CN106789239A - Towards the information application system failure trend prediction method and device of power business - Google Patents
Towards the information application system failure trend prediction method and device of power business Download PDFInfo
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Abstract
The present invention relates to a kind of computer realm, more particularly to a kind of information application system failure trend prediction method and device towards power business.Method includes being monitored at least one equipment included in information application system, and obtains Monitoring Data；Using default failure trend prediction rule, the Monitoring Data to getting carries out data processing, obtains corresponding fault trend information；The fault trend information is carried out into visual presentation in given display device.Device includes：Monitoring modular, for being monitored at least one equipment included in information application system, and obtains Monitoring Data；Processing module, for using default failure trend prediction rule, the Monitoring Data to getting to carry out data processing, obtains corresponding fault trend information；Display module, for the fault trend information to be carried out into visual presentation in given display device.
Description
Technical Field
The invention relates to the field of computers, in particular to a method and a device for predicting a fault trend of an information application system facing power business.
Background
With the continuous promotion of the informatization construction of the national network company, the types and the number of information systems are continuously increased, the requirements on the safe and reliable operation of the information systems are continuously improved, and the national network company uniformly constructs an IMS (IP Multimedia Subsystem) system to strengthen the centralized monitoring capability on the operation condition of the information systems. Especially, when a dispatcher is on duty at night and a major fault occurs in an information application system, operation and maintenance personnel need a certain time to arrive at the site to remove the fault. In order to further improve the reliable operation and maintenance management level of the information application and improve the safe and reliable operation guarantee capability of the information system, the operation monitoring analysis and emergency disposal mechanism of the existing information application needs to be innovated and improved by actively researching and applying the information technology in combination with the actual situation of a company information dispatching and transportation system.
Review of research level at home and abroad:
1) foreign research level:
the data center is a complex set of facilities, which not only comprises an information system and other matched servers, communication, storage and other equipment, but also comprises redundant data communication connection, environment control equipment, monitoring equipment and various safety devices. For common monitoring requirements, the most ideal monitoring effect cannot be achieved by a basic monitoring method.
The operation and maintenance monitoring object of the information system mainly comprises a host and a network, and the host monitoring can be divided into application layer monitoring, service layer monitoring, server layer monitoring and network interface layer monitoring. An information system network is in fact a collection of different devices, routers, switches, firewalls, etc. can be considered as special "servers", and the connections between them constitute the network. Thus, the network monitoring object is actually a device based on the network environment.
At present, the mainstream commercial IT monitoring tool products abroad include IBM Tivoli, HP Open View, microsoft sccm, BMC Patrol, CA Unicenter, etc., and the commercial products are expensive, generally hundreds of thousands to millions, and the functions are not easy to customize and expand. Open source IT monitoring technologies include Cacti, Nagios, Zenoss, Zabbix, Hyperic HQ and the like, are provided in a free form, can effectively monitor the host states of Windows, Linux and Unix, network devices such as switches and routers and the like, can support protocols such as WMI, PerfMon, SNMP, JMX, HTTP, Telnet, SSH, Syslog, ICMP, FTP, SMTP and the like, and generally lack friendly user interfaces.
2) Level of domestic research
In recent years, the development of IT monitoring theory and technology research field in China is fast, based on the open source IT monitoring technology, domestic commercial IT monitoring tool products and solutions are rapidly developed, and more mature products comprise IT operation and maintenance monitoring management systems of companies such as North Tower, east China, China Taiyue, Moka and Taihao.
Even if the existing information application system can realize fault detection, the corresponding fault can be detected only after the fault occurs, reliable fault trend prediction cannot be realized, and the function of 'warning in advance' cannot be realized.
Disclosure of Invention
In view of the above, the present invention is proposed so as to provide a power service oriented information application system failure trend prediction method and apparatus that overcomes or at least partially solves the above problems.
The method for predicting the fault trend of the information application system facing the power service is characterized by comprising the following steps:
monitoring at least one device contained in the information application system, and acquiring monitoring data;
performing data processing on the obtained monitoring data by adopting a preset fault trend prediction rule to obtain corresponding fault trend information;
and visually displaying the fault trend information on a specified display device.
The at least one device comprises: any one or more of a server, a storage device, a switch, and a router node designated in the information application system;
the monitoring data comprises network interface layer data, server layer data, service layer data and application layer data; wherein,
the data of the network interface layer comprises an IP address, an MAC address, a routing table, a port survival state and uplink and downlink flow;
the server layer data comprises CPU load, memory occupancy rate, process state and disk I/O;
the service layer data comprises middleware and state data of database platform software;
the application layer data includes performance state data of the information application system.
When adopting intelligent agent's distributed monitoring mode, install intelligent monitoring agent SMA on every monitored equipment, monitor at least one equipment that contains in the information application system to acquire monitoring data, include:
the intelligent monitoring agent SMA monitors at least one device contained in the information application system to obtain monitoring data;
the monitoring server acquires the monitoring data monitored by the intelligent monitoring agent SMA, and the monitoring server periodically and alternately patrols the intelligent monitoring agent SMA according to a set time interval so as to acquire the monitoring data monitored by the intelligent monitoring agent SMA;
and the monitoring server acquires the monitoring data transmitted between the intelligent monitoring agents SMA through an XML format.
