Data storage method, data obtaining method, and apparatus
A method for storing data includes: generating data for current data based on historical data and a change rule of the historical data; obtaining a delta between the data and the current data; storing the delta instead of the current data.
1. A method for storing data, comprising:
generating, by a processor of a storage device, predicted data for current data based on historical data and a change rule of the historical data, wherein the predicted data represents an amount of data to be stored in the storage device subsequently to reduce storage overheads of the storage device;
obtaining, by the processor, a delta between the predicted data and the current data;
determining, by the processor, a storage space occupied by the delta is smaller than a storage space occupied by the current data; and
storing, by the processor, first information used to restore the current data, wherein the first information includes the delta without including the current data.
2. The method according to claim 1 , wherein storing the delta comprises:
compressing the delta.
3. The method according to claim 1 , wherein the generating the predicted data for the current data comprises:
generating the predicted data using an artificial intelligence (AI) neural algorithm.
4. The method according to claim 3 , further comprising:
storing a correspondence between the delta and the AI neural algorithm.
5. The method according to claim 3 , wherein a type of the AI neural algorithm is a normalized least mean square (NLMS) type.
6. The method according to claim 3 , wherein a type of the AI neural algorithm is a single-layer perceptron (SLP) type.
7. The method according to claim 3 , wherein a type of the AI neural algorithm is a multilayer perceptron (MLP) type.
8. The method according to claim 3 , wherein a type of the AI neural algorithm is recurrent neural network (RNN) type.
9. A device, comprising:
an interface; and
a processor coupled to the interface to:
generate predicted data for current data based on historical data and a change rule of the historical data, wherein the predicted data represents an amount of data to be stored subsequently to reduce storage overheads,
obtain a delta between the predicted data and the current data,
determine a storage space occupied by the delta is smaller than a storage space occupied by the current data; and
store first information used to restore the current data, wherein the first information includes the delta without including the current data.
10. The device according to claim 9 , wherein the processor is further configured to:
compress the delta.
11. The device according to claim 9 , wherein the processor is further configured to:
generate the predicted data using an artificial intelligence (AI) neural algorithm.
12. The device according to claim 11 , wherein the processor is further configured to:
store a correspondence between the delta and the AI neural algorithm.
13. The device according to claim 11 , wherein a type of the AI neural algorithm is a normalized least mean square (NLMS) type.
14. The device according to claim 11 , wherein a type of the AI neural algorithm is a single-layer perceptron (SLP) type.
15. The device according to claim 11 , wherein a type of the AI neural algorithm is a multilayer perceptron (MLP) type.
16. The device according to claim 11 , wherein a type of the AI neural algorithm is recurrent neural network (RNN) type.
17. A non-transitory machine-readable storage medium having instructions stored therein, which when executed by a processor, cause the processor to:
obtain a delta,
generate predicted data for current data based on historical data and a change rule of the historical data; wherein the delta is a delta between the predicted data and the current data wherein the predicted data represents an amount of data to be stored subsequently to reduce storage overheads,
determine a storage space occupied by the delta is smaller than a storage space occupied by the current data; and
restore first information used to restore the current data, wherein the first information includes the delta without including the current data.
18. The non-transitory machine-readable storage medium according to claim 17 , wherein the processor is further configured to:
generate the predicted data using an artificial intelligence (AI) neural algorithm.
19. The non-transitory machine-readable storage medium according to claim 18 , wherein the processor is further configured to:
store a correspondence between the delta and the AI neural algorithm.