196results about How to "Good forecast" patented technology

The invention provides a traffic flow prediction method based on a dual hypergraph neural network. The method comprises the following steps: preprocessing traffic data collected by a sensor in a traffic road network in a certain time period; forming a data set by the preprocessed traffic data, and dividing the data set into a training set, a test set and a verification set; respectively constructing a traffic flow graph and a dual hypergraph according to the topological graph of the traffic network; constructing a traffic flow prediction model based on the space-time dual hypergraph convolution layer, wherein the traffic flow prediction model comprises an input layer, the space-time dual hypergraph convolution layer and an output layer which are connected in sequence; inputting the preprocessed traffic data in the training set into a traffic flow prediction model for training; testing the trained traffic flow prediction model by using the preprocessed traffic data in the test set to obtain a final traffic flow prediction model; and inputting the traffic data preprocessed in the verification set into the final traffic flow prediction model to obtain the predicted traffic flow. According to the method, multi-source data are fused and external knowledge is utilized, so that the spatial-temporal correlation in the data can be better understood and utilized, and the accuracy of a traffic prediction model is improved.

The invention discloses a photovoltaic power prediction method based on seasonal weather classification and an RLHF large model, and the method comprises the following steps: 1, data acquisition: obtaining power generation data and historical meteorological data of a photovoltaic power station, and constructing a data set; 2, constructing a prediction model: constructing a weather classification and photovoltaic output prediction model, dividing a year into a plurality of seasons, dividing weather into a plurality of weather types, and constructing a photovoltaic output prediction model for each weather type of each season; an RLHF large model is constructed, a deep learning and reinforcement learning combined method is used, and the photovoltaic power generation power prediction precision and interpretability are improved through pre-training and human feedback; and step 3, constructing a fusion model: fusing prediction output of the weather typing and photovoltaic output prediction model and the RLHF large model to obtain a final prediction result of the photovoltaic power generation power. The invention also discloses a photovoltaic power prediction system based on seasonal weather typing and the RLHF large model.

The invention discloses a broadband wide frequency spectrum analysis method and system based on deep learning. The method comprises the steps of spectrum signal acquisition, spectrum preprocessing, abnormal interference detection, spectrum trend prediction and spectrum scheduling management. The invention relates to the technical field of wide-frequency spectrum analysis, in particular to a broadband wide-frequency spectrum analysis method and system based on deep learning, which adopts a three-step method of abnormal interference detection, spectrum trend prediction and spectrum scheduling management, and adopts a layer-by-layer progressive deep learning and reinforcement learning technology. The real-time detection of spectrum abnormity, the accurate prediction of future trend and the optimal allocation of dynamic resources can be realized; carrying out abnormal interference detection by adopting a joint improvement multi-classification support vector machine method combined with feature enhancement; performing spectrum trend prediction by adopting a time sequence integrated network combined with one-dimensional convolution feature extraction; and performing spectrum scheduling management by adopting a reinforcement learning method enhanced by a near-end strategy optimization algorithm.

The invention discloses an electric vehicle charging load prediction method, which comprises the following steps: firstly, screening variables with high correlation through a maximum information coefficient, selecting features with high correlation as model input, and taking a charging load as output; reconstructing the data training set by adopting cross validation; a charging load integration model is constructed, the integration model is divided into a base learning device and a meta learning device, the base learning device comprises a DCformer model, a BiTCN model and an artificial neural network ANN model, and a lightweight gradient elevator LightGBM is used as the meta learning device; the parameters of the LightGBM model are optimized by adopting an improved star graffiti optimization algorithm INOA; an error sequence is obtained after a preliminary prediction value is obtained through model prediction, and a final prediction value is obtained by taking LightGBM as an error prediction model. According to the method, data features are effectively extracted by integrating a plurality of single models, the prediction accuracy is improved, and compared with a traditional model, the stability is better, and the generalization ability is higher.

