291 results about "Cross-validation" patented technology

This invention discloses a clutter discrimination and suppression method based on multiple echo features, comprising: acquiring radar front-end clutter and target echo detection video data and dividing it into several connected regions; extracting multi-dimensional features and labeling prior information for each connected region; using the Relief feature selection algorithm to calculate and filter the weights of the multi-dimensional features, removing redundant features to obtain the optimal feature vector; using this feature vector to train a support vector machine classifier, and obtaining the optimal parameter model through cross-validation; acquiring measured clutter data, extracting corresponding features and inputting them into the model; and determining whether to remove clutter and retain targets based on the output. This invention effectively filters out dynamic clutter spots, avoids false deletion of weak targets, significantly reduces the false alarm rate of the system, and alleviates the computational burden of subsequent track processing.

This application discloses a method, apparatus, and medium for joint prediction of multiple steel quality indicators under small sample conditions, relating to the field of material performance prediction. The method includes: determining a standardized dataset comprising steel chemical composition, process parameter characteristics, and multi-dimensional mechanical performance indicators; configuring the target order of the multi-dimensional mechanical performance indicators; constructing a regression chain model based on the target order, wherein the predicted values ​​of preceding mechanical performance indicators serve as additional input features for the regressors corresponding to subsequent mechanical performance indicators; employing a nested cross-validation strategy to search and optimize the hyperparameters of the regression chain model to determine the target hyperparameter combination; performing full retraining of the regression chain model based on the target hyperparameter combination to obtain a trained joint prediction model for steel performance; and inputting the chemical composition and production process parameters of the steel to be predicted into the joint prediction model to obtain the joint prediction results for each steel performance indicator corresponding to the steel to be predicted.

The application discloses an ophthalmic disease image classification system based on a big data model, which comprises a data set construction module, an image segmentation module and a model training module.The data set construction module is used for collecting eye images of patients with different ophthalmic diseases in an ophthalmic clinic and performing image marking processing to construct training and test data sets.The image segmentation module adopts YOLOv7 to perform image segmentation on the images taken by the smart phone to obtain eye images containing only the part below the eyebrows and above the zygomatic arch.The model training module adopts a five-fold cross-validation method to train the model on the images, and applies data enhancement technology, white balance adjustment and a transfer learning algorithm.The application relates to the technical field of ophthalmic disease image classification.The ophthalmic disease image classification system based on the big data model has high classification accuracy for diseases such as cataracts, keratitis and pterygium in the development stage test set and different clinical evaluation stages.

The application belongs to the technical field of artificial intelligence medical treatment, and discloses a medical health service system and method based on a multi-granularity semantic analysis and knowledge reasoning engine, which comprises the following steps: through a multi-granularity semantic analysis module, coarse-grained intention recognition and fine-grained medical entity state extraction are performed on the unstructured input of a user; a dynamic probability reasoning engine starts two reasoning paths in parallel: a generative reasoning path generates a group of candidate diagnosis hypotheses by using an LLM, and a symbolic reasoning path performs multi-hop probability reasoning on a unique probability knowledge graph to calculate another group of candidate diagnosis paths and their cumulative probabilities; a reasoning fusion and verification module cross-verify the results of the two paths, and only when the hypothesis of the LLM is verified by the high-probability path of the PKG, a final health service response containing a conclusion and an interpretable path is generated. The application significantly improves the accuracy, reliability and transparency of automated medical consultation services.

The application provides a kind of management method and system for scenic spot multi-source heterogeneous data, belong to data management technical field;The application obtains comprehensive credibility by real-time calculation of the accuracy, real-time performance, coverage and stability index of each data source, weighted fusion;The measured value is converted into the basic probability assignment of evidence theory, and the comprehensive credibility is used for discount correction;The Jousselme distance between evidences is calculated as the conflict degree, and the fusion is carried out by using Dempster combination rule, credibility weighted average and deep conflict resolution according to low, medium and high levels;The fusion result is verified by multi-source cross, time sequence consistency and physical constraint, and the quality evaluation parameter, conflict threshold and fusion strategy are dynamically adjusted according to the deviation to form a closed loop feedback;The application significantly improves the accuracy, robustness and adaptive ability of multi-source heterogeneous data fusion, and is suitable for scenic spot passenger flow statistics, congestion warning and other scenes.

