71 results about "PROJECTIONS PREDICTIONS" patented technology

The invention discloses a wind speed prediction method and system based on a hybrid convolutional network and a parallel prediction model, and relates to the technical field of weather forecast, and the method comprises the steps: firstly carrying out the cluster screening through a clustering method, and then selecting a certain turbine as a target data set for prediction. During prediction, a GCN model is used to carry out spatial feature learning on historical wind speed data, then a TCN model is used to carry out time feature learning on the historical wind speed data, and then time sequence format data constructed by fusing spatial and temporal features are input into a Transform-LSTM parallel structure prediction model for prediction. According to the wind speed prediction method and system based on the hybrid convolutional network and the parallel prediction model, which adopt the structure and the steps, the prediction result is highly consistent with the observation value in numerical value, and relatively high accuracy is also shown in the aspect of trend prediction.

The invention relates to the technical field of concrete quality control, in particular to a concrete strength prediction system and method based on SVR. According to the invention, multi-dimensional data of rebound test, compressive strength test and carbonization depth value measurement are collected through the data acquisition module, rich input characteristics are provided for the model, and the prediction capability of the model is enhanced; and abnormal data is eliminated by adopting a Grubbs criterion, so that the quality and consistency of input data are ensured, and the influence of noise on model training is reduced. According to the method, a concrete rebound curved surface regression equation and a support vector regression model are established respectively and serve as prediction criteria, data fusion is carried out through a Kalman filter, and the prediction precision and reliability are further improved. Through automatic and standardized data processing procedures including data acquisition, preprocessing, regression prediction, SVR prediction, data fusion and the like, rapid prediction of the concrete strength is realized, and the prediction efficiency is improved.

The invention discloses a tailing pond displacement dynamic prediction method and system based on a multi-algorithm coupling model. The prediction method comprises the following steps: S1, data acquisition and preprocessing; s2, decomposing a displacement signal; s3, trend term displacement prediction; s4, performing feature derivation; s5, periodic term displacement prediction; and S6, predicting result fusion. The prediction system comprises the following modules: a data acquisition module, a data preprocessing module, a displacement signal decomposition module, a trend term displacement prediction module, a feature derivation and optimization module, a periodic term displacement prediction module and a prediction result fusion module. Through the advanced multi-algorithm coupling model, the high-precision dynamic prediction of the displacement of the tailing pond is realized, the precision of the displacement prediction of the tailing dam and the long-term dependence modeling capability are improved, the generalization capability of the model to a complex displacement mode is enhanced, and powerful technical support is provided for the safety monitoring and disaster early warning of the tailing dam.

The invention discloses a product forging data intelligent management system and method based on 5G cloud computing, and relates to the field of intelligent manufacturing. According to the system, multi-source data such as temperature, stress, load and images are collected through a process data module and weighted labeling is carried out; the multi-model reasoning module selects support vector regression, a random forest and a convolutional neural network for parallel prediction according to data features; the prediction fusion module generates a unified prediction result by adopting a nonlinear function based on confidence score; the cloud-edge collaborative scheduling module determines cloud or edge deployment in combination with task complexity, edge computing power and a network state; the parameter optimization module adopts an EPQ-Opt algorithm to optimize process parameters based on energy consumption prediction and residual score; and the security guarantee module adjusts the strategy through consistency detection and robustness evaluation and guarantees data security. The method significantly improves the prediction precision and response speed, reduces the energy consumption, enhances the system safety, and is suitable for the field of high-end forging and intelligent manufacturing.

The invention provides an electric power material demand prediction method, system and device and a medium, and belongs to the technical field of electric power material regulation, and the method comprises the steps: collecting historical electric power material demand data and influence factor data, and carrying out the preprocessing of cleaning, transformation and feature extraction; building a deep learning model based on a TensorFlow deep learning framework; performing training, model parameter adjustment and performance evaluation on the deep learning model by using the preprocessed historical data to obtain a final prediction model; and collecting real-time electric power material demand data and influence factor data, pre-processing the data, and inputting the data into the final prediction model for electric power material demand prediction. According to the method, the deep learning model is constructed through the TensorFlow deep learning framework, prediction of the electric power material demand data is realized in combination with the processed historical electric power material demand data, and the prediction precision is high.