The invention discloses a method and device for predicting the geological storage amount of carbon dioxide and a medium, and relates to the technical field of carbon dioxide capture, utilization and burying. The method comprises the steps that whether a to-be-predicted oil and gas reservoir and a control oil and gas reservoir are in the same block or not is judged; if yes, inputting the geological data, the production dynamic data and the injection parameter combination scheme of the to-be-predicted oil and gas reservoir into the optimal control oil and gas reservoir carbon dioxide geological sequestration quantity prediction model to determine the whole-area accumulated carbon dioxide geological sequestration quantity of the to-be-predicted oil and gas reservoir; if not, geological data, production data and an injection parameter value range of the to-be-predicted oil and gas reservoir are obtained, migration training is conducted on the optimal control oil and gas reservoir carbon dioxide geological sequestration quantity prediction model, and a to-be-predicted oil and gas reservoir carbon dioxide geological sequestration quantity prediction model is obtained; and determining the whole-area accumulated carbon dioxide geological sequestration amount of the to-be-predicted oil and gas reservoir. According to the invention, the precision and efficiency of carbon dioxide geological sequestration quantity prediction are improved.

The invention discloses an intestinal flora marker related to Parkinson's syndrome and application of the intestinal flora marker. The invention discloses an intestinal flora marker, which comprises one or a combination of more of bacteroides faecalis, bacteroides vulgaris, bacteroides thetaotamicrobe, ruminococcus ruminococcus and lactobacillus salivarius, and is characterized in that the intestinal flora marker comprises one or a combination of more of bacteroides faecalis, bacteroides vulgaris, bacteroides thetaotamicrobe, ruminococcus ruminococcus and lactobacillus salivarius, and the intestinal flora marker comprises one or a combination of more of the bacteroides vulgaris, the bacteroides vulgaris, the bacteroides thetaotamicrobe, the ruminococcus ruminococcus and the lactobacillus salivarius. The intestinal flora marker is used as a detection marker to be applied to diagnosis of Parkinson's syndrome patients, and is completely noninvasive and high in accuracy.

The invention provides a hyperspectrum-based tea tree canopy leaf nitrogen estimation method, which comprises the following steps of: firstly, smoothing original spectral data of a tea tree canopy by using a Savitzky-Golay method so as to reduce random noise and improve a signal-to-noise ratio; then, the original spectrum is converted into 600 wave bands at the interval of 1 nm through spline interpolation, so that the continuity and convenience of calculation are ensured; the method comprises the following steps of: firstly, carrying out first-order derivative processing on a spectrum, then carrying out second-order derivative processing on the processed spectrum, calculating a normalized difference spectrum index (NDSI) by using a derivative spectrum, calculating a Pearson's correlation coefficient (PCC) between the NDSI and a measured nitrogen element, carrying out principal component analysis (PCA) on the highest 20 indexes of the correlation coefficient, and finally constructing a multiple linear regression model by using the principal components with the first 10 PCA scores as parameters. The method is used for predicting the nitrogen content (LNC) of tea tree canopy leaves. Prediction experiments of a plurality of tea gardens show that the model shows a good prediction effect which is close to the actual nitrogen content, and the generalization performance is good.

The invention discloses a power grid abnormal situation awareness method and system. The method comprises the following steps: acquiring power grid data in real time and calculating different power grid situation evaluation indexes; weighting each power grid situation assessment index, correcting the weight of an abnormal index, and then performing linear weighting on all the power grid situation assessment indexes to obtain a situation assessment value; constructing a prediction model with a double-layer gating recurrent neural network, an attention layer and an output layer cascade, inputting historical data of a power grid situation assessment index, and outputting a future situation assessment prediction value; and inputting new historical data of the power grid situation assessment index into the trained prediction model to obtain a corresponding future situation assessment prediction value, and matching the future situation assessment prediction value with a pre-divided security level to determine whether an abnormality occurs. According to the method, a deep learning framework is utilized, the model for predicting the power grid operation situation of the double-layer gating recurrent neural network based on the attention mechanism is constructed, and a better prediction effect and higher accuracy are achieved.

