CN112948223B - Method and device for monitoring running condition - Google Patents
Method and device for monitoring running condition Download PDFInfo
- Publication number
- CN112948223B CN112948223B CN201911172200.4A CN201911172200A CN112948223B CN 112948223 B CN112948223 B CN 112948223B CN 201911172200 A CN201911172200 A CN 201911172200A CN 112948223 B CN112948223 B CN 112948223B
- Authority
- CN
- China
- Prior art keywords
- early warning
- index
- data
- running condition
- monitored system
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 238000000034 method Methods 0.000 title claims abstract description 55
- 238000012544 monitoring process Methods 0.000 title claims abstract description 37
- 238000007477 logistic regression Methods 0.000 claims description 13
- 238000004590 computer program Methods 0.000 claims description 9
- 238000010219 correlation analysis Methods 0.000 claims description 7
- 238000010586 diagram Methods 0.000 description 13
- 238000004458 analytical method Methods 0.000 description 10
- 230000000694 effects Effects 0.000 description 8
- 238000004891 communication Methods 0.000 description 7
- 230000006870 function Effects 0.000 description 6
- 238000012545 processing Methods 0.000 description 5
- 230000003287 optical effect Effects 0.000 description 4
- 230000008569 process Effects 0.000 description 4
- 230000004044 response Effects 0.000 description 4
- 238000012986 modification Methods 0.000 description 3
- 230000004048 modification Effects 0.000 description 3
- 230000008901 benefit Effects 0.000 description 2
- 238000004364 calculation method Methods 0.000 description 2
- 230000001364 causal effect Effects 0.000 description 2
- 238000001514 detection method Methods 0.000 description 2
- 239000000835 fiber Substances 0.000 description 2
- 235000003642 hunger Nutrition 0.000 description 2
- 238000010801 machine learning Methods 0.000 description 2
- 230000000644 propagated effect Effects 0.000 description 2
- 239000004065 semiconductor Substances 0.000 description 2
- 230000037351 starvation Effects 0.000 description 2
- 230000032683 aging Effects 0.000 description 1
- 238000013459 approach Methods 0.000 description 1
- 238000012098 association analyses Methods 0.000 description 1
- 230000005540 biological transmission Effects 0.000 description 1
- 238000004422 calculation algorithm Methods 0.000 description 1
- 230000008859 change Effects 0.000 description 1
- 238000013145 classification model Methods 0.000 description 1
- 238000004140 cleaning Methods 0.000 description 1
- 238000010276 construction Methods 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 230000007812 deficiency Effects 0.000 description 1
- 238000013461 design Methods 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 239000000284 extract Substances 0.000 description 1
- 239000004973 liquid crystal related substance Substances 0.000 description 1
- 239000013307 optical fiber Substances 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F11/00—Error detection; Error correction; Monitoring
- G06F11/30—Monitoring
- G06F11/34—Recording or statistical evaluation of computer activity, e.g. of down time, of input/output operation ; Recording or statistical evaluation of user activity, e.g. usability assessment
- G06F11/3409—Recording or statistical evaluation of computer activity, e.g. of down time, of input/output operation ; Recording or statistical evaluation of user activity, e.g. usability assessment for performance assessment
Landscapes
- Engineering & Computer Science (AREA)
- General Engineering & Computer Science (AREA)
- Theoretical Computer Science (AREA)
- Computer Hardware Design (AREA)
- Quality & Reliability (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Debugging And Monitoring (AREA)
Abstract
The invention discloses a method and a device for monitoring running conditions, and relates to the technical field of computers. One embodiment of the method comprises the following steps: acquiring index data of a monitored system; acquiring index association relations corresponding to the index data; predicting the predicted running condition of the monitored system according to the index data and the index association relation; and sending out early warning based on the predicted running condition and the early warning threshold value. According to the method and the device, the predicted running condition of the monitored system can be predicted, and the intelligent early warning is performed in advance in a complex scene by combining the index association relation.
Description
Technical Field
The present invention relates to the field of computer technologies, and in particular, to a method and an apparatus for monitoring an operating condition.
Background
Currently, the internet runs a large number of systems that are serving users, and therefore stability of the systems is an important point. There are many factors that can affect the stability of the system: network conditions, server hardware conditions, system itself conditions, etc. When these factors that affect the stability of the system have negative effects, or may have negative effects, a method and system are needed to timely detect these effects and send a targeted warning to related personnel such as the responsible person of the system in real time.
