Off the road tire maintenance using machine learning

1. A system for off road tire maintenance, comprising:

a data processing system comprising one or more processors and memory configured to:

receive, via one or more sensors of a tire, one or more values comprising sensor data and a unique identifier associated with the tire equipped on a vehicle, the sensor data comprising pressure data and temperature data;

retrieve, from a tire data structure comprising at least the unique identifier of the tire and a plurality of timestamps associated with the unique identifier, historical inspection data associated with the unique identifier, wherein each of the plurality of timestamps corresponds to the sensor data and the historical inspection data;

generate a matrix comprising a first dimension based on timestamps and a second dimension based on the one or more values and the historical inspection data;

predict, via input of the matrix into a machine learning model constructed to output condition data of the tire, an output matrix comprising an indication to perform a type of maintenance selected from a plurality of types of maintenance for the tire during a time interval and at least one tire maintenance category; and

provide, responsive to the prediction, the indication to perform the type of maintenance for the tire during the time interval and the at least one tire maintenance category.

2. The system of claim 1 , wherein the data processing system is configured to train, via a machine learning engine, the machine learning model using historical sensor data and other historical inspection data of a plurality of tires to predict the output matrix.

3. The system of claim 1 , wherein the historical inspection data is first historical inspection data, and wherein the data processing system is configured to:

retrieve, from the tire data structure, second historical inspection data of a plurality of tires comprising condition data of the plurality of tires, a plurality of tire maintenance categories, and the plurality of types of maintenance performed for the plurality of tires;

compare the first historical inspection data of the tire to the second historical inspection data of the plurality of tires; and

input, based on the comparison, a subset of the second historical inspection data corresponding to the historical inspection data into the machine learning model to output the condition data of the tire.

4. The system of claim 3 , wherein the second historical inspection data comprise one or more thresholds associated with the plurality of tire maintenance categories, and wherein the data processing system is configured to:

compare the one or more values of the tire to the one or more thresholds; and

determine, based on the comparison between the one or more values and the one or more thresholds and the comparison between the first historical inspection data and the second historical inspection data, the condition data of the tire and the at least one tire maintenance category.

5. The system of claim 1 , wherein the data processing system is configured to:

link the one or more values comprising the sensor data to the unique identifier associated with the tire; and

store, to the tire data structure responsive to linking the sensor data to the unique identifier, an association between the one or more values and the unique identifier.

6. The system of claim 1 , wherein the data processing system is configured to:

receive, from a remote computing device, the historical inspection data via an inspection on the tire; and

store, in the tire data structure, an association between the historical inspection data and the unique identifier associated with the tire.

7. The system of claim 1 , wherein the at least one tire maintenance category comprises at least one of worn out category, impact damage category, or durability category for performing the type of maintenance for the tire.

8. The system of claim 1 , wherein the historical inspection data comprises location data, tire position data, tire structure data, and appearance data, and wherein the sensor data further comprises mechanical data and load data of the tire.

9. The system of claim 8 , wherein the mechanical data comprises rotation data of the tire, wherein the load data comprises the pressure data and compression data of the tire, wherein the location data comprises a first coordinate and a second coordinate of the tire on a map, and wherein the tire position data comprises a distance from a center of a wheel to ground and a position equipped on the vehicle.

10. A method for off road tire maintenance, comprising:

receiving, by a data processing system comprising one or more processors and memory, via one or more sensors of a tire, one or more values comprising sensor data and a unique identifier associated with the tire equipped on a vehicle, the sensor data comprising pressure data and temperature data;

retrieving, by the one or more processors, from a tire data structure comprising at least the unique identifier of the tire and a plurality of timestamps associated with the unique identifier, historical inspection data associated with the unique identifier, wherein each of the plurality of timestamps corresponds to the sensor data and the historical inspection data;

generating, by the one or more processors, a matrix comprising a first dimension based on timestamps and a second dimension based on the one or more values and the historical inspection data;

predicting, by the one or more processors, via input of the matrix into a machine learning model constructed to output condition data of the tire, an output matrix comprising an indication to perform a type of maintenance selected from a plurality of types of maintenance for the tire during a time interval and at least one tire maintenance category; and

providing, by the one or more processors, responsive to the prediction, the indication to perform the type of maintenance for the tire during the time interval and the at least one tire maintenance category.

11. The method of claim 10 , further comprising:

training, by the one or more processors, via a machine learning engine, the machine learning model using historical sensor data and other historical inspection data of a plurality of tires to predict the output matrix.

12. The method of claim 10 , wherein the historical inspection data is first historical inspection data, and wherein the method further comprises:

retrieving, by the one or more processors, from the tire data structure, second historical inspection data of a plurality of tires comprising condition data of the plurality of tires, a plurality of tire maintenance categories, and the plurality of types of maintenance performed for the plurality of tires;

comparing, by the one or more processors, the first historical inspection data of the tire to the second historical inspection data of the plurality of tires; and

inputting, by the one or more processors, based on the comparison, a subset of the second historical inspection data corresponding to the historical inspection data into the machine learning model to output the condition data of the tire.

13. The method of claim 12 , wherein the second historical inspection data comprise one or more thresholds associated with the plurality of tire maintenance categories, and wherein the method further comprises:

comparing, by the one or more processors, the one or more values of the tire to the one or more thresholds; and

determining, by the one or more processors, based on the comparison between the one or more values and the one or more thresholds and the comparison between the first historical inspection data and the second historical inspection data, the condition data of the tire and the at least one tire maintenance category.

14. The method of claim 10 , further comprising:

linking, by the one or more processors, the one or more values comprising the sensor data to the unique identifier associated with the tire; and

storing, by the one or more processors, to the tire data structure responsive to linking the sensor data to the unique identifier, an association between the one or more values and the unique identifier.

15. The method of claim 10 , further comprising:

receiving, by the one or more processors, from a remote computing device, the historical inspection data via an inspection on the tire; and

storing, by the one or more processors, in the tire data structure, an association between the historical inspection data and the unique identifier associated with the tire.

16. The method of claim 10 , wherein the at least one tire maintenance category comprises at least one of worn out category, impact damage category, or durability category for performing the type of maintenance for the tire.

17. The method of claim 10 , wherein the historical inspection data comprises location data, tire position data, tire structure data, and appearance data, and wherein the sensor data further comprises mechanical data and load data of the tire.

18. The method of claim 17 , wherein the mechanical data comprises rotation data of the tire, wherein the load data comprises the pressure data and compression data of the tire, wherein the location data comprises a first coordinate and a second coordinate of the tire on a map, and wherein the tire position data comprises a distance from a center of a wheel to ground and a position equipped on the vehicle.