The monitoring at least one device included in the information application system and acquiring monitoring data further includes: the intelligent monitoring agent SMA establishes heartbeat connection with the monitoring server; when the monitoring server monitors that the intelligent monitoring agent SMA heartbeat connection is overtime, obtaining that equipment corresponding to the intelligent monitoring agent SMA is out of order and generating a corresponding fault message; wherein the fault message is contained within the monitoring data;
when a network monitoring mode of an SNMP protocol is adopted, the monitoring at least one device included in an information application system and acquiring monitoring data includes: monitoring the network performance and network errors of at least one device contained in the information application system, and acquiring monitoring data;
when adopting intelligent agent's host computer fault diagnosis monitoring mode, when installing intelligent monitoring agent SMA on every monitored equipment, monitor at least one equipment that contains in the information application system to obtain the monitoring data, include: the intelligent monitoring agent SMA monitors at least one device contained in the information application system according to a specified monitoring strategy; the monitoring main server receives an alarm or fault message sent by the intelligent monitoring agent SMA when the intelligent monitoring agent SMA monitors that the equipment operates abnormally; wherein the alarm or fault message is contained in the monitoring data;
the monitoring at least one device included in the information application system and acquiring monitoring data further includes: the intelligent monitoring agent SMA establishes heartbeat connection with the monitoring main server; when the monitoring main server monitors that the intelligent monitoring agent SMA heartbeat connection is overtime, obtaining that equipment corresponding to the intelligent monitoring agent SMA is out of order and generating a corresponding fault message; wherein the fault message is contained within the monitoring data;
the method for acquiring the fault trend information comprises the following steps of adopting a preset fault trend prediction rule to perform data processing on the acquired monitoring data to acquire corresponding fault trend information:
performing data processing on the acquired monitoring data by adopting a preset linear regression algorithm and an exponential regression algorithm to obtain corresponding fault future trend information;
performing data processing on the obtained monitoring data by adopting a preset trigonometric function regression algorithm to obtain corresponding fault periodic trend information;
wherein the fault trend information comprises fault future trend information and fault periodic trend information.
The method for processing the acquired monitoring data by adopting a preset trigonometric function regression algorithm to obtain corresponding fault periodicity trend information comprises the following steps:
taking out the last acquired state factor parameter value in the monitoring data state factor parameter sequence and m-1 state factor parameter values before the last acquired state factor parameter value to perform periodic analysis, calculating the periodic parameter of the state factor parameter value change in the period of time according to the acquired state factor parameter value to obtain a periodic regression analysis function, and then drawing a periodic curve of the state factor parameter value change according to the function;
the periodicity analysis algorithm is specifically as follows: the collected state factor parameter series is { y1, y2, … …, yn }, the collection time series is { t1, t2, … …, tn }, and the regression function of the trigonometric function is adopted as follows:
where k is a preset fractional number for controlling the precision of the trigonometric periodic regression, m is the magnitude of the state factor parameter sequence, ej(j ═ 0,1,. times, k) and fj(j ═ 1, 2.. times, k) are parameters of the periodic regression function of the trigonometric function, wherein the parameter calculation method is as follows:
and after each analysis is finished, continuously acquiring the state factor parameter value of the next period, putting the state factor parameter value at the tail of the state factor parameter sequence, deleting the state factor parameter value acquired earliest in the original state factor parameter sequence, and keeping the size of the state factor parameter sequence as m.
An information application system failure trend prediction device, comprising:
the monitoring module is used for monitoring at least one device contained in the information application system and acquiring monitoring data;
the processing module is used for carrying out data processing on the acquired monitoring data by adopting a preset fault trend prediction rule to obtain corresponding fault trend information;
and the display module is used for visually displaying the fault trend information on the appointed display equipment.
When the distributed monitoring mode of intelligent agent is adopted, when intelligent monitoring agent SMA is installed on each monitored equipment, the monitoring module comprises:
the intelligent monitoring agent SMA is used for monitoring at least one device contained in the information application system to obtain monitoring data;
the monitoring server is used for acquiring the monitoring data monitored by the intelligent monitoring agent SMA;
and the monitoring server acquires the monitoring data transmitted between the intelligent monitoring agents SMA through an XML format.
