federated governance rule of a data weaving platform to generate a metadata governance list; and generating a unified metadata directory based on the metadata governance list, so that a user of the data knitting platform can obtain key segments containing complete data consanguinity records. According to the method, the problem of insufficient accuracy and efficiency in self-service data preparation can be effectively relieved, and the efficient preparation requirement of a data weaving platform for multi-source data is met.

Disclosed is an explainable method for monitoring a state of a generator of a wind turbine generator system on the basis of a spatio-temporal graph. The method includes: S1: acquiring data collected by a supervisory control and data acquisition (SCADA) system; S2: carrying out data understanding on the SCADA data, selecting features associated with the generator, and carrying out data preparation on the selected feature data, and obtaining valid data; S3: embedding the SCADA data, and forming a directed spatio-temporal graph data sequence; and S4: carrying out modeling of a normal behavior model of the generator on the constructed directed spatio-temporal graph data sequence, computing a full-graph-level residual and a node-level residual, computing a residual through an exponentially weighted moving average (EWMA) control chart method, carrying out full-graph-level state monitoring on the generator, forming a fault information transmission chain relation, and enhancing explainability and robustness of a monitoring result.

The invention relates to a multi-class archive knowledge question and answer agent based on a large language model, and belongs to the technical field of artificial intelligence and archive information management. The intelligent agent comprises a data preparation module, a semantic knowledge base construction module, a perception and analysis module, a decision and matching module, an execution and retrieval module, a cognition and reasoning module and an interaction and generation module. According to the method, the complex query intention of the user can be accurately understood, automatic association and semantic reasoning across archive categories are achieved, and the semantic splitting problem of a traditional retrieval mode in cross-category and multi-level query is effectively solved.

The invention discloses a method for applying linear programming to CDN (Content Delivery Network) scheduling, which relates to the technical field of content delivery networks and comprises the steps of data preparation, strategy layer version smooth configuration, macroscopic layer and microscopic layer linear solution and online execution. Basic data are collected, cleaned and repaired, and a version change rule is set; the macroscopic layer constructs a linear programming model, and the cross-provincial bearing quota is solved with the aim of minimizing the cross-provincial cost; the micro layer takes the quota as a boundary and generates domain name class-node weight vectors in parallel; and adapting a routing request online through weighted rendezvous hashing and request features. According to the method, a dynamic cost matrix and a weight granularity control technology are integrated, the engineering problem of linear programming is solved, second-level response, approximate global optimal scheduling and accurate execution of floating-point-level weight are realized, memory overhead is reduced, smooth updating of a strategy and system stability are guaranteed, and CDN service quality and operation efficiency are improved.

The invention belongs to the technical field of computer vision, and discloses a target identification tracking method based on a self-supervision mechanism, and the method comprises the steps: enhancing a self-supervision pre-training module through causality, constructing a causal sample pair through unlabeled video data, learning universal features through combining with comparison loss, and achieving the high-precision tracking without large-scale manual labeling. A multi-modal feature fusion and dynamic calibration mechanism further reduces dependence on annotated data, is especially suitable for industrial inspection, field monitoring and other scenes where data acquisition is difficult, significantly reduces time and labor costs in a data preparation stage, and broadens the application range of the technology in resource limited scenes; a causal reasoning and physical constraint mechanism is introduced, a dynamic relation between targets is modeled through a space-time causal graph, unreasonable tracks are filtered in combination with a physical rule, and complex conditions such as shielding, rapid movement and extreme weather are effectively dealt with; the dynamic feature calibration module corrects feature drift in real time, and ensures stable model performance in long-term tracking.

The invention belongs to the field of explosion load and response prediction, and particularly relates to an impact load and response intelligent prediction system and method based on machine vision. An impact load and response intelligent prediction system based on machine vision comprises an image data preparation module, an image recognition and segmentation module, a numerical calculation module and a load and response prediction module, and the image data preparation module obtains, marks and enhances an image data set required by a target detection and tracking model; the image recognition and segmentation module recognizes and segments splashing fragments and dynamically tracks the splashing fragments; the numerical calculation module generates a sample database required for training the machine learning model; and the load and response prediction module outputs a forward prediction model, a reverse prediction model and a prediction result. According to the prediction system and method, computer vision, a numerical calculation method and machine learning are fused together, and the problems that a traditional method is low in efficiency and cannot conduct reverse prediction according to fragment distribution after impact explosion are solved.