The existing detection mode is to acquire the memory condition of each server on the line in real time, and send alarm information to the responsible person who responds when the memory is insufficient. The thread number early warning is also carried out in a similar way, the current system thread number is detected in real time, and when the current system thread number exceeds a threshold value, alarm information is issued. The interface call exception is to take the total call times in the last time range and warn according to the times.
In the process of implementing the present invention, the inventor finds that at least the following problems exist in the prior art:
The association relation among the early warnings is lacking, the early warning rule is single, the intelligent early warning can not be realized in a complex scene, and the early warning can not be predicted in advance.
Disclosure of Invention
In view of the above, the embodiment of the invention provides a method and a device for monitoring operation conditions, which can predict the predicted operation conditions of a monitored system and combine with index association relations to intelligently early warn in advance in a complex scene.
To achieve the above object, according to one aspect of an embodiment of the present invention, there is provided a method of monitoring an operation condition.
The method for monitoring the running condition comprises the following steps:
Acquiring index data of a monitored system;
Acquiring an index association relation corresponding to the index data;
Predicting the predicted running condition of the monitored system according to the index data and the index association relation;
and sending out early warning based on the predicted running condition and an early warning threshold value.
Optionally, collecting index data of the monitored system includes:
Collecting system operation data of a monitored system and server operation data of each server in the system operation data;
Index data corresponding to each reference index is extracted from the system operation data and the server operation data.
Optionally, obtaining the index association relationship corresponding to the index data includes:
Acquiring a history early warning result of a monitored system;
And analyzing the historical early warning result by using a correlation analysis method to obtain index correlation relations among the reference indexes.
Optionally, predicting the predicted operation condition of the monitored system according to the index data and the index association relation includes:
And calculating the index data and the index association relation based on a logistic regression model to obtain the predicted running condition of the monitored system.
Optionally, the early warning threshold includes a dynamic threshold of each reference index corresponding to each time period; and
Sending out an early warning based on the predicted running condition and an early warning threshold value, comprising:
Obtaining early warning results of the reference indexes based on the predicted running condition and the dynamic threshold value;
sending out an early warning corresponding to the reference index according to the early warning result; or, sending out early warning corresponding to the reference index according to the index data and the dynamic threshold;
and recording the early warning result.
To achieve the above object, according to still another aspect of an embodiment of the present invention, there is provided an apparatus for monitoring an operation condition.
The device for monitoring the running condition comprises:
the acquisition module is used for acquiring index data of the monitored system;
The acquisition module is used for acquiring the index association relation corresponding to the index data;
the prediction module is used for predicting the predicted running condition of the monitored system according to the index data and the index association relation;
and the early warning module is used for sending out early warning based on the predicted running condition and the early warning threshold value.
Optionally, the acquisition module is further configured to:
Collecting system operation data of a monitored system and server operation data of each server in the system operation data;
Index data corresponding to each reference index is extracted from the system operation data and the server operation data.
Optionally, the acquiring module is further configured to:
Acquiring a history early warning result of a monitored system;
And analyzing the historical early warning result by using a correlation analysis method to obtain index correlation relations among the reference indexes.
Optionally, the prediction module is further configured to:
And calculating the index data and the index association relation based on a logistic regression model to obtain the predicted running condition of the monitored system.
Optionally, the early warning threshold includes a dynamic threshold of each reference index corresponding to each time period; and
The early warning module is also used for:
Obtaining early warning results of the reference indexes based on the predicted running condition and the dynamic threshold value;
sending out an early warning corresponding to the reference index according to the early warning result; or, sending out early warning corresponding to the reference index according to the index data and the dynamic threshold;
and recording the early warning result.
To achieve the above object, according to still another aspect of the embodiments of the present invention, there is provided an electronic device for monitoring an operation condition.
An electronic device for monitoring an operation condition according to an embodiment of the present invention includes: one or more processors; and the storage device is used for storing one or more programs, and when the one or more programs are executed by the one or more processors, the one or more processors are enabled to realize the method for monitoring the running condition.
To achieve the above object, according to still another aspect of the embodiments of the present invention, there is provided a computer-readable storage medium.
A computer readable storage medium of an embodiment of the present invention has stored thereon a computer program which, when executed by a processor, implements a method of monitoring an operating condition of an embodiment of the present invention.
