| CPC G06Q 10/087 (2013.01) [G06Q 10/0833 (2013.01)] | 18 Claims |
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1. A system for tracing a transport, comprising:
a computing device configured to:
identify at least a transport entity of a plurality of transport entities of a transport using a unique identifier of a first node of an immutable sequential listing, wherein the unique identifier comprises at least a barcode;
verify the unique identifier of the first node as a function of a verification criteria, wherein the verification criteria comprises a digital signature of the immutable sequential listing;
record the verified unique identifier of the at least a transport entity in a first data block of the immutable sequential listing;
communicate transport data with the at least a transport entity through the first node of the immutable sequential listing;
verify the transport data of the first node as a function of the verification criteria of the immutable sequential listing;
record at least a transport datum of the verified transport data in a second data block of the immutable sequential listing;
generate, using a transport parameter machine learning model, a transport parameter threshold,
wherein the transport parameter machine learning model is trained with transport training data correlating transport data to transport parameter thresholds;
compare the first data block and the second data block of the immutable sequential listing separately to a transport parameter threshold;
generate, as a function of the comparison, an error mapping of the at least a transport entity;
determine, as a function of the transport parameter machine-learning model, a transport status of at least a transport entity, wherein the transport parameter machine learning model is configured to receive a plurality of transport data and output a transport status of a transport entity, and the transport parameter machine-learning model is further configured to output corrective actions as a function of data of the immutable sequential listing, and wherein determining the transport status comprises:
receiving training data, wherein the training data correlates a plurality of transport data to a transport status; and
training the machine learning model as a function of the training data;
update the plurality of transport data in real-time; and
retrain the machine learning model as a function of the updated training data correlating a plurality of transport data to the transport status:
select at least one transport entity of the plurality of transport entities to be used in a transport,
wherein selecting at least one transport entity comprises:
comparing a first transport entity to a second transport entity using an optimization model;
generating a process score for a matrix of transports and the plurality of transport entities as a function of the comparison; and
pairing, as a function of the optimization model, a transport and transport entity with a highest process score; and
determine a transport deviance mitigation plan as a function of the transport status and the error mapping, wherein the transport deviance mitigation plan comprises one or more transport handoff adjustments configured to update at least one handoff location, and wherein the transport deviance mitigation plan further comprises selecting at least one transport entity of the plurality of transport entities to be used in a transport.
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