The invention relates to the technical field of artificial intelligence precision medical treatment, in particular to a multi-mode hepatocellular carcinoma recurrence risk prediction method and system and application, and the prediction method comprises the steps of multi-mode data acquisition, data standardization preprocessing, model training and recurrence risk prediction. The method comprises the following steps: firstly, collecting multi-modal data of a patient, wherein the multi-modal data comprises preoperative data, postoperative data and a recurrence risk label; secondly, carrying out standardized preprocessing on the collected multi-modal data; and updating parameters of the pre-operation model and the post-operation model by using an adaptive moment estimation algorithm to obtain a trained multi-modal hepatocellular carcinoma recurrence risk prediction model which comprises the pre-operation model and the post-operation model and can be used for performing recurrence risk prediction before and after an operation respectively. According to the method, the deep learning technology is used, the multi-modal data before and after the operation can be respectively utilized, the recurrence risk of the HCC patient can be accurately predicted before and after the operation, and a targeted treatment scheme is formulated for the HCC patient.

The invention relates to a plug-in hybrid electric vehicle hierarchical energy management method based on historical data and real-time speed prediction, and belongs to the technical field of new energy automobiles, and the method comprises the following steps: S1, obtaining and preprocessing historical commuting data of a single vehicle, establishing a historical commuting data set, and predicting a future global driving condition; s2, constructing a vehicle part simplified model, and solving an optimal reference SOC track by using a dynamic programming algorithm; s3, constructing a real-time speed predictor by using a neural network to realize real-time speed prediction; and S4, optimizing a real-time speed prediction result in combination with the optimal reference SOC trajectory, and realizing energy management by using a PMP-MPC algorithm.

The invention discloses a pet health monitoring method and system based on a prediction model. The method comprises the following steps: acquiring physiological data and image data of a target pet object; determining a first health prediction result of the target pet object according to the physiological data based on a preset health condition prediction model; determining a second health prediction result of the target pet object according to a trained image recognition algorithm and the image data; according to the first health prediction result and the second health prediction result, performing training optimization on the prediction model to obtain an optimized prediction model; the optimization prediction model is used for further predicting the health condition of the target pet object according to the physiological data. Therefore, the method can achieve the optimization of the prediction model based on the correction training between the prediction relation and image recognition, so as to effectively improve the accuracy and efficiency of pet health monitoring.

The invention relates to the technical field of information retrieval, in particular to an intelligent query system and method based on semantic analysis, and the system comprises an entity relation mapping module which collects user query data, carries out part-of-speech tagging and syntactic analysis operation on user query, constructs connection mapping according to syntactic relations among key elements, and sends the connection mapping to an entity query module; and calculating the correlation degree between each pair of keywords in the mapping, quantifying the attribute interaction strength, and generating a keyword relation mapping table. According to the method and the device, the precision and the efficiency of information retrieval are improved by deeply analyzing the query data of the user and combining the technical innovation of natural language processing. When part-of-speech tagging and syntactic analysis are carried out on a user query statement, key elements are accurately identified, connection mapping is constructed, and accurate capture of core information is ensured. And the information retrieval precision is enhanced by quantifying the association degree between the keywords and the attribute interaction strength. And a coherence score is calculated, so that the system can screen out the most relevant information.

The invention discloses a CNN-XGBoost power load prediction method based on an attention mechanism, and the method comprises the steps: obtaining related historical load data and feature data, carrying out the data correlation analysis, employing the CNN to construct a feature extraction module based on the advantages of the CNN in feature extraction, and carrying out the calculation of the data correlation. Aiming at the problem that a traditional convolutional neural network cannot distinguish whether the extracted features are effective for prediction, Attention is introduced, different weights are given to the features, the features with different output contribution degrees are distinguished, the extracted features are emphasized consciously, the relation between the extracted features and labels is explored, and an XGBoost algorithm is combined to determine whether the extracted features are effective for prediction or not. According to the method, the advantages of the CNN algorithm in the aspect of feature extraction and the advantages of the XGBoost algorithm of high nonlinear load prediction precision and high prediction convergence speed can be integrated, and the accuracy and stability of short-term load prediction of power consumption of the whole network are improved.