One embodiment of the above invention has the following advantages or benefits: because the index data of the monitored system is collected; acquiring index association relations corresponding to the index data; predicting the predicted running condition of the monitored system according to the index data and the index association relation; the technical means of sending out early warning based on the predicted running condition and the early warning threshold value overcomes the technical problems that the incidence relation is lacking among early warning, the early warning rule is single, intelligent early warning cannot be achieved in a complex scene, and the early warning cannot be predicted in advance, so that the predicted running condition of a monitored system can be predicted, and the technical effect of intelligent early warning in advance in the complex scene is achieved by combining the index incidence relation.
Further effects of the above-described non-conventional alternatives are described below in connection with the embodiments.
Drawings
The drawings are included to provide a better understanding of the invention and are not to be construed as unduly limiting the invention. Wherein:
FIG. 1 is a schematic diagram of the main steps of a method of monitoring an operating condition according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of the main flow of a method of identity authentication according to one referenceable embodiment of the invention;
FIG. 3 is a schematic diagram of the major modules of an apparatus for monitoring operation in accordance with an embodiment of the present invention;
FIG. 4 is an exemplary system architecture diagram in which embodiments of the present invention may be applied;
fig. 5 is a schematic diagram of a computer system suitable for use in implementing an embodiment of the invention.
Detailed Description
Exemplary embodiments of the present invention will now be described with reference to the accompanying drawings, in which various details of the embodiments of the present invention are included to facilitate understanding, and are to be considered merely exemplary. Accordingly, those of ordinary skill in the art will recognize that various changes and modifications of the embodiments described herein can be made without departing from the scope and spirit of the invention. Also, descriptions of well-known functions and constructions are omitted in the following description for clarity and conciseness.
It should be noted that the embodiments of the present invention and the technical features in the embodiments may be combined with each other without collision.
The existing detection mode is to acquire the running condition of each server in real time, and know the running condition of the monitored system according to the running condition of each server, so as to analyze whether each reference index of the monitored system needs early warning or not, but the mode has the following defects:
1. The association relation among the early warnings is lacking, in actual operation, the early warnings seem to be independent, some association relation exists among the early warnings, even some causal relation exists among the early warnings, for example, the more the interface call times are, the more the memory consumption is, but the association relation among the early warnings is not established, so that the capability of preventing the occurrence of the failure is lacking;
2. the intelligent and early warning method is not intelligent enough, for example, the memory deficiency warning is not realized, the threshold value of the warning is estimated by a system responsible person according to personal experience, then the system is set in a monitoring system, and when the real-time memory of the system reaches the early warning, the early warning can be carried out, and the warning is often carried out on the late Zhuge;
3. The early warning rule is single, intelligent early warning can not be achieved under complex scenes, for example, interface calling times are early warning, the same interface has great difference in calling times under different scenes, commodity information interfaces in a large promotion period and a non-large promotion period have great difference in maximum calling amount and minimum calling amount, the maximum calling times in the large promotion period can be 1000 times per second, the minimum times can be 200 times per second, the maximum times in the non-large promotion period can be 100 times per second, and the minimum times can be 20 times per second.
In the method for monitoring the running condition, disclosed by the embodiment of the invention, not only is the current running condition data of the monitored system and the server analyzed in real time by using a logistic regression algorithm, but also the correlation degree analysis is carried out again on all the historical analysis results and is used for analyzing the relation among all the reference indexes, so that the accuracy of alarming and pre-judging is greatly enhanced.
FIG. 1 is a schematic diagram of the main steps of a method of monitoring an operating condition according to an embodiment of the present invention.
As shown in fig. 1, the method for monitoring an operation condition according to an embodiment of the present invention mainly includes the following steps:
step S101: and collecting index data of the monitored system.
In order to predict the running condition of the monitored system for a period of time in the future, index data are collected when the monitored system runs, and the index data are used for reflecting the current running condition of the monitored system.
In the embodiment of the present invention, step S101 may be implemented in the following manner: collecting system operation data of a monitored system and server operation data of each server in the system operation data; index data corresponding to each reference index is extracted from the system operation data and the server operation data.
The monitored system refers to various systems and application software deployed and running on servers, and typically, the monitored system will run on multiple servers. The server refers to a dedicated server running various systems and applications. The system operation data is data generated by the monitored system in operation, and represents the overall operation condition of the monitored system, such as the total memory quantity of the JVM (Java virtual machine), the current use quantity of the JVM, the current thread number or the maximum call quantity within the latest N minutes of the interface, and the like. The server operation data is data generated by a server in the monitored system during operation, and represents the operation condition of the server, such as the total memory size, the current memory size, the total network connection number or the current network transmission rate. The reference index is a reference standard for evaluating the running condition, such as the current consumed memory value of the JVM, the number of calls or threads, and the like.
