WO2019233047A1

WO2019233047A1 - Power grid dispatching-based operation and maintenance method

Info

Publication number: WO2019233047A1
Application number: PCT/CN2018/117328
Authority: WO
Inventors: 徐丽燕; 陆继翔; 胡俊; 王纪立; 刘金波; 徐春雷; 严亚勤; 翟明玉; 彭晖; 季学纯; 余璟; 宋旭日; 吴海伟; 孙云枫; 章兵; 季惠英; 沙一川; 邹庆
Original assignee: 国电南瑞科技股份有限公司; 国家电网有限公司; 国网江苏省电力有限公司
Priority date: 2018-06-07
Filing date: 2018-11-23
Publication date: 2019-12-12
Also published as: CN109034521A; CN109034521B

Abstract

A power grid dispatching-based operation and maintenance method comprises the following steps: at least collecting monitored data associated with hardware equipment and system software of a power grid dispatching and control system; determining, at least according to a data type and/or time validity of the collected monitored data, a storage method for the collected monitored data, and using the determined storage method to store the collected monitored data; performing detection on the monitored data to obtain anomaly index data; constructing, on the basis of a multidimensional association analysis of the anomaly index data, a fault diagnosis tree with respect to operation and maintenance, and diagnosing and locating a fault on the basis of which the anomaly index data is generated; and presenting the fault as a multi-dimensional visualization.

Description

Operation and maintenance method based on grid dispatch

Cross-reference to related applications

This application is based on a Chinese patent application with an application number of 201810578582.X and an application date of June 07, 2018, and claims the priority of the Chinese patent application. The content of the Chinese patent application is incorporated herein by reference. .

Technical field

This application relates to data processing technology, and in particular, to an operation and maintenance method based on grid dispatch.

Background technique

The current power grid dispatching control system still uses traditional operation and maintenance methods, that is, after the failure occurs, the operation and maintenance personnel take corresponding remedial measures; this passive operation and maintenance method has low work efficiency, weak monitoring and management methods, and cannot be quickly located Faults, lack of early warning and pre-control functions, etc. As the scale of the power grid continues to expand, the scale of equipment continues to increase, and the degree of informatization continues to increase, the operation and maintenance of the power grid dispatching control system itself is more complicated, and it is difficult to meet the current demand for traditional power grid dispatching control system passive operation and maintenance. How to ensure the safe and stable operation of the control system and optimize the performance of the operating environment have brought new requirements and challenges to the system's operation and maintenance level.

Summary of the Invention

The embodiment of the present application discloses an operation and maintenance method based on grid dispatching, which includes comprehensive monitoring data collection, monitoring data storage, real-time monitoring and alarming, fault diagnosis and positioning, and multi-dimensional visual display; it can monitor the operating status of the entire system in real time, which is effective Accurately and timely evaluate the health status of the host, storage, and application software and hardware, provide effective information for system optimization and fault detection, and improve system operation and maintenance efficiency.

The technical solutions adopted in the embodiments of the present application are as follows:

An embodiment of the present application provides an operation and maintenance method based on power grid scheduling, and the method includes:

Collect at least monitoring data of hardware equipment and system software of the power grid dispatching control system;

Determine the storage method of the collected monitoring data based on at least the data type and / or timeliness of the collected monitoring data, and store the collected monitoring data in the determined storage method;

Detect monitoring data to obtain abnormal index data;

Based on multi-dimensional correlation analysis of abnormal indicator data, construct an operation and maintenance fault diagnosis tree, at least diagnose and locate faults that generate abnormal indicator data;

Display faults in a multi-dimensional visualization.