The invention discloses an energy storage battery health state prediction method, device and equipment and a medium, and the method comprises the steps: collecting operation data of a target battery, carrying out the preprocessing of the operation data, obtaining battery data, and dividing the battery data into a training set and a test set; establishing a Transform model to perform health state prediction on the target battery; a genetic algorithm is adopted, and an optimal hyper-parameter combination of the Transform model is determined; according to the optimal hyper-parameter combination, using the training set to train a Transform model; and inputting the test set into the trained Transform model to carry out health state prediction so as to obtain a health state prediction value of the target battery. According to the method, the prediction precision can be improved, the complexity of model training is reduced, and the accuracy of health state prediction is improved.

The invention discloses a thermal generator set load prediction system based on machine learning, and the system overcomes the defects of a conventional load prediction method through an end-to-end solution composed of a comprehensive data collection module, a multi-level feature engineering module, a multi-model integration module, a self-adaptive optimization module and the like. The system collects multi-dimensional data from a thermal generator set control system, an environment monitoring system, a fuel detection system and a historical database, wherein the multi-dimensional data comprises load data, environment data, fuel characteristics and equipment operation data. According to the multi-dimensional data integration mode, the model not only depends on single historical load data, but combines multiple influence factors such as environment, fuel and equipment states, the problem that a traditional method depends on a single data source is solved, the load prediction precision is effectively improved, and the load prediction efficiency is improved. And the adaptive capacity of the prediction model to different environment conditions and equipment states is stronger.

The invention provides a heart great vessel postoperative pulmonary complication prediction method and device and processing equipment, which are used for constructing a heart great vessel postoperative pulmonary complication prediction model on the basis of a machine learning model, so that the heart great vessel postoperative pulmonary complication prediction model has high pertinence to heart great vessel postoperative pulmonary complication and is high in accuracy. And a series of optimal configuration schemes are provided in the aspect of details, so that a good prediction effect can be realized, the high-quality prediction requirement on the high-precision pulmonary complications after the heart great vessel operation in clinical work is met, and high-quality data support is provided for the clinical work.

The invention relates to a modeling method and device for a lithium ion battery multi-physics field aging coupling model, and relates to the technical field of lithium ion batteries. The method comprises the following steps: obtaining a pre-constructed equivalent circuit model, and obtaining a pre-constructed evolution model of solid electrolyte interfacial film growth; the equivalent circuit model is coupled with the evolution model of solid electrolyte interfacial film growth, and a lithium ion battery multi-physical field aging coupling model is constructed; and on the basis of the test data corresponding to the lithium ion battery, calibrating various parameters in the lithium ion battery multi-physics field aging coupling model to obtain a calibrated aging coupling model. By adopting the method, the simulation and prediction effects of the aging model of the battery in a simulation experiment can be improved.

According to the method and the system for predicting the mass moisture content of the roadbed based on the dielectric property and the compaction degree, the dielectric property and the compaction state of a soil body are comprehensively considered, and a prediction model of the relationship among the dielectric constant, the compaction degree of the roadbed and the mass moisture content of the roadbed is constructed; the mass moisture content of different types of soil bodies under different compaction conditions is accurately predicted, and the method has the advantages of being high in precision and high in adaptability. Key parameters are obtained through regression analysis fitting, and the method can be widely applied to different types of soil bodies and various compaction degree conditions. In practical engineering application, model parameters can be adjusted according to actual conditions on site, accuracy and applicability of measurement are ensured, and the method can show an excellent prediction effect in various construction environments due to the characteristic.

The invention discloses an improved YOLOv8-seg-based chip surface defect segmentation model and a training method and application thereof, a network of an improved YOLOv8-seg segmentation detection model is built, a Backbone module comprises a multi-layer ShuffleNetV2 network structure, a picture is subjected to multiple convolution processing, and three effective feature layers are extracted; the Neck module adopts a ConcatSDI network structure and an EMA attention mechanism network structure, the ConcatSDI network structure is used for carrying out multi-level feature fusion on an effective feature layer output by the Backbone module to obtain a feature map, and the EMA attention mechanism network structure is used for carrying out feature weighting on the feature map; the head module is a segmentation head and outputs final feature information based on an output result of the Neck module; and after the network structure of the improved YOLOv8-seg segmentation detection model is constructed, training, verification and testing are carried out. According to the method, the improved yov8-seg segmentation detection model is used for accurately segmenting and detecting the surface defects of the chip, and particularly, the detection precision can still be ensured when the method faces complex backgrounds and low-contrast defects.

