Systems and methods for tracking equipment through use of distributed ledger technologies and non-fungible tokens

1 . A computerized method, comprising:

receiving, at one or more hardware processors, an electronic identifier associated with a piece of equipment, wherein the electronic identifier comprises information associated with one or more data elements selected from a set of data elements comprising: a service history, input from one or more equipment sensors, equipment performance data, abnormality alert data, equipment breakdown data, and smart contract data;

retrieving, via the one or more hardware processors, the one or more data elements from an electronically provided distributed ledger; generating an updated resale value for the piece of equipment associated with the electronic identifier, based at least in part on the one or more data elements;

identifying, via the one or more hardware processors, from at least the input from the one or more equipment sensors, an equipment abnormality associated with the piece of equipment, and generating, via the one or more hardware processors, an analysis of the equipment abnormality;

applying, by the one or more processors, a machine-learning model to (i) the equipment breakdown data, wherein the equipment breakdown data is based at least in part on the equipment abnormality and the analysis of the equipment abnormality, and (ii) a type of the piece of equipment, to identify a type of repair personnel needed to repair the piece of equipment, wherein the machine learning model has been trained using historical equipment breakdown records correlated with completed repair outcomes;

transmitting, via the one or more hardware processors, an electronic dispatch instruction to a repair-dispatch system to automatically cause deployment of repair personnel of the identified type to repair the piece of equipment,

recording, on the distributed ledger, a repair completion record associated with the electronic identifier after completion of the repair; and

providing, via the one or more hardware processors, the updated resale value to a user.

2 . The computerized method of claim 1 , wherein the electronic identifier is a non-fungible token.

3 . The computerized method of claim 1 , further comprising receiving input from one or more equipment sensors selected from the group comprising one or more voltage sensors, one or more amperage sensors, one or more energy sensors, one or more temperature sensors, one or more gas leak sensors, one or more power failure sensors, and one or more time of operation sensors.

4 . The computerized method of claim 1 , further comprising receiving a notification of an equipment operation abnormality, associated with the piece of equipment; and sending an

equipment operation abnormality notification to one or more of an owner of the piece of equipment and a repair person.

5 . The computerized method of claim 3 , further comprising identifying, from the input from one or more equipment sensors, an equipment operation abnormality associated with the piece of equipment; generating an analysis of the equipment operation abnormality; and sending an equipment operation abnormality notification to one or more of an owner of the piece of equipment and a repair person, based at least in part on the analysis of the equipment operation abnormality.

6 . The computerized method of claim 5 , wherein the repair person is identified, based at least in part on a type of equipment operation abnormality identified in the analysis of the equipment operation abnormality.

7 . The computerized method of claim 1 , further comprising: receiving equipment performance data; and identifying, from the equipment performance data, an equipment performance abnormality associated with the piece of equipment;

generating an analysis of the equipment performance abnormality; and sending an equipment performance abnormality notification to one or more of an owner of the piece of equipment and a repair person, based at least in part on the analysis of the equipment performance abnormality.

8 . The computerized method of claim 1 , further comprising analyzing, via a machine learning model, the updated resale value for the piece of equipment, wherein the machine learning model comprises a data model comprising data points associated with one or more of the group comprising data related to commercial secondary market practices, data related to previous equipment auctions, data related to auction methods, data related to reverse auction methods, data related to performance of other pieces of equipment, and data related to maintenance of other pieces of equipment generating, based at least in part on use of the machine learning model, an optimized resale value for the piece of equipment.

9 . The computerized method of claim 8 , further comprising identifying a final resale price of the piece of equipment; and updating said machine learning model based at least in part on the optimized resale value for the piece of equipment and the final resale price of the piece of equipment.

10 . A computerized method for tracking and trading pieces of equipment, comprising:

receiving, via one or more hardware processors, an electronic identifier associated with a piece of equipment; identifying an electronic smart contract associated with the electronic identifier;

identifying, via said one or more hardware processors, a first owner of the piece of equipment, based at least in part on the electronic identifier;

providing, via said one or more hardware processors, data points to one or more potential owners of the piece of equipment, wherein the data points are identified based at least in part on the electronic identifier, and wherein the data points are selected from a group comprising provenance tracking data, service history data, repair history data, performance data and sensor data, the data points stored on a, distributed ledger;

analyzing, via the one or more hardware processors using a machine learning model, a resale value for the piece of equipment, wherein the machine learning model comprises a data model comprising data points associated with one or more of the group comprising data related to commercial secondary market practices, data related to previous equipment auctions, data related to auction methods, data related to reverse auction methods, data related to performance of other pieces of equipment, and data related to maintenance of other pieces of equipment;

generating, via the one or more hardware processors, based at least in part on use of the machine learning model and the data points, an optimized resale value for the piece of equipment;

providing, via the one or more hardware processors, said optimized resale value to the one or more potential owners of the piece of equipment;