In the above solution, the method includes:

Storing the collected monitoring data in a corresponding database at least according to the data type and / or timeliness of the collected monitoring data;

Among them, according to the data type, the collected monitoring data is divided into index data and log data; according to the data effectiveness, the collected monitoring data is divided into real-time data and historical data; according to the data type and data effectiveness, The collected monitoring data is divided into real-time index data and real-time log data;

At least the monitoring data divided into real-time index data is stored in a time-series database; the monitoring data divided into real-time log data is stored in a full-text search database; the monitoring data divided into historical monitoring data is stored in a columnar database; among them, The historical data includes at least historical monitoring data.

In the above solution, the method includes:

Real-time anomaly detection of monitoring data using constant threshold setting method and dynamic threshold setting method;

Among them, if the monitoring data is data with periodic characteristics, the dynamic threshold setting method is used for abnormal detection; if the monitoring data is data without periodic characteristics, the constant threshold setting method is used for abnormal detection.

In the above solution, the construction of an operation and maintenance fault diagnosis tree based on a multi-dimensional correlation analysis of abnormal indicator data, at least diagnosing and locating faults that generate abnormal indicator data, includes:

Perform correlation analysis and information drilling on the monitoring data from multiple dimensions to find the operation and maintenance indicators that have a strong correlation with the fault. Use a logic-based method to construct the operation and maintenance fault diagnosis tree. After selecting a system fault, The cause of the fault is to construct the fault branch of the operation and maintenance fault diagnosis tree according to the logical causality and build the tree from top to bottom. Calculate the fault correlation factor of the fault branch. If the fault correlation factor is higher than the threshold, the fault will be described. The branch is added to the fault tree; if the fault correlation factor is lower than a threshold, the fault branch is removed from the fault tree.

In the above solution, the fault display in a multi-dimensional visualization form includes:

The monitoring data of hardware, middleware and application systems in the grid dispatch control system are displayed visually.

In the above scheme, an alarm is generated when abnormal index data is detected;

Among them, when a constant threshold setting method is used for real-time anomaly detection, an alarm is generated when an index set for monitoring data exceeds a predetermined alarm threshold range;

When the dynamic threshold setting method is used for real-time anomaly detection, different alarm threshold ranges are calculated for monitoring data in different time periods; the monitoring data collected in one of the time periods exceeds the calculation for one of the time periods When the alarm threshold range is exceeded, an alarm is generated.

In the above scheme,

The monitoring data of the hardware device includes at least configuration information, status information, and performance information;

Among them, the configuration information includes at least: host name, manufacturer, device model, number of central processing unit (CPU) cores, CPU frequency, number of CPUs, virtual memory size, memory size, swap memory size, Internet Protocol IP address, network segment IP , Network card type, network card model, media access control MAC address, subnet mask, default gateway address, disk mount point, mount point path, partition size, total space of the file system, file system type, operating system vendor and Operating system version;

The status information includes at least: host running status, network port status, remote ping status, cluster running status, key process running status, power status, and fan status;

Performance information includes at least: CPU usage; disk capacity, disk usage, disk read and write rates; file system name, type, capacity, used space, remaining space, file system usage; memory capacity, memory usage; network link Speed, utilization; process ID, process user and CPU usage.

In the above scheme,

The hardware equipment acquisition scope includes at least:

a) host equipment;

b) network equipment;

c) storage equipment;

d) auxiliary equipment;

e) special power equipment;

The system software acquisition scope includes at least:

a) Power system energy efficiency management (EMS) system software;

b) database software;

c) World Wide Web Web middleware;

d) other automation system application software.

In the above scheme,

The monitoring data of the system software includes at least the data status, application status and key indicators;

a) Data status: data imbalance check status, data refresh status, and data transition status;

b) Application status: application running status, node application status, key process running status, database connection status, and front link running status;

c) Key indicators: availability of data acquisition and monitoring system (SCADA), pass rate of state estimation / convergence rate / measurement coverage, availability of remote control system at the plant, availability of remote control device, availability of PMU device at the factory, Plant station monitoring system availability and computer communication forwarding interruption time.

