9results about How to "Accurate and Efficient Prediction" patented technology

This invention provides a training and prediction method, system, device, and storage medium for a pressure prediction model, belonging to the field of urban intelligent water supply network technology. The training method for a multi-monitoring-point network pressure prediction model includes: acquiring historical pressure data of the monitored urban water supply network; constructing a pressure prediction model, which includes a temporal convolutional network model and a long short-term memory network model; jointly training the convolutional network model and the long short-term memory network model based on historical pressure data to obtain a trained pressure prediction model; and using this trained pressure model to predict the pressure data of each monitoring point on the monitored urban water supply network based on the historical pressure data. This invention provides a training method for a pressure prediction model that, based on the correlation and periodicity of multiple monitoring points on an urban water supply network, efficiently and accurately predicts the pressure data of each monitoring point on the network.

This invention discloses an intelligent analysis method and system for the bottom support status of caissons based on correlation features, relating to the field of caisson construction monitoring technology. The method includes: acquiring strain time-series data of the target monitoring location at the bottom of the caisson during the drainage sinking stage; preprocessing the strain time-series data and classifying and labeling the support status, constructing labeled sample data, where the support status includes both voided and non-voided states; performing various correlation analyses on the labeled sample data to mine multiple correlation information between the sample data and the support status; constructing a classification prediction model for each correlation information, and training the classification prediction model using each correlation information; deploying the trained classification prediction models to the non-drainage sinking stage, and comprehensively judging the bottom support status of the caisson during the non-drainage sinking stage based on the prediction results of each classification prediction model. This invention can achieve accurate and efficient identification of the bottom support status of caissons during the non-drainage sinking stage.

The application discloses a converted wave multiple wave prediction method and related devices, and the method comprises the following steps: establishing multiple wave prediction models corresponding to various types of converted wave multiple wave propagation paths according to the various types of converted wave multiple wave propagation paths; transforming P-wave seismic time domain data and converted wave seismic time domain data to obtain P-wave seismic frequency domain data and converted wave seismic frequency domain data; predicting converted wave multiple wave frequency domain data according to the P-wave seismic frequency domain data, the converted wave seismic frequency domain data and the multiple wave prediction models corresponding to the various types of converted wave multiple wave propagation paths; and inversely transforming the converted wave multiple wave frequency domain data to obtain converted wave multiple wave time domain data. According to the P-wave seismic frequency domain data, the converted wave seismic frequency domain data and the multiple wave prediction models corresponding to the various types of converted wave multiple wave propagation paths, the converted wave multiple wave of the complex multiple propagation paths is efficiently and accurately predicted.

The application discloses a 6G wireless communication prediction channel modeling method based on large language model fine tuning, relates to the technical field of channel prediction, and comprises the following steps: processing channel measurement data, matching corresponding text data, dividing a training set and a test set, and constructing a channel prediction data set; designing a channel encoder and a dual-domain fusion module to extract channel features, and designing a text-driven encoder to extract text features; fine tuning a large language model by using the extracted channel and text features, enhancing multi-modal perception and transfer learning capability; designing a fine tuning module, fine tuning the output of the large language model, and projecting to predicted future channel state information; designing an angle consistency loss function, training a prediction algorithm based on large language model fine tuning in combination with prediction loss, and obtaining a trained network architecture; and iteratively predicting the space-time domain channel state by using the trained network, and outputting a channel prediction result. The system has high-precision prediction performance, and has outstanding practical value and popularization prospect.

The application discloses a coastal city flood rapid prediction method based on LightGBM and a hydrological and hydrodynamic model, and comprises the following steps: 1, obtaining sample data, including: designing multiple rainfall-tide level combination scenarios; constructing a flood simulation model of a target region based on a PCSWMM hydrological and hydrodynamic model; simulating the maximum water depth of each selected flood point under different rainfall-tide level combination scenarios by using the flood simulation model; extracting rainfall-related characteristic variables and tide-related characteristic variables, and taking the flood point position and the maximum water depth as sample data; 2, constructing and training a city flood rapid prediction model based on LightGBM; 3, predicting the maximum water depth of a submerged point in city flood by using the trained city flood prediction model. The application has excellent performance and calculation efficiency, and can realize accurate and rapid prediction of city flood. The application can also realize efficient and accurate prediction of a city region with a large space range or high spatial resolution.

The application discloses a square resistance prediction model training method and a square resistance prediction method, and belongs to the technical field of semiconductors. The method comprises the following steps: obtaining a training set; determining a plurality of relative differences between an optimized process and a plurality of key parameters of each sample in the training set according to a design rule of the optimized process, wherein the optimized process is obtained by optimizing a source process; obtaining a square resistance prediction model, wherein the square resistance prediction model is constructed based on a physical relationship between square resistance and the plurality of key parameters; and training the square resistance prediction model by using a plurality of samples in the training set and the plurality of relative differences of each sample. The method can realize high-precision square resistance prediction.

The present application relates to the technical field of wine brewing, and particularly relates to a method for predicting the generation potential of wine aliphatic lactone based on fruit precursors. The concentration of aliphatic precursors in grape fruits is detected, and the generation potential of wine aliphatic lactone is obtained according to the concentration of the aliphatic precursors through a pre-trained potential prediction model. The prediction method can efficiently and accurately predict and evaluate the aroma potential of wine after fermentation according to the concentration of precursors in grape fruits before fermentation, so as to achieve the purpose of guiding harvesting and brewing.

The present application relates to the field of biotechnology, and particularly relates to a SNP molecular marker related to the average daily weight gain trait of Ningxiang pigs and application thereof. The molecular marker comprises SNP1 and SNP2; the SNP1 molecular marker corresponds to the 12624189th site from the 5' end on the chromosome 1 of the reference genome Sus Scrofa Build11.1, and is C or A; the SNP2 molecular marker corresponds to the 13057273th site from the 5' end on the chromosome 1 of the reference genome Sus Scrofa Build11.1, and is G or A. The present application obtains a molecular marker significantly related to the average daily weight gain of Ningxiang pigs, and provides guidance for the breeding of Ningxiang pigs by using the molecular marker, so that the average daily weight gain value and typing can be accurately and efficiently predicted, and the pig species with high average daily weight gain value can be identified and screened, and the economic benefit can be improved.

Embodiments of the present application provide a method and device for predicting wireless coverage, electronic equipment and storage medium. The method comprises: obtaining input data of a target area, and obtaining an input feature vector of the target area according to the input data; inputting the input feature vector into a pre-trained wireless coverage prediction model to obtain a predicted coverage result of the target area output by the wireless coverage prediction model; wherein the wireless coverage prediction model is trained based on an automatic hyperparameter tuning gradient boosting decision tree (GBDT) model. In the embodiments of the present application, the automatic hyperparameter tuning gradient boosting decision tree model is used to predict the wireless coverage of the target area, which reduces the complexity of predicting the wireless coverage of the actual antenna, saves a large amount of computing resources, and realizes efficient and accurate wireless coverage prediction.