after providing said optimized resale value to the one or more potential owners, receiving a purchase request from a first potential owner of the one or more potential owners, wherein the purchase request comprises an offer to purchase the piece of equipment:

receiving, via the one or more hardware processors, an acceptance of the purchase request from the owner of the piece of equipment; and

transferring, via the one or more hardware processors, ownership from the owner of the piece of equipment to the first potential owner according to the smart contract by:

updating via the one or more hardware processors, information associated with the electronic identifier associated with the piece of equipment such that the first potential owner is made the new owner of the piece of equipment,

wherein the method further comprises,

applying, by the one or more hardware processors, the machine learning model to (i) breakdown data associated with the piece of equipment and (ii) a type of the piece of equipment to identify a type of repair personnel needed to repair the piece of equipment, wherein the machine learning model is trained using historical equipment breakdown records correlated with completed repair outcomes

recording, via the one or more hardware processors on the distributed ledger, a repair completion record associated with the electronic identifier after completion of the repair; and

transmitting, via the one or more hardware processors, an electronic dispatch instruction to a repair-dispatch system to automatically cause deployment of repair personnel of the identified type to repair the piece of equipment.

11 . The computerized method of claim 10 , wherein a value associated with the purchase request exceeds the value of one or more other purchase requests received from the one or more potential owners.

12 . A computerized system, comprising:

one or more hardware processors and a non-transitory computer-readable medium storing machine-readable instructions that, when executed by the one or more hardware processors, cause the one or more hardware processors to:

receive an electronic identifier associated with a piece of equipment, wherein the

electronic identifier comprises information associated with one or more data elements selected from a set of data elements comprising, a service history, input from one or more equipment sensors, equipment performance data, abnormality alert data, equipment breakdown data, and a smart contract data;

retrieve, via electronic communication, the one or more data elements from an electronically provided distributed ledger; generate an updated resale value for the piece of equipment associated with the electronic identifier, based at least in part on the one or more data elements;

identify, from at least the input from the one or more equipment sensors, an equipment abnormality associated with the piece of equipment, and generate an analysis of the equipment abnormality;

apply a machine learning model, trained using historical equipment breakdown records correlated with completed repair outcomes, to (i) the equipment breakdown data, wherein the equipment breakdown data comprises the equipment abnormality and the analysis of the equipment abnormality, and (ii) a type of the piece of e equipment, to identify a type of repair personnel needed to repair the piece of equipment;

transmit an electronic dispatch instruction to cause deployment of repair personnel of the identified type to repair the piece of equipment;

recording, on the distributed ledger, a repair completion record associated with the electronic identifier after completion of the repair; and

provide the updated resale value to a user via a network interface.

13 . The computerized system of claim 12 , wherein the electronic identifier is a non-fungible token.

14 . The computerized system of claim 12 , wherein the one or more hardware processors are further configured by machine-readable instructions to receive input from one or more equipment sensors, wherein the input from one or more equipment sensors comprises information from sensors selected from the group comprising one or more voltage sensors, one or more amperage sensors, one or more energy sensors, one or more temperature sensors, one or more gas leak sensors, one or more power failure sensors, and one or more time of operation sensors.

15 . The computerized system of claim 12 , wherein the one or more hardware processors are further configured by machine-readable instructions to: receive a notification of an equipment operation abnormality, associated with the piece of equipment; and send an equipment operation abnormality notification to one or more of an owner of the piece of equipment and a repair person.

16 . The computerized system of claim 14 , wherein the one or more hardware processors are further configured by machine-readable instructions to: identify, from the input from one or more equipment sensors, an equipment operation abnormality associated with the piece of equipment; generate an analysis of the equipment operation abnormality; and send an equipment operation abnormality notification to one or more of an owner of the piece of equipment and a repair person, based at least in part on the analysis of the equipment operation abnormality.

17 . The computerized system of claim 16 , wherein the repair person is identified, based at least in part on a type of equipment operation abnormality identified in the analysis of the equipment operation abnormality.

18 . The computerized system of claim 12 , wherein the one or more hardware processors are further configured by machine-readable instructions to: receive equipment performance data; and identify, from the equipment performance data, an equipment performance abnormality associated with the piece of equipment; generate an analysis of the equipment performance abnormality; and send an equipment performance abnormality notification to one or more of an owner of the piece of equipment and a repair person, based at least in part on the analysis of the equipment performance abnormality.

19 . The computerized system of claim 12 , wherein the one or more hardware processors are further configured by machine-readable instructions to: analyze, via a machine learning model, the updated resale value for the piece of equipment, wherein the machine learning model comprises a data model comprising data points associated with one or more of the group comprising data related to commercial secondary market practices, data related to previous equipment auctions, data related to auction systems, data related to reverse auction systems, data related to performance of other pieces of equipment, and data related to maintenance of other pieces of equipment generate, based at least in part on use of the machine learning model, an optimized resale value for the piece of equipment.