In the above scheme,

Set different collection frequencies according to the properties of the monitoring data, and collect monitoring data at the set collection frequency, where the attributes at least indicate the importance of the monitoring data;

Monitoring data collection must meet at least the following performance indicators:

a) Real-time data update performance: more than 20000 pieces / second;

b) Sampling success rate: greater than 99.5%;

c) Meet the requirements of 7 * 24 hours of uninterrupted operation, and the system performance during the operation remains unchanged.

The beneficial effects brought by the embodiments of the present application include:

The operation and maintenance method based on grid dispatching provided in the embodiments of the present application adopts a comprehensive collection of monitoring data, and the comprehensive monitoring data collection mainly includes monitoring data collection of business systems, basic software and hardware equipment, including index data (performance Information and status information) and log data. The real-time monitoring and alerting technology provided by the software and hardware operation and maintenance indicators in real-time monitoring, can automatically alert when an abnormal event occurs. Multi-dimensional correlation analysis technology based on artificial intelligence for massive operation and maintenance data, quickly digs the root cause of system failures and realizes automatic fault location.

In the embodiment of the present application, monitoring data (including index data and log data) is collected based on equipment such as business systems, basic software, and hardware, so as to realize comprehensive monitoring data collection.

In the embodiment of the present application, a constant threshold setting method and a dynamic threshold setting method are adopted. Through real-time monitoring of software and hardware operation and maintenance indicators, an automatic alarm can be performed when an abnormal event occurs.

In the embodiment of the present application, a multi-dimensional correlation analysis technology based on artificial intelligence based on massive operation and maintenance data is used to construct a fault diagnosis tree, which can quickly discover the root cause of system faults and realize automatic fault location.

The embodiments of the present application can realize intelligent monitoring of the operation status of the power grid control system, accurately grasp the changing trend of the operation status of the control system, comprehensively analyze the operation data of the control system, improve the ability of online diagnosis and fault location, and enhance the intelligent level of operation and maintenance of the control system. To ensure the safe and stable operation of the system.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a schematic diagram of an intelligent operation and maintenance architecture of a power grid dispatching control system of the present application;

FIG. 2 is a schematic diagram of storing monitoring data for this application;

FIG. 3 is a schematic diagram for implementing fault diagnosis and positioning in this application.

Detailed ways

The technical solution of the present application is described in further detail below with reference to the accompanying drawings and specific embodiments, so that those skilled in the art can better understand the application and implement it, but the examples given are not intended to limit the application.

As shown in Figure 1, first, comprehensive monitoring data collection is performed on hardware equipment, basic software, and business systems. The collected data includes index data and log data. Data is collected through a unified data centralized interface, and then data processing and data storage. For the real-time monitoring data, the constant threshold (setting) method and the dynamic threshold (setting) method are used for real-time monitoring and alarming according to the actual situation. For historical data, the artificial intelligence-based multi-dimensional correlation analysis technology is used to construct an operation and maintenance fault diagnosis tree to achieve fault diagnosis and location. The above monitoring data, monitoring alarms and other information can be displayed in multiple dimensions through a variety of visual graphics.

The embodiment of the present application provides an operation and maintenance method based on power grid dispatching, and particularly relates to a method for designing an intelligent operation and maintenance architecture based on power grid dispatching control system, including the following steps:

S1. Comprehensive collection of operation and maintenance monitoring data: at least the monitoring data of hardware equipment and system software of the power grid dispatching control system;

S2. Store monitoring data: at least according to the data type and / or data effectiveness of the collected monitoring data, determine the storage method of the collected monitoring data, and store the collected monitoring data in the determined storage method;

As shown in Figure 2, the collected monitoring data is divided into indicator data and log data according to different data types; real-time data and historical data are divided according to different data effectiveness; according to the data type and data effectiveness, the collected data can be collected The received monitoring data is divided into real-time index data and real-time log data; when data is stored, different storage methods are used according to different data; for example, real-time index data is stored in a time-series database; real-time log data is stored in full-text retrieval Library; historical monitoring data is stored in a columnar database. The historical data includes at least historical monitoring data.

