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The present application relates to a method for optimizing energy consumption of an electric-thermal integrated energy system with reduced loss, comprising: obtaining energy consumption data of the electric-thermal integrated energy system; processing the obtained energy consumption data, classifying it according to energy consumption regions, and dividing time periods; analyzing the energy consumption data of different regions respectively, obtaining consumption sources; optimizing the energy consumption of different regions respectively, generating improvement suggestions for the consumption sources; implementing loss reduction measures, and analyzing and comparing the loss reduction effects, recording the effective loss reduction measures for popularization and use. The present application also discloses an energy consumption optimization system. The present application obtains energy consumption data of different regions and different time intervals, monitors energy consumption abnormal nodes in real time; uses a normal distribution probability model of an expectation maximization algorithm to calculate the optimal energy consumption data distribution, generates improvement suggestions for the consumption sources, and optimizes energy consumption.

This invention provides a method and system for detecting damage to the glass back panel of an OLED display, relating to the field of defect detection technology. The method includes: performing grayscale conversion and image enhancement processing on the original glass back panel image P0 to obtain an enhanced glass back panel image P1; performing threshold segmentation processing on the enhanced glass back panel image P1 to obtain the contour coordinates of the glass back panel; removing edge protrusion noise from the binarized glass back panel image P2 to obtain a standard glass back panel contour image P3; generating a glass back panel mask image P4 using the contour coordinates of the glass back panel; performing differential processing between the glass back panel mask image P4 and the standard glass back panel contour image P3 to obtain a glass back panel damage detection image P5; and determining whether the glass back panel is damaged based on the glass back panel damage detection image P5. This solves the problem in the prior art of OLED display glass back panel defect detection where the small difference between the defect and the background leads to over-detection or under-detection.

The embodiment of the invention provides an agricultural disaster risk prediction method and device for agricultural insurance policies, electronic equipment and a medium, belongs to the technical field of artificial intelligence, and is suitable for the field of financial science and technology. The method comprises the following steps: acquiring original multi-source environment data; obtaining an agricultural insurance policy resource pool; the agricultural insurance policy resource pool comprises an original agricultural insurance policy, and the original agricultural insurance policy is used for underwriting a current crop; performing information extraction on the original agricultural insurance policy to obtain underwriting area information and current growth information of a current crop; based on the original multi-source environment data, the insured area information and the current growth information, performing knowledge retrieval on a preset agricultural disaster information base to obtain reference agricultural disaster information; performing risk prediction based on the reference agricultural disaster information to obtain agricultural disaster risk prediction data; and performing insurance policy screening on the original agricultural insurance policies based on the agricultural disaster risk prediction data to obtain disaster-affected agricultural insurance policies. According to the embodiment of the invention, the accuracy of agricultural disaster risk prediction can be improved.