Flexible automated sorting and transport arrangement (fast) asset monitor

Abstract

A disclosed system for transport asset monitoring, for example monitoring truck trailer unloading progress at large retail locations, includes an artificial intelligence (AI) solution for managing operations at a facility. The AI solution analyzes current load percentage and other data to predict availability for moving the transport asset and ability to accept a new incoming transport asset. Predictions of availability can reduce response times, resulting in higher utilization rates for assets, thereby improving efficiency. An exemplary system includes a sensor configured to sense operation progress parameter data for a transport asset; and logic to receive the operation progress parameter data from the sensor; determine, using the AI solution and based at least on the operation progress parameter data, a predicted milestone parameter; and report the predicted milestone parameter to a remote node.

What is claimed is: 
     
         1 . A system for transport asset monitoring, the system comprising:
 a first sensor configured to sense a first operation progress parameter data for a first transport asset;   a processor; and   a non-transitory computer-readable medium storing instructions that are operative when executed by the processor to:
 receive the first operation progress parameter data from the first sensor; 
 determine, using an artificial intelligence (AI) solution and based at least on the first operation progress parameter data, a predicted milestone parameter; and 
 report the predicted milestone parameter to a remote node. 
   
     
     
         2 . The system of  claim 1  wherein the transport asset comprises a trailer. 
     
     
         3 . The system of  claim 1  wherein the first sensor comprises at least one sensor selected from the list consisting of:
 an RFID sensor, a barcode scanner, and a computer vision (CV) sensor. 
 
     
     
         4 . The system of  claim 1  wherein the first operation progress parameter data comprises identification of items unloaded from the transport asset. 
     
     
         5 . The system of  claim 1  further comprising:
 a second sensor configured to sense a second operation progress parameter data for the transport asset, wherein the second operation progress parameter data comprises identification of items remaining on the transport asset; and 
 wherein the instructions are further operative to:
 receive the second operation progress parameter data from the second sensor; and 
 
 wherein determining the predicted milestone parameter comprises determining, using the AI solution and based at least on the first operation progress parameter data and the second operation progress parameter data, the predicted milestone parameter. 
 
     
     
         6 . The system of  claim 1  wherein the instructions are further operative to:
 receive, from a user interface (UI), confirmation or correction of the predicted milestone parameter. 
 
     
     
         7 . The system of  claim 1  further comprising:
 a machine learning (ML) component to generate the AI solution using at least historical operation progress parameter data. 
 
     
     
         8 . The system of  claim 1  further comprising:
 a wireless communication module; and 
 an automated ground vehicle (AGV) in communication with the processor via the wireless communication module. 
 
     
     
         9 . The system of  claim 1  wherein the instructions are further operative to:
 generate, using the AI solution and based at least on the first operation progress parameter data, logistical instructions for a second transport asset. 
 
     
     
         10 . A method of transport asset monitoring, the method comprising:
 receiving a first operation progress parameter data for a first transport asset from a first sensor, wherein receiving the first operation progress parameter data from the first sensor data comprises receiving the first operation progress parameter data from at least one sensor selected from the list consisting of:
 an RFID sensor, a barcode scanner, and a computer vision (CV) sensor; 
   determining, using an artificial intelligence (AI) solution and based at least on the first operation progress parameter data, a predicted milestone parameter; and   reporting the predicted milestone parameter to a remote node.   
     
     
         11 . The method of  claim 10  wherein receiving the first operation progress parameter data for the first transport asset comprises receiving the first operation progress parameter data for a trailer. 
     
     
         12 . The method of  claim 10  wherein the first operation progress parameter data comprises identification of items unloaded from the transport asset. 
     
     
         13 . The method of  claim 10  further comprising:
 receiving a second operation progress parameter data from a second sensor, wherein the second operation progress parameter data comprises identification of items remaining on the transport asset; and 
 wherein determining the predicted milestone parameter comprises determining, using the AI solution and based at least on the first operation progress parameter data and the second operation progress parameter data, the predicted milestone parameter. 
 
     
     
         14 . The method of  claim 10  further comprising:
 receiving, from a user interface (UI), confirmation or correction of the predicted milestone parameter. 
 
     
     
         15 . The method of  claim 10  further comprising:
 generating, with a machine learning (ML) component, the AI solution using at least historical operation progress parameter data. 
 
     
     
         16 . The method of  claim 10  further comprising:
 wirelessly communicating, with an automated ground vehicle (AGV), logistical data related to the AGV. 
 
     
     
         17 . The method of  claim 10  further comprising:
 generating, using the AI solution and based at least on the first operation progress parameter data, logistical instructions for a second transport asset. 
 
     
     
         18 . One or more computer storage devices having computer-executable instructions stored thereon for transport asset monitoring, which, on execution by a computer, cause the computer to perform operations comprising:
 receiving a first operation progress parameter data for a first transport asset from a first sensor, wherein receiving the first operation progress parameter data for the first transport asset comprises receiving the first operation progress parameter data for a trailer, wherein the first operation progress parameter data comprises identification of items unloaded from the transport asset, and wherein receiving the first operation progress parameter data from the first sensor data comprises receiving the first operation progress parameter data from at least one sensor selected from the list consisting of:
 an RFID sensor, a barcode scanner, and a computer vision (CV) sensor; 
   determining, using an artificial intelligence (AI) solution and based at least on the first operation progress parameter data, a predicted milestone parameter;   receiving, from a user interface (UI), confirmation or correction of the predicted milestone parameter;   reporting the predicted milestone parameter to a remote node;   generating, with a machine learning (ML) component, the AI solution using at least historical operation progress parameter data; and   generating, using the AI solution and based at least on the first operation progress parameter data, logistical instructions for a second transport asset.   
     
     
         19 . The one or more computer storage devices of  claim 18  wherein the operations further comprise:
 receiving a second operation progress parameter data from a second sensor, wherein the second operation progress parameter data comprises identification of items remaining on the transport asset; and 
 wherein determining the predicted milestone parameter comprises determining, using the AI solution and based at least on the first operation progress parameter data and the second operation progress parameter data, the predicted milestone parameter. 
 
     
     
         20 . The one or more computer storage devices of  claim 18  wherein the predicted milestone parameter comprises an expected completion time of unloading the trailer.