This type of monitoring data is stored in different types of databases, such as time series data in a time series database; log data is stored in a full-text search database; the main considerations are:

Real-time indicator data is a series of data with time series characteristics. It can be considered that real-time indicator data is data with time stamp-time series data and should be stored in a time series database for storing time series data. The time-series database can store time-series data and support the fast writing and persistence of time-series data. Storing time-series data in the time-series database can realize the rapid writing of data and provide a certain basis for efficient operation and maintenance effects.

Log data is unstructured text data that should be stored in a full-text search library. The full-text search database can support real-time full-text search of unstructured data. Usually the full-text search database can carry a limited amount of data, so it can only store recent log data such as real-time log data.

For historical monitoring data, the quantity is large and needs to be stored in a large-capacity columnar database to facilitate subsequent large-scale data processing.

S3. Detect monitoring data to obtain abnormal index data;

Real-time monitoring and alarming: constant threshold (setting) method and dynamic threshold (setting) method are used for real-time monitoring and alarming to detect abnormal index data of the system;

In related technologies, the configuration of monitoring alarms basically depends on the experience of operation and maintenance engineers or continuous iterative corrections, or even pure manual troubleshooting. In the embodiments of the present application, the standardization and automation of operation and maintenance monitoring are achieved through the development of monitoring standards and automatic monitoring deployment, so as to implement intelligent alarms.

In steps S1 and S2, the operation status of the power grid dispatching control system is collected by collecting the operation monitoring data of the resource pool (physical machine, virtual machine, container, storage device, network device, security device), platform, database, and application system. Perform real-time monitoring and issue alarms based on corresponding alarm thresholds.

When the indicator data fluctuates in the system, it is necessary to first determine whether it is indeed an abnormal situation, and then implement an accurate alarm after determining the abnormality. There are two main methods for automatically detecting abnormal indicators of business: constant threshold setting method and dynamic threshold setting method.

It should be noted that if the monitoring data is data with strong periodic characteristics, the dynamic threshold setting method is preferably used; if the data has no periodic characteristics after analysis, the constant threshold setting method can be used.

Among them, the constant threshold setting method: when the index set on the server side exceeds a predetermined alarm threshold range, an automatic alarm is immediately issued. It can be understood that the constant threshold setting method is to set a fixed value (alarm threshold range) for one type of monitoring data within a period of time. An alarm is generated when it is detected that the monitoring data exceeds the alarm threshold range.

Dynamic threshold setting method: The threshold setting method of dynamic time window is adopted to solve the abnormal judgment of periodic data. The threshold setting method of the dynamic time window can be understood as follows. For different types of monitoring data collected, different alarm threshold ranges are automatically calculated according to the 3sigma principle. For monitoring data collected in different time periods, an alarm is generated when it exceeds the alarm threshold range calculated for the monitoring data for the corresponding time period. For example, the alarm threshold range calculated for monitoring data A based on the 3sigma principle during time period 1 is B1, and over time, the alarm threshold range calculated for monitoring data A according to the 3sigma principle during period 2 is B2 It can be understood that B2 calculated for the monitoring data A in the time period 2 should be an update of B1 according to the 3sigma principle.