Step S102: and acquiring an index association relation corresponding to the index data.
In actual operation, each reference index looks independent, and even some reference indexes have a causal relationship, for example, the more the interface call times, the more the memory consumption is, after the index data is collected, the reference indexes can be analyzed, and the association relationship (i.e. index association relationship) between the reference indexes can be obtained.
The association relation between the reference indexes can be obtained and stored in advance through analyzing the historical data (namely the historical early warning result) so as to facilitate the subsequent analysis and use. In the embodiment of the present invention, step S102 may be implemented in the following manner: acquiring a history early warning result of a monitored system; and analyzing the historical early warning result by using a correlation analysis method to obtain index correlation relations among all the reference indexes.
Association analysis is a simple and practical analysis technique that finds associations or correlations that exist in a large number of data sets, describing the rules and patterns in which certain attributes appear simultaneously in a thing. For example, the probability of occurrence of the B event after the occurrence of the a event is calculated, and if the probability is large, the event B can be described as being associated with the event a, and when the a event occurs again, the event B is likely to occur.
Step S103: and predicting the predicted running condition of the monitored system according to the index data and the index association relation.
After the index data and the index association relation are obtained through the steps, the next operation condition of the monitored system can be deduced, namely, how the data corresponding to each reference index of the monitored system is predicted to change.
In the embodiment of the present invention, step S103 may be implemented in the following manner: and calculating the association relation between the index data and the index by using a logistic regression model to obtain the predicted running condition of the monitored system.
The logistic regression model is a simple and common classification model, and the classification of the object is obtained by inputting the attribute feature sequence of the unknown class object. The method for monitoring the running condition of the embodiment of the invention introduces a machine learning mode, uses a logistic regression model to analyze and predict the running data of the server of the current server and the running data of the system to be monitored running on the server so as to pre-judge the possible problems in advance and give out the probability of the problems, and if the probability of the problems is larger, the monitoring system gives out an alarm. Based on the machine learning mode, the method does not depend on experience of system developers and configuration thresholds, so the method can be applied to complex scenes. In addition, the use of the logistic regression model may refer to the existing technical solution, and will not be described here.
Step S104: and sending out early warning based on the predicted running condition and the early warning threshold value.
The early warning threshold value can be determined according to actual needs or historical data and the like, and when the predicted running condition reaches the early warning threshold value, early warning can be sent out to remind related personnel, for example: network anomaly alerts, server disk starvation alerts, memory starvation alerts, system thread anomaly alerts, server performance alerts, interface call volume exceeding an upper threshold alert, interface call volume below a lower threshold alert, and so on.
In the embodiment of the present invention, step S104 may be implemented in the following manner: obtaining early warning results of various reference indexes based on the predicted running condition and the dynamic threshold value; sending out an early warning corresponding to the reference index according to the early warning result; or, sending out early warning corresponding to the reference index according to the index data and the dynamic threshold value; and recording an early warning result.
The method for monitoring the running condition adopts a mode of combining a logistic regression model and a relevance analysis to analyze, namely, real-time alarm of the monitored system is realized, risk possibly occurring in the future of the monitored system is also predicted, if the monitored system or a server where the monitored system is located is predicted to possibly have problems, the monitored system or the server can be actively contacted with a responsible person of the monitored system or the server through early warning modes such as mail, short messages, even telephones and the like to inform the responsible person of the possible risk.
Because the running condition of the monitored system may be changed due to some external factors, for example, the interface call amount of some specific time is extremely high or the memory is more used, or the performance is reduced due to the aging of the hardware, the early warning threshold may include dynamic thresholds of each reference index corresponding to each time period, i.e. dynamic thresholds matched with the use requirement may be set for different reference indexes of different time. In addition, the early warning result can be recorded so as to facilitate subsequent analysis.
The method for monitoring the running condition according to the embodiment of the invention can be seen because the index data of the monitored system is collected; acquiring index association relations corresponding to the index data; predicting the predicted running condition of the monitored system according to the index data and the index association relation; the technical means of sending out early warning based on the predicted running condition and the early warning threshold value overcomes the technical problems that the incidence relation is lacking among early warning, the early warning rule is single, intelligent early warning cannot be achieved in a complex scene, and the early warning cannot be predicted in advance, so that the predicted running condition of a monitored system can be predicted, and the technical effect of intelligent early warning in advance in the complex scene is achieved by combining the index incidence relation.
Fig. 2 is a schematic diagram of the main flow of a method of monitoring operation according to one referenceable embodiment of the invention.