The invention discloses a power grid full-cycle operation safety supervision system based on artificial intelligence, and belongs to the field of power grid safety supervision, and the system comprises the steps: collecting historical charge data and corresponding timestamp records, carrying out the preprocessing, and dividing the obtained time series data into a training sample set and a verification sample set; through constructing a prediction model and carrying out training and optimization evaluation, an optimal prediction model is obtained; real-time charge data are collected and input into the optimal prediction model, predicted charge data of the next time step are obtained, the charge change rate is calculated and compared with a preset change threshold value, and whether the charge data of the next time step are abnormal data or normal data is judged; performing fault type prediction on the abnormal data by using a preset random forest model; and periodically traversing the fault record to obtain all fault types, setting a power grid operation safety level, and performing risk assessment on power grid operation by calculating a risk index to obtain the power grid operation safety level.

The invention provides a Bayesian-based parameter optimization method and system for quantum key distribution, relates to the technical field of quantum information processing, and aims to solve the problems that in the prior art, the calculation amount is large, the efficiency is low, and real-time parameter optimization of a high-speed quantum key distribution system and the optimal configuration requirement of large quantum communication network resources are difficult to realize. The method comprises the following steps: acquiring system parameters, a sending probability in an MDI-QKD protocol, and intensities of a signal state and a decoy state; the obtained data is preprocessed; according to the preprocessed data, a parameter optimal model based on Bayesian optimization is constructed, training and testing are carried out, and a trained model is obtained; and inputting to-be-predicted system parameters and the sending probability, signal state and decoy state intensity data in the MDI-QKD protocol into the trained model, and outputting optimal parameters. According to the method, the to-be-optimized system parameters can be accurately predicted, the system parameter optimization efficiency is improved, the time cost is reduced, and the key generation rate is greatly improved.

The invention discloses an aluminum electrolysis cell internal magnetic field prediction method based on a physical information network (PINN), and the method comprises the following steps: inputting related data of a to-be-predicted magnetic field into a trained PINN model, and outputting prediction data; the specific content of constructing the trained PINN model comprises the following steps: S1, constructing a physical model of an internal magnetic field of a target aluminum electrolysis cell; s2, a PINN model is constructed, a loss function is constructed according to the physical model of the internal magnetic field of the target aluminum electrolysis cell, physical loss in the back propagation process is calculated through the loss function through iterative training and back propagation, if a preset condition is met, training is completed, and the PINN model used for predicting the internal magnetic field of the target is obtained; and S3, verification of the PINN model is completed, and a trained PINN model is obtained.

The invention discloses an air jet loom auxiliary jet air consumption prediction method and device, equipment and a medium, and belongs to the technical field of industrial automation. The method comprises the following steps: obtaining to-be-measured structure parameters of a to-be-measured nozzle in the air jet loom, wherein the to-be-measured structure parameters comprise an inlet diameter, a straight pipe diameter, a nozzle taper, an elliptical outlet long axis and an elliptical outlet short axis; the to-be-measured structure parameters are input into an air flow prediction model, the nozzle air consumption, output by the air flow prediction model, of the to-be-measured nozzle is obtained, and the air flow prediction model is obtained based on sample structure parameters of the sample nozzle and air consumption label training. According to the method, the gas consumption of the nozzle can be accurately predicted in a complex industrial environment.

The invention discloses a dependent task unloading method, a terminal device and a storage medium, and provides a multi-dependent task double-path dynamic unloading and scheduling scheme with two state embedding layers based on the dependent task unloading method, and characteristic vectors of a plurality of dependent tasks are captured by using a double-path GRU, so that the dynamic unloading and scheduling of the dependent tasks is realized. Remodeling the dependency task into a directed acyclic graph according to the feature vector, performing feature fusion of the multi-heterogeneous dependency task to obtain an optimal scheduling path of the multi-heterogeneous dependency task, training an unloading model according to the obtained optimal scheduling path, and making an optimal unloading decision; in consideration of the limitation of local edge model training, a new edge-cloud cooperative unloading framework is provided, and a trained unloading scheduling model in a heterogeneous edge server is uploaded to a cloud for weighted aggregation updating so as to improve the generalization ability of the model. The method has the characteristics of improving the calculation efficiency to more accurately and quickly process tasks and multi-task low-overhead processing.

