5 results about "Weather factor" patented technology

The short-term load forecasting method based on SARIMA-random forest combination model comprises the following steps: grouping the original load data by using a sliding window, decomposing the to-be-tested week-before-next day data set of each group to obtain a trend item, a seasonal item and a residual item; establishing a SARIMA model, predicting the trend item to obtain a preliminary prediction result and a residual; clustering weather factors to obtain similar days, grouping to construct a weather-residual data set and establishing a random forest regression model, learning the influence of the weather factors on the residual, and selecting model parameters by using a grid search method; combining the prediction results of the model, and comparing the influence of weather clustering and residual training on the load prediction accuracy. The method can accurately predict the next day load under the condition that the historical load and weather factors of the to-be-tested day are known, and improves the prediction accuracy.

The application discloses a rain weather multi-view feedforward Gaussian reconstruction method and system based on weather factor gating, and the method comprises the following steps: acquiring rain weather context images under different views and camera parameters thereof; inputting the rain weather context images into a weather factor decomposition network to generate predicted weather factors; constructing a matching reliability graph for representing the corresponding reliability degree across views according to the predicted weather factors; extracting multi-scale features of the rain weather context images; generating gated features according to the multi-scale features, the matching reliability graph and reliability gating; performing multi-view aggregation based on the gated features to generate fusion representation; inputting the fusion representation into a three-dimensional reconstruction network for feedforward prediction to obtain a three-dimensional Gaussian set; inputting the three-dimensional Gaussian set into a differentiable renderer to render a clean predicted image of a target view in combination with target view camera parameters; acquiring clean context images of multiple views to construct rain weather training samples; and constructing a loss function to train the weather factor decomposition network and the three-dimensional reconstruction network.

The utility model relates to high altitude operation equipment technical field, and disclose a kind of welding platform with telescopic canopy suitable for high altitude welding, including lifting platform and the operating frame installed in the top of lifting platform, further include: canopy cloth is set to the top of operating frame one side, the side of operating frame is provided with the support member for supporting or storing canopy cloth, support member includes movable rod and support frame, the side of movable rod is rotatably connected with operating frame by pivot. By the setting of support member, the outward thrust generated by the compressed gas of first gas spring is utilized, the movable rod can be easily lifted up, the support frame is pushed up synchronously during the lifting of movable rod, and then the canopy cloth is unfolded smoothly, the unfolded canopy cloth can effectively block sunlight and rain for the working area, which improves the working environment of workers, avoids the interference of weather factors on construction, and significantly improves the practical value and use convenience of canopy cloth.

The application discloses a kind of over-ocean fishery safety early warning method, system and medium based on big data, by obtaining and analyzing the marine historical weather data of multiple marine fishing sub-regions and the marine environmental information of current region, obtain current warning information, determine the warning level of warning information, generate different navigation correction information for different warning levels, according to correction information, obtain suitable fishing route, thereby effectively improve fishing yield and economic efficiency of marine fishery.In addition, by analyzing the safe marine sub-region in the fishing area, a suitable return route is obtained, which can effectively reduce the economic loss caused by abnormal weather factors in marine production.