The intelligent monitoring agent SMA is also used for establishing heartbeat connection with the monitoring server; the monitoring server is further used for obtaining that equipment corresponding to the intelligent monitoring agent SMA fails and generating corresponding failure information when monitoring that the heartbeat connection of the intelligent monitoring agent SMA is overtime; wherein the fault message is contained within the monitoring data;
when a network monitoring mode of an SNMP protocol is adopted, the monitoring module is specifically configured to: monitoring the network performance and network errors of at least one device contained in the information application system, and acquiring monitoring data;
when adopting intelligent agent's host computer fault diagnosis monitoring mode, install intelligent monitoring agent SMA on every monitored equipment, monitoring module includes: the intelligent monitoring agent SMA is used for monitoring at least one device contained in the information application system according to a specified monitoring strategy; the monitoring main server is used for receiving an alarm or fault message sent by the intelligent monitoring agent SMA when the intelligent monitoring agent SMA monitors that the equipment operates abnormally; wherein the alarm or fault message is contained in the monitoring data;
the intelligent monitoring agent SMA is also used for establishing heartbeat connection with the monitoring main server; the monitoring main server is also used for monitoring the heartbeat connection timeout of the intelligent monitoring agent SMA, obtaining the fault of the equipment corresponding to the intelligent monitoring agent SMA and generating a corresponding fault message; wherein the fault message is contained within the monitoring data;
the processing module comprises:
the first processing unit is used for performing data processing on the acquired monitoring data by adopting a preset linear regression algorithm and an exponential regression algorithm to obtain corresponding fault future trend information;
the second processing unit is used for performing data processing on the acquired monitoring data by adopting a preset trigonometric function regression algorithm to obtain corresponding fault periodic trend information;
wherein the fault trend information comprises fault future trend information and fault periodic trend information.
The second processing unit is specifically configured to:
taking out the last acquired state factor parameter value in the monitoring data state factor parameter sequence and m-1 state factor parameter values before the last acquired state factor parameter value to perform periodic analysis, calculating the periodic parameter of the state factor parameter value change in the period of time according to the acquired state factor parameter value to obtain a periodic regression analysis function, and then drawing a periodic curve of the state factor parameter value change according to the function;
the periodicity analysis algorithm is specifically as follows: the collected state factor parameter series is { y1, y2, … …, yn }, the collection time series is { t1, t2, … …, tn }, and the regression function of the trigonometric function is adopted as follows:
where k is a preset fractional number for controlling the precision of the trigonometric periodic regression, m is the magnitude of the state factor parameter sequence, ej(j ═ 0,1,. times, k) and fj(j ═ 1, 2.. times, k) are parameters of the periodic regression function of the trigonometric function, wherein the parameter calculation method is as follows:
and after each analysis is finished, continuously acquiring the state factor parameter value of the next period, putting the state factor parameter value at the tail of the state factor parameter sequence, deleting the state factor parameter value acquired earliest in the original state factor parameter sequence, and keeping the size of the state factor parameter sequence as m.
By the technical scheme, the technical scheme provided by the embodiment of the invention at least has the following advantages:
according to the technical scheme provided by the embodiment of the invention, the equipment contained in the information application system is monitored, the preset fault trend prediction rule is adopted, the obtained monitoring data is subjected to data processing, the corresponding fault trend information is obtained, the accurate prediction of the fault trend, namely 'advance warning' is realized, and the safe and reliable operation guarantee capability of the information system is further improved.
The foregoing description is only an overview of the technical solutions of the present invention, and in order to make the technical solutions of the present invention more clearly understood and to implement them in accordance with the contents of the description, the following detailed description is given with reference to the preferred embodiments of the present invention and the accompanying drawings.
Drawings
Various other advantages and benefits will become apparent to those of ordinary skill in the art upon reading the following detailed description of the preferred embodiments. The drawings are only for purposes of illustrating the preferred embodiments and are not to be construed as limiting the invention. Also, like reference numerals are used to refer to like parts throughout the drawings. In the drawings:
FIG. 1 is a flow chart illustrating a method for predicting failure trend of an information application system according to the present invention;
fig. 2 is a schematic structural diagram of the information application system failure trend prediction device of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, but not all, embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Before explaining the technical solutions provided by the present invention in detail, the basic concept of the present invention will be described first. The principle framework of the technical scheme provided by the embodiment of the invention comprises an acquisition layer, a data layer and a display layer from bottom to top respectively. The acquisition layer is responsible for collecting state data of each layer of nodes such as designated servers, managed switches, routers and the like in the network in real time through protocols such as SNMP, WMI and the like. The data layer is responsible for storing and further processing the monitoring data collected by the collection layer, such as early warning and warning calculation according to a formula or a model. The display layer is responsible for providing monitoring data for the data layer and visually displaying the processing result of the data.
The monitoring data of the information application system can be divided into four levels of categories of a network interface layer, a server layer, a service layer and an application layer. The network interface layer mainly comprises host network state data, such as IP addresses, MAC addresses, port survival, uplink and downlink flow, speed, routing tables, network card transmission/packet/bad packet flow and the like. The server layer mainly comprises a host BIOS and operating system state data, including operating system/temperature/fan/voltage/server state, CPU/load/memory/disk/IO use condition, installed hardware and software information and the like. The service layer mainly comprises state data of platform software such as middleware and databases, and comprises service ports/service processes, IIS/Apache/Webloglc, Mssql/Mysql/Oracle/DB2 and other application services. The application layer mainly aims at the state data of the availability, the performance and the like of the business application system, and comprises the performance based on user access, such as WEB page access response time and the like.