As shown in fig. 2, the method for monitoring the operation condition according to the embodiment of the present invention is implemented with reference to the following flow:
1. starting an intelligent monitoring system;
2.1. collecting various operation data (i.e. system operation data) of a specified application system (i.e. a monitored system) in real time;
2.2. collecting various operation data (namely server operation data) of a designated server in real time;
3. Some other data which can influence the analysis result, wherein the most main time parameter is used for distinguishing different threshold ranges required by various early warning in different time periods, and other types of early warning result information in the latest time range can also be provided;
4. index data are extracted from the data collected in 2.1, 2.2 and 3 and used as the input parameters of a logistic regression model (LR model), and the LR model is used for calculation and analysis;
The formula of logistic regression of the LR model is: Wherein P is a decision value, x is a characteristic value, and e is a natural logarithm. Taking JVM memory early warning as an example, when x represents parameters such as a total memory value of a server, a current consumed memory value of the JVM, a current thread number, a method call number, response time and the like, the more the call times are, the more the thread number is, the response time is reduced, so that the frequency of cleaning heap space (GC) is increased, and finally, the risk that the JVM memory is consumed may occur. JVM memory: the application system is divided into a memory area from the physical memory of the server, and the memory area is only used by the current application system; number of method calls: the application system is provided with a plurality of interfaces, namely methods, which are used for completing certain logic calculation tasks, such as commodity detail methods, and are mainly used for acquiring the belief information of commodities, the method is provided for all users, and when more and more users open commodity detail pages, the calling quantity of the commodity detail methods is larger and larger; response time: one approach is to complete a particular logical computing task from the beginning to the length of time it takes to complete the entire process. The units may be units of milliseconds, or seconds, etc.;
5. results analyzed based on LR model: whether each reference index needs to be pre-warned;
6. Writing the analyzed result into a hive database;
7. the monitoring system extracts various analysis results (namely historical early warning results) of the last N days from hive;
8. by using a correlation analysis method, the correlation among different types of early warning is analyzed, for example, the calling number early warning also predicts the early warning of response time possibly to a certain extent, and even accompanies the early warning that the JVM memory is possibly exhausted;
9. the association degree analysis result (namely index association relation) obtained in the last step is also used as a parameter of the LR model and is used as one of the influence factors of the current early warning;
10. If the early warning is needed, alarm information is sent out through various notification modes, such as mail alarm, short message alarm, enterprise office chat tool pushing alarm information and the like
Fig. 3 is a schematic diagram of the main modules of an apparatus for monitoring operation according to an embodiment of the present invention.
As shown in fig. 3, an apparatus 300 for monitoring an operation condition according to an embodiment of the present invention includes: the system comprises an acquisition module 301, an acquisition module 302, a prediction module 303 and an early warning module 304.
Wherein,
The acquisition module 301 is configured to acquire index data of a monitored system;
an obtaining module 302, configured to obtain an index association relationship corresponding to the index data;
A prediction module 303, configured to predict a predicted operation condition of the monitored system according to the index data and the index association relationship;
and the early warning module 304 is configured to send out an early warning based on the predicted running condition and an early warning threshold.
In the embodiment of the present invention, the acquisition module 301 is further configured to:
Collecting system operation data of a monitored system and server operation data of each server in the system operation data;
Index data corresponding to each reference index is extracted from the system operation data and the server operation data.
In the embodiment of the present invention, the obtaining module 302 is further configured to:
Acquiring a history early warning result of a monitored system;
And analyzing the historical early warning result by using a correlation analysis method to obtain index correlation relations among the reference indexes.
In the embodiment of the present invention, the prediction module 303 is further configured to:
And calculating the index data and the index association relation based on a logistic regression model to obtain the predicted running condition of the monitored system.
In addition, the early warning threshold includes dynamic thresholds of the reference indicators corresponding to the time periods.
In the embodiment of the present invention, the early warning module 304 is further configured to:
Obtaining early warning results of the reference indexes based on the predicted running condition and the dynamic threshold value;
sending out an early warning corresponding to the reference index according to the early warning result; or, sending out early warning corresponding to the reference index according to the index data and the dynamic threshold;
and recording the early warning result.