S4. Based on multi-dimensional correlation analysis of abnormal index data, construct an operation and maintenance fault diagnosis tree, and at least diagnose and locate faults that generate abnormal index data;

Step S4 can be regarded as a solution for implementing fault diagnosis and positioning: a multi-dimensional association analysis technology based on artificial intelligence, constructing an operation and maintenance fault diagnosis tree, quickly digging the root cause of system faults, and achieving fault diagnosis and automatic positioning;

Facing the massive operation and maintenance data in the power grid dispatching control system, it is necessary to fully consider the time, space, and application of the multidimensional characteristics of the operation and maintenance data and the complex relationship between the operation and maintenance data and other factors, as shown in Figure 3. Dimensional correlation analysis, which analyzes the operation and maintenance data from multiple dimensions, drills down the information, finds the operation and maintenance indicators that have a strong correlation with the fault, and uses a logic-based method to construct the operation and maintenance fault diagnosis tree. Fault diagnosis tree is a kind of inverted tree-like logical causality diagram, which is used to describe the causality between various events in the system. After selecting a certain system fault, according to the cause of the fault, a fault branch is constructed according to the logical cause and effect relationship, and trees are built from top to bottom. In order to avoid the fault tree being too complicated, the fault correlation factor of the fault branch is calculated. If the fault correlation factor is higher than the threshold (which can be regarded as a fault threshold), it is added to the fault tree; if the fault correlation factor is lower than the threshold, the fault correlation factor is calculated. It is removed from the fault tree.

Among them, the O & M fault diagnosis tree is intuitive, clear, clear in thinking, and logical. Automatic positioning can improve the efficiency and accuracy of automatic fault location, improve the intelligent level of system operation and maintenance, and reduce system operation and maintenance costs.

S5. Display the monitoring data in a multi-dimensional visualization form.

The hardware, middleware, and application system monitoring data in the power grid dispatching control system are displayed visually through a variety of visualization methods (graphics, tables, dashboards), from the entire network to specific equipment, from comprehensive information display to important node monitoring, Multi-dimensional and multi-level visual display of operation and maintenance data enables regulators to quickly and accurately judge whether the automation equipment and systems of the entire network are operating normally from the visualized operation and maintenance graphics, and timely grasp the dangerous points of system operation and the existing operational risks. Handle faults and risks in a timely manner to improve operation and maintenance efficiency.

The hardware equipment collection range includes:

a) Host equipment, including servers and workstations

b) network equipment, including switches, firewalls and isolation devices;

c) Storage equipment, including disk arrays and fiber switches

d) auxiliary equipment, including system virtualization module (KVM), large screen;

e) Power-specific equipment, including forward and reverse isolation devices and vertical encryption authentication gateways.

The monitoring data of the hardware device includes configuration information, status information, and performance information;

a) Configuration information includes: host name, manufacturer, device model, number of CPU cores, CPU frequency, number of CPUs, virtual memory size (MB), memory size (KB), swap memory size (MB), IP address, network segment IP, NIC type, NIC model, MAC address, subnet mask, default gateway address, disk mount point, mount point path, partition size (MB), total space of the file system (MB), file system type, operation System manufacturer and operating system version;

b) Status information includes: host operating status, network port status (transmission control protocol (TCP) / user datagram protocol (UDP) port status and occupied processes), remote ping status, cluster operating status, critical process operating status, power supply And fan status, etc .;

c) Performance information includes: CPU usage; disk capacity, disk usage, read and write speed; file system name, type, capacity, used space, remaining space, file system usage; memory capacity, memory usage; network link Speed, utilization; process ID, process user, CPU usage.

The collection range of system software (basic software) includes:

a) EMS system software;

b) Database software, including Oracle, Dameng, Jincang;

c) Web middleware, supporting mainstream web middleware, such as Tomcat, Weblogic, etc .;

d) other automation system application software.

The monitoring data of the system software includes data status, application status and key indicators;

a) Data status: data imbalance check status (line, main transformer), data refresh status, data transition status;

b) Application status: application running status, node application status (on duty, standby), key process running status, database connection status, front link running status (communication with 104 links, 101 links, TASE.2 of each plant station) link);

c) Key indicators: SCADA availability rate, status estimation qualification rate / convergence rate / measurement coverage rate, plant station remote control system availability rate, telecontrol device availability rate, plant station PMU device availability rate, plant station monitoring system availability rate, Computer communication forwarding interruption time.

