WO2020026071A1 - Procédé permettant de prédire les performances de modules d'un système de commande distribué par le biais d'un réseau et système associé - Google Patents

Procédé permettant de prédire les performances de modules d'un système de commande distribué par le biais d'un réseau et système associé Download PDF

Info

Publication number
WO2020026071A1
WO2020026071A1 PCT/IB2019/056275 IB2019056275W WO2020026071A1 WO 2020026071 A1 WO2020026071 A1 WO 2020026071A1 IB 2019056275 W IB2019056275 W IB 2019056275W WO 2020026071 A1 WO2020026071 A1 WO 2020026071A1
Authority
WO
WIPO (PCT)
Prior art keywords
data packet
modules
servers
type
network
Prior art date
Application number
PCT/IB2019/056275
Other languages
English (en)
Inventor
Utkarsh Verma
Lohith HC
Rahulkumar D
Raoul JETLEY
Original Assignee
Abb Schweiz Ag
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Abb Schweiz Ag filed Critical Abb Schweiz Ag
Publication of WO2020026071A1 publication Critical patent/WO2020026071A1/fr

Links

Classifications

    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L41/00—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks
    • H04L41/14—Network analysis or design
    • H04L41/147—Network analysis or design for predicting network behaviour
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L43/00—Arrangements for monitoring or testing data switching networks
    • H04L43/08—Monitoring or testing based on specific metrics, e.g. QoS, energy consumption or environmental parameters
    • H04L43/0852—Delays
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L41/00—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks
    • H04L41/14—Network analysis or design
    • H04L41/142—Network analysis or design using statistical or mathematical methods