The invention discloses a ground feature classification method based on a frequency-space collaborative learning hierarchical fusion network. The method comprises the following steps: step 1, data preparation; step 2, the HMFE module extracts frequency domain features; step 3, extracting spatial domain features by an MLSA module; 4, the AWF module dynamically integrates the features; 5, executing a classification task; the MLSA module strengthens cross-scale interaction through a tree fusion structure and a cross attention mechanism, realizes efficient fusion of HSI and LiDAR data in a spatial domain, improves classification consistency of complex scenes, and reduces boundary blur phenomena; the HMFE module introduces a learnable frequency coding mechanism into the Mmba module so as to enhance spectrum-frequency components with strong discrimination; the AWF module realizes dynamic integration of spatial domain and frequency domain features through adaptive weighted fusion, fully mines deep complementarity of the spatial domain and the frequency domain, and improves the utilization efficiency of the model for heterogeneous data.

The invention provides a mine car full load detection method based on a multi-branch gating residual network, and the method comprises the steps: preparing model training data, collecting mine car image data in a mining area monitoring video, and constructing a training set, a verification set and a test set for model training; constructing a multi-branch gating residual network model, and training the mine car full load detection network model based on the training set, the verification set and the constructed multi-branch gating residual network model; and inputting a mine car full-load detection test set obtained based on model training data preparation into the mine car full-load detection network model which completes network model training, and then starting mine car full-load state detection on mine car image data. The method has the beneficial effects that the accuracy rate, the precision rate, the recall rate and the F1-score are all superior to those of a traditional network, the method can effectively adapt to mine car full load state identification in a complex environment of a mining area, and the method has good practical application value.

The invention discloses a semi-supervised text data multi-label classification method, system and device and a storage medium. According to the method, a double-branch model of a shared feature processing network is constructed, a pseudo label generator is utilized to automatically generate a pseudo label for an unlabeled sample on the basis of limited labeled data, and the pseudo label generator and a classifier are jointly trained to realize collaborative learning of labeled data and unlabeled data; by introducing an adaptive threshold mechanism and an improved loss function design, the recognition precision of minority class labels is effectively improved. Compared with a traditional full supervision model, the method has the advantages that the dependence on large-scale manual annotation is reduced, the data preparation cost is remarkably reduced, and the application performance of the classification model in multi-label scenes such as medical text analysis, public opinion monitoring and personalized recommendation is improved. The system, the device and the storage medium provided by the invention can realize the method, and have good expandability and engineering application value.

The invention relates to the technical field of internet data services, and particularly discloses a multi-graph hidden danger prediction method based on retrieval enhancement and full-graph sub-graphs. The method constructs a complete technical closed loop from data preparation, model training to intelligent application by combining external knowledge retrieval, multi-granularity visual perception and hierarchical reasoning, and specifically comprises three core stages: firstly, automatically constructing a high-quality field specific training data set by using a pre-training large model; secondly, specialized cultivation of a lightweight target multi-modal large model is carried out through supervised fine tuning and reinforcement learning alignment; and finally, introducing'full graph-sub graph 'multi-vision input and regulation knowledge retrieval enhancement in a prediction stage, performing hierarchical reasoning, and outputting a structured hidden danger report.

The invention provides a unified supervision fine tuning and reinforcement learning training method based on dynamic weight fusion, and belongs to the field of artificial intelligence. According to the unified supervision fine tuning and reinforcement learning training method based on dynamic weight fusion, knowledge imitation and strategy exploration are balanced based on unified SFT and RL training targets, and therefore the purpose of improving training stability is achieved. Comprising the following implementation steps: step (1), data preparation; step (2), performing double-path parallel processing; step (2.1), an SFT path is established; (2.2) an RL path; step (3), a dynamic weight fusion mechanism; the loss weights of the SFT path and the RL path are dynamically adjusted through the global coefficient mu, and progressive transition from imitation learning to exploration learning is achieved.

The invention discloses a numerical simulation method and system for a coal underground gasification process, and belongs to the technical field of energy development. The method comprises the following steps: constructing a gasification cavity, cavity wall and raw coal three-area conceptual model; establishing a kinetic model coupled with five types of chemical reactions (complete oxidation, water vapor conversion, Boudouard reaction, hydrogenization and water-gas shift), and associating porosity change and cavity wall temperature gradient distribution; dynamic evolution prediction of the semi-teardrop-shaped gasification cavity is realized through a numerical flow of cyclically updating the porosity, marking the cavity, calculating the reaction and outputting the synthesis gas; a cavity form and synthesis gas component curve is generated based on a multi-module system (data preparation, numerical calculation, result analysis and visualization). The problem that the gasification cavity evolution simulation precision is insufficient in the prior art is solved, and the method is suitable for UCG engineering design and safety evaluation.