The invention discloses a formation resistivity prediction method and device based on horizontal well array induction logging. The method comprises the following steps: dividing a permeable layer and a non-permeable layer in a horizontal well stratum according to logging information of an adjacent well and horizontal well array induction logging data; according to the three-dimensional complex interpretation model of the horizontal well stratum, the divided pervious bed and the divided non-pervious bed, a one-dimensional horizontal stratified interpretation model and a one-dimensional radial stratified interpretation model are constructed; performing inversion according to the obtained multiple groups of interpretation initial values and the one-dimensional horizontal stratigraphic interpretation model to obtain equivalent macroscopic resistivity of each horizontal stratigraphic stratum; and performing radial inversion on the equivalent macroscopic resistivity of each horizontal stratified stratum obtained by the one-dimensional horizontal stratified interpretation model according to the one-dimensional radial stratified interpretation model to obtain a resistivity prediction result of each horizontal stratified stratum. According to the method, stratum information prediction is carried out based on the simplified model by utilizing the sub-array parameters, the prediction speed is high, the precision is high, and the real information of the predicted stratum is truly reflected.

The invention discloses a deep large-mining-height working face surrounding rock stress dynamic prediction method. The method comprises the following steps: S1, data acquisition and selection; s2, decomposing a stress signal; s3, trend term stress prediction; s4, periodic term stress prediction; and S5, predicting result fusion. The prediction system comprises the following modules: a data acquisition module, a data preprocessing module, a stress signal decomposition module, a trend term stress prediction module, a periodic term stress prediction module and a prediction result fusion module. Through the advanced CEEMDAN-Kmeans algorithm and multi-algorithm coupling model, the high-precision dynamic prediction of the surrounding rock stress is realized, the signal processing efficiency and accuracy and the prediction precision are improved, the generalization ability of the model to a complex stress change mode is also enhanced, and the prediction precision of the surrounding rock stress is improved. And powerful technical support is provided for coal mine safety production and roadway surrounding rock stress management.

The invention discloses a server energy consumption prediction method and device, computing equipment and a storage medium. First. The power of the first server at the multiple sampling moments is predicted according to the rated power and the no-load power of the first server and the load rates of the first server at the multiple sampling moments in the prediction time period, and the load rates of the multiple sampling moments correspond to the power of the multiple sampling moments one to one. And then, predicting the energy consumption of the first server in the prediction time period according to the power at the plurality of sampling moments. According to the scheme of the embodiment of the invention, server energy consumption prediction is performed based on the load rate, and the prediction result is relatively accurate.

An example of processing a point cloud includes responsive to determining to predict a current point in the point cloud using predictive geometry coding, selecting, from a set of prediction modes, a prediction mode for the current point, wherein the set of prediction modes includes at least an intra prediction mode and an inter prediction mode; and responsive to selecting the inter prediction mode for the current point, predicting the current point of the point cloud using inter prediction.

A prediction device 1X mainly includes: an acquisition means 24X; a prediction model execution means 25X; and a correction model execution means 27X. The acquisition means 24X acquires prediction data to be used for prediction by a prediction model. The prediction model execution means 25X acquires prediction-target prediction results output by the prediction model on the basis of the prediction data. The correction model execution means 27X acquires correction results obtained by correcting the prediction results on the basis of the prediction results, the prediction data, and a correction model that is a machine learning model.

The invention relates to an encrypted domain three-dimensional grid model steganography method and system based on ripple prediction. The method comprises the steps that S1, a model owner executes vertex coordinate conversion on a three-dimensional grid model; s2, a model owner predicts the vertexes of the three-dimensional grid model by applying a ripple prediction method, adjacent vertexes are predicted layer by layer with a reference point as a starting center during prediction until all the vertexes of the whole three-dimensional grid model are expanded, and finally prediction errors of all the vertexes are generated; s3, the model owner carries out coding processing on the obtained prediction error; s4, the model owner encrypts the encoded data to generate an encrypted three-dimensional grid model, and uploads the encrypted three-dimensional grid model to a cloud for storage; and S5, the cloud service provider embeds secret data in the encrypted three-dimensional grid model to generate a marked three-dimensional grid model containing the secret data. According to the method, the data embedding capacity is remarkably improved on the premise of ensuring the privacy of the model, and the method is suitable for three-dimensional grid model data transmission with high privacy requirements.

This prediction device comprises: a verification prediction unit that predicts electricity prices in a predetermined verification period, on the basis of each of a plurality of trained prediction models; a model selection unit that selects a prediction model having high accuracy for predicting electricity prices among the prediction models; and a target prediction unit that predicts electricity prices in a prediction target period, on the basis of the selected prediction model.

