Intelligent system for minting digital assets with power consumption optimization
Intelligent and dynamic power consumption optimization during minting of a digital asset, such as an NFT or the like. Metadata is extracted from a digital file and the extracted metadata is applied to Machine Learning (ML) models, such as Deep Learning (DL) models to determine an optimal power consumption scheme for minting the digital asset from the digital file. In response to determining the optimal power consumption, the digital asset is minted using the optimal power consumption scheme. The optimal power consumption scheme may define (i) a distributed trust computing network for minting the digital asset, including the geographic location of the distributed trust computing network, (ii) a minting algorithm used to mint the digital asset, (iii) a consensus algorithm for validating minting of the digital asset, and (iv) a storage location for the digital file once the digital asset has been minted.
1. A system for intelligently determining and implementing power consumption optimization for minting digital assets, the system comprising:
a first computing platform including a first memory and one or more first computing processor devices in communication with the first memory, wherein the first memory stores:
a digital file metadata extractor executable by at least one of the one or more first computing processor devices and configured to:
extract metadata from a digital file, and
a digital asset power consumption optimization engine including one or more first machine learning models, executable by at least one of the one or more first computing processor devices and configured to:
apply, at least, the extracted metadata to the one or more first machine learning models to determine an optimal power consumption scheme for minting a digital asset of the digital file; and
a second computing platform including a second memory and one or more second computing processor devices in communication with the second memory, wherein the second memory stores a digital asset minting application, executable by at least one of the one or more second computing processor devices and configured to:
receive the digital file, and
mint a digital asset of the digital file according to the optimal power consumption scheme.
2. The system of claim 1 , wherein the digital asset power consumption optimization engine is further configured to apply, at least, the extracted metadata to the one or more machine learning models to determine an optimal power consumption scheme, wherein the optimal power consumption scheme includes at least one of (i) a distributed trust computing network for minting the digital asset, (ii) a minting algorithm used to mint the digital asset, (iii) a consensus algorithm for validating minting of the digital asset.
3. The system of claim 1 , wherein the digital asset power consumption optimization engine is further configured to receive real-time network performance parameters from a plurality of distributed trust computing networks and further apply the real-time network performance parameters to the one or more machine learning models to determine the optimal power consumption scheme including the distributed trust computing network for minting the digital asset from amongst the plurality of distributed trust computing networks.
4. The system of claim 1 , wherein the digital asset power consumption optimization engine is further configured to apply, at least, the extracted metadata to the one or more machine learning models to determine an optimal power consumption scheme, wherein the optimal power consumption scheme includes a geographic location for minting the digital asset.
5. The system of claim 1 , wherein the digital asset power consumption optimization engine is further configured to apply, at least, the extracted metadata to the one or more machine learning models to determine an optimal power consumption scheme, wherein the optimal power consumption scheme includes a storage location for the digital file after minting the digital asset.
6. The system of claim 1 , further comprising a third computing platform including a third memory and one or more third computing processor devices in communication with the third memory, wherein the third memory stores:
a certification rules engine including a plurality of certification rules, executable by at least one of the one or more second computing processor devices and configured to:
select one or more of the plurality certification rules based at least on a type of the digital asset, and
apply at least the optimal power consumption scheme to the selected one or more certification rules to determine a certification status for the digital asset, wherein the certification status indicates a level of power consumption used to mint the digital asset.
7. The system of claim 6 , wherein the certification rules engine is further configured to in response to determining the certification status, generate a set of ingestion validate rules.
8. The system of claim 7 , wherein the third memory further stores a digital asset ingestion module executable by at least one of the one or more second computing processor devices and configured to execute the set of ingestion rules to receive and validate the digital asset into a digit asset exchange platform.
9. The system of claim 8 , wherein the digital asset ingestion module is further configured to, in response to ingesting the digital asset into a digit asset exchange platform, classify the digital asset based on the certification status and assign an index tag based on the classification.