The invention discloses a face privacy protection-oriented generated image detection method and a face privacy protection-oriented generated image detection system. The method comprises the following steps of: firstly, preparing face and text pairing data, and finely adjusting a diffusion model; secondly, on the basis of the diffusion model after fine tuning, potential vectors are extracted and clustered, and text prompts and center vectors obtained through clustering form a dictionary; and finally, based on the obtained dictionary, obtaining a pseudo image and a label through the fine-tuned diffusion model, and outputting a detection result through a classifier. According to the method, potential spatial clustering and conditional diffusion generation are combined, privacy protection and data diversity are taken into consideration, and the security and generalization ability of forged face image detection are remarkably improved.

The invention belongs to the technical field of natural language processing and power system intelligent information processing. Comprising the following steps: S1, preparing data; s2, performing named entity recognition and relation extraction on the text block by utilizing a pre-trained large language model; s3, constructing a knowledge graph in the urban power distribution field by using the triple summary; s4, generating a semantic abstract with a hierarchical structure for each theme community; s5, querying and retrieving: retrieving related information from the vector index and the knowledge graph in parallel by receiving a natural language query of a user, and fusing results into a context knowledge set; and S6, an answer generation step: inputting the query and the context knowledge set thereof into a large language model, and generating an answer which is coherent in semantics, accurate in facts and covers query requirements through a preset prompt strategy guide model. According to the method, the accuracy and practicability of the question-answering system are remarkably improved, and core technical support is provided for intelligent operation and maintenance of the power distribution network.

The invention discloses an unmanned aerial vehicle path planning method based on an intelligent optimization algorithm. The method aims to overcome the defects that a traditional ant colony algorithm is low in convergence speed, prone to falling into local optimum and prone to falling into blind search in the initial stage of the algorithm. The unmanned aerial vehicle path planning method suitable for the complex environment is provided from the characteristics that a fitness function and a heuristic function of an ant colony algorithm can be customized to meet actual requirements, a search strategy can be customized to adapt to a specific environment, and the ant colony algorithm is easily combined with other intelligent optimization algorithms. Based on a search mode of an unmanned aerial vehicle, three-dimensional grid topographic data and a basic unmanned aerial vehicle kinematics constraint and path evaluation principle, after environment modeling and topographic data preparation are carried out on a three-dimensional mountainous area scene based on a grid method, a classical ant colony algorithm is improved; comprising the steps of introducing an improved heuristic function, a self-adaptive step length search strategy based on a visual area and a pheromone updating strategy comprehensively considering path smoothness.

The invention relates to a gas concentration prediction method based on irregular sampling data multi-scale feature fusion, and belongs to the technical field of coal mine gas concentration prediction. The method comprises the following steps: S1, data preparation: carrying out data sequence division according to shift time set by a coal mine; s2, continuous time coding and feature fusion: performing potential space mapping on the divided sequence by using Shenchang differential; dividing multiple time windows, and performing multi-scale data feature extraction and fusion; s3, shift self-adaptive design: adopting a hybrid expert structure and a gating network to perform self-adaptive processing on coal mine shift changes, and outputting weighting and features by an expert layer; s4, failure fault-tolerant design: performing failure fault-tolerant processing, and compensating a data missing problem caused by common sensor abnormality to obtain reconstruction features; and S5, splicing the expert layer output features with the reconstruction features, inputting the spliced features into a prediction network, and predicting the gas concentration at the next moment. The accuracy of toxic and harmful gas can be remarkably improved.

The invention discloses a method for predicting the remaining service life of a control moment gyroscope based on parameter fusion, and relates to the technical field of health management of spacecraft attitude control systems. According to the method, optimal fusion of measurement parameters and implicit parameters is realized through adversarial learning feature extraction and a channel attention mechanism, and the method is particularly suitable for high-precision residual life prediction of the spacecraft control moment gyroscope under variable working conditions. The method comprises four main steps of data preparation and multi-source input, adversarial learning feature extraction, SE-Attention feature fusion and three-stage training optimization. In the data preparation step, original time sequences of voltage and current of a rotor motor are collected through a built-in electrical sensor of a CMG, and key physical parameters of a full-film lubrication friction coefficient, lubricating oil viscosity, a bearing clearance and contact stiffness are inverted based on an electromechanical coupling model; in the adversarial learning step, a game framework of a feature extractor and a working condition discriminator is constructed, and working condition-independent robust feature extraction is realized through a gradient inversion layer; in the SE-Attention feature fusion step, importance weights are adaptively distributed to different feature channels through an extrusion-excitation mechanism; and the three-stage training optimization step adopts a preheating-confrontation-fine tuning strategy to ensure the network convergence. The method solves the problem of domain adaptation, has the technical advantages of strong robustness, high precision, strong generalization, interpretability and self-adaptation, and can realize high-precision RUL prediction under variable working conditions.

