System and method for data storage, transfer, synchronization, and security using automated model monitoring and training with a load-adaptive cache

A computer-implemented method comprising the steps of:

• processing received input data to generate a test dataset;
• analyzing the test dataset to determine a probability distribution;
• retraining encoding and decoding algorithms using the test dataset and the probability distribution;
• applying the retrained algorithms to generate one or more data units from the test dataset;
• associating each of the one or more data units with a corresponding identifier;
• and
• storing the one or more data units and their corresponding identifiers in an updated data structure;
• monitoring system performance metrics and resource utilization across system components;
• analyzing temporal and spatial relationships in data access patterns;
• calculating likelihood scores for potential prefetch operations based on historical accuracy tracked percentages of previously prefetched data units that were actually used, wherein each likelihood score represents a probability that a corresponding data unit will be accessed data element will be accessed within a predetermined time window;
• dynamically adjusting cache parameters based on current performance metrics;
• maintaining multiple cache levels with different performance characteristics for different data types;
• and
• executing prefetch operations for data units whose likelihood scores exceed a threshold based on prediction models while considering available resources, wherein the prefetch operations are selected from the potential prefetch operations based on prediction models that incorporate the calculated likelihood scores and adjust the threshold value based on and available system resources.