As shown in fig. 1, a flow diagram of the method for predicting the failure trend of the information application system of the present invention is shown.
S1: the acquisition layer collects state monitoring data of all layers of a designated server, storage equipment, a managed switch and a router node in a network in real time through a communication protocol;
s2: the data layer stores the monitoring data collected by the collection layer, and performs fault detection analysis and fault trend prediction analysis;
s3: the display layer provides monitoring data for the data layer and visually displays the processing result of the data.
The execution subject of the method provided by this embodiment may be a hardware device capable of implementing the method provided by this embodiment, and/or an application installed on the hardware device. Specifically, the method provided in this embodiment includes:
step 101, monitoring at least one device included in an information application system, and acquiring monitoring data.
Wherein the at least one device may include: any one or more of servers, storage devices, switches, and router nodes designated in the information application system. That is, when there is one device under test, the device may be any one of the above devices, and when there are a plurality of devices under test, the device may be any plurality of the above devices.
The monitoring data comprises network interface layer data, server layer data, service layer data and application layer data, wherein the network interface layer data comprises host network state data comprising an IP address, an MAC address, a routing table, a port survival state and uplink and downlink flow; the server layer data comprises host BIOS and operating system state data, including CPU load, memory occupancy rate, process state and disk I/O; the service layer data comprises middleware and state data of database platform software; the application layer data includes availability, performance status data of the information application system.
Specifically, the present embodiment can be implemented by different methods according to different monitoring modes:
(1) distributed monitoring mode adopting intelligent agent
Namely, when the distributed monitoring mode of intelligent agent is adopted, and intelligent monitoring agent SMA is installed on each monitored device, at least one device contained in the information application system is monitored, and monitoring data is obtained, which includes:
step S11, the intelligent monitoring agent SMA monitors at least one device contained in the information application system to obtain monitoring data.
And step S12, the monitoring server side acquires the monitoring data monitored by the intelligent monitoring agent SMA.
And the monitoring server acquires the monitoring data transmitted between the intelligent monitoring agents SMA through an XML format.
Based on a distributed monitoring structure, an intelligent monitoring agent SMA is installed on each cluster computer. The intelligent monitoring agent SMA collects the working state information of the computer, and installs the operation monitoring server on the monitoring host; the intelligent monitoring agent SMA and the monitoring server side transmit monitoring data through an XML format, the monitoring server side periodically and cyclically patrols the intelligent monitoring agent SMA to obtain monitoring information, and the monitoring host detects the running state of any computer in the cluster by heartbeat.
Namely, further, the above steps: the monitoring server side acquires the monitoring data monitored by the intelligent monitoring agent SMA, and the monitoring data can specifically be: and the monitoring server periodically and alternately patrols the intelligent monitoring agent SMA according to a set time interval so as to acquire the monitoring data monitored by the intelligent monitoring agent SMA.
Further, the steps are as follows: the monitoring at least one device included in the information application system and acquiring monitoring data may further include:
and step S13, the intelligent monitoring agent SMA establishes heartbeat connection with the monitoring server.
And step S14, when the monitoring server monitors that the intelligent monitoring agent SMA heartbeat connection is overtime, obtaining that equipment corresponding to the intelligent monitoring agent SMA is out of order and generating a corresponding fault message.
Wherein the fault message is contained within the monitoring data.
(2) Network monitoring mode using SNMP protocol
When a network monitoring mode of an SNMP protocol is adopted, the monitoring at least one device included in an information application system and acquiring monitoring data includes:
and monitoring the network performance and network errors of at least one device contained in the information application system, and acquiring monitoring data.
In specific implementation, the network monitoring function based on the SNMP comprises monitoring network performance, detecting and analyzing network errors and configuring network equipment, and when the network works normally, the SNMP realizes the functions of statistics, configuration and test; in the event of a network failure, various error monitoring and recovery functions are implemented.
(3) Host fault diagnosis monitoring mode adopting intelligent agent
When adopting intelligent agent's host computer fault diagnosis monitoring mode, when installing intelligent monitoring agent SMA on every monitored equipment, monitor at least one equipment that contains in the information application system to obtain the monitoring data, include:
and step S21, the intelligent monitoring agent SMA monitors at least one device contained in the information application system according to the specified monitoring strategy.
And step S22, the monitoring main server receives an alarm or a fault message sent by the intelligent monitoring agent SMA when the intelligent monitoring agent SMA monitors that the equipment operates abnormally.
Wherein the alarm or fault message is included in the monitoring data.
Further, the monitoring at least one device included in the information application system and acquiring the monitoring data may further include:
and step S23, the intelligent monitoring agent SMA establishes heartbeat connection with the monitoring main server.
And step S24, when the monitoring master server monitors that the intelligent monitoring agent SMA heartbeat connection is overtime, obtaining that equipment corresponding to the intelligent monitoring agent SMA is out of order and generating a corresponding failure message.
Wherein the fault message is contained within the monitoring data.
And 102, performing data processing on the acquired monitoring data by adopting a preset fault trend prediction rule to obtain corresponding fault trend information.