10. A computer-implemented method for intelligently determining and implementing power consumption optimization for minting digital assets, the method being executable by one or more computing device processors and comprising:
extracting metadata from a digital file;
applying, at least, the extracted metadata to one or more first machine learning models to determine an optimal power consumption scheme for minting a digital asset of the digital file; and
minting a digital asset of the digital file according to the optimal power consumption scheme.
11. The computer-implemented method of claim 10 , wherein applying further comprises applying, at least, the extracted metadata to the one or more first machine learning models to determine the optimal power consumption scheme, wherein the optimal power consumption scheme includes at least one of (i) a distributed trust computing network for minting the digital asset, (ii) a minting algorithm used to mint the digital asset, (iii) a consensus algorithm for validating minting of the digital asset.
12. The computer-implemented method of claim 10 , further comprising receiving real-time network performance parameters from a plurality of distributed trust computing networks, and
wherein applying further comprises applying the real-time network performance parameters to the one or more machine learning models to determine the optimal power consumption scheme including the distributed trust computing network for minting the digital asset from amongst the plurality of distributed trust computing networks.
13. The computer-implemented method of claim 10 , wherein applying further comprises applying, at least, the extracted metadata to the one or more first machine learning models to determine the optimal power consumption scheme, wherein the optimal power consumption scheme includes at least one of (i) a geographic location for minting the digital asset and (ii) a storage location for the digital file after minting the digital asset.
14. The computer-implemented method of claim 10 , further comprising:
selecting one or more of the plurality certification rules based at least on a type of the digital asset; and
applying at least the optimal power consumption scheme to the selected one or more certification rules to determine a certification status for the digital asset, wherein the certification status indicates a level of power consumption used to mint the digital asset.
15. The computer-implemented method of claim 14 , further comprising:
in response to determining the certification status, generating a set of ingestion validate rules; and
executing the set of ingestion rules to receive and validate the digital asset into a digit asset exchange platform.
16. A computer program product comprising:
a non-transitory computer-readable medium comprising sets of codes for causing one or more computing devices to:
extract metadata from a digital file;
apply, at least, the extracted metadata to the one or more first machine learning models to determine an optimal power consumption scheme for minting a digital asset of the digital file; and
mint a digital asset of the digital file according to the optimal power consumption scheme.
17. The computer program product of claim 16 , wherein the set of codes for causing the one or more computing devices to apply are further configured to cause the one or more computing devices to apply, at least, the extracted metadata to the one or more first machine learning models to determine the optimal power consumption scheme, wherein the optimal power consumption scheme includes at least one of (i) a distributed trust computing network for minting the digital asset, (ii) a minting algorithm used to mint the digital asset, (iii) a consensus algorithm for validating minting of the digital asset.
18. The computer program product of claim 16 , wherein the set of codes further comprise a set of codes for causing the one or more computing devices to receive real-time network performance parameters from a plurality of distributed trust computing networks, and
wherein the set of codes for causing the one or more computing devices to apply are further configured to cause the one or more computing devices to apply the real-time network performance parameters to the one or more machine learning models to determine the optimal power consumption scheme including the distributed trust computing network for minting the digital asset from amongst the plurality of distributed trust computing networks.
19. The computer program product of claim 16 , wherein the set of codes for causing the one or more computing devices to apply are further configured to cause the one or more computing devices to apply, at least, the extracted metadata to the one or more first machine learning models to determine the optimal power consumption scheme, wherein the optimal power consumption scheme includes at least one of (i) a geographic location for minting the digital asset and (ii) a storage location for the digital file after minting the digital asset.
20. The computer program product of claim 16 , wherein the sets of codes further comprise sets of codes for causing the one or more computing devices to:
select one or more of the plurality certification rules based at least on a type of the digital asset; and
apply at least the optimal power consumption scheme to the selected one or more certification rules to determine a certification status for the digital asset, wherein the certification status indicates a level of power consumption used to mint the digital asset.