Combiner circuitry generates a combined prediction associated with a given address based on combining respective sets of prediction information generated by two or more predictors. Predictor control circuitry determines, based on a lookup of a prediction input address in a combiner hint data structure, whether a second predictor lookup suppression condition is satisfied for the prediction input address indicating that the combined prediction that would be determined by the combiner circuitry for the prediction input address is likely to be derivable from a prediction outcome predicted by the first predictor for the prediction input address. If this condition is satisfied, a lookup of the second predictor is suppressed and the prediction associated with the prediction input address is generated based on the prediction outcome predicted by the first predictor for the prediction input address.

Methods, systems, and techniques for training an implicit neural representation (INR) network to perform time series data forecasting. Lookback and horizon time series of data are obtained, with the lookback time series of data spanning a lookback time window and the horizon time series of data spanning a horizon time window following the lookback time window. A lookback basis and a horizon basis are determined by processing the lookback time series of data using the INR network. Weight and bias parameters are determined using a regression based on the lookback basis and time series. Predicted horizon values are forecast from the horizon basis values and the weight and bias parameters. The INR network is trained to reduce forecast error between the horizon time series of data and the predicted horizon values, with the training involving penalizing linear redundancies between pairs of bases selected from the lookback and horizon bases.

The invention discloses a physical examination score prediction method and platform. The method comprises the following steps: a) analyzing historical data; b) analyzing a data trend; c) performing simulation test; step d) expert assessment; and e) personal evaluation: step a) historical data analysis: firstly analyzing physical examination score data in past several years, knowing an average score, a highest score, a lowest score and the like, comparing data in different years, observing trends and changes, predicting a possible score range of students according to training conditions, historical scores and examination difficulty of the students, and b) analyzing a data trend: analyzing the collected historical data. According to the method, the phenomena that traditional prediction is too troublesome and the prediction period is long are changed, multiple prediction modules and scientific methods are adopted for prediction, multiple algorithms are not needed for prediction, the prediction period is not very long, and the phenomenon that the prediction accuracy is not high does not occur; and the physical examination quality of the trainee is not influenced.

The invention provides a replenishment prediction model training method and a replenishment method and device, and relates to the technical field of supply chain intellectualization, and the method comprises the steps: obtaining a training sample and an initial replenishment prediction model; inputting the training data into a prediction unit to predict and predict replenishment data; simulating the predicted replenishment data through a simulation unit, and inputting the simulated replenishment data into a prediction unit for iterative prediction; and repeating the above steps until training is completed, determining a loss value of the initial replenishment prediction model based on the predicted replenishment data and the simulated replenishment data, adjusting the initial replenishment prediction model based on the loss value, and outputting a target replenishment prediction model. The target replenishment prediction model containing the prediction unit and the simulation unit at the same time is generated through training, prediction and simulation can be achieved through the same model, intermediate processes are reduced, decision errors are reduced, finally, the theoretically optimal replenishment decision is directly generated, and meanwhile the generation cost of the replenishment decision can be reduced.

The invention discloses a time sequence prediction method and device, a program product, a storage medium and application thereof, relates to the field of time sequence analysis, and aims to solve the problems of high calculation complexity and poor reliability of a prediction result in a time sequence prediction task. The method comprises the following steps: training an inference model by using a time sequence, wherein the inference model extracts a dependency relationship among variables based on an autocorrelation matrix of an input original time sequence; integrating the dependency relationship into an original time sequence to obtain a key time sequence feature; learning a long-term dependency relationship between variables from the key time sequence features; and mapping a time sequence of future time steps according to the long-term dependency relationship. According to the method, the accurate prediction of the time sequence is realized with low calculation complexity, and the prediction credibility is high.

A power grid short-term load prediction method comprises the following steps: determining a prediction target period; and predicting the power grid short-term load in the prediction target period by using a pre-trained prediction model, wherein the prediction model is: The present application provides a power grid short-term load prediction method, system, computer device and storage medium, comprehensively considers the influence of long-term trend, seasonal factors, special events such as holidays and weather changes on the power grid load, establishes a load prediction model to accurately predict the future load of the power grid.