In specific implementation, the step 102 can be implemented by the following method:
firstly, a preset linear regression algorithm and an exponential regression algorithm are adopted to perform data processing on the acquired monitoring data to obtain corresponding fault future trend information.
And then, performing data processing on the acquired monitoring data by adopting a preset trigonometric function regression algorithm to obtain corresponding fault periodic trend information.
Wherein the fault trend information comprises fault future trend information and fault periodic trend information.
More specifically, the above-mentioned performing data processing on the obtained monitoring data by using a preset linear regression algorithm and an exponential regression algorithm to obtain corresponding future fault trend information may include:
(1) the linear regression algorithm
Collecting historical data of various faults of the information application system by taking monitoring data related to the faults in the information application system as a sample data set of a linear regression algorithm, wherein the historical data comprises specific time of the various faults, the frequency of the faults within a period of time and corresponding state factor data when the faults occur each time;
the linear regression algorithm model is as follows: y ═ a + b1x1+ b2x2+ b3x3+ …;
wherein y is a dependent variable and is also a future trend of the fault of the prediction object; x1, x2 and x3 are independent variables, are monitoring data related to faults in the information application system, namely fault state factors, and are related factors of y; a is linear regression coefficient, and b1, b2, b3 are linear partial regression coefficient.
Performing partial correlation analysis, namely determining main faults expected to occur in the future period set by the information application system, wherein the main faults are faults of which any two partial correlation coefficients are more than or equal to-1 and less than or equal to 1;
respectively establishing a mapping relation equation of the fault and the state factor data for each main fault determined in the step two by adopting a stepwise regression method, carrying out F inspection, if the significance level P cannot meet the condition that P is less than a set threshold value, rejecting the main fault, and otherwise, keeping the mapping relation equation of the fault and the state factor data established by the main fault;
predicting the state factor parameter values of the monitoring data of the information application system in the set future period, and substituting the predicted state factor parameter values into the reserved mapping relation equation of the fault and the state factor data to obtain the probability value of the occurrence of the corresponding fault and the future trend information of the fault;
(2) the exponential regression algorithm
Calculating the predicted values of the monitoring data in a plurality of periods in the future of the information application system by utilizing a preset exponential regression algorithm according to the acquired parameter sequence values of the state factors of the monitoring data:
the collected state factor parameter series is { y }1，y2，……，ynThe acquisition time sequence is { t }1，t2，……，tnThe exponential regression function adopted is: y is cedt；
Wherein c and d are parameters of an exponential regression function, and the parameter calculation method comprises the following steps:
wherein,
and calculating the predicted value of the state factor parameter of a future period according to the parameter calculation result by adopting the following formula:
the above-mentioned adopting the preset trigonometric function regression algorithm, performing data processing on the obtained monitoring data, and obtaining corresponding fault periodicity trend information may include:
taking out the last acquired state factor parameter value in the monitoring data state factor parameter sequence and m-1 state factor parameter values before the last acquired state factor parameter value to perform periodic analysis, calculating the periodic parameter of the state factor parameter value change in the period of time according to the acquired state factor parameter value to obtain a periodic regression analysis function, and then drawing a periodic curve of the state factor parameter value change according to the function;
the periodicity analysis algorithm is specifically as follows: the collected state factor parameter series is { y1, y2, … …, yn }, the collection time series is { t1, t2, … …, tn }, and the regression function of the trigonometric function is adopted as follows:
where k is a preset fractional number for controlling the precision of the trigonometric periodic regression, m is the magnitude of the state factor parameter sequence, ej(j ═ 0,1,. times, k) and fj(j ═ 1, 2.. times, k) are parameters of the periodic regression function of the trigonometric function, wherein the parameter calculation method is as follows:
and after each analysis is finished, continuously acquiring the state factor parameter value of the next period, putting the state factor parameter value at the tail of the state factor parameter sequence, deleting the state factor parameter value acquired earliest in the original state factor parameter sequence, and keeping the size of the state factor parameter sequence as m.
And 103, visually displaying the fault trend information on a specified display device.
According to the technical scheme provided by the embodiment, the equipment contained in the information application system is monitored, the preset fault trend prediction rule is adopted, the obtained monitoring data is subjected to data processing, the corresponding fault trend information is obtained, the accurate prediction of the fault trend is realized, namely 'advance warning', and the safe and reliable operation guarantee capability of the information system is further improved.
It should be noted that: while, for purposes of simplicity of explanation, the foregoing method embodiments have been described as a series of acts or combination of acts, it will be appreciated by those skilled in the art that the present invention is not limited by the illustrated ordering of acts, as some steps may occur in other orders or concurrently with other steps in accordance with the invention. Further, those skilled in the art should also appreciate that the embodiments described in the specification are preferred embodiments and that the acts and modules referred to are not necessarily required by the invention.
