Abstract: Provided herein is a method and a system for data-driven prediction based on spatial information constraints, belonging to the technical field of intelligent information processing. The method comprises: prediction target interpolation based on collocated Co-Kriging; sample weight calculation based on sequential Gaussian simulation and loss function construction based on spatial information constraints; optimization of loss function and data-driven prediction based on deep fully connected neural network. The system comprises: data acquisition module, data preprocessing module, prediction target interpolation module, sample weight calculation module, loss function construction module, loss function optimization module, data-driven prediction module.

Abstract: The present invention provides an electric power material distribution and allocation method based on an optimized branch and bound method, including: sorting the distribution of each batch of electric power materials to obtain a material set R; screening available vehicle data to obtain a vehicle deadweight set W and a vehicle unique identification set C; determining whether available vehicles meet requirements; and using an optimized queued branch and bound method to obtain an optimal distribution and allocation scheme. According to the present invention, scientific scheme support is provided for the distribution and allocation of the electric power materials, the scientificity of the distribution of power grid materials is improved, the influence of human factors is reduced, the utilization rate of distribution vehicles is increased, the distribution cost of the materials is reduced, and the economic benefit is improved.

Abstract: A method for determining abnormality of a temperature sensor and the image forming apparatus, the method comprises: detecting an actual temperature of a fixing apparatus and an input voltage; calculating an actual temperature variation amount in a predetermined time period; comparing the detected input voltage and a predetermined voltage; comparing the calculated actual temperature variation amount and a first reference temperature variation amount if the input voltage is greater than the predetermined voltage; determining that the temperature sensor is abnormal if the actual temperature variation amount is less than the first reference temperature variation amount; comparing the calculated actual temperature variation amount and a smaller second reference temperature variation amount if the input voltage is equal to or less than the predetermined voltage; determining that the temperature sensor is abnormal if the actual temperature variation amount is less than the second reference temperature variation amou

Abstract: A method for determining abnormality of a temperature sensor and the image forming apparatus, the method comprises: detecting an actual temperature of a fixing apparatus and an input voltage; calculating an actual temperature variation amount in a predetermined time period; comparing the detected input voltage and a predetermined voltage; comparing the calculated actual temperature variation amount and a first reference temperature variation amount if the input voltage is greater than the predetermined voltage; determining that the temperature sensor is abnormal if the actual temperature variation amount is less than the first reference temperature variation amount; comparing the calculated actual temperature variation amount and a smaller second reference temperature variation amount if the input voltage is equal to or less than the predetermined voltage; determining that the temperature sensor is abnormal if the actual temperature variation amount is less than the second reference temperature variation amou