Method and system for shipping container loading and unloading estimation

Patent metadata

Field	Value
Publication number	US-2021123795-A1
Application number	US-201916664387-A
Country	US
Kind code	A1
Filing date	Oct 25, 2019
Priority date	Oct 25, 2019
Publication date	Apr 29, 2021
Grant date	—

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

1 . A method at a computing device, the method comprising:
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.

2 . The method of claim 1 , 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.

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

4 . The method of claim 1 , further comprising, prior to the obtaining, deriving the weighting constants.

5 . The method of claim 4 , 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.

6 . The method of claim 5 , wherein the machine learning algorithm is a minimum-mean squared error algorithm.

7 . The method of claim 6 , wherein each of the plurality of known energies of the low frequency passband and the high frequency passband pairs and energy ratios, along with a 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

8 . The method of claim 5 , wherein the machine learning algorithm is a support-vector machine algorithm.

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

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

11 . A computing device comprising:
a processor; and a communications subsystem,
wherein the computing device is configured to:
obtain sensor data for a 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;
apply 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.

12 . The computing device of claim 11 , 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.

13 . The computing device of claim 11 , wherein the computing device is further configured to repeat the calculating, finding and applying for a plurality of bandpass filter pairs.

14 . The computing device of claim 11 , wherein the computing device is further configured to derive the weighting constants.

15 . The computing device of claim 14 , 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 energy ratios, along with a known loading status.

16 . The computing device of claim 15 , wherein the machine learning algorithm is a minimum-mean squared error algorithm.

17 . The computing device of claim 16 , wherein each of the plurality of known energies of the low frequency passband and the high frequency passband pairs and energy ratios, along with a 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 ]
And
w =[ w 1 w 2 . . . w j ] T

18 . The computing device of claim 15 , wherein the machine learning algorithm is a support-vector machine algorithm.

19 . The computing device of claim 11 , wherein the computing device is a sensor apparatus on a vehicle.

20 . The computing device of claim 11 , wherein the computing device is a server remote from the vehicle.

21 . A computer readable medium for storing instruction code, which, when executed by a processor on a computing device cause the computing device to:
obtain sensor data for a 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; apply 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.

Assignees

• Blackberry Ltd

Inventors

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

Classifications

• G06F30/27 Primary

  using machine learning, e.g. artificial intelligence, neural networks, support vector machines [SVM] or training a model · CPC title

• G01V1/00

  Seismology; Seismic or acoustic prospecting or detecting · CPC title

• G06F2111/10

  Numerical modelling · CPC title

• G06N20/10

  using kernel methods, e.g. support vector machines [SVM] · CPC title

• G01G19/086

  wherein the vehicle mass is dynamically estimated · CPC title