56 results about "PROJECTIONS PREDICTIONS" patented technology

A data analysis and combination primary function nerve network-based wind power prediction method is disclosed and relates to the field of wind power prediction. The method comprises the following steps: collected wind power data is subjected to data analyzing operation via variable mode decomposition operation, sample entropy technologies and phase space reconstruction technologies; four groups of subsequences are obtained; primary function nerve networks are built via orthogonal polynomials, and a combined primary function prediction model containing four groups of primary function nerve networks is built; a state transition algorithm is used for optimizing weight and threshold values of the primary function nerve networks, reconstructed subsequences are used as input for the primary function nerve networks, and the optimized prediction model combination primary function nerve networks are used for predicting wind power. Prediction accuracy of the method is markedly higher than that of a BP network and that of an RBF nerve network.

The invention discloses a sales prediction method and system, and relates to the field of sales prediction, and the method comprises the steps: obtaining a predicted category and corresponding time information; obtaining corresponding feature data according to the predicted category and the corresponding time information; and inputting the feature data into a preset model combination correspondingto the predicted category, and predicting the sales volume of the predicted category in the time corresponding to the time information. According to the invention, the system is directly adopted to replace manpower to predict the sales volume, so that the capability requirement on purchasing personnel is reduced; and a computer system is adopted for prediction, so that the dimensions and the datavolume considered during prediction are greatly increased, and a foundation is laid for accurate prediction. In addition, the sales volume is predicted by using a multi-model combination, and the precision is greatly improved compared with prediction by using only one model. And each category has a corresponding preset model combination, so that the matching degree is better, and the prediction result of each category is further improved.

The invention discloses a red tide grade prediction method. The method comprises: prediction algorithm optimization, prediction model construction and prediction result analysis. According to optimization of a prediction algorithm, a C4.5 decision tree classification contribution optimal feature selection method is adopted, the problem that input parameters of the BP neural network are difficult to select is solved, and the problem that the number of nodes of a hidden layer of the BP neural network is difficult to determine is solved by adopting a binary segmentation algorithm. A prediction model construction comprises: constructing a red tide level prediction model by adopting the optimized BP neural network, training the model by using historical case data, and ending the training when the prediction error is within an allowable range or the network training reaches the maximum iteration times. And the prediction result is analyzed, the trained model is used to predict the red tide level, the root-mean-square error of the prediction result is smaller than the prediction result of the traditional BP neural network before optimization, and the prediction precision is higher. The method can provide a new solution for red tide grade prediction.

The invention discloses an intra-frame prediction method and device, an encoder and a storage device. The intra-frame method includes defining the reference lines at a first side, a second side, a third side and a fourth side of a current encoded block, wherein the first side and the second side are adjacent and in an encoding direction of the current encoded block, and the third side and the fourth side are adjacent and in an encoding reverse direction of the current encoded block; obtaining a projection prediction value corresponding to the compensation pixel in the current coding block in each angle mode on a reference line, wherein the projection prediction value comprises a first projection prediction value in an angle mode direction and a second projection prediction value in an angle mode reverse direction; and respectively carrying out weighted average on the first prediction value and the second prediction value of each compensation pixel to obtain an angle mode prediction value of each compensation pixel, the first prediction value being obtained by using the first projection prediction value, and the second prediction value being obtained by using the second projection prediction value. In this way, the spatial redundancy removal effect can be improved.

The invention relates to a sand table manufacturing method based on machine learning, and belongs to the technical field of sand table manufacturing. The problems that a sand table is long in manufacturing cycle, authenticity is lacked during displaying and an adjustment cannot be made in real time in the prior art are solved. The method includes the following steps of establishing a laser holographic projection prediction model and a geomorphic information prediction model to be trained, adjusting projection parameters and geomorphic information of a laser holographic projector in real time through the trained models, conducting sand table projection imaging through the laser holographic projector according to the projection parameter value, and conducting sand table geomorphic manufacturing through a mechanical arm according to the geomorphic information. The sand table manufacturing process is rapid and efficient, the geomorphic state can be truly displayed and simulated through laser holographic projection, the terrain is rapidly piled and adjusted through the mechanical arm, the optimal projection parameters and geomorphic information can be obtained in real time through the models trained through machine learning, the projection state and geomorphology are automatically corrected and adjusted in real time, and the manufactured sand table reaches the optimal projection size and effect.

Disclosed is a machine learning system with date alignment features for improved demand forecasting for products and/or services. The system includes an appliance for more accurately aligning days and weeks between years, including adapting to holidays and special days, in order to ascertain the date in a previous year that most closely aligns with the date in the future for which the forecast is sought. The corresponding day in one or more previous years can then be computed and demand data associated therewith can be retrieved from data storage to be used in forecasting demand on the forecast date. The most closely aligned day from a previous year can be selected such that the aligned day is positioned appropriately within the calendar week and year and the aligned day falls within a week that is positioned appropriately within the calendar month (i.e., first week, last week or middle-month weeks).