This invention discloses a method and system for identifying abnormal wear conditions of pantograph carbon sliding plates based on random forest, belonging to the field of fault diagnosis and intelligent operation and maintenance. The method collects characteristic data of pantograph carbon sliding plate wear; preprocesses the characteristic data, and establishes an input matrix and a target vector based on the preprocessed characteristic data; uses the input matrix and target vector as training data to train a random forest regression model, and fine-tunes the parameters of the random forest regression model through cross-validation to obtain a parametric model of abnormal wear of the pantograph carbon sliding plate; based on the feature importance index of the parametric model of abnormal wear of the pantograph carbon sliding plate, it identifies the key causes of abnormal wear of the pantograph carbon sliding plate. This method can identify the wear condition of the pantograph carbon sliding plate and analyze the key factors under abnormal wear conditions, playing a significant role in maintaining the safe operation of trains.

This invention discloses a method and system for classifying pear varieties based on a minimum feature set, belonging to the field of agricultural product classification technology. The method involves acquiring raw physicochemical index data of pear samples, standardizing the data to obtain a standardized dataset, and then performing principal component analysis (PCA) to verify the separability of the raw physicochemical index data. An orthogonal partial least squares discriminant analysis (OPLS-DA) model is used for pairwise comparisons across multiple groups. After screening and ranking, a subset of key difference components is obtained. This subset is then ranked by importance and sequentially input into a random forest model for training. After cross-validation, a minimum feature set is obtained, and the final random forest model is derived based on this minimum feature set for classifying pear varieties. This invention ensures the reliability of national standard testing methods while effectively reducing the number of testing indicators, achieving high-efficiency and low-cost accurate identification of agricultural products, effectively reducing testing costs, and promoting industry development.

The application discloses a small-sample steel quality multi-index joint prediction method and device and a medium, relates to the field of material performance prediction, and comprises the following steps: determining a standardized data set comprising steel chemical composition, process parameter characteristics and multi-dimensional mechanical performance index data; configuring a target order of the multi-dimensional mechanical performance index; constructing a regression chain model based on the target order; in the regression chain model, the prediction value of a previous mechanical performance index is used as an additional input feature of a corresponding regressor of a subsequent mechanical performance index; adopting a nested cross-validation strategy to perform hyperparameter search and optimization on the regression chain model, and determining a target hyperparameter combination; performing full-amount retraining on the regression chain model based on the target hyperparameter combination, and obtaining a trained steel performance joint prediction model; and inputting the chemical composition and production process parameters of the steel to be predicted into the steel performance joint prediction model, and obtaining a joint prediction result of each steel performance index corresponding to the steel to be predicted.

The application relates to an optimal decoupling method, device, equipment and medium of a six-dimensional moment sensor, wherein the optimal decoupling method of the six-dimensional moment sensor comprises the following steps: constructing a six-dimensional moment sensor, calibrating the six-dimensional moment sensor to construct an initial sensor data set; constructing a one-dimensional convolutional neural network for decoupling output data of the six-dimensional moment sensor; offline training the one-dimensional convolutional neural network based on the initial sensor data set, and obtaining an initial sensor decoupling model; iteratively optimizing the initial sensor decoupling model based on the initial sensor data set and using a K-fold cross-validation method to obtain a trained sensor decoupling model; and collecting real-time output data of the six-dimensional moment sensor based on the obtained sensor decoupling model, and outputting real-time measurement results of the six-dimensional moment sensor based on the real-time output data. The scheme can more accurately fit the nonlinear data of the six-dimensional moment sensor.

This specification discloses a method, apparatus, and device for identifying multimodal self-certifying materials. The scheme includes: acquiring image information corresponding to the self-certifying material and preprocessing the image information; determining the required key fields based on the material type corresponding to the self-certifying material; generating a corresponding number of cross-validation tasks for each key field based on the corresponding task window length and redundant validation coverage; independently executing each corresponding cross-validation task using multiple multimodal large models, outputting local recognition results for some key fields included in the cross-validation task; and fusing the local recognition results to obtain the overall recognition result for the key fields.

