8 results about "Stock prediction" patented technology

The invention discloses a forest accumulation prediction method and system, and relates to the technical field of image data processing, and the method comprises the steps: obtaining a target region SAR image through an L-band synthetic aperture radar, and generating a polarization interference coherence matrix; combining a digital elevation model to extract a terrain gradient and a local incident angle, and calculating a polarization azimuth angle offset; performing model polarization decomposition based on the coherence matrix and the offset, and estimating a pure body decoherence coefficient and body scattering intensity; introducing a three-layer S-RVoG model in combination with the interference fringe pattern to invert the forest canopy height and the extinction coefficient, and constructing a multi-dimensional feature vector; and inputting the result into a pre-training model, and outputting a forest stock prediction result, thereby achieving the technical effects of reducing the terrain influence and improving the prediction precision.

The application discloses a kind of big data intelligent warehouse operation management method and system, it is related to data processing and intelligent warehouse management technical field, including, acquisition multi-source heterogeneous data and divide into grid unit, while fusion time, space and environmental characteristics, generate space-time data matrix, input space-time data matrix into space-time graph convolution network model, through multilayer space-time convolution analysis the relevance between grid unit, obtain the safety stock prediction value of each area, the present application can accurately obtain the safety stock prediction value of each area by integrating multi-source heterogeneous data and using space-time graph convolution network model to analyze the relevance between these data, improve the accuracy of inventory management decision, use generative adversarial network model to deeply analyze the distribution characteristics of abnormal candidate area, and accurately locate abnormal area and its deviation level by calculating deviation degree, greatly reduce the false positive rate, reduce the dependence on artificial review.

The invention discloses a convolution gating cycle unit stock prediction method based on limit gradient lifting feature selection and secondary decomposition. The method comprises the following steps: acquiring historical market data of an A stock; preprocessing the A stock data; secondary decomposition is carried out on the A-strand time sequence data through EMD and VMD; EMD decomposition is carried out on the original time sequence data, and then secondary decomposition is carried out on the high-frequency IMF through the VMD; phase space reconstruction: converting the one-dimensional time sequence data into a two-dimensional phase space; performing feature selection by using a limit gradient lifting method; constructing a convolutional gating cycle unit model framework: extracting depth features by using a convolutional layer, and performing data prediction by using two GRU layers; using a genetic algorithm to optimize the parameters of the convolution gating cycle unit; predicting the decomposed IMF by using a gating cycle unit model with optimal parameters; and calculating a model error: evaluating the model through indexes such as a mean square error, a mean absolute error, a root-mean-square error and a mean relative error. The method can improve the accuracy of stock price prediction.

Systems and methods for determining out-of-stock predictions for merchant items and performing intervention actions based on the out-of-stock predictions using machine-learned models. The method includes obtaining feature data including at least (i) aggregations of historical found rate data, (ii) item level metadata, and (iii) merchant signal data into an out-of-stock model. The method includes determining a number of output scores for a number of items. The output scores can be indicative of a likelihood of an item being out-of-stock. The method includes determining a first output score for a first item of the plurality of items satisfies an intervention criterion. The method includes responsive to determining the first output score satisfies the intervention criterion, performing an intervention action.

The invention provides a simulation-based MilkRun stockout material calling logistics management method, which comprises the following steps of S1, constructing a MilkRun logistics simulation model, and inputting inventory data, transportation equipment parameters and production equipment parameters; s2, simulating a material delivery mode by using the simulation model, and monitoring the operation state change of each device in real time; the material distribution mode is a mixed distribution mechanism of'dynamic response type MilkRuns + stock-out material calling '; according to the method, the accuracy of out-of-stock prediction is improved, the misjudgment rate is reduced, and the unnecessary material calling cost is reduced; quick response to the stockout condition is realized, the replenishment period is shortened, and the logistics efficiency and the equipment utilization rate are improved; the distribution plan is optimized, the utilization efficiency of logistics resources is improved, and the overall operation cost is reduced.

The invention discloses a stock extreme fluctuation prediction method and device based on dual-channel emotion propagation and cross-modal attention fusion, and belongs to the field of stock prediction. A dual-channel sentiment factor DCSF is provided, the dual-channel sentiment factor comprises two indexes, namely a newspaper influence index NII constructed by utilizing an industry vocabulary proportion and a media coverage degree and a network influence index SII constructed by utilizing social interaction popularity, sentiment analysis is carried out on news texts of different sources through different methods, and the sentiment analysis result is obtained. And the robustness of market prediction is improved. A nonlinear time decay model is provided, the decay of market emotions in a time window is dynamically adjusted through a historical market fluctuation rate, and the model is further enabled to pay attention to key information by combining a convolutional network and a dynamic distribution weight.

This invention discloses a regression method for predicting urban road material inventory based on the GBDT algorithm, comprising the following steps: segmenting road network information into regions using ArcMap on GIS road network vector maps at various time points, determining parameters such as road width, pavement thickness, and density and admixture of road construction materials; calculating the material inventory of different road construction materials using Python programming; summarizing and organizing the material inventory data, and obtaining area, population, and economic data within the calculation area to construct a feature variable dataset, while converting the categorical feature variables into binary vectors using One-hot encoding; dividing the sample set into a training set and a validation set; training a material inventory prediction regression model based on the GBDT algorithm; evaluating the model's adaptability and validating it on an independent test set. This invention establishes a prediction regression model for the material inventory of various road construction materials in the road system, achieving high prediction accuracy.

An object of a system according to an embodiment is to make an efficient delivery route and plan and minimize an environmental load.SOLUTION: A system according to an embodiment includes a traffic information analysis unit, a route generation unit, a stock prediction unit, a delivery planning unit, and an eco-transportation proposal unit. The traffic information analysis unit analyzes real-time traffic information. The route generation unit generates an optimal delivery route based on the traffic information analyzed by the traffic information analysis unit. The inventory forecasting component analyzes inventory levels and demand forecasts. The delivery planning unit makes an optimal delivery plan based on the inventory level and the demand forecast analyzed by the inventory forecasting unit. The eco-friendly transportation proposal unit proposes the most eco-friendly transportation method in consideration of the cost and efficiency of the transportation method and the environmental load.SELECTED DRAWING: Figure 1