Different monitoring data and different collection frequencies are set according to different importance. The monitoring data collection meets the following performance indicators:

a) Real-time data update performance: more than 20000 pieces / second;

b) Sampling success rate: greater than 99.5%;

In the above solution, an integrated intelligent operation and maintenance solution for the power grid dispatching control system is provided. The overall architecture of the integrated intelligent operation and maintenance includes comprehensive monitoring data collection, monitoring data storage, real-time monitoring and alarming, fault diagnosis and positioning, and multidimensional visualization. The five-part display will improve system operation and maintenance efficiency and reduce operation and maintenance costs.

The above are only the preferred embodiments of the present application. It should be noted that, for those of ordinary skill in the art, without departing from the principles of the present application, several improvements and retouches can be made. These improvements and retouches should also be viewed as The scope of protection of this application.

Those skilled in the art should understand that the embodiments of the present application may be provided as a method, a system, or a computer program product. Therefore, this application may take the form of a hardware embodiment, a software embodiment, or an embodiment combining software and hardware aspects. Moreover, this application may take the form of a computer program product implemented on one or more computer-usable storage media (including, but not limited to, magnetic disk storage, optical storage, and the like) containing computer-usable program code.

This application is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present application. It should be understood that each process and / or block in the flowcharts and / or block diagrams, and combinations of processes and / or blocks in the flowcharts and / or block diagrams can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing device to produce a machine such that the instructions generated by the processor of the computer or other programmable data processing device are used to generate instructions Means for implementing the functions specified in one or more flowcharts and / or one or more blocks of the block diagrams.

These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to work in a specific manner such that the instructions stored in the computer-readable memory produce a manufactured article including an instruction device, the instructions The device implements the functions specified in one or more flowcharts and / or one or more blocks of the block diagram.

These computer program instructions can also be loaded onto a computer or other programmable data processing device, so that a series of steps can be performed on the computer or other programmable device to produce a computer-implemented process, which can be executed on the computer or other programmable device. The instructions provide steps for implementing the functions specified in one or more flowcharts and / or one or more blocks of the block diagrams.

The above descriptions are merely preferred embodiments of the present application, and are not intended to limit the protection scope of the present application.

Industrial applicability

The embodiment of the present application can monitor the running status of the entire system in real time, and effectively, accurately and timely evaluate the health status of software, hardware, such as hosts, storage, and applications, and provide effective information for system optimization and fault detection, and improve system operation and maintenance efficiency. .

Claims

An operation and maintenance method based on grid dispatching, characterized in that the method includes:

Collect at least monitoring data of hardware equipment and system software of the power grid dispatching control system;

Determine the storage method of the collected monitoring data based on at least the data type and / or timeliness of the collected monitoring data, and store the collected monitoring data in the determined storage method;

Detect monitoring data to obtain abnormal index data;

Based on multi-dimensional correlation analysis of abnormal indicator data, construct an operation and maintenance fault diagnosis tree, at least diagnose and locate faults that generate abnormal indicator data;

Display of monitoring data in multi-dimensional visualization.
The method according to claim 1, wherein the method comprises:

Storing the collected monitoring data in a corresponding database at least according to the data type and / or timeliness of the collected monitoring data;

Among them, according to the data type, the collected monitoring data is divided into index data and log data; according to the data effectiveness, the collected monitoring data is divided into real-time data and historical data; according to the data type and data effectiveness, The collected monitoring data is divided into real-time index data and real-time log data;

At least the monitoring data divided into real-time index data is stored in a time-series database; the monitoring data divided into real-time log data is stored in a full-text search database; the monitoring data divided into historical monitoring data is stored in a columnar database; among them, The historical data includes at least historical monitoring data.
The method according to claim 1, wherein the method comprises:

Real-time anomaly detection of monitoring data using constant threshold setting method and dynamic threshold setting method;