The embodiment of the invention provides a network attack prediction method and device, and relates to the technical field of network security. The network attack prediction method comprises the following steps: firstly, obtaining to-be-detected network attack behavior data; preprocessing the network attack behavior data to obtain preprocessed data; constructing an attack event sequence according to the preprocessed data; further performing prediction processing on the attack event sequence through a pre-constructed network attack prediction model to obtain a prediction result; and finally, determining an attacked target address and an attacked probability corresponding to the attacked target address according to the prediction result. Therefore, network attack behaviors can be predicted, the prediction accuracy is high, and the network security can be further maintained in time.

The invention discloses a gridding-based power distribution network top-down load prediction information method. The method comprises the following steps: dividing L1 and L2 planning grids, establishing an L2 grid load prediction formula, establishing an L1 grid load prediction formula, establishing a variable measurement economic equation, constructing a scene model, predicting a variable, predicting a predicted value of a future power load, establishing an annual maximum load prediction curve and querying a grid load predicted value. According to the invention, L1 and L2 are used to plan a grid division mode; load prediction is carried out by using L1 and L2 grid regions; each area is predicted separately, the prediction result is clearer, assignment of various variables is introduced ina scene model mode, various factors such as area development data are considered in load prediction, the credibility and accuracy of the prediction result are demonstrated, the load prediction information is more reasonable, and the method has positive significance for future power grid development planning.

Embodiments predict/forecast demand of a product by receiving historical sales data for the product and, using a plurality of different seasonality estimation methods, estimating a plurality of different seasonality estimations for future time periods and determining an approximate error amount for each of the different seasonality estimations. Embodiments determine a weight for each of the plurality of different seasonality estimation methods based on the corresponding approximate error amount and generate an aggregate seasonality model based on the plurality of different seasonality estimations and the weights. Embodiments then determine a demand forecast using the aggregate seasonality model.

The invention provides a mine water inflow prediction method and system based on coal mine big data, and the method comprises the steps of obtaining coal mine geographic position natural conditions and mining area information, and obtaining coal hydrogeological detection data; determining a main control factor of the mine water inflow according to the acquired information; building a mapping relation between the mine water inflow and the determined main control factors, building a water inflow prediction model, using a TRUST-TECH method to find a plurality of local optimal solutions so as to determine a global optimal solution, and obtaining a working face water inflow prediction result. According to the invention, the main control factors of mine water gushing are extracted, the TRUST-TECH technology is used for establishing the water gushing prediction model, the water gushing damage degree of the working face is predicted, the prediction process is simple, and the prediction result is accurate.

Systems and methods for determining a source of anomaly in a cyber-physical system (CPS) are disclosed. A forecasting tool can obtain a plurality of CPS feature values during an input window and forecast the plurality of CPS feature values for a forecast window. An anomaly identification tool can determine a total forecast error for the plurality of CPS features in the forecast window, identify ananomaly in the cyber-physical system when the total forecast error exceeds a total error threshold, and identify at least one CPS feature as the source of the anomaly.

The invention discloses a medium and long term load prediction method, and the method comprises the steps: finding out an economic factor which has a long term equilibrium relation with electric quantity through a co-integration test in measurement economics based on a Granger causality test and an LSTM; determining economic factors beneficial to electric quantity prediction by using a Granger causality test method so as to reduce the number of input variables of the prediction model; and finally, inputting the economic factor data into the LSTM model for load prediction. A method of combining the Granger causal relationship test and the LSTM time sequence prediction model is introduced into a multivariable system, the medium-and-long-term load prediction model which is not easy to overfit and high in expandability is constructed, and the model is used for medium-and-long-term load prediction. And the predicted medium-and-long-term load has relatively high accuracy.

The invention discloses an inorganic arsenic content prediction method and device based on XGBoost and a medium. The method comprises the following steps: acquiring original data; preprocessing the original data to generate modeling data; according to the modeling data, an XGBoost model is trained, and a prediction model is formed; and inputting process condition information and raw material information of a to-be-detected object into the prediction model, and generating a predicted value of the inorganic arsenic content of the to-be-detected object. The XGBoost model is used for predicting the inorganic arsenic content of the to-be-detected object, the prediction precision is high, in addition, the XGBoost model has the unique advantages of short training time and the like, the prediction efficiency of the method can be further improved, the safety of the related to-be-detected object is further improved, and the method can be widely applied to the technical field of data processing.

The embodiment of the invention discloses a load comprehensive prediction method, which comprises the following steps of: analyzing and counting average power load data at the same time point in short-term time and meteorological data corresponding to each time point, and determining meteorological data parameters influencing the change of the power load data; establishing a short-term fractionalBrownian motion model between the power load data and the meteorological data in a short period of time; establishing a medium and long term load prediction model between the power load data and the economic policy data; optimizing the short-term fractional Brownian motion model and the medium-and-long-term load prediction model to obtain a fractional Brownian motion optimization model about the power load within a short medium-and-long term limit; predicting short-term, medium-term and long-term power load data by using a fractional Brownian motion optimization model; according to the scheme,short-term and medium-and-long-term prediction is carried out on the power load, comprehensive prediction on the power load is realized, and the accuracy of prediction data is high.