The device for monitoring the running condition according to the embodiment of the invention can be seen in that the device for collecting the index data of the monitored system is adopted; acquiring index association relations corresponding to the index data; predicting the predicted running condition of the monitored system according to the index data and the index association relation; the technical means of sending out early warning based on the predicted running condition and the early warning threshold value overcomes the technical problems that the incidence relation is lacking among early warning, the early warning rule is single, intelligent early warning cannot be achieved in a complex scene, and the early warning cannot be predicted in advance, so that the predicted running condition of a monitored system can be predicted, and the technical effect of intelligent early warning in advance in the complex scene is achieved by combining the index incidence relation.
Fig. 4 illustrates an exemplary system architecture 400 to which the method of monitoring operation or the apparatus of monitoring operation of the embodiments of the present invention may be applied.
As shown in fig. 4, the system architecture 400 may include terminal devices 401, 402, 403, a network 404, and a server 405. The network 404 is used as a medium to provide communication links between the terminal devices 401, 402, 403 and the server 405. The network 404 may include various connection types, such as wired, wireless communication links, or fiber optic cables, among others.
A user may interact with the server 405 via the network 404 using the terminal devices 401, 402, 403 to receive or send messages or the like. Various communication client applications, such as shopping class applications, web browser applications, search class applications, instant messaging tools, mailbox clients, social platform software, etc., may be installed on the terminal devices 401, 402, 403.
The terminal devices 401, 402, 403 may be various electronic devices having a display screen and supporting web browsing, including but not limited to smartphones, tablets, laptop and desktop computers, and the like.
The server 405 may be a server providing various services, such as a background management server providing support for shopping-type websites browsed by the user using the terminal devices 401, 402, 403. The background management server can analyze and other processing on the received data such as the product information inquiry request and the like, and feed back processing results (such as target push information and product information) to the terminal equipment.
It should be noted that, the method for monitoring the operation condition provided in the embodiment of the present invention is generally executed by the server 405, and accordingly, the device for monitoring the operation condition is generally disposed in the server 405.
It should be understood that the number of terminal devices, networks and servers in fig. 4 is merely illustrative. There may be any number of terminal devices, networks, and servers, as desired for implementation.
Referring now to FIG. 5, there is illustrated a schematic diagram of a computer system 500 suitable for use in implementing an embodiment of the present invention. The terminal device shown in fig. 5 is only an example, and should not impose any limitation on the functions and the scope of use of the embodiment of the present invention.
As shown in fig. 5, the computer system 500 includes a Central Processing Unit (CPU) 501, which can perform various appropriate actions and processes according to a program stored in a Read Only Memory (ROM) 502 or a program loaded from a storage section 508 into a Random Access Memory (RAM) 503. In the RAM 503, various programs and data required for the operation of the system 500 are also stored. The CPU 501, ROM 502, and RAM 503 are connected to each other through a bus 504. An input/output (I/O) interface 505 is also connected to bus 504.
The following components are connected to the I/O interface 505: an input section 506 including a keyboard, a mouse, and the like; an output portion 507 including a Cathode Ray Tube (CRT), a Liquid Crystal Display (LCD), and the like, and a speaker, and the like; a storage portion 508 including a hard disk and the like; and a communication section 509 including a network interface card such as a LAN card, a modem, or the like. The communication section 509 performs communication processing via a network such as the internet. The drive 510 is also connected to the I/O interface 505 as needed. A removable medium 511 such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, or the like is mounted on the drive 510 as needed so that a computer program read therefrom is mounted into the storage section 508 as needed.
In particular, according to embodiments of the present disclosure, the processes described above with reference to flowcharts may be implemented as computer software programs. For example, embodiments of the present disclosure include a computer program product comprising a computer program embodied on a computer readable medium, the computer program comprising program code for performing the method shown in the flow chart. In such an embodiment, the computer program may be downloaded and installed from a network via the communication portion 509, and/or installed from the removable media 511. The above-described functions defined in the system of the present invention are performed when the computer program is executed by a Central Processing Unit (CPU) 501.
The computer readable medium shown in the present invention may be a computer readable signal medium or a computer readable storage medium, or any combination of the two. The computer readable storage medium can be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or a combination of any of the foregoing. More specific examples of the computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the context of this document, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. In the present invention, however, the computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, with the computer-readable program code embodied therein. Such a propagated data signal may take any of a variety of forms, including, but not limited to, electro-magnetic, optical, or any suitable combination of the foregoing. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to: wireless, wire, fiber optic cable, RF, etc., or any suitable combination of the foregoing.
The flowcharts and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present invention. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams or flowchart illustration, and combinations of blocks in the block diagrams or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
The modules involved in the embodiments of the present invention may be implemented in software or in hardware. The described modules may also be provided in a processor, for example, as: the processor comprises an acquisition module, a prediction module and an early warning module. The names of these modules do not in any way limit the module itself, for example, the acquisition module may also be described as a "module that acquires index data of the monitored system".