As shown in fig. 2, the structure of the device for predicting the failure trend of the information application system of the present invention is schematically illustrated. The device provided by the embodiment can realize the method provided by the first embodiment. Specifically, the apparatus provided in this embodiment includes:
the monitoring module 1 is used for monitoring at least one device contained in the information application system and acquiring monitoring data;
the processing module 2 is used for performing data processing on the acquired monitoring data by adopting a preset fault trend prediction rule to obtain corresponding fault trend information;
and the display module 3 is used for visually displaying the fault trend information on the appointed display equipment.
According to the technical scheme provided by the embodiment, the equipment contained in the information application system is monitored, the preset fault trend prediction rule is adopted, the obtained monitoring data is subjected to data processing, the corresponding fault trend information is obtained, the accurate prediction of the fault trend is realized, namely 'advance warning', and the safe and reliable operation guarantee capability of the information system is further improved.
Further, when adopting intelligent agent's distributed monitoring mode, install intelligent monitoring agent SMA on every monitored equipment, monitoring module, include:
the intelligent monitoring agent SMA is used for monitoring at least one device contained in the information application system to obtain monitoring data;
the monitoring server is used for acquiring the monitoring data monitored by the intelligent monitoring agent SMA;
and the monitoring server acquires the monitoring data transmitted between the intelligent monitoring agents SMA through an XML format.
Further, the monitoring server is specifically configured to:
and periodically polling the intelligent monitoring agent SMA according to a set time interval so as to obtain the monitoring data monitored by the intelligent monitoring agent SMA.
Further, the intelligent monitoring agent SMA is also used for establishing heartbeat connection with the monitoring server;
the monitoring server is further used for obtaining that equipment corresponding to the intelligent monitoring agent SMA fails and generating corresponding failure information when monitoring that the heartbeat connection of the intelligent monitoring agent SMA is overtime;
wherein the fault message is contained within the monitoring data.
Further, when a network monitoring mode of the SNMP protocol is adopted, the monitoring module is specifically configured to:
and monitoring the network performance and network errors of at least one device contained in the information application system, and acquiring monitoring data.
Further, when adopting intelligent agent's host computer fault diagnosis monitoring mode, when installing intelligent monitoring agent SMA on every monitored equipment, monitoring module includes:
the intelligent monitoring agent SMA is used for monitoring at least one device contained in the information application system according to a specified monitoring strategy;
the monitoring main server is used for receiving an alarm or fault message sent by the intelligent monitoring agent SMA when the intelligent monitoring agent SMA monitors that the equipment operates abnormally;
wherein the alarm or fault message is included in the monitoring data.
Furthermore, the intelligent monitoring agent SMA is also used for establishing heartbeat connection with the monitoring main server;
the monitoring main server is also used for monitoring the heartbeat connection timeout of the intelligent monitoring agent SMA, obtaining the fault of the equipment corresponding to the intelligent monitoring agent SMA and generating a corresponding fault message;
wherein the fault message is contained within the monitoring data.
Further, the processing module includes:
the first processing unit is used for performing data processing on the acquired monitoring data by adopting a preset linear regression algorithm and an exponential regression algorithm to obtain corresponding fault future trend information;
the second processing unit is used for performing data processing on the acquired monitoring data by adopting a preset trigonometric function regression algorithm to obtain corresponding fault periodic trend information;
wherein the fault trend information comprises fault future trend information and fault periodic trend information.
Further, the first processing unit is specifically configured to:
(1) the linear regression algorithm
Collecting historical data of various faults of the information application system by taking monitoring data related to the faults in the information application system as a sample data set of a linear regression algorithm, wherein the historical data comprises specific time of the various faults, the frequency of the faults within a period of time and corresponding state factor data when the faults occur each time;
performing partial correlation analysis, namely determining main faults expected to occur in a future period set by the information application system, wherein the main faults are faults of which any two partial correlation coefficients are greater than or equal to-1 and less than or equal to 1;
respectively establishing a mapping relation equation of the fault and the state factor data for each determined main fault by adopting a stepwise regression method, carrying out F test, if the significance level P cannot meet that P is less than a set threshold value, rejecting the main fault, and otherwise, keeping the mapping relation equation of the fault and the state factor data established by the main fault;
predicting the monitoring data state factor parameter value of the information application system in the set future period, and substituting the predicted state factor parameter value into the reserved mapping relation equation of the fault and the state factor data to obtain the probability value of the occurrence of the corresponding fault and the future trend information of the fault;
(2) the exponential regression algorithm
Calculating the predicted values of the monitoring data in a plurality of periods in the future of the information application system by utilizing a preset exponential regression algorithm according to the acquired parameter sequence values of the state factors of the monitoring data:
the collected state factor parameter series is { y }1，y2，……，ynThe acquisition time sequence is { t }1，t2，……，tnThe exponential regression function adopted is: y is cedt；
Wherein c and d are parameters of an exponential regression function, and the parameter calculation method comprises the following steps:
wherein,
and calculating the predicted value of the state factor parameter of a future period according to the parameter calculation result by adopting the following formula:
further, the second processing unit is specifically configured to:
taking out the last acquired state factor parameter value in the monitoring data state factor parameter sequence and m-1 state factor parameter values before the last acquired state factor parameter value to perform periodic analysis, calculating the periodic parameter of the state factor parameter value change in the period of time according to the acquired state factor parameter value to obtain a periodic regression analysis function, and then drawing a periodic curve of the state factor parameter value change according to the function;
the periodicity analysis algorithm is specifically as follows: the collected state factor parameter series is { y1, y2, … …, yn }, the collection time series is { t1, t2, … …, tn }, and the regression function of the trigonometric function is adopted as follows:
where k is a preset fractional number for controlling the precision of the trigonometric periodic regression, m is the magnitude of the state factor parameter sequence, ej(j ═ 0,1,. times, k) and fj(j ═ 1, 2.. times, k) are parameters of the periodic regression function of the trigonometric function, wherein the parameter calculation method is as follows:
and after each analysis is finished, continuously acquiring the state factor parameter value of the next period, putting the state factor parameter value at the tail of the state factor parameter sequence, deleting the state factor parameter value acquired earliest in the original state factor parameter sequence, and keeping the size of the state factor parameter sequence as m.
