Path study and antenna locating systems and methods

Patent metadata

Field	Value
Publication number	US-12598534-B2
Application number	US-202318129423-A
Country	US
Kind code	B2
Filing date	Mar 31, 2023
Priority date	Mar 31, 2023
Publication date	Apr 7, 2026
Grant date	Apr 7, 2026

Abstract

Method and system for automating a path study to establish a private network between an access point and a remote asset along an optimal path. A predictive model identifies potential communications paths between the access point and the remote asset based on site data. The potential communications paths each specifies at least one antenna parameter. The machine learning also includes selecting the optimal path from the potential communications paths based at least in part on the network performance requirements of the private network and predicted signal quality along the potential communications paths using the antenna parameter.

First claim

The invention claimed is:

1 . A method of managing remote telemetry communications in a process control system, the process control system comprising a Supervisory Control and Data Acquisition (SCADA) server configured to monitor telemetry data from a remote industrial asset and provide supervisory control via a private network, the private network comprising a plurality of data radios, the method comprising:
receiving input representative of network performance requirements for the private network; retrieving site data from a site database in response to a request to perform a path study, wherein the site data stored in the site database comprises information relating to a designated area for establishing remote telemetry communications via the private network, and wherein the plurality of data radios of the private network comprises a data radio associated with the remote industrial asset and an access point associated with the SCADA server; executing machine learning to perform the path study for establishing the private network between the access point and the data radio associated with the remote industrial asset within the designated area, wherein the machine learning includes:
executing a predictive model based on the retrieved site data to identify one or more potential communications paths between the access point and the data radio associated with the remote industrial asset, wherein the one or more potential communications paths each specifies at least one antenna parameter of the plurality of data radios of the private network; and
selecting an optimal communication path from the one or more potential communications paths based at least in part on the network performance requirements of the private network and predicted signal quality along the potential communications paths, wherein the selected optimal communications path includes a location of at least one antenna within the designated area; and
establishing remote telemetry communications on the private network via the antenna between the access point and the data radio associated with the remote industrial asset within the designated area along the optimal communication path.

2 . The method of claim 1 , wherein the at least one antenna parameter comprises at least one of the following; a location within the designated area, a tower height, a radiation pattern, power, gain, and directivity.

3 . The method of claim 1 , wherein the site data comprises at least one of the following: a map; property boundary information; satellite imagery; Global Positioning System (GPS) coordinates; legal restrictions; physical restrictions; and obstruction information.

4 . The method of claim 1 , wherein the plurality of data radios comprises one or more repeaters between the access point and the data radio associated with the remote industrial asset.

5 . The method of claim 4 , wherein selecting the optimal communication path comprises balancing a cost associated with the one or more repeaters along the potential communications paths and a cost associated with the specified antenna parameter for each antenna along the potential communications paths while meeting the network performance requirements of the private network.

6 . The method of claim 5 , wherein the specified antenna parameter comprises a tower height, and wherein the balancing is based on a number of the repeaters along the potential communication paths and the tower height of each antenna along the potential communication paths.

7 . The method of claim 5 , wherein the cost associated with the specified antenna parameter is a function of one or more of the following for each antenna along the potential communication paths: make and model, configuration information, installation options, installation and maintenance, existing infrastructure, needed infrastructure, and power requirements.

8 . The method of claim 1 , wherein the machine learning further includes executing the predictive model based on the retrieved site data to identify one or more no-go areas in the designated area between the access point and the data radio associated with the remote industrial asset.

9 . The method of claim 1 , further comprising:
analyzing the retrieved site data to determine whether the information relating to the designated area is sufficient for performing the path study; and in response to determining additional information relating to the designated area is required to perform the path study, requesting and retrieving the additional information until the information relating to the designated area is sufficient for performing the path study.

10 . The method of claim 1 , further comprising training the predictive model, wherein training the predictive model comprises teaching artificial intelligence (AI) to calculate signal quality at the access point following one or more preset paths from a training set.

11 . The method of claim 10 , wherein the training set comprises a catalog of previous manual path studies and wherein training the predictive model comprises adjusting the predictive model until the potential communications paths identified by the predictive model based on the training set match the manual path studies.

12 . The method of claim 1 , wherein selecting the optimal communication path comprises ranking the potential communications paths based on at least one of the following: indicated customer preferences, learned customer preferences, cost, and ease or difficulty of installation.

13 . The method of claim 1 , wherein the industrial assets comprise one or more of the following: a remote terminal unit (RTU) device, a programmable logic controller (PLC), and a peripheral device.

14 . An automation system comprising:
a private network having a plurality of data radios, each of the data radios having an antenna coupled thereto; a remote industrial asset coupled to the private network; a Supervisory Control and Data Acquisition (SCADA) server configured to monitor telemetry data from the remote industrial asset and provide supervisory control via the private network, wherein the plurality of data radios of the private network comprises a data radio associated with the remote industrial asset and an access point associated with the SCADA server; a site database storing information relating to a designated area for establishing remote telemetry communications via the private network; a path study processor coupled to the site database, the path study processor receiving input representative of network performance requirements for the private network and retrieving site data from the site database in response to a request to perform a path study; and a memory device storing computer-executable instructions that, when executed by the path study processor, configure the path study processor for:
executing machine learning to perform the path study for establishing the private network between the access point and the data radio associated with the remote industrial asset within the designated area, wherein the machine learning includes:
executing a predictive model based on the retrieved site data to identify one or more potential communications paths between the access point and the data radio associated with the remote industrial asset, wherein the one or more potential communications paths each specifies at least one antenna parameter of the plurality of data radios of the private network; and
selecting an optimal communication path from the one or more potential communications paths based at least in part on the network performance requirements of the private network and predicted signal quality alon

Assignees

• Schneider Electric Systems Usa Inc

Inventors

• Redmond James
• Sobin Zackery

Classifications

• H04L41/16

  using machine learning or artificial intelligence · CPC title

• H04L41/147

  for predicting network behaviour · CPC title

• H04L67/12

  specially adapted for proprietary or special-purpose networking environments, e.g. medical networks, sensor networks, networks in vehicles or remote metering networks · CPC title

• H04W16/18

  Network planning tools · CPC title

• G06N20/00

  Machine learning · CPC title