This invention relates to the field of
computer data processing technology and discloses a dynamic regularization and collaborative scheduling method for time-series
information data. This method accesses asynchronous multi-source time-
series data streams, initializes a regularization
pool based on a global tracking identifier, and sets a basic survival time window. It scans the regularization
pool to calculate the associated state feedback factor and collects execution layer load data to calculate the resource
water level. When the feedback factor reaches the high readiness state locking threshold and the resource
water level is below the congestion threshold, the regularization
pool is locked, a dedicated thread is allocated, and a data block access descriptor is generated for reading and computation. If the regularization pool approaches the time window and the data is not fully ready, the execution state
dimensionality reduction or time window compensation operation is dynamically selected based on the resource
water level status. This invention avoids memory accumulation and effective
data truncation problems caused by
fixed time windows by establishing a joint determination mechanism of data readiness and underlying resources, thereby improving the utilization and
throughput of computing resources in concurrent scenarios.