In the foregoing embodiments, the descriptions of the respective embodiments have respective emphasis, and for parts that are not described in detail in a certain embodiment, reference may be made to related descriptions of other embodiments.
Claims (9)
1. The method for predicting the fault trend of the information application system facing the power service is characterized by comprising the following steps:
monitoring at least one device contained in the information application system, and acquiring monitoring data;
performing data processing on the obtained monitoring data by adopting a preset fault trend prediction rule to obtain corresponding fault trend information;
and visually displaying the fault trend information on a specified display device.
2. The method of claim 1, wherein the at least one device comprises: any one or more of a server, a storage device, a switch, and a router node designated in the information application system;
the monitoring data comprises network interface layer data, server layer data, service layer data and application layer data; wherein,
the data of the network interface layer comprises an IP address, an MAC address, a routing table, a port survival state and uplink and downlink flow;
the server layer data comprises CPU load, memory occupancy rate, process state and disk I/O;
the service layer data comprises middleware and state data of database platform software;
the application layer data includes performance state data of the information application system.
3. The method of claim 1,
when adopting intelligent agent's distributed monitoring mode, install intelligent monitoring agent SMA on every monitored equipment, monitor at least one equipment that contains in the information application system to acquire monitoring data, include:
the intelligent monitoring agent SMA monitors at least one device contained in the information application system to obtain monitoring data;
the monitoring server acquires the monitoring data monitored by the intelligent monitoring agent SMA, and the monitoring server periodically and alternately patrols the intelligent monitoring agent SMA according to a set time interval so as to acquire the monitoring data monitored by the intelligent monitoring agent SMA;
and the monitoring server acquires the monitoring data transmitted between the intelligent monitoring agents SMA through an XML format.
The monitoring at least one device included in the information application system and acquiring monitoring data further includes: the intelligent monitoring agent SMA establishes heartbeat connection with the monitoring server; when the monitoring server monitors that the intelligent monitoring agent SMA heartbeat connection is overtime, obtaining that equipment corresponding to the intelligent monitoring agent SMA is out of order and generating a corresponding fault message; wherein the fault message is contained within the monitoring data;
when a network monitoring mode of an SNMP protocol is adopted, the monitoring at least one device included in an information application system and acquiring monitoring data includes: monitoring the network performance and network errors of at least one device contained in the information application system, and acquiring monitoring data;
when adopting intelligent agent's host computer fault diagnosis monitoring mode, when installing intelligent monitoring agent SMA on every monitored equipment, monitor at least one equipment that contains in the information application system to obtain the monitoring data, include: the intelligent monitoring agent SMA monitors at least one device contained in the information application system according to a specified monitoring strategy; the monitoring main server receives an alarm or fault message sent by the intelligent monitoring agent SMA when the intelligent monitoring agent SMA monitors that the equipment operates abnormally; wherein the alarm or fault message is contained in the monitoring data;
the monitoring at least one device included in the information application system and acquiring monitoring data further includes: the intelligent monitoring agent SMA establishes heartbeat connection with the monitoring main server; when the monitoring main server monitors that the intelligent monitoring agent SMA heartbeat connection is overtime, obtaining that equipment corresponding to the intelligent monitoring agent SMA is out of order and generating a corresponding fault message; wherein the fault message is contained within the monitoring data.
4. The method according to claim 3, wherein the obtaining of the corresponding fault trend information by performing data processing on the obtained monitoring data by using a preset fault trend prediction rule includes:
performing data processing on the acquired monitoring data by adopting a preset linear regression algorithm and an exponential regression algorithm to obtain corresponding fault future trend information;
performing data processing on the obtained monitoring data by adopting a preset trigonometric function regression algorithm to obtain corresponding fault periodic trend information;
wherein the fault trend information comprises fault future trend information and fault periodic trend information.