A Seq2Seq multi-step 4D flight path prediction method based on gating error correction comprises the following steps: data preparation: selecting data of a plurality of real flight paths of the same flight; data preprocessing, including data normalization and data set division; a TCN-GRU-GECM model is constructed, and the TCN-GRU Carrying out the training of the TCN-GRU-GECM model; inputting a test set data sample as an input sequence into the stored optimal model to obtain a predicted value of the tth time step; and S6, traversing all the input sequences, circularly executing the previous process, and finally realizing a multi-step 4D prediction task for the whole track. Error accumulation is effectively inhibited, an error window is maintained through a gating error correction module, an error trend is analyzed based on a mean value and a standard deviation of historical errors, a correction value is dynamically generated by utilizing a gating mechanism and is fed back to a decoder, and the problem of error propagation amplification in autoregression prediction is remarkably relieved.

The invention belongs to the technical field of medical image processing, and particularly relates to a left-hand X-ray image double-stage bone age evaluation system based on cross-modal learning. The system comprises a data preparation module, a coarse granularity classification module, a fine granularity regression module and an output module. The method comprises the following steps: firstly, preprocessing a hand X-ray image, and constructing cross-modal feature input in combination with structured text description of a standard bone age map; through an SBA-CLIP + cross-modal learning model, alignment and fusion of image features and text features are realized, and coarse-grained classification of bone age intervals is completed; and according to a classification result, dynamically calling a fine classification sub-model of a corresponding interval, and realizing fine regression prediction of the bone age. In the process, a bimodal attention alignment module is introduced to enhance internal feature representation of images and texts, and cross-modal alignment and classification are improved in combination with a comparison loss function guided by cyclic consistency. According to the method, the prediction precision and interpretability are remarkably improved, and the method has wide application prospects and clinical value.

The invention discloses a dumb resource management system automatic test method, system, device, medium and product, and belongs to the technical field of optical network communication test.The method comprises the steps that configuration information of passive dumb resources is obtained according to a passive dumb resource management system, and a relation graph G = (V, E) of the passive dumb resources is constructed; based on a predefined state machine model, controlling a test process to be automatically switched among a plurality of preset states so as to execute a full-process test from passive dummy resource data preparation to optical path verification; wherein after at least one non-terminated preset state is experienced in the test process, automatic verification is carried out based on the relation graph, and an automatic verification result is used as a trigger condition of state transition of the state machine model and a judgment basis of a whole-process test result. According to the invention, efficient, comprehensive and accurate automatic testing of the optical network passive dumb resource management system with extremely complex service logic is realized.

The invention discloses an unsupervised silicon wafer surface defect detection method and device based on hierarchical clustering, relates to the field of image processing and computer vision, and utilizes an unsupervised clustering technology to detect silicon wafer surface defects. The method comprises the following steps: extracting multi-scale features from a plurality of normal silicon wafer images through a pre-trained convolutional neural network; integrating the multi-scale features of the plurality of pictures by adopting a hierarchical fusion strategy; constructing a feature memory library by using a clustering algorithm, and generating a clustering center; generating patch features for the test image through a consistent fusion process; through comparative supervision, normal patches are close to a clustering center, and abnormal patches are pushed away; calculating the distance between the patch and the clustering center, and judging abnormity; and mapping the position of the abnormal patch, and positioning scratches, cracks, pollution or recesses. According to the method, through an unsupervised mode, a large amount of defect labeling data is not needed, the data preparation cost is effectively reduced, and the accuracy and robustness of silicon wafer surface defect detection are remarkably improved.

The invention discloses a railway line selection effect evaluation method and system based on artificial intelligence, and belongs to the technical field of railway intelligent line selection, and the method comprises the steps of data preparation, toughness feature extraction, climate toughness scoring and multi-dimensional effect evaluation. According to the method, line climate toughness feature extraction based on multi-source space-time toughness is adopted, line structure features, environmental conditions and climate disturbance are comprehensively analyzed, and the stability and adaptability of different line sections under different space-time scales are quantified, so that the overall toughness level of the line can be reflected more accurately; climate toughness scoring combining multiple disaster risks and dynamic correction is adopted, multiple disaster types and interaction influences thereof can be comprehensively considered in the line selection process, key fragile sections and potential risk points are identified through dynamic correction, and therefore a scientific basis is provided for line scheme comparison and optimization.