According to the in-province power flow calculation and report generation method and platform based on the digital intelligent AI, an integrated solution of data input, model calculation and report output is formed, standardization and processizing of provincial level power flow planning work are achieved, and the planning efficiency and the planning precision are improved; the method comprises the steps of 1, predicting load electric quantity, comparing and selecting data, and forming a high-medium-low scheme; historical load data, historical electric quantity data and historical economic data are uploaded, the load electric quantity is predicted by adopting 10 methods, and 10 groups of load electric quantities are predicted and generated; calculating an average value of the 10 groups of data, taking data similar to the average value as a middle scheme, taking a group slightly higher than the middle scheme as a high scheme, and taking a group slightly lower than the middle scheme as a low scheme; step 2, power balance and power flow calculation; step 3, report generation; and calling the DeepSeek-R1-0528 model to complete the writing of the report.

The invention discloses an inter-city industry migration prediction method and system and related equipment, and the method comprises the steps: obtaining enterprise registration data and migration information, and constructing an inter-city industry migration matrix according to the migration information; the spatial distance, the time distance and the connection strength between the cities are calculated, and standardization processing and weighted summation are carried out on the spatial distance, the time distance and the connection strength between every two cities. According to the method, the industry migration volume prediction model based on the improved gravitation model and the industry migration direction prediction model based on the field potential energy model are adopted, city-level and industry-oriented industry migration prediction can be realized, and the prediction result is more detailed and comprehensive. According to the method, the dynamic model of urban industry migration situation prediction is improved by using the time sequence model, and long-time prediction of the overall trend of urban industry migration can be realized.

This invention discloses a time series forecasting method, apparatus, program product, storage medium, and its applications, relating to the field of time series analysis, to address the problems of high computational complexity and poor reliability of prediction results in time series forecasting tasks. This invention trains an inference model using time series data. This inference model extracts dependencies between variables based on the autocorrelation matrix of the input original time series; integrates these dependencies into the original time series to obtain key time-series features; learns long-term dependencies between variables from these key time-series features; and maps the time series for future time steps from these long-term dependencies. This invention achieves accurate time series forecasting with high reliability at a computationally low level.

The application discloses a kind of day-ahead wind power power ramp event prediction methods, comprising the following steps: S1.day-ahead wind power prediction: using extreme value driving model, based on historical wind power and meteorological characteristics, produce the wind power prediction of Q time points of next day;S2.predicting confidence interval construction: using wind power oriented conformal inference method, for each future time point, construct confidence interval C;S3.ramp event detection based on confidence interval: the prediction result of next day wind power ramp event is obtained by confidence perception detection algorithm.The application improves the prediction accuracy and reliability of wind power ramp event with significant operation risk by fusing multi-scale time series feature analysis, customized loss function for extreme event and adaptive confidence interval construction technology with statistical guarantee.

The present invention provides a resource adjustment method, device, server, and storage medium, relating to the field of cloud computing technology. The method comprises: predicting a first load for a prediction time period based on target user workloads for multiple first historical time periods, and predicting a second load for a prediction time period based on target user workloads for multiple second historical time periods; predicting a load for the prediction time period based on the first load and the second load; classifying the load for the prediction time period to obtain the total amount of resources for the prediction time period, and determining a target user resource adjustment value based on the total amount of resources for the prediction time period. An embodiment of the present invention provides a method for predicting load based on historical workloads on different dates and historical workloads on the same date but different time periods, and then determining a target user resource adjustment value based on the load, thereby improving not only prediction accuracy but also work efficiency.

The invention discloses a BiLSTM-XGBoost settlement prediction method based on an NGSA-II multi-objective optimization algorithm. The BiLSTM-XGBoost settlement prediction method specifically comprises the following steps: preprocessing engineering monitoring acquisition data; carrying out time sequence feature screening by adopting an ADF; generating new features by adopting BiLSTM, and fusing the new features with the static features; performing optimization on the hyper-parameters of the BiLSTM and the XGBoost by using the NSGA-II; on the basis of the optimized hyper-parameters, a fused BiLSTM-XGBoost model is trained, and efficient prediction of sedimentation is achieved; and evaluating and applying the prediction model. Through efficient fusion and multi-objective optimization of multi-source features, dynamic balance of model prediction precision and complexity is realized, the accuracy of settlement prediction is remarkably improved, meanwhile, the training time is shortened, the calculation complexity is reduced, and the method is suitable for real-time prediction and decision support in engineering construction.