The present invention relates to the field of big data, and specifically refers to an innovative display solution system based on big data, comprising a data acquisition module, a data storage module, an interaction module, a data analysis module, a communication module and a display module. The solution performs labeled classification in the data acquisition module, optimizes the classification of labels by using a residual diversified integration method, forms a diversified model structure by combining a deep classifier and a shallow classifier, and introduces a generative adversarial network to generate tail label samples; introduces a C-WOA optimization algorithm in the training of the diversified integration model, adopts an adaptive parameter adjustment mechanism, and dynamically optimizes the model parameter search range and step size; constructs a graph convolutional network model in the data analysis module to analyze user behavior data and data packets, introduces a dynamic graph structure, forms a high-dimensional graph structure distribution by a variational inference method, and further optimizes the model's processing capability for time series data.

The invention discloses a photovoltaic power generation power prediction method and system based on similar daily clustering and FEDform, and the method comprises the steps: obtaining photovoltaic power generation power historical data of a photovoltaic power station, carrying out the preprocessing of the photovoltaic power generation power historical data, and replacing a missing value with a mean value of the data of a same time node; clustering the replaced photovoltaic power generation power historical data set by using a KFCM, and evaluating a clustering effect in combination with a clustering evaluation index; performing correlation analysis on historical data sets after similar daily clustering of the KFCM clustering algorithm through an SRCC method, and determining meteorological characteristic factors with relatively high correlation with photovoltaic power generation power as input variables of an FEDformer prediction model; decomposing the correlation data set by using VMD to obtain a plurality of groups of subsequences so as to further extract features of different time scales; and predicting each sub-sequence by adopting an FEDformer prediction model, and accumulating prediction results of the sub-sequences to obtain a final photovoltaic power generation power prediction result. The stability of the photovoltaic power generation power and the accuracy of the prediction effect can be remarkably improved.

The invention discloses a drug interaction prediction method and system based on a graph neural network and multiple attention mechanisms. The method comprises the following steps: converting an SMILES sequence of a drug pair into graph data, and obtaining an embedding matrix of the drug pair by utilizing a multi-layer GIN operation; extracting a self-attention feature matrix of the drug pair from the embedding matrix of the drug through a self-attention mechanism encoder, calculating a cosine similarity matrix of the drug pair, and obtaining a self-attention feature vector of the drug pair after leveling operation; processing the embedded matrix of the drug pair through an interactive attention mechanism encoder to obtain a feature matrix with interactive attention coefficients, performing average pooling operation on the interactive attention coefficient feature matrixes of the two drugs, and then performing standardization to obtain an interactive feature vector of the drug pair; and carrying out splicing operation on the self-attention feature vectors and the interaction feature vectors of the drug pairs, and inputting a multi-layer perceptron to predict the interaction between the drug pairs. According to the invention, the interaction between drugs can be better predicted.

The invention discloses a micro-seismic quantitative prediction method based on theoretical model constraint and data driving fusion. The method comprises the following steps: 1, acquiring original micro-seismic data of a coal mine working face to form an initial sample data set; 2, preprocessing data and dividing the data into a training set and a test set; 3, constructing a long and short term memory network LSTM model; 4, sampling historical micro-seismic data as a micro-seismic event quantitative prediction sample; 5, constructing a multi-objective optimization model containing seven sub-objective functions including a micro-seismic time fractal dimension, a micro-seismic space fractal dimension, a micro-seismic energy fractal dimension, micro-seismic predicted total energy, micro-seismic reconstructed mining-induced stress, a micro-seismic activity process and a micro-seismic energy-frequency power law b value; and 6, solving an optimal sampling sample as a predicted value. According to the method, quantitative prediction of the micro-seismic event based on theoretical model constraint and data driving fusion is achieved through the steps, the prediction effect is good, and the prediction result can effectively guide on-site production activity process regulation and control, implementation of pressure relief measures and disaster early warning.