As another aspect, the present invention also provides a computer-readable medium that may be contained in the apparatus described in the above embodiments; or may be present alone without being fitted into the device. The computer readable medium carries one or more programs which, when executed by a device, cause the device to include: step S101: acquiring index data of a monitored system; step S102: acquiring index association relations corresponding to the index data; step S103: predicting the predicted running condition of the monitored system according to the index data and the index association relation; step S104: and sending out early warning based on the predicted running condition and the early warning threshold value.
According to the technical scheme of the embodiment of the invention, the index data of the monitored system is collected; acquiring index association relations corresponding to the index data; predicting the predicted running condition of the monitored system according to the index data and the index association relation; the technical means of sending out early warning based on the predicted running condition and the early warning threshold value overcomes the technical problems that the incidence relation is lacking among early warning, the early warning rule is single, intelligent early warning cannot be achieved in a complex scene, and the early warning cannot be predicted in advance, so that the predicted running condition of a monitored system can be predicted, and the technical effect of intelligent early warning in advance in the complex scene is achieved by combining the index incidence relation.
The above embodiments do not limit the scope of the present invention. It will be apparent to those skilled in the art that various modifications, combinations, sub-combinations and alternatives can occur depending upon design requirements and other factors. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the scope of the present invention.
Claims (6)
1. A method of monitoring an operating condition, comprising:
Acquiring index data of a monitored system; the method specifically comprises the following steps: collecting system operation data of a monitored system and server operation data of each server in the system operation data; extracting index data corresponding to each reference index from the system operation data and the server operation data;
acquiring an index association relation corresponding to the index data; the method specifically comprises the following steps: acquiring a history early warning result of a monitored system; analyzing the historical early warning result by using a correlation analysis method to obtain index correlation relations among the reference indexes;
Predicting the predicted running condition of the monitored system according to the index data and the index association relation; the method specifically comprises the following steps: calculating the index data and the index association relation based on a logistic regression model to obtain the predicted running condition of the monitored system;
and sending out early warning based on the predicted running condition and an early warning threshold value.
2. The method of claim 1, wherein the pre-warning threshold comprises a dynamic threshold for each time period corresponding to each reference indicator; and
Sending out an early warning based on the predicted running condition and an early warning threshold value, comprising:
Obtaining early warning results of the reference indexes based on the predicted running condition and the dynamic threshold value;
sending out an early warning corresponding to the reference index according to the early warning result; or, sending out early warning corresponding to the reference index according to the index data and the dynamic threshold;
and recording the early warning result.
3. An apparatus for monitoring an operating condition, comprising:
The acquisition module is used for acquiring index data of the monitored system; also used for: collecting system operation data of a monitored system and server operation data of each server in the system operation data; extracting index data corresponding to each reference index from the system operation data and the server operation data;
the acquisition module is used for acquiring the index association relation corresponding to the index data; also used for:
Acquiring a history early warning result of a monitored system; analyzing the historical early warning result by using a correlation analysis method to obtain index correlation relations among the reference indexes;
The prediction module is used for predicting the predicted running condition of the monitored system according to the index data and the index association relation; the method is also used for calculating the index data and the index association relation based on a logistic regression model so as to obtain the predicted running condition of the monitored system;
and the early warning module is used for sending out early warning based on the predicted running condition and the early warning threshold value.
4. The apparatus of claim 3, wherein the pre-warning threshold comprises a dynamic threshold for each time period corresponding to each reference indicator; and
The early warning module is also used for:
Obtaining early warning results of the reference indexes based on the predicted running condition and the dynamic threshold value;
sending out an early warning corresponding to the reference index according to the early warning result; or, sending out early warning corresponding to the reference index according to the index data and the dynamic threshold;
and recording the early warning result.
5. An electronic device for monitoring an operating condition, comprising:
One or more processors;
Storage means for storing one or more programs,
When executed by the one or more processors, causes the one or more processors to implement the method of any of claims 1-2.