The application provides an interpretable aerodynamic coefficient prediction method and system based on dynamic weighting. The method comprises: obtaining a flight state parameter feature dataset composed of a plurality of sample points and a corresponding aerodynamic coefficient label dataset, performing normalization processing on the feature dataset to obtain standardized sample features; training a group of heterogeneous machine learning models in parallel based on the standardized sample features and the label dataset, and obtaining unbiased prediction values of each model for each sample point through cross-validation; for each sample point, calculating and fusing three types of sample-level evaluation weights based on the unbiased prediction values of each model corresponding to the sample point to generate dynamic fusion weights of each model for the sample point; for each sample point, performing weighted summation on the unbiased prediction values of each model for the sample point by using the dynamic fusion weights corresponding to the sample point to obtain a final fusion prediction value of the sample point; and summarizing the final fusion prediction values of all sample points to output an aerodynamic coefficient prediction result and a corresponding interpretability analysis report.

This invention discloses a carbon emission prediction system and method for papermaking processes based on BO-GBDT. The system collects process parameters and energy consumption data during papermaking production, preprocesses the input data, constructs a feature set containing key influencing factors, and obtains a carbon emission dataset for model training. A Bayesian optimization algorithm is introduced to intelligently search for the optimal hyperparameters of four gradient boosting models, constructing a hyperparameter optimization space and setting an optimization objective function. Model performance is evaluated through cross-validation, and the optimal model is updated and selected. BO-GBDT is selected as the final prediction model. The trained model is then used to accurately predict carbon emissions from the papermaking process. By automatically optimizing the hyperparameters of the gradient boosting decision tree model using Bayesian optimization and combining it with multi-source data feature engineering, high-precision and high-efficiency prediction of carbon emissions from the papermaking process is achieved, providing an effective tool for carbon management in the production process.

This invention discloses an automatic identification and evaluation system for cable-stayed bridge cable forces based on dynamic modeling, specifically relating to the field of dynamic modeling. The system includes: a sensor preprocessing module, a baseline modeling module, a hybrid prediction module, an evaluation and early warning module, and a self-optimizing iterative module. It proposes a pure data-driven AI model that integrates multi-source data fusion. This model fuses vibration signal data features with prior knowledge of string vibration through a dual-channel architecture and is trained using a composite loss function constrained by physics and rate of change, achieving intelligent identification of cable forces under complex working conditions. Furthermore, it constructs an intelligent evaluation framework with online cross-validation, trend prediction, and multi-source correlation, achieving model self-optimization and long-term adaptation through incremental learning. Addressing the problems of weak generalization, physical inconsistency, and poor adaptability of data-driven AI in engineering, this invention proposes a general architecture integrating multiple technologies to achieve accurate identification of tension states in multiple scenarios.

The application discloses a question bank intelligent recording method, a storage medium and equipment, utilizes a heterogeneous large language model to generate multiple answers for cross verification, and combines confidence evaluation to effectively filter high-risk answers; through logical dependency graph and branch rewriting technology, accurate synchronization of analysis is realized, and the cost of manual review is reduced; through double-person review and arbitration mechanism, the authority of dispute processing is guaranteed, and through behavior portrait, dynamic management and control of editing authority are realized; through the RAG strategy, the processing capacity of complex questions is enhanced, and multiple scene teaching needs are adapted.

This invention relates to the field of network security technology, specifically a method, system, device, and storage medium for detecting and assessing isolated web resources based on natural language. The method uses a semantic task conversion mechanism to parse the natural language detection requirements of operations and maintenance personnel into structured task instructions, initiating a multi-source detection fusion strategy to scan the target website. This multi-source detection strategy includes access log tracing, website link structure detection, and content semantic association. Cross-validation is used to identify sets of suspected isolated resources. An evolutionary trend prediction model is established, collecting historical page behavior data to predict state transition trends, and using the prediction results as a preliminary warning for isolation determination. A layered confirmation mechanism performs dual verification of suspected isolated resources at both the structural and semantic levels, outputting a list of isolated resources.

