4results about How to "Reduce or avoid the impact" patented technology

The application discloses an Internet of Things data acquisition device for high-cold and high-altitude application, which comprises a box body, a supporting column, a solar panel, an anti-static and radiation layer, a thermal insulation layer and an Internet of Things data acquisition device. In use, the basic processing work of the collected terminal equipment data can be completed through the setting of the Internet of Things data acquisition device, and label management. The edge calculation of the collected data is focused on, the data is cleaned, effective characteristic parameters are extracted and transmitted, so that the power consumption of the transmitting unit is minimized. The setting of the solar panel can provide energy for the working of the Internet of Things data acquisition device. The anti-static and radiation layer and the thermal insulation layer can protect the Internet of Things data acquisition device, so that the Internet of Things data acquisition device can work at a reasonable temperature, and the influence of a dry environment, ultraviolet rays and radiation on the collection result is reduced or avoided, and the reliability of the result collection is ensured.

Embodiments of the present disclosure disclose a feature data-based attribution analysis method, device and electronic equipment. A specific implementation of the method includes: generating training data according to feature data belonging to in-module features in a swap-in data set and a swap-out data set, wherein the in-module features are in-module features of an original decision model that appears a swap-in swap-out phenomenon; training a preset classification model through the training data, and determining index data of each in-module feature of the original decision model based on the trained preset classification model; selecting a set number of in-module features from each in-module feature of the original decision model as target features according to the determined index data; and analyzing key features that cause the original decision model to produce swap-in swap-out data based on the target features, the swap-in data set and the swap-out data set. The implementation is related to model bottom layer analysis technology, and reveals key features that cause swap-in swap-out by analyzing in-module features of the original decision model.

The present application relates to the field of freeze-dried preparations of leeches, and provides a freeze-dried powder containing multiple active enzymes and a preparation method thereof, to solve the problem that the prior art cannot simultaneously ensure the release of active ingredients and the maintenance of the stability of active ingredients, comprising the following steps: S100, cooling leeches to obtain freeze-dried leeches; S200, putting the freeze-dried leeches into a solution system, crushing, centrifuging, and taking the supernatant; the pH value of the solution system is 6.5-8.5; the solution system at least comprises water; the crushing speed is 150-250 r / min, and the time is 5-8 min; S300, after the supernatant is separated and purified, freeze-drying is performed to obtain the freeze-dried powder of leeches. The present application adopts a freeze-crushing process, and after freezing, not only can the crushing operation be simply realized, but also the loss of active ingredients caused by high temperature can be reduced by utilizing the crushing process at low temperature of raw materials.

Embodiments of the present disclosure disclose a decision mechanism-based attribution analysis method, device and electronic equipment. A specific implementation of the method includes: using feature data belonging to a target feature in the swap-in data set to train decision tree models of multiple depths to obtain multiple swap-in candidate decision tree models; using feature data belonging to the target feature in the swap-out data set to train decision tree models of multiple depths to obtain multiple swap-out candidate decision tree models; selecting swap-in decision tree models and swap-out decision tree models of the same depth according to index values of each candidate decision tree model and the original decision model on a preset constraint index; and based on decision rules generated by the swap-in and swap-out decision tree models, analyzing differences in the decision mechanism of the original decision model for the swap-in and swap-out data sets. The implementation is related to model bottom layer analysis technology, and by analyzing the decision rules of the decision tree, the bottom layer decision logic causing the swap-in and swap-out is revealed.