Among them, if the monitoring data is data with periodic characteristics, the dynamic threshold setting method is used for abnormal detection; if the monitoring data is data without periodic characteristics, the constant threshold setting method is used for abnormal detection.
The method according to claim 1, wherein the constructing an operation and maintenance fault diagnosis tree based on multi-dimensional correlation analysis of abnormal index data, at least diagnosing and locating faults that generate abnormal index data, comprises:

Perform correlation analysis and information drilling on the monitoring data from multiple dimensions to find the operation and maintenance indicators that have a strong correlation with the fault. Use a logic-based method to construct the operation and maintenance fault diagnosis tree. After selecting a system fault, The cause of the fault is to construct the fault branch of the operation and maintenance fault diagnosis tree according to the logical causality and build the tree from top to bottom. Calculate the fault correlation factor of the fault branch. If the fault correlation factor is higher than the threshold, the fault will be described. The branch is added to the fault tree; if the fault correlation factor is lower than a threshold, the fault branch is removed from the fault tree.
The method according to claim 1, wherein the displaying the fault in a multi-dimensional visualization form comprises:

The monitoring data of hardware, middleware and application systems in the grid dispatch control system are displayed visually.
The method according to claim 3, wherein an alarm is generated when abnormal data is detected;

Among them, when a constant threshold setting method is used for real-time anomaly detection, an alarm is generated when an index set for monitoring data exceeds a predetermined alarm threshold range;

When the dynamic threshold setting method is used for real-time anomaly detection, different alarm threshold ranges are calculated for monitoring data in different time periods; the monitoring data collected in one of the time periods exceeds the calculation for one of the time periods When the alarm threshold range is exceeded, an alarm is generated.
The method according to any one of claims 1 to 6, characterized in that:

The monitoring data of the hardware device includes at least configuration information, status information, and performance information;

Among them, the configuration information includes at least: host name, manufacturer, device model, central processing unit CPU core number, CPU frequency, CPU number, virtual memory size, memory size, swap memory size, Internet Protocol IP address, network segment IP, network card Type, network card model, media access control MAC address, subnet mask, default gateway address, disk mount point, mount point path, partition size, total file system space, file system type, operating system vendor and operating system version;

The status information includes at least: host running status, network port status, remote ping status, cluster running status, key process running status, power status, and fan status;

Performance information includes at least: CPU usage; disk capacity, disk usage, disk read and write rates; file system name, type, capacity, used space, remaining space, file system usage; memory capacity, memory usage; network link Speed, utilization; process ID, process user and CPU usage.
The method according to any one of claims 1 to 6, characterized in that:

The hardware equipment acquisition scope includes at least:

a) host equipment;

b) network equipment;

c) storage equipment;

d) auxiliary equipment;

e) special power equipment;

The system software acquisition scope includes at least:

a) EMS software for power system energy efficiency management;

b) database software;

c) World Wide Web Web middleware;

d) other automation system application software.
The method according to any one of claims 1 to 6, characterized in that:

The monitoring data of the system software includes at least the data status, application status and key indicators;

a) Data status: data imbalance check status, data refresh status, and data transition status;

b) Application status: application running status, node application status, key process running status, database connection status, and front link running status;

c) Key indicators: SCADA availability rate of data acquisition and monitoring system, qualified rate of state estimation / convergence rate / measurement coverage rate, availability rate of plant remote control system, availability rate of remote control device, availability rate of plant PMU device, plant Monitor system availability and computer communication interruption time.
The method according to claim 1, wherein:

Set different collection frequencies according to the properties of the monitoring data, and collect monitoring data at the set collection frequency, where the attributes at least indicate the importance of the monitoring data;

Monitoring data collection must meet at least the following performance indicators:

a) Real-time data update performance: more than 20000 pieces / second;

b) Sampling success rate: greater than 99.5%;

c) Meet the requirements of 7 * 24 hours of uninterrupted operation, and the system performance during the operation remains unchanged.