This invention provides a method and system for rapid online detection of fertilizer nutrients based on near-infrared spectroscopy. The method includes: acquiring the online near-infrared spectrum and corresponding nutrient concentration data of the fertilizer to be tested, constructing and preprocessing a calibration set; screening characteristic wavelengths through multiple rounds of iteration: in each round, a sample subset is generated by randomly sampling from the calibration set, and a partial least squares regression model is established; determining the importance weight of each wavelength, and determining the number of wavelengths to retain by combining an exponential decay function, and screening high-weight wavelengths to obtain a candidate subset; evaluating the accuracy of the candidate subset using optimized k-fold cross-validation: calculating the leverage value and Mahalanobis distance of all samples, mapping them to a two-dimensional space and dividing them into grids; sampling and grouping by grid as a stratum, and calculating the root mean square error of cross-validation; comparing all iteration results, selecting the candidate wavelength with the smallest error as the optimal characteristic spectrum; and using the optimal characteristic spectrum and nutrient concentration data, training a quantitative prediction model for fertilizer nutrients to achieve rapid detection.

This invention relates to the field of intrinsic ability prediction technology and discloses a method for predicting the intrinsic ability characteristics of the elderly based on machine learning. First, intrinsic ability score data of the elderly in five dimensions, including cognition and psychology, are acquired, and heterogeneity classification is performed using latent profile analysis. Using the classification results as labels, random forest and LASSO regression are used in parallel to screen key predictive factors to form a feature subset. A classification prediction model is built based on XGBoost, and hyperparameters are optimized through cross-validation and grid search. The SHAP and LIME algorithms are integrated to achieve both global and local interpretability. Finally, the model is deployed on an online interactive platform, where inputting individual characteristics outputs the IC propensity classification, probability, and feature contribution explanation. This invention achieves accurate classification and interpretable prediction of the intrinsic abilities of the elderly, reduces the dependence of assessment on professionals and the environment, and is suitable for large-scale application in grassroots elderly care scenarios.

This invention discloses a train control and management system based on multi-bus fusion in rail transit, relating to the field of onboard network control technology. Specifically, it includes: a multi-path acquisition module, a confidence assessment module, a cross-validation module, and a simulation prediction module; deploying three independent sensing paths, which are uploaded in parallel to the fusion processor via different onboard multi-buses at 100ms intervals; calculating confidence scores for the estimated data output by each sensing path based on Bayesian methods to determine the path's reliability and mapping it to discrete quality levels; performing three-way voting through weighted K-fold cross-validation to calculate a consistency index and compare threshold judgment instructions; constructing a digital twin model, and in case of conflict, performing Monte Carlo simulation for the next cycle, outputting the probability of the result to switch the bus fusion management strategy, effectively avoiding system blind spots caused by single sensor or bus failures, and improving the energy utilization efficiency of trains under high-speed sudden changes, severe load fluctuations, or strong interference scenarios.

This invention discloses a visual management and automated execution method and system for time series forecasting, relating to the field of forecasting technology. The method includes: configuring and storing task parameters in a visual management interface; accessing and validating time series data according to the task parameters, and aligning training data according to the forecast frequency; constructing model input data and adaptively determining cross-validation window parameters according to the time span; optimizing hyperparameters under window constraints or training the forecasting model using preset hyperparameters; generating future time point sequences based on the forecast period and outputting forecast results; generating evaluation results and outputting and storing them according to the output parameters; determining thresholds for forecast results based on alarm parameters and recording alarm history; and automatically triggering execution according to task scheduling parameters and displaying the execution status and history. Through the technical solution of this invention, a closed loop of configuration, training prediction, evaluation output, alarming, and scheduling is formed, improving forecast stability and reproducibility, and reducing operation and maintenance costs.

The application discloses a type 2 diabetes heart failure risk prediction system and method, and belongs to the technical field of medical diagnosis and risk assessment. The prediction system comprises the following modules: a data and feature engineering module, which is responsible for standardization processing of data and screening of key prediction factors, and obtains a core feature set for machine learning; a model construction and selection module, which uses the core feature set and trains multiple machine learning algorithms in parallel; through cross-validation and comprehensive performance evaluation, the best model is selected as a prediction model; and a model deployment and application module, which converts output results of the prediction model into a clinically usable static nomogram or online tool, and performs visual output. The application predicts by integrating clinical variables and adopting a machine learning algorithm, and provides a static nomogram and a dynamic Web application, realizes heart failure risk assessment without relying on NT-proBNP detection, and can improve the prevention and management efficiency of cardiovascular diseases.

