Abstract: A leakage measurement error compensation method based on cloud-edge collaborative computing is implemented on a communication network formed by an interconnection between a leakage current edge monitoring terminal and a power consumption management cloud platform. The method includes the following steps: iteratively training, by the power consumption management cloud platform, a pseudo-leakage compensation model by using the received leakage current data, continuously updating pseudo-leakage model parameters, and feeding the pseudo-leakage model parameters back to the leakage current edge monitoring terminal; and processing, by the leakage current edge monitoring terminal, the leakage current data according to the pseudo-leakage compensation model parameters.

Abstract: A feeding device for an intelligent cooking machine, including a base frame, at least one ingredient box, at least one door panel, at least one door opening mechanism, at least one door opening motor, at least one door closing mechanism, and at least one door closing motor. The at least one ingredient box matches with the at least one door panel in number; at least one door panel is disposed on the base frame; the at least one door opening mechanism and the at least one door opening motor are configured to control an opening of the at least one door panel; the at least one door closing mechanism and the at least one door closing motor are configured to control a reset and closure of the at least one door panel; the at least one door opening mechanism is disposed on the base frame.

Abstract: The present disclosure provides a leakage measurement error compensation method based on cloud-edge collaborative computing, implemented on a communication network formed by interconnection between a leakage current edge monitoring terminal and a power consumption management cloud platform, and including the following steps: monitoring, by the leakage current edge monitoring terminal, leakage current data, and sending the leakage current data to the power consumption management cloud platform; iteratively training, by the power consumption management cloud platform, a pseudo-leakage compensation model by using the received leakage current data, continuously updating pseudo-leakage model parameters, and feeding the pseudo-leakage model parameters back to the leakage current edge monitoring terminal; and processing, by the leakage current edge monitoring terminal, the leakage current data according to the pseudo-leakage compensation model parameters, so as to eliminate the influence of a pseudo-leakage phenomen

Abstract: A method for prediction of dynamic rescheduling with a digital twin workshop for circuit breakers includes: building a circuit breaker digital manufacturing twin workshop system; determining a circuit breaker circuit breaker workshop dynamic rescheduling mathematical model that is based on rush order events, and performing prediction of information related to the rush order events on twin data of the circuit breaker digital manufacturing twin workshop system based on a prediction mechanism that relies on time window setting and order inquiry, and further updating the circuit breaker workshop dynamic rescheduling mathematical model with the predicted information related to the rush order events; and based on the updated circuit breaker workshop dynamic rescheduling mathematical model, developing a workshop dynamic rescheduling prediction model for multi-objective optimization focused on production efficiency and equipment energy consumption, and further finding an optimal solution to the workshop dynamic resch