According to certain embodiments, a provisioning manager comprises an interface and processing circuitry. The interface is configured to obtain provisioning data from a provisioning database. The processing circuitry is configured to prepare one or more configuration files based on the provisioning data. The configuration file(s) indicate how to provision one or more service instances. The processing circuitry is further configured to commit the configuration file(s) to one or more repositories in order to make the configuration file(s) available to at least one of the service instances. The processing circuitry is further configured to send one or more notifications indicating to one or more of the service instances that the configuration file(s) have been committed to the one or more repositories.

The invention relates to the technical field of system compliance management, and discloses a system compliance early warning method and system, electronic equipment and a storage medium, and the method comprises the steps: collecting system file data, and carrying out the standardization processing and structural analysis; data preparation is carried out based on the system metadatabase and the supervision fine tuning corpus set, an initial model is constructed and trained, a trained system large model is formed, and version management is carried out; determining early warning types, establishing a system revision early warning model for different early warning types, and triggering early warning reminding according to an output result of the early warning model; performing difference comparative analysis on the same-theme systems based on the system large model, and generating a compliance inspection report; receiving a natural language question, generating an answer and providing system traceability information; a system outline is generated according to system revision requirements, a chapter summary and clause content are generated based on the system outline, and real-time compliance check and clause recommendation are supported. According to the invention, the intelligent demand of enterprise system full-life-cycle management can be met.

The invention discloses an end face tool sharpener control system, and relates to the technical field of numerical control systems. The end face tool sharpener control system comprises a data preparation and recording module, a tool type parameter dynamic modeling module, a multi-axis linkage control module, a grinding wheel pressure self-adaption control module, a grinding wheel abrasion compensation module and a technological parameter optimization module. By comprehensively collecting multi-dimensional historical processing data and constructing a set framework, automatic classified storage of parameters is realized; the NURBS curve interpolation algorithm is adopted, and the machining precision and smoothness of the complex shape are guaranteed; instructions are synchronized in real time, errors are compensated, vibration is monitored in combination with an acoustic emission sensor, and machining stability is improved; a fuzzy PID model and machine learning are utilized to dynamically adjust parameters and optimize the parameters in advance; through machine vision and digital twinning, abrasion is accurately recognized, and the finishing period is predicted. And based on the hybrid model and reinforcement learning, predicting a processing result, dynamically adjusting parameters, and performing error compensation and sensitivity analysis.

This invention relates to a method and apparatus for predicting molecular properties by integrating three-dimensional structure and prior features. The method includes: identifying a set of predicted properties and a set of prior properties; constructing a molecular property prediction model based on the predicted and prior property sets; constructing a model training dataset and training the molecular property prediction model based on the dataset; after model training, receiving first molecular information input by the user, preparing model input data based on the first molecular information and the prior property set, and inputting the three-dimensional molecular structure M and prior characteristic vector X obtained from this data preparation into the molecular property prediction model to predict the corresponding property prediction vector Y, which is then fed back to the current user. This invention can reduce prediction complexity, shorten prediction time, and improve prediction efficiency.

The invention belongs to the technical field of remote sensing image processing, computer vision and deep learning, and particularly relates to a remote sensing image erosion gully semantic segmentation method based on improved OfficientNet-UNet. Comprising the following steps of 1, data preparation and data preprocessing; 2, constructing and enhancing a data set; step 3, model construction and strategy training; and 4, performing contrast experiment and result evaluation. According to the method, detail features of ground features can be more accurately captured, an overfitting phenomenon caused by too high model complexity is reduced, so that the classification precision and boundary recognition accuracy of land coverage data are effectively improved, weights of different types of samples can be automatically adjusted in the training process, and the training efficiency is improved. Particularly, the contribution of background pixels to a loss function is reduced, so that the problem of dominant training of the background pixels is effectively relieved; the method has good expansibility.

The invention discloses a traffic flow prediction method based on a large language model, and the method comprises the steps: data preparation, construction of a road network diagram structure, collection of traffic sensor data, construction of a traffic prediction model, formatting of the data into a mixed format in which a natural language and a structure are combined, spatial feature extraction through LLM, formatting of time series data, and reprogramming. And then fusing the spatial features with the spatial-temporal features of the reprogrammed time series data, performing lightweight fine tuning on the language model by using LoRA, then performing forward propagation, abandoning a suffix part and obtaining an output representation, performing a flattening operation on the output representation, and obtaining a prediction result through a linear projection layer. According to the method, the LLM is conveniently used for traffic flow prediction under the condition that the backbone language model is kept complete, and the complexity and parameter quantity of model training are remarkably reduced while the prediction accuracy is improved.