This invention relates to the field of signal interference identification technology, and discloses a distributed communication cabinet group collaborative control method and system based on the Internet of Things, including: automatically collecting and semantically mapping full-dimensional data; calculating node collaborative adaptability to complete heterogeneous node admission judgment; dividing a decentralized dynamic collaborative domain; electing domain coordination nodes and building a distributed collaborative infrastructure; calculating decentralized distributed clock synchronization and deviation within the domain; planning a unified collaborative timing window and completing full-link timing binding; combining node twins to complete cross-validation and anomaly identification of collaborative data; building a three-layer hierarchical decision architecture; screening feasible collaborative strategies through multi-objective rigid constraints; completing distributed command interlocking and priority control; calculating and pre-diagnosing node failure probability through node twins; planning fault pre-collaborative backup strategies and executing three-level gradient self-healing; and completing incremental iteration of the collaborative decision model and full lifecycle collaborative operation and maintenance through federated learning.

This invention discloses a method and system for identifying cross-connection issues in low-voltage distribution areas. It acquires voltage and current time-series data of users in the low-voltage distribution area, cleans the data using a mask interpolation strategy, and adaptively extracts voltage drop and current surge features based on the median absolute deviation algorithm. It constructs a voltage trend similarity model based on local regularization kernels and a mutation co-occurrence similarity model based on event level weights, employing a dual-track strategy combining daily rigorous assessment and feature revival to generate suspected voltage correlation pairs. A density-based clustering algorithm is used to generate an initial population, and a voltage similarity-weighted centroid screening mechanism is introduced to remove loose outliers in the feature space. A pairwise cross-validation model is constructed based on electrical causality, and the population is topologically refined using relative voltage response criteria and a maximum fully connected clique extraction algorithm to determine the final cross-connection population. This invention significantly improves the accuracy and robustness of cross-connection identification.

The present application relates to a method, device, equipment and medium for verifying the commissioning status of power equipment, and relates to the technical fields of power engineering auditing and artificial intelligence. It includes: parsing the submitted review materials using a multimodal document parsing model; inputting the dispatching operation power time series into a variational autoencoder model for sequence reconstruction calculation to obtain the reconstruction error and generate a time series anomaly feature component; generating a trajectory space anomaly feature component based on the spatio-temporal data of project personnel clock-in and the benchmark coordinates of the equipment ledger, and at the same time performing error level analysis on the on-site commissioning image data to generate an image anti-counterfeiting anomaly feature component; submitting the above multi-source heterogeneous data to a multimodal large model for cross-comparison and logical reasoning, breaking the limitation of only relying on the submitted review materials by a single party, identifying forgery behaviors from the source. With the equipment identifier as the only primary key, in the face of false commissioning scenarios with strong concealment and across multiple links, a multi-dimensional cross-verification closed loop is formed to identify deep造假行为 is misspelled. It should be "deep造假行为 is misspelled. It should be "deep forgery behaviors" here. So the corrected translation is as follows: The present application relates to a method, device, equipment and medium for verifying the commissioning status of power equipment, and relates to the technical fields of power engineering auditing and artificial intelligence. It includes: parsing the submitted review materials using a multimodal document parsing model; inputting the dispatching operation power time series into a variational autoencoder model for sequence reconstruction calculation to obtain the reconstruction error and generate a time series anomaly feature component; generating a trajectory space anomaly feature component based on the spatio-temporal data of project personnel clock-in and the benchmark coordinates of the equipment ledger, and at the same time performing error level analysis on the on-site commissioning image data to generate an image anti-counterfeiting anomaly feature component; submitting the above multi-source heterogeneous data to a multimodal large model for cross-comparison and logical reasoning, breaking the limitation of only relying on the submitted review materials by a single party, identifying forgery behaviors from the source. With the equipment identifier as the only primary key, in the face of false commissioning scenarios with strong concealment and across multiple links, a multi-dimensional cross-verification closed loop is formed to identify deep forgery behaviors.