Definitions

  • the present disclosure relates in general to performance prediction of modules. More particularly, the present disclosure relates to a method for predicting performance of modules through a network in a Distributed Control System (DCS) and a system thereof.
  • DCS Distributed Control System
  • DCS Distributed Control System
  • modules are configured in the one or more servers to perform a specific functionality repeatedly throughout their runtime lifecycle. Though the modules perform the specific functionality repeatedly, performance of the modules may vary based on runtime environment at site.
  • Performance is one of the key criteria in selection and success of any modules configured in the one or more servers. Performance is generally predicted based on debug information of the modules which is generated upon execution of the modules. Accessing the debug information in the runtime environment is simple when the modules are configured in a development lab. However, once the modules are deployed in the servers or end user systems outside the development lab, accessing the debug information in the runtime environment becomes complex, due to which predicting performance of the modules becomes challenging. This leads to questions on which modules should be preferred or selected for certain operations in the DCS.
  • the performance of the modules is predicted by installing additional performance calculation software or by introducing plugins in the modules configured in the one or more servers.
  • additional software or the plugins take substantive amount of time to access the necessary debug information and predict the performance of the modules.
  • the excess time introduced by the additional software or the plugins adds to execution time of the modules, thus increasing total execution time of the modules, and causing undue delay in the execution. Therefore, performance of the modules predicted using the conventional methods may not result in accurate performance prediction.
  • the present disclosure discloses a method for predicting performance of one or more modules of a Distributed Control System (DCS) through a network, in real-time, and a system thereof.
  • the one or modules are configured in one or more servers of the DCS.
  • the one or more servers are connected to each of plurality of controllers through the network. Further, each of the plurality of controllers are connected to each other through the network.
  • a plurality of equipment is associated with at least one controller for performing one or more operations in the DCS.
  • the one or more modules configured in the one or more servers may transmit a plurality of data packets to at least one controller, through the network, while performing a corresponding functionality.
  • At least one server in the DCS captures, from the network, each of the plurality of data packets transmitted from the one or more servers to at least one controller, through the network.
  • the at least one server (also referred as a“server”) of the DCS, capturing each of the plurality of data packets may be a server dedicated to perform the method disclosed herein, wherein the server is different from the one or more servers transmitting the plurality of data packets to at least one controller.
  • the one or more servers transmit the plurality of data packets one at a time upon receiving an acknowledgement for indicating receipt of each data packet, from at least one controller receiving the plurality of data packets.
  • the one or more servers may transmit more than one data packet in parallel to at least one controller.
  • the server determines a type of each data packet based on unique identifier associated with each data packet.
  • each type of the data packet comprises a predefined packet format and a predefined size.
  • the server determines values of a plurality of parameters impacting the performance of each module, for each type of the data packet.
  • the server determines the values of the plurality of parameters based on the corresponding data packet and data related to the one or more servers configured with the one or more modules transmitting each data packet.
  • the one or more parameters may include, but not limited to, size of the data packet, quantity of the data packets, time period between transmission of each data packet, time period for receiving the acknowledgement from at least one controller, Random Access Memory (RAM) size of the one or more servers transmitting the plurality of data packets and Central Processing Unit (CPU) core utilization of the one or more servers while transmitting the plurality of data packets.
  • the time period between the transmission of each data packet to at least one controller is a variable time period and the time period for receiving the acknowledgment from at least one controller is a fixed time period.
  • the server generates a predictive model representing total time taken for transmitting each type of the data packet by analysing the values of the plurality of parameters determined for each type of the data packet.
  • the server predicts the performance of each of the one or more modules based on the predictive model generated for each type of the data packet.
  • the predictive model is generated using one or more predefined curve fitting graphical techniques.
  • FIG.l illustrates a block diagram of a Distributed Control System (DCS) illustrating a method for predicting performance of one or more modules configured in one or more servers of the DCS, through a network, in accordance with an embodiment of the present disclosure
  • DCS Distributed Control System
  • FIG.2A shows a sequence diagram illustrating a method for determining transmission time for each type of a data packet, in accordance with an embodiment of the present disclosure
  • FIG.2B illustrates a table representing exemplary values of two parameters for generating a 2-Dimensional predictive model, in accordance with an embodiment of the present disclosure
  • FIG.2C (1) - FIG.2C (4) illustrate exemplary 2-Dimensional predictive models for exemplary type of data packet, in accordance with an embodiment of the present disclosure
  • FIG.2D illustrates a table representing exemplary values of three parameters for generating a 3 -Dimensional predictive model, in accordance with an embodiment of the present disclosure
  • FIG.2E illustrates exemplary 3-Dimensional predictive models for exemplary type of data packet, in accordance with an embodiment of the present disclosure.
  • FIG.3 illustrates a flowchart for predicting performance of one or more modules through a network, in accordance with an embodiment of the present disclosure.
  • the present disclosure discloses a method for predicting performance of modules of a DCS through a network and a system thereof.
  • the present disclosure aims to predict the performance of the modules without intruding in execution of the one or more modules configured in one or more servers. Therefore, the server of the DCS configured to perform the method disclosed herein, monitors network traffic by hardware-based sniffing or tapping techniques, to gather required information for predicting performance of the one or more modules.
  • the non-intrusive technique disclosed in the present disclosure eliminates the need for installation of additional performance calculation software or plugins in the one or more modules to predict the performance. Therefore, the total execution time of the one or more modules remains unaffected due to the process of performance prediction.
  • the method disclosed in the present disclosure results in accurate performance prediction, thereby providing an advantage for end users or customers to plan upgrades to the one or more modules and scaling of the overall infrastructure.
  • FIG.l illustrates a block diagram of a Distributed Control System (DCS) 100.
  • the DCS 100 comprises multiple layers of network.
  • Bottom most layer of the DCS 100 is a field network (Layer 1) that comprises plurality of equipment (equipment lOli to equipment 101 n ).
  • Next layer of the DCS 100 in bottom- up direction is a control network (Layer 2) that comprises a plurality of controllers 103 (controller 103i to controller 103n).
  • Each of the plurality of equipment 101 is associated with at least one controller 103.
  • the plurality of equipment 101 may be instruments, motors, sensors, actuators and the like which are configured to perform one or more operations in the DCS 100.
  • each of the plurality of controllers 103 are configured to control the one or more operations of the corresponding plurality of equipment 101.
  • each of the plurality of controllers 103 communicate with each other via a network 105.
  • the network 105 may be a wired network, a wireless network or a combination of both wired and wireless network.
  • the next layer in bottom- up direction is a operator network (Layer 3), that comprises one or more servers 107 (server 107i to server 107 n ).
  • the one or more servers 107 are connected to each of the plurality of controllers 103 via the network 105.
  • the one or more servers 107 may be at least one of operational servers and engineering servers.
  • the one or more servers 107 may act as a client or a server depending on a requirement at any given instance.
  • the one or more servers 107 are configured with one or more modules 102 (module 102i to module 102 n ).
  • the next layer of the DCS 100 is a plant network (Layer 4) that comprises one or more workstations 109 (workstation 109i to workstation 109 n ) that communicate with the one or more servers 107.
  • the one or more workstations 109 are in turn connected to a remote terminal 111 over the Internet. Data transmission to a remote terminal 111 over the Internet is subject to security measures that are provided by construction of routers/firewalls 110.
  • the one or more workstations 109 enable operators or engineers to configure, monitor and control operations of the plurality of controllers 103 via the one or more servers 107.
  • the one or more servers 107 are configured to act as a bridge between the one or more workstations 109 and the one or more controllers 103 for interacting and controlling each of the plurality of controllers 103 based on the information received from the one or more workstations 109.
  • At least one server from the one or more servers 107 is configured to predict performance of the one or more modules 102 of the DCS 100. To predict the performance of the one or more modules 102, initially, at least one server from the one or more servers 107 captures, from the network 105, each of a plurality of data packets transmitted by the one or more modules 102 configured in the one or more servers 107 to at least one controller among the plurality of controllers 103, through the network 105.
  • the at least one server (also referred as“server 107- X”) is a server configured with the method disclosed herein and is different from the one or more servers 107 transmitting the plurality of data packets.
  • the one or more servers 107 transmit the plurality of data packets to at least one controller when the one or more modules 102 perform at least one corresponding functionality.
  • the plurality of data packets are transmitted one at a time upon receiving an acknowledgement for indicating receipt of each data packet, from at least one controller receiving the plurality of data packets.
  • more than one data packet may be transmitted in parallel as well.
  • the DCS engineering tool may be configured in the controller 107i to perform various functionalities.
  • an exemplary functionality of the DCS engineering tool is“configuration download to a controller”.
  • the configuration to be downloaded is a summation block.
  • the DCS engineering tool downloads the summation block to the controller 1032 over an Internet Protocol (IP) based Ethernet network (network 105).
  • IP Internet Protocol
  • the DCS engineering tool downloads the summation block by generating the plurality of data packets related to the summation block.
  • the DCS engineering tool is configured to transmit the plurality of data packets to the controller 1032.
  • the server 107-X may capture each of the plurality of data packets from the IP based Ethernet network (network 105).
  • the server 107-X captures each of the plurality of data packets using hardware-based sniffing or tapping techniques. Further, for each of the plurality of data packets captured from the network 105, the server 107-X determines a type of each data packet. In an embodiment, the type of each data packet is determined based on unique identifier associated with each data packet. Each type of the data packet comprises a predefined packet format and a predefined size. Considering the example of DCS engineering tool, consider the data packets generated while downloading the summation block belong to type 1. As an example, data packet belonging to type 1 is of size“270 bytes” and in a format specified for data packets of type 1. In an embodiment, a given configuration can also generate l-N data packets and l-N types of the data packets.
  • the server 107-X determines values of a plurality of parameters impacting the performance of each module. In an embodiment, the server 107-X determines the values of the plurality of parameters based on the corresponding data packet and data related to the one or more servers 107 configured with the one or more modules 102 transmitting each data packet.
  • the one or more parameters may include, but not limited to, size of the data packet, quantity of the data packets, time period between transmission of each data packet, time period for receiving the acknowledgement from at least one controller (controller 1032), Random Access Memory (RAM) size of the one or more servers 107 transmitting the plurality of data packets and Central Processing Unit (CPU) core utilization of the one or more servers 107 while transmitting the plurality of data packets.
  • the size of the data packet can be derived from the corresponding data packet and the quantity of the data packets can be determined by monitoring network traffic while transmitting each type of the data packet.
  • the time period between the transmission of each data packet to at least one controller (controller 1032) and the time period for receiving the acknowledgment from at least one controller (controller 1032) is determined as illustrated in FIG.2A.
  • FIG.2A shows transmission of data packets belonging to Type 1.
  • same method can be used for estimating time required for transmission for other types of data packets as well.
  • the server 107-X may measure start time and end time for transmission of each data packet i.e. data packet 1 of type 1 to data packet n of type 1.
  • Start Time (ST) of the data packet 1 of type 1 is ST 11 as shown in the FIG.2A and End Time (ET) of the data packet 1 of type 1 is ET11.
  • the server 107-X determines time taken for transmission of each data packet by subtracting the start time from end time of the corresponding data packet. Further, consider the end time of the last data packet N is ET1N. In such scenario, the server 107-X determines time taken for transmission of all the data packets of the type 1 by subtracting the start time ST11 from end time ET1N. It is to be noted that, while determining the transmission time, time consumed by Transmission Control Protocol/Internet Protocol (TCP/IP) connection management for transmission of the plurality of data packets is ignored.
  • TCP/IP Transmission Control Protocol/Internet Protocol
  • the time period between the transmission of each data packet to at least one controller is a variable time period since each module operate in a non-real time environment.
  • the time period for receiving the acknowledgment from at least one controller is a fixed time period since the controller is a hard real-time system.
  • FIG.2B illustrates a table representing exemplary values of one or more parameters for various execution cycles of a certain functionality of the module.
  • the table represented in FIG.2B indicates values corresponding to two parameters for each type of the data packet i.e. quantity of the data packets and the transmission time for each type of the data packet.
  • time taken for transmission of 2 data packets of type 1 is Tl.
  • time taken for transmission of 5 data packets of type 3 is T3.
  • the server 107-X generates a predictive model representing total time taken for transmitting each type of the data packet by analysing the values of the plurality of parameters determined for each type of the data packet.
  • the server 107-X predicts the performance of each of the one or more modules 102 based on the predictive model generated for each type of the data packet.
  • the predictive model is generated using one or more predefined curve fitting graphical techniques.
  • the server 107-X plots a graph with X- axis representing quantity of each type of the data packets and Y-axis representing transmission time for each type of the data packets.
  • FIG.2C (1) - FIG.2C (4) show graphs plotted for exemplary type of data packets i.e. type 1, type 2, type 3 and type 4 of the data packets. In this scenario, since two parameters are used for plotting the graphs, the graphs thus plotted indicate 2-Dimensional curves.
  • the server 107-X analyses the values of the plurality of parameters and the graphs plotted for each type of the data packet, to generate an equation for each type of the data packet, using the one or more predefined curve fitting graphical techniques.
  • the server 107- X may generate an exemplary equation 1 as shown below.
  • f(xType 1) ax + b - Equation 1
  • a and b are constants whose value has to be determined.
  • the one or more servers 107 consider multiple sets of the recorded values of the same type of the data packet for determining best suited values of a and b.
  • the server 107-X may generate an exemplary equation 2 as shown below.
  • f(xType 2) ax 2 + b - Equation 2
  • the server 107-X may generate an exemplary equation 3 as shown below.
  • f(xType 3) 2 ax 3 + b - Equation 3
  • the server 107-X may generate an exemplary equation 4 as shown below.
  • f(xType 4) 2 ax 3 + 3 b - Equation 4
  • Equation 5 [0039] In the above Equation 5,
  • n indicates the type of the data packets, wherein total number of types of the data packets transmitted is represented by N;
  • t is the time taken for transmitting each type of the data packet.
  • table represented in FIG.2B represents only values of 2 parameters.
  • the number of parameters considered for predicting performance of the one or more modules 102 can be more than 2 parameters.
  • the server 107- X can consider RAM size of the one or more servers 107 transmitting the plurality of data packets to at least one controller (controller 1032). The RAM size is directly proportional to performance of the one or modules 102.
  • FIG.2D illustrates a table representing exemplary values of three parameters for various execution cycles of a certain functionality of the module.
  • the three parameters for each type of the data packet represented in the table shown in FIG.2D indicates values corresponding to i.e. RAM size of the one or more servers 107 transmitting the data packets, quantity of the data packets and the transmission time for each type of the data packet.
  • the above exemplary values represent that, time taken for transmission of 2 data packets of type 1, by an exemplary server configured with a RAM of size 1 GB is Tl.
  • the above exemplary values represent that, time taken for transmission of 3 data packets of type 2, by an exemplary server configured with a RAM of size 2 GB is T2.
  • the server 107-X plots a graph with X-axis representing quantity of each type of the data packets, Y-axis representing transmission time for each type of the data packet and Z-axis representing RAM size.
  • FIG.2E shows three curves upon plotting the graph, wherein curve 1 indicates time taken for transmission of the data packets of type 1 when the RAM size is 10GB, curve 2 indicates time taken for transmission of the data packets of type 1 when the RAM size is 20GB and curve 3 indicates time taken for transmission of the data packets of type 1 when the RAM size is“M” GB.
  • the server 107-X analyses the values of the plurality of parameters and the graphs plotted for each type of the data packet, to generate an equation for each type of the data packet, using the one or more predefined curve fitting graphical techniques.
  • the server 107-X uses multidimensional (N-dimensional) curve fitting graphical techniques.
  • n indicates the type of the data packets, wherein total number of types of the data packets transmitted is represented by N;
  • t(m) is the time taken for transmitting each type of the data packet by a server of RAM size“m” GB.
  • the server 107-X predicts the performance of the one or more modules 102 by adapting to add multiple parameters and thereby increasing the accuracy in predicting the performance of the one or more modules 102.
  • FIG.3 illustrates a flowchart for predicting performance of one or more modules 102 through a network, in accordance with an embodiment of the present disclosure.
  • At block 301 at least one server (server 107-X) from one or more servers 107, of the Distributed Control System (DCS) 100, captures each of a plurality of data packets transmitted by one or more modules 102 configured in the one or more servers 107, to at least one controller (1032) among the plurality of controllers 103, through a network
  • DCS Distributed Control System
  • the server 107-X determines a type of each data packet based on a unique identifier associated with each data packet.
  • each type of the data packet comprises a predefined packet format and a predefined size.
  • the server 107-X determines values of a plurality of parameters impacting the performance of each module, for each type of the data packet, based on the corresponding data packet and data related to the one or more servers 107 configured with the one or more modules 102, transmitting each data packet.
  • the server 107-X generates a predictive model representing total time taken for transmitting each type of the data packet by analysing the values of the plurality of parameters determined for each type of the data packet, for predicting the performance of each of the one or more modules 102.
  • the present disclosure discloses a non-intrusive method for predicting performance of the one or more modules 102 in the DCS environment.
  • the non-intrusive method eliminates the need for installation of additional performance calculation software or plugins in the one or more modules 102 to predict the performance. Therefore, the total execution time of the one or more modules 102 remains unaffected due to the process of performance prediction.
  • the method disclosed in the present disclosure results in accurate performance prediction, thereby providing an advantage for end users or customers to plan upgrades to the one or more modules 102 and scaling of the overall infrastructure.
  • the present disclosure allows usage of multidimensional graphs to predict the performance of the one or more modules 102, thereby enabling addition of multiple parameters.

Landscapes

  • Engineering & Computer Science (AREA)
  • Computer Networks & Wireless Communication (AREA)
  • Signal Processing (AREA)
  • Environmental & Geological Engineering (AREA)
  • Debugging And Monitoring (AREA)

Abstract

La présente invention porte sur un procédé permettant de prédire les performances d'un ou de plusieurs modules d'un système DCS par le biais d'un réseau, ainsi que sur un système associé. Au moins un serveur du système DCS capture, à partir du réseau, chaque paquet de données transmis par les modules lorsque le ou les modules exécutent au moins une fonctionnalité correspondante. En outre, le serveur détermine un type de chaque paquet de données sur la base d'un identifiant unique associé à chaque paquet de données. Par la suite, le serveur détermine des valeurs d'une pluralité de paramètres impactant les performances de chaque module, pour chaque type du paquet de données, sur la base du paquet de données correspondant et de données se rapportant à un ou plusieurs serveurs configurés avec les modules. Enfin, le serveur génère un modèle prédictif par analyse des valeurs de la pluralité de paramètres déterminées pour chaque type du paquet de données, pour prédire les performances de chaque module.
PCT/IB2019/056275 2018-07-31 2019-07-23 Procédé permettant de prédire les performances de modules d'un système de commande distribué par le biais d'un réseau et système associé WO2020026071A1 (fr)

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
IN201841028820 2018-07-31
IN201841028820 2018-07-31

Publications (1)

Publication Number Publication Date
WO2020026071A1 true WO2020026071A1 (fr) 2020-02-06

Family

ID=68062965

Family Applications (1)

Application Number Title Priority Date Filing Date
PCT/IB2019/056275 WO2020026071A1 (fr) 2018-07-31 2019-07-23 Procédé permettant de prédire les performances de modules d'un système de commande distribué par le biais d'un réseau et système associé

Country Status (1)

Country Link
WO (1) WO2020026071A1 (fr)

Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2001006415A1 (fr) * 1999-07-19 2001-01-25 Netpredict Inc. Utilisation de l'etalonnage d'un modele pour l'analyse haute precision de reseaux informatiques
US20150057973A1 (en) * 2012-03-01 2015-02-26 Nuovo Pignone Srl Method and system for real-time performance degradation advisory for centrifugal compressors

Patent Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2001006415A1 (fr) * 1999-07-19 2001-01-25 Netpredict Inc. Utilisation de l'etalonnage d'un modele pour l'analyse haute precision de reseaux informatiques
US20150057973A1 (en) * 2012-03-01 2015-02-26 Nuovo Pignone Srl Method and system for real-time performance degradation advisory for centrifugal compressors

Similar Documents

Publication Publication Date Title
US20190052551A1 (en) Cloud verification and test automation
US9762471B2 (en) Methods and systems for estimating and analyzing flow activity and path performance data in cloud or distributed systems
CN102473132B (zh) 监控软件应用的性能的方法
US20090296593A1 (en) Method and apparatus of measuring and reporting data gap from within an analysis tool
US20140215077A1 (en) Methods and systems for detecting, locating and remediating a congested resource or flow in a virtual infrastructure
CN109150610B (zh) 基于规则适配的网络事件采集方法
JP2005521359A (ja) ソフトウェアアプリケーションのネットワーク動作特性を測定する方法、システム及びコンピュータプログラム
WO2002091112A2 (fr) Calcul du temps de reponse d'un site serveur pour des applications arbitraires
US8966117B1 (en) System for discovering routers in a communication path of a supervisory control and data acquisition system
Seo et al. Component-level energy consumption estimation for distributed java-based software systems
CN104618341B (zh) 用于安全的远程访问的系统和方法
US7623527B2 (en) Method for analyzing network trace, method for judging order among nodes, processor for analyzing network trace, computer-executable program for controlling computer as processor, and method for correcting time difference among nodes in network
CN109194545B (zh) 一种网络试验平台流量生成系统、方法、装置及电子设备
CN102780612B (zh) 在tcp流/套接字连接的生命周期中估计发送方拥塞窗口的方法和装置
US9100318B1 (en) Method for discovering routers in a communication path of a supervisory control and data acquisition system
Tang et al. Key performance indicators for process control system cybersecurity performance analysis
CN105530137A (zh) 流量数据分析方法及流量数据分析系统
CN112671596A (zh) 一种远程控制的自动化测试系统及测试方法
Garitano et al. A method to construct network traffic models for process control systems
WO2020026071A1 (fr) Procédé permettant de prédire les performances de modules d'un système de commande distribué par le biais d'un réseau et système associé
Tao et al. IP sensor and its distributed networking application in e-maintenance
JP6566070B1 (ja) フロー管理サーバ、フロー管理システム、フロー管理方法およびフロー管理プログラム
Nakutis et al. Round-trip delay estimation in opc ua server-client communication channel
YUCESAN et al. A Reliability Assessment of an Industrial Communication Protocol on a Windows OS Embedded PC for an Oil Rig Control Application
JP7010791B2 (ja) 情報提供装置、中央監視装置及びプログラム

Legal Events

Date Code Title Description
121 Ep: the epo has been informed by wipo that ep was designated in this application

Ref document number: 19773898

Country of ref document: EP

Kind code of ref document: A1

NENP Non-entry into the national phase

Ref country code: DE

122 Ep: pct application non-entry in european phase

Ref document number: 19773898

Country of ref document: EP

Kind code of ref document: A1