This invention discloses an ozone forecasting method based on multi-model fusion, which consists of the following steps: data preprocessing, feature engineering, model training, model evaluation, model prediction, and model correction. This process ultimately yields reliable ozone prediction results. This method is easy to operate, data acquisition is readily available, and it does not require the collection of large amounts of precursor pollutant emission data. It can predict most ozone pollution conditions using only meteorological data, significantly improving prediction accuracy and compensating for the large biases in predictions based on a single model.

The invention discloses an intra-day electricity price probabilistic prediction method fusing time sequence depth representation and a gradient boosting tree. The method comprises the following steps: constructing a market state space and generating a multi-scale time sequence; feature depth engineering is carried out based on time-varying heterogeneity and a microstructure; performing time sequence depth representation and probabilistic prediction based on a hybrid model; and generating prediction probability density and deducing a multi-step path. The invention provides a brand new intra-day electricity price probabilistic prediction method. The method aims to deeply mine an internal dynamic rule of a time sequence through a hybrid neural network model, organically fuse the internal dynamic rule with heterogeneous features representing market states, and finally realize normal form transformation from traditional point prediction to probabilistic prediction with higher information value in combination with a quantile regression thought.

The invention discloses a technical acceptance and behavior intention prediction method based on a behavior intention derivative model. The prediction method comprises the following steps: S1, data acquisition: providing necessary input data for behavior intention prediction; s2, multiple interaction modes: providing more-dimensional information support for subsequent behavior intention prediction; s3, a behavior intention analysis model: constructing a behavior intention calculation formula through quantitative analysis of a plurality of influence factors, and providing accurate prediction of the user behavior intention; s4, construction of a prediction model: further predicting future behaviors of the user through analysis and training of user behavior intentions, and providing a basis for decision making; and S5, continuous iteration and updating of the model: performing real-time updating and optimization according to new data by adopting a continuous learning mechanism, and constructing an optimization prediction algorithm formula. The method is high in prediction accuracy, has extremely high applicability, can accurately grasp user requirements, and provides valuable decision support for technical design and popularization strategies.

The application discloses a product forging data intelligent management system and method based on 5G cloud computing, and relates to the field of intelligent manufacturing.The system collects temperature, stress, load and image and other multi-source data through a process data module and marks them with weights; a multi-model reasoning module selects support vector regression, random forest and convolutional neural network in parallel prediction according to data characteristics; a prediction fusion module generates unified prediction results by using a nonlinear function based on confidence score; a cloud-edge collaborative scheduling module determines cloud or edge deployment in combination with task complexity, edge computing capability and network state; a parameter optimization module optimizes process parameters by using an EPQ-Opt algorithm based on energy consumption prediction and residual score; and a security guarantee module adjusts strategies and guarantees data security through consistency detection and robustness evaluation.The application significantly improves prediction accuracy and response speed, reduces energy consumption and enhances system security, and is suitable for high-end forging and intelligent manufacturing fields.

Methods and systems for managing inference models are disclosed. To manage inference models, a plurality of predictions that each indicate whether a state will occur may be obtained, the plurality of predictions being generated by respective inference models of at least one inference model. The plurality of predictions may be analyzed to obtain a statistical characterization regarding agreement in the plurality of predictions. A determination may be made regarding whether the statistical characterization meets criteria. In a first instance of the determination in which the statistical characterization meets the criteria, an action set may be obtained, the action set being based on an occurrence of the state predicted by the plurality of predictions to occur and usable to update an operating state of the data processing system to hedge against a risk of an undesired outcome from the occurrence of the state.

The invention relates to the technical field of digital economy, and provides a digital economy integration and optimization method and system based on an information chain, and the method comprises the following steps: data collection and integration: building a data integration platform, collecting market data, customer data and supply chain data, and integrating the data into a centralized data warehouse through ETL (extraction, conversion and loading); data analysis and mining: analyzing the integrated data based on a Transform model, and mining the market trend and demand; and real-time monitoring and prediction: predicting future market dynamics through an autoregression model by using an information chain and historical data, and supporting strategic decisions. Through ETL and standardization processing, the problem of multi-source data isomerism is solved, through combination of a Transform model and a self-attention mechanism, the market trend analysis accuracy is improved by 30%, the method is significantly superior to a traditional statistical method, through combination of an autoregression model and an information chain, real-time market monitoring and prediction are achieved, and the prediction error rate is reduced to 5% or below.