The present disclosure provides a processing method and device of training samples and electronic equipment, and relates to the technical fields of data processing, artificial intelligence, classification model and the like. The method comprises: obtaining a training sample set of a classification model; wherein the training samples in the training sample set are labeled with real categories; performing cross-validation based on the training sample set to obtain probability information of any training sample under multiple categories; for any training sample, determining a classification result of the training sample according to the probability information of the training sample under multiple categories and the real category of the training sample; wherein the classification result is used to indicate the category recognition difficulty of the training sample; and performing screening on the training sample set according to the classification result of the training samples in the training sample set to obtain a screened training sample set; wherein the screened training sample set is used to train the classification model.

The invention discloses a construction method and application of a safety evaluation risk prediction model after child medication marketing, and the method comprises the steps: constructing a three-level index system, and determining the weight of each level of index through an analytic hierarchy process; based on the three-level index system, determining a risk feature set, constructing a three-level model architecture, and performing model training by adopting a machine learning algorithm; collecting multi-source real world data, and establishing a standardized data pool; the performance of the model is improved through hyper-parameter optimization and integrated learning, the performance of the model is verified and evaluated by adopting internal cross validation and an external independent data set, and the transparency of the model is enhanced through an interpretability technology; and establishing a dynamic updating mechanism, deploying the model as a cloud API service, and realizing risk prediction and early warning functions. According to the invention, multi-source real world data can be effectively integrated, the children medication safety risk can be accurately identified, the method is used for safety evaluation after children medication is listed, and the method is of great significance to supplement clinical test insufficiency before children medication is listed, reduce medication risk and guarantee children medication safety.

The application discloses a bearing fault diagnosis method and system based on a CS-SHAP model, bearing operation signals are collected through acceleration, temperature and acoustic emission multi-sensor, multi-dimensional fault features are extracted from time domain, frequency domain and time-frequency domain after wavelet transform or EMD algorithm denoising to complete preprocessing, feature data is divided into similar clusters by K-means clustering, SHAP values are calculated cluster by cluster and weighted according to cluster data density or diagnosis influence degree to obtain feature importance score, a SVM classifier based on RBF kernel function is trained with screened key features, model parameters are optimized through 5-fold cross validation, new collected data is preprocessed and input into the trained model to obtain diagnosis results, feature SHAP values are calculated and visualized by using the CS-SHAP model, and key factors of faults are analyzed. The application realizes accurate diagnosis of bearing faults and interpretability of diagnosis results, and provides technical support for intelligent operation and maintenance of industrial equipment.

This invention relates to the field of microservice anomaly detection, and more particularly to a microservice software optimization method based on reinforcement learning. This invention acquires historical operation data from the user terminal to extract the user terminal's operation rhythm features; combines these operation rhythm features with the uniformity of the repeated attack interval to analyze the user terminal's operation rhythm representation value, thereby marking the user terminal's rhythm steady-state category; in response to the user terminal being marked as having a low rhythm steady-state category, anomaly assessment analysis of the user terminal's game operations is performed; based on the conditional judgment results, combined with the matching degree between the aiming timestamp and the target's updated position timestamp, and the angle between the screen viewpoint and the firing direction, it is determined whether the user terminal is using unauthorized microservice software. This invention focuses on the accurate identification of unauthorized microservice software tools in terminal scenarios, improving the accuracy of anomaly detection and reducing the probability of false positives and false negatives through multi-dimensional feature fusion, hierarchical detection logic, and cross-validation mechanisms.

The application discloses a kind of based on the experience updating method, system and medium of security research and judge intelligent agent, is especially related to network security technical field, the method includes: under the condition that the reuse value score of candidate experience entity meets preset threshold condition, candidate experience entity is compared with the historical experience entity stored in experience memory base, and the comparison result is obtained;The comparison result is input into the preset multi-node decision rule tree, and according to the entity matching degree condition, attribute change type condition and confidence difference condition set in the multi-node decision rule tree, the update operation of candidate experience entity relative to historical experience entity is determined;The write request for candidate experience entity sent by security research and judge intelligent agent is obtained, and semantic logic verification and cross-source cross-validation are executed on candidate experience entity;In the case where semantic logic verification and cross-source cross-validation are both passed, candidate experience entity is updated into experience memory base according to update operation.