5. The method according to claim 4, wherein the step of performing data processing on the acquired monitoring data by using a preset trigonometric function regression algorithm to obtain corresponding fault periodicity trend information comprises:
taking out the last acquired state factor parameter value in the monitoring data state factor parameter sequence and m-1 state factor parameter values before the last acquired state factor parameter value to perform periodic analysis, calculating the periodic parameter of the state factor parameter value change in the period of time according to the acquired state factor parameter value to obtain a periodic regression analysis function, and then drawing a periodic curve of the state factor parameter value change according to the function;
the periodicity analysis algorithm is specifically as follows: the collected state factor parameter series is { y1, y2, … …, yn }, the collection time series is { t1, t2, … …, tn }, and the regression function of the trigonometric function is adopted as follows:
where k is a preset fractional number for controlling the precision of the trigonometric periodic regression, m is the magnitude of the state factor parameter sequence, ej(j ═ 0,1,. times, k) and fj(j ═ 1, 2.. times, k) are parameters of the periodic regression function of the trigonometric function, wherein the parameter calculation method is as follows:
and after each analysis is finished, continuously acquiring the state factor parameter value of the next period, putting the state factor parameter value at the tail of the state factor parameter sequence, deleting the state factor parameter value acquired earliest in the original state factor parameter sequence, and keeping the size of the state factor parameter sequence as m.
6. An information application system failure trend prediction device, comprising:
the monitoring module is used for monitoring at least one device contained in the information application system and acquiring monitoring data;
the processing module is used for carrying out data processing on the acquired monitoring data by adopting a preset fault trend prediction rule to obtain corresponding fault trend information;
and the display module is used for visually displaying the fault trend information on the appointed display equipment.
7. The apparatus of claim 6,
when the distributed monitoring mode of intelligent agent is adopted, when intelligent monitoring agent SMA is installed on each monitored equipment, the monitoring module comprises:
the intelligent monitoring agent SMA is used for monitoring at least one device contained in the information application system to obtain monitoring data;
the monitoring server is used for acquiring the monitoring data monitored by the intelligent monitoring agent SMA;
and the monitoring server acquires the monitoring data transmitted between the intelligent monitoring agents SMA through an XML format.
The intelligent monitoring agent SMA is also used for establishing heartbeat connection with the monitoring server; the monitoring server is further used for obtaining that equipment corresponding to the intelligent monitoring agent SMA fails and generating corresponding failure information when monitoring that the heartbeat connection of the intelligent monitoring agent SMA is overtime; wherein the fault message is contained within the monitoring data;
when a network monitoring mode of an SNMP protocol is adopted, the monitoring module is specifically configured to: monitoring the network performance and network errors of at least one device contained in the information application system, and acquiring monitoring data;
when adopting intelligent agent's host computer fault diagnosis monitoring mode, install intelligent monitoring agent SMA on every monitored equipment, monitoring module includes: the intelligent monitoring agent SMA is used for monitoring at least one device contained in the information application system according to a specified monitoring strategy; the monitoring main server is used for receiving an alarm or fault message sent by the intelligent monitoring agent SMA when the intelligent monitoring agent SMA monitors that the equipment operates abnormally; wherein the alarm or fault message is contained in the monitoring data;
the intelligent monitoring agent SMA is also used for establishing heartbeat connection with the monitoring main server; the monitoring main server is also used for monitoring the heartbeat connection timeout of the intelligent monitoring agent SMA, obtaining the fault of the equipment corresponding to the intelligent monitoring agent SMA and generating a corresponding fault message; wherein the fault message is contained within the monitoring data.
8. The apparatus of claim 7, wherein the processing module comprises:
the first processing unit is used for performing data processing on the acquired monitoring data by adopting a preset linear regression algorithm and an exponential regression algorithm to obtain corresponding fault future trend information;
the second processing unit is used for performing data processing on the acquired monitoring data by adopting a preset trigonometric function regression algorithm to obtain corresponding fault periodic trend information;
wherein the fault trend information comprises fault future trend information and fault periodic trend information.
9. The apparatus according to claim 8, wherein the second processing unit is specifically configured to:
taking out the last acquired state factor parameter value in the monitoring data state factor parameter sequence and m-1 state factor parameter values before the last acquired state factor parameter value to perform periodic analysis, calculating the periodic parameter of the state factor parameter value change in the period of time according to the acquired state factor parameter value to obtain a periodic regression analysis function, and then drawing a periodic curve of the state factor parameter value change according to the function;
the periodicity analysis algorithm is specifically as follows: the collected state factor parameter series is { y1, y2, … …, yn }, the collection time series is { t1, t2, … …, tn }, and the regression function of the trigonometric function is adopted as follows:
where k is a preset fractional number for controlling the precision of the trigonometric periodic regression, m is the magnitude of the state factor parameter sequence, ej(j ═ 0,1,. times, k) and fj(j ═ 1, 2.. times, k) are parameters of the periodic regression function of the trigonometric function, wherein the parameter calculation method is as follows:
and after each analysis is finished, continuously acquiring the state factor parameter value of the next period, putting the state factor parameter value at the tail of the state factor parameter sequence, deleting the state factor parameter value acquired earliest in the original state factor parameter sequence, and keeping the size of the state factor parameter sequence as m.
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