Abstract: A method of deploying microservice includes: determining whether a current load of a task queue of a target edge host is not smaller than a load alert level; if the current load is not smaller than the load alert level, calculating a task migration number of a first queue of the task queue to deploy a microservice corresponding to the first queue at at least one of a first available edge host and a cloud host; if the current load is smaller than the load alert level, calculating a long-term load according to a history pushing rate, a history consumption rate and a default time period; and when a sum of the long-term load and a current load of a second queue of the task queue is not smaller than the load alert level, deploying a microservice corresponding to the second queue at a second available edge host.

Abstract: A model adjustment method, comprises: by a processing device, performing: obtaining inferred data that is inferred using a model, performing a feedback mechanism on the inferred data to obtain a feedback command associated with correctness of the inferred data, adjusting the inferred data according to the feedback command to generate adjusted data, and using the adjusted data as one of a plurality of pieces of training data for retraining the model. The present disclosure further provides a model adjustment system and non-transitory computer readable medium.

Abstract: A missing data compensation method, missing data compensation system and non-transitory computer-readable medium are provided in this disclosure. The method includes the following operations: inputting a sensing signal by a sensor; searching for a historical data sections similar to a first data section from the plurality of historical data sections to generate a plurality of candidate data sections; calculating a plurality of data relation diagrams according to the first data section and the candidate data sections, respectively; utilizing a feature recognition model to calculate a plurality of similarity values according to the data relation diagrams; selecting a candidate data section corresponding to the maximum similarity value as a sample data section; and utilizing the data in the sample data section to compensate the data in the first data section to generate compensated data section.

Abstract: An apparatus and method for generating a motor diagnosis model. Each first dimension-reduced datum of a first motor corresponds to a normal state, an offline state, or an abnormal state, while each second dimension-reduced datum corresponds to the normal state or the offline state. The apparatus rotates the first dimension-reduced data or the second dimension-reduced data and integrates the rotated results with the dimension-reduced data that have not been rotated as a to-be-analyzed dataset. The apparatus trains a classification model for distinguishing data sources by using a subset of the to-be-analyzed dataset, derives an accuracy rate by testing the classification model for distinguishing data sources by using another subset of the to-be-analyzed dataset, and determines whether to use the to-be-analyzed dataset to generate the motor diagnosis model for the second motor.

Abstract: A missing data compensation method, missing data compensation system and non-transitory computer-readable medium are provided in this disclosure. The method includes the following operations: inputting a sensing signal by a sensor; searching for a historical data sections similar to a first data section from the plurality of historical data sections to generate a plurality of candidate data sections; calculating a plurality of data relation diagrams according to the first data section and the candidate data sections, respectively; utilizing a feature recognition model to calculate a plurality of similarity values according to the data relation diagrams; selecting a candidate data section corresponding to the maximum similarity value as a sample data section; and utilizing the data in the sample data section to compensate the data in the first data section to generate compensated data section.