Method and system for shipping container loading and unloading estimation

Patent metadata

Field	Value
Publication number	US-11333547-B2
Application number	US-201916664387-A
Country	US
Kind code	B2
Filing date	Oct 25, 2019
Priority date	Oct 25, 2019
Publication date	May 17, 2022
Grant date	May 17, 2022

Abstract

A method at a computing device, the method including obtaining sensor data for a vehicle providing vibration frequency and magnitude; calculating an energy for each of a low frequency passband and a high frequency passband of a bandpass filter pair; finding an energy ratio based on the energy for the low frequency passband and the energy for the high frequency passband; applying weighting constants to each of the energy for the low frequency passband, the energy for the high frequency passband and the energy ratio to calculate a decision variable; and finding that the vehicle is unloaded if the decision variable is below a threshold and finding that the vehicle is loaded if the decision variable is above a threshold.

First claim

The invention claimed is:

1. A method at a computing device, the method comprising:
deriving weighting constants for a type of chassis of a vehicle;
obtaining sensor data for the vehicle providing vibration frequency and magnitude;
calculating an energy for each of a low frequency passband and a high frequency passband of a bandpass filter pair;
finding an energy ratio based on the energy for the low frequency passband and the energy for the high frequency passband, wherein the energy ratio comprises:

eRatio
=

E
L

E
L

+

E
H

where eRatio is the energy ratio; E L is the energy for the low frequency passband; and E H is the energy for the high frequency passband;
applying the weighting constants to each of the energy for the low frequency passband, the energy for the high frequency passband and the energy ratio to calculate a decision variable; and
finding that the vehicle is unloaded if the decision variable is below a threshold and finding that the vehicle is loaded if the decision variable is above a threshold.

2. The method of claim 1 , further comprising repeating the calculating, finding and applying for a plurality of bandpass filter pairs.

3. The method of claim 1 , wherein the deriving comprising applying a machine learning algorithm a plurality of known energies each of the low frequency passband and the high frequency passband pairs and energy ratios, along with a known loading status.

4. The method of claim 3 , wherein the machine learning algorithm is a minimum-mean squared error algorithm.

5. The method of claim 4 , wherein each of the plurality of known energies of the low frequency passband and the high frequency passband pairs and the energy ratio, along with the known loading status are represented as
y n =w 0 +w 1 x n1 +w 2 x n2 +. . . +w j x nj

and wherein the plurality of equations may be solved for:
y=Xw

where

X
=

[

1

…

x

1
⁢
j

⋮

⋱

⋮

1

…

x
nj

]

and
y =[ y 1 y 2 . . . y n ] T

and
w =[ w 1 w 2 . . . w j ] T .

6. The method of claim 3 , wherein the machine learning algorithm is a support-vector machine algorithm.

7. The method of claim 1 , wherein the computing device is a sensor apparatus on the vehicle.

8. The method of claim 1 , wherein the computing device is a server remote from the vehicle.

9. A computing device comprising:
a processor; and
a communications subsystem,
wherein the computing device is configured to:
derive weighting constants for a type of chassis of a vehicle;
obtain sensor data for the vehicle providing vibration frequency and magnitude;
calculate an energy for each of a low frequency passband and a high frequency passband of a bandpass filter pair;
find an energy ratio based on the energy for the low frequency passband and the energy for the high frequency passband, wherein the energy ratio comprises:

eRatio
=

E
L

E
L

+

E
H

where eRatio is the energy ratio; E L is the energy for the low frequency passband; and E H is the energy for the high frequency passband;
apply the weighting constants to each of the energy for the low frequency passband, the energy for the high frequency passband and the energy ratio to calculate a decision variable; and
find that the vehicle is unloaded if the decision variable is below a threshold and finding that the vehicle is loaded if the decision variable is above a threshold.

10. The computing device of claim 9 , wherein the computing device is further configured to repeat the calculating, finding and applying for a plurality of bandpass filter pairs.

11. The computing device of claim 9 , wherein the computing device is configured to derive by applying a machine learning algorithm a plurality of known energies each of the low frequency passband and the high frequency passband pairs and the energy ratio, along with a known loading status.

12. The computing device of claim 11 , wherein the machine learning algorithm is a minimum-mean squared error algorithm.

13. The computing device of claim 12 , wherein each of the plurality of known energies of the low frequency passband and the high frequency passband pairs and the energy ratio, along with the known loading status are represented as
y n =w 0 +w 1 x n1 +w 2 x n2 +. . . +w j x nj

and wherein the plurality of equations may be solved for:
y=Xw

where

X
=

[

1

…

x

1
⁢
j

⋮

⋱

⋮

1

…

x
nj

Assignees

• Blackberry Ltd

Inventors

• Qu Shouxing
• Martin-Cocher Gaëlle Christine
• Periyalwar Suresh
• Gao Yu
• Ayoub Sameh

Classifications

• G06N20/00

  Machine learning · CPC title

• G06F2111/10

  Numerical modelling · CPC title

• G01G19/086

  wherein the vehicle mass is dynamically estimated · CPC title

• G01V1/00

  Seismology; Seismic or acoustic prospecting or detecting · CPC title

• G06Q10/083 Primary

  Shipping · CPC title