One example method includes receiving a data stream, collecting a sequence of one or more batches of data from the data stream, analyzing the batches of data in the sequence, obtaining compressor choices for the batches of data in the sequence, obtaining a new batch of data from the data stream, analyzing the new batch of data, based on the analyzing and the compressor choices for the batches of data in the sequence, and the analyzing of the new batch of data, generating a prediction that identifies recommended data compressor for the new batch of data, and in response to a change in the data stream, compressing the new batch of data using the recommended data compressor.

One example method includes receiving a data stream, collecting a sequence of one or more batches of data from the data stream, analyzing the batches of data in the sequence, obtaining compressor choices for the batches of data in the sequence, obtaining a new batch of data from the data stream, analyzing the new batch of data, based on the analyzing and the compressor choices for the batches of data in the sequence, and the analyzing of the new batch of data, generating a prediction that identifies recommended data compressor for the new batch of data, and in response to a change in the data stream, compressing the new batch of data using the recommended data compressor.

An intelligent collaborative operation control strategy for an electro-hydraulic suspension system of a high-horsepower tractor is provided, including: S1, establishing an operation task database; S2, collecting information of the electro-hydraulic suspension system during an operation of the high-horsepower tractor in real time as real-time data; S3, preprocessing the real-time data; S4, analyzing a dataset and generating a preliminary control strategy for the electro-hydraulic suspension system based on parameters in the operation task database; S5, performing data interaction and collaborative control with operation units; S6, identifying deviations between operation status data and expected control parameters; S7, dynamically adjusting the preliminary control strategy. Through implementation of sensing technology and machine learning algorithms, real-time and precise adjustment of operation parameters for the suspension system of the tractor is achieved, significantly enhancing a level of automation and intelligence in operations, thereby effectively improving efficiency and economic benefits of agricultural operations.

An intelligent collaborative operation control strategy for an electro-hydraulic suspension system of a high-horsepower tractor is provided, including: S1, establishing an operation task database; S2, collecting information of the electro-hydraulic suspension system during an operation of the high-horsepower tractor in real time as real-time data; S3, preprocessing the real-time data; S4, analyzing a dataset and generating a preliminary control strategy for the electro-hydraulic suspension system based on parameters in the operation task database; S5, performing data interaction and collaborative control with operation units; S6, identifying deviations between operation status data and expected control parameters; S7, dynamically adjusting the preliminary control strategy. Through implementation of sensing technology and machine learning algorithms, real-time and precise adjustment of operation parameters for the suspension system of the tractor is achieved, significantly enhancing a level of automation and intelligence in operations, thereby effectively improving efficiency and economic benefits of agricultural operations.