6. A computer readable medium, on which a computer program is stored, characterized in that the program, when being executed by a processor, implements the method according to any of claims 1-2.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201911172200.4A CN112948223B (en) | 2019-11-26 | 2019-11-26 | Method and device for monitoring running condition |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201911172200.4A CN112948223B (en) | 2019-11-26 | 2019-11-26 | Method and device for monitoring running condition |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN112948223A CN112948223A (en) | 2021-06-11 |
| CN112948223B true CN112948223B (en) | 2024-09-20 |
Family
ID=76224876
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201911172200.4A Active CN112948223B (en) | 2019-11-26 | 2019-11-26 | Method and device for monitoring running condition |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN112948223B (en) |
Families Citing this family (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113610344B (en) * | 2021-06-30 | 2023-11-14 | 江苏省生态环境监控中心(江苏省环境信息中心) | Pollution early warning method and device, electronic equipment and storage medium |
| CN115687052B (en) * | 2023-01-03 | 2023-03-31 | 北京宇信科技集团股份有限公司 | Visual monitoring method, server and system for operation state |
| CN116880395B (en) * | 2023-07-19 | 2024-06-07 | 天津市易控科技发展有限公司 | Monitoring method, device, equipment and medium based on DCS system |
Citations (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108199920A (en) * | 2017-12-29 | 2018-06-22 | 北京星河星云信息技术有限公司 | Monitor the method and server of the performance of the server in cloud platform |
Family Cites Families (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20170147985A1 (en) * | 2015-11-25 | 2017-05-25 | International Business Machines Corporation | Predicting an outcome of the execution of a schedule |
| CN108181105B (en) * | 2017-11-28 | 2020-08-07 | 杭州安脉盛智能技术有限公司 | Rolling bearing fault pre-diagnosis method and system based on logistic regression and J divergence |
| CN109597676A (en) * | 2018-10-25 | 2019-04-09 | 平安科技（深圳）有限公司 | Monitoring and early warning method, apparatus and storage medium based on JVM |
| CN110275814A (en) * | 2019-06-28 | 2019-09-24 | 深圳前海微众银行股份有限公司 | A kind of monitoring method and device of operation system |
| CN110428018A (en) * | 2019-08-09 | 2019-11-08 | 北京中电普华信息技术有限公司 | A kind of predicting abnormality method and device in full link monitoring system |
-
2019
- 2019-11-26 CN CN201911172200.4A patent/CN112948223B/en active Active
Patent Citations (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108199920A (en) * | 2017-12-29 | 2018-06-22 | 北京星河星云信息技术有限公司 | Monitor the method and server of the performance of the server in cloud platform |
Also Published As
| Publication number | Publication date |
|---|---|
| CN112948223A (en) | 2021-06-11 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN107809331B (en) | Method and device for identifying abnormal flow | |
| WO2020259421A1 (en) | Method and apparatus for monitoring service system | |
| CN109471783B (en) | Method and device for predicting task operation parameters | |
| CN112948223B (en) | Method and device for monitoring running condition | |
| CN110929799B (en) | Method, electronic device, and computer-readable medium for detecting abnormal user | |
| CN112615742A (en) | Method, device, equipment and storage medium for early warning | |
| US10705940B2 (en) | System operational analytics using normalized likelihood scores | |
| CN114091704B (en) | Alarm suppression method and device | |
| CN107392259B (en) | Method and device for constructing unbalanced sample classification model | |
| CN114201278A (en) | Task processing method, task processing device, electronic device, and storage medium | |
| CN112532435B (en) | Operation and maintenance method, operation and maintenance management platform, equipment and medium | |
| CN110677271B (en) | Big data alarm method, device, equipment and storage medium based on ELK | |
| CN114610561A (en) | System monitoring method, device, electronic equipment and computer readable storage medium | |
| CN111897700B (en) | Application index monitoring method and device, electronic equipment and readable storage medium | |
| Hammer et al. | Exploiting usage statistics for energy-efficient logical status inference on mobile phones | |
| CN116471174B (en) | Log data monitoring system, method, device and storage medium | |
| CN108255710B (en) | Script abnormity detection method and terminal thereof | |
| CN114697247B (en) | Fault detection method, device, equipment and storage medium of streaming media system | |
| CN117573412A (en) | System fault early warning method and device, electronic equipment and storage medium | |
| CN116755974A (en) | Cloud computing platform operation and maintenance method and device, electronic equipment and storage medium | |
| CN114661562A (en) | Data warning method, device, equipment and medium | |
| CN113010571B (en) | Data detection method, device, electronic equipment, storage medium and program product | |
| CN116668264A (en) | Root cause analysis method, device, equipment and storage medium for alarm clustering | |
| CN116112342A (en) | Alarm information processing method, device, electronic equipment and storage medium | |
| CN115187364A (en) | Method and device for monitoring deposit risk under bank distributed scene |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |