CN105491164B - SIS network diagnosis methods based on distributed intelligence monitoring arrangement and data analysis center - Google Patents

Abstract

An SIS network diagnosis method based on a distributed intelligent monitoring device and a data analysis center is composed of a distributed intelligent monitoring device and a data analysis center. The intelligent monitoring devices are distributed on the network terminals of the nodes at all levels of the SIS, monitor and diagnose the activity of the local machine and auxiliary programs in real time, and report to the upper-level nodes in time; the data analysis center is at the top node, responsible for receiving and summarizing the information of each intelligent monitoring device, and Perform analysis and processing to form a real-time display of network diagnostic reports for IT maintenance personnel to inspect.

Description

SIS Network Diagnosis Based on Distributed Intelligent Monitoring Device and Data Analysis Center method

technical field

The invention relates to a network diagnosis method of a plant-level monitoring information system (SIS), in particular to a network diagnosis method of an SIS based on a distributed intelligent monitoring device and a data analysis center.

Background technique

The plant-level monitoring information system (SIS) of the power plant is a plant-level automation information system that integrates process real-time monitoring, optimization control and production process management. A set of real-time plant-level monitoring information system, the main functions include unit working condition monitoring, statistical reports, performance calculation, consumption difference analysis, working condition analysis and fault diagnosis, etc.

SIS needs to use calculation engines such as data mining, data processing and optimization to analyze, diagnose and optimize the operating conditions of the unit and even the entire plant. In order to achieve accurate and timely calculation and analysis, the real-time performance of the system is very important. At present, the SIS of each power plant lacks the judgment of the real-time data. Even if the data interruption information is detected through trend analysis and other means, the location of the interruption cannot be determined, and because the discovery time is too late, the system has caused irreparable data loss.

Although there are many types of network management software on the market, and they have already implemented modular architecture, they are all only for local area networks and cannot be applied to cross-gateway and cross-system network structures such as SIS. Network O&M only stays at the level of manually troubleshooting problems one by one. It is difficult to ensure real-time and accurate SIS data, and the availability rate is low.

Contents of the invention

In order to improve the availability of SIS, ensure the real-time performance and reliability of SIS, and reduce the workload of operation and maintenance personnel. The invention provides a set of intelligent monitoring devices distributed in each node in the SIS network, and feeds back the monitoring information to the data analysis center in real time to form the SIS network diagnosis report. The present invention can effectively solve the following problems: help maintenance personnel to find SIS data interruption in time; help maintenance personnel locate the location of SIS data interruption, reduce maintenance time investment; help maintenance personnel sort out and summarize SIS network stability and availability.

The invention discloses a SIS network diagnosis method based on a distributed intelligent monitoring device and a data analysis center. The method includes the following steps:

Step 1: The intelligent monitoring device of each interface machine collects the network communication status information and program running status information of the machine;

Step 2: Encapsulate the information described in step 1 into a data message, and send it to the database layer through the UDP protocol in real time;

Step 3: the intelligent monitoring device at the database layer receives the message information of each interface machine described in step 1, and forms timeout information if the reception is overtime;

Step 4: the database layer intelligent monitoring device collects the local network communication status information and program running status information;

Step 5: the database layer intelligent monitoring device encapsulates the information described in step 3 and step 4 to form a message, and sends it to the application service layer through the UDP protocol in real time;

Step 6: the application service layer intelligent monitoring device receives the message information of the database layer described in step 5, and forms timeout information if the reception is overtime;

Step 7: The application service layer intelligent monitoring device collects the local network communication status information and program running status information;

Step 8: The application service layer intelligent monitoring device encapsulates the information described in step 6 and step 7 to form a message, and sends it to the data analysis center through the UDP protocol in real time;

Step 9: The data analysis center analyzes the message information described in step 8 to form a diagnosis report. If the reception timeout occurs, an alarm message of abnormal communication with the application service layer is formed and fed back to the maintenance personnel.

Compared with the prior art, the present invention has the following technical effects:

1) Real-time. This method has the characteristics of real-time, can provide timely feedback, and helps operation and maintenance personnel to find abnormalities in the first time.

2) Accuracy. Since the intelligent monitoring device is distributed at any node in the SIS network, the diagnostic report of the data analysis center can accurately locate the abnormal position, effectively reducing the maintenance time.

3) Long-term effect. The diagnostic report of the data analysis center can be stored for a long time, which will provide the basis for the stability assessment of the interface (software and hardware), and also provide the basis for the calculation of the comprehensive availability of the system.

4) Scalability. The method is aimed at network monitoring isolated across networks, not only can realize SIS network diagnosis, but also can be applied to other cross-system network diagnosis.

Description of drawings

Fig. 1 is a schematic diagram of SIS network structure according to the present invention;

Fig. 2 is a structural diagram of an intelligent monitoring device according to the present invention;

Fig. 3 is a data flow diagram of an intelligent analysis network according to the present invention.

Detailed ways

The structure of the present invention is shown in Fig. 1. An intelligent monitoring device is installed in each network node of the SIS, and the monitoring information is summarized to the data analysis center in real time by using the SIS network, and a diagnosis report is formed and displayed in real time. When a network abnormality occurs, the data analysis center will change the color or pop-up an alarm according to the abnormality level.

As shown in Figure 2, according to the SIS network structure, the entire set of intelligent monitoring devices is divided into three levels of distribution. The first level is the interface layer, the second level is the database layer, and the third level is the application service layer. The monitoring information will be distributed in order Finally, the application service layer is aggregated to the data analysis center.

As shown in Figure 3, the distributed intelligent monitoring device of the present invention is divided into interface machine monitoring device, database layer monitoring device and application service layer monitoring device, and these monitoring devices form an intelligent analysis network together with the data analysis center. Specifically include the following steps:

Step 1: the interface machine n intelligent monitoring device collects the local network communication status information and program running status information, these information include whether to receive the data message information of the control system, the activity of the local application program (such as whether the interface program is running normally etc.), native network connectivity, stability;

Step 2: Encapsulate the information described in step 1 into a data message, and send it to the database layer through the UDP protocol in real time;

Step 3: the intelligent monitoring device of the database n receives the message information of the interface machine n described in the step 1, and if the reception is overtime, a timeout message is formed, and an overtime alarm is issued;

Step 4: The database n intelligent monitoring device collects the local network communication status information and program running status information. These information include whether there is any abnormality in the interface machine to which it belongs, whether there is any abnormality in the database cluster service (if it is deployed in a cluster mode), whether the disk array disk is abnormal There are exceptions (if deployed in cluster mode), local application program activity (such as whether the database program is running normally, etc.), local network connectivity and stability;

Step 5: the database n intelligent monitoring device encapsulates the information described in step 3 and step 4 to form a message, and sends it to the application service layer through the UDP protocol in real time;

Step 6: the application server n intelligent monitoring device receives the message information of the database n described in step 5, if receiving overtime then forms overtime information, and overtime reports to the police;

Step 7: the application server n intelligent monitoring device collects the local network communication status information and the program operation status information, whether these information include the database server to which there is abnormality, the local application program activity (such as application programs such as publishing platform, computing platform), Local network connectivity, stability;

Step 8: the application server n intelligent monitoring device encapsulates the information described in step 6 and step 7 to form a message, and sends it to the data analysis center in real time through the UDP protocol;

Step 9: The data analysis center analyzes the message information described in step 8 to form a diagnosis report; if the reception timeout, it forms an alarm message of abnormal communication with the application service layer. The specific work includes analyzing the monitoring information of each monitoring device, understanding the working conditions of SIS equipment at each level, calculating the time interval (overtime calculation) if the monitoring information is not received in real time, displaying the diagnostic report in real time, and generating early warning or alarm according to abnormal conditions. Diagnostic reports and alarm information are fed back to maintenance personnel in real time. At the same time, annual summary reports can be generated to warn equipment with long-term problems, and diagnostic reports are stored to facilitate historical query by maintenance personnel.

Claims (1)

1. A SIS network diagnosis method based on distributed intelligent monitoring devices and data analysis centers. Based on the SIS network architecture of thermal power plants, the final diagnosis report is formed by monitoring node information at all levels and using the original SIS network to transmit to the data analysis center. , the distributed intelligent monitoring device includes an interface machine intelligent monitoring device, a database layer intelligent monitoring device and an application service layer intelligent monitoring device, wherein the SIS is a plant-level monitoring information system of a power plant; it is characterized in that: Step 1: The intelligent monitoring device of each interface machine collects the network communication status information and program running status information of the machine; Step 2: Encapsulate the information described in step 1 into a data message, and send it to the database layer through the UDP protocol in real time; Step 3: the intelligent monitoring device at the database layer receives the message information of each interface machine described in step 1, and forms timeout information if the reception is overtime; Step 4: the database layer intelligent monitoring device collects the local network communication status information and program running status information; Step 5: the database layer intelligent monitoring device encapsulates the information described in step 3 and step 4 to form a message, and sends it to the application service layer through the UDP protocol in real time; Step 6: the application service layer intelligent monitoring device receives the message information of the database layer described in step 5, and forms timeout information if the reception is overtime; Step 7: The application service layer intelligent monitoring device collects the local network communication status information and program running status information; Step 8: The application service layer intelligent monitoring device encapsulates the information described in step 6 and step 7 to form a message, and sends it to the data analysis center through the UDP protocol in real time; Step 9: The data analysis center analyzes the message information described in step 8 to form a diagnosis report. If the reception timeout occurs, an alarm message of abnormal communication with the application service layer is formed and fed back to the maintenance personnel.