CN111984495A - Big data monitoring method and device and storage medium - Google Patents
Big data monitoring method and device and storage medium Download PDFInfo
- Publication number
- CN111984495A CN111984495A CN201910725296.6A CN201910725296A CN111984495A CN 111984495 A CN111984495 A CN 111984495A CN 201910725296 A CN201910725296 A CN 201910725296A CN 111984495 A CN111984495 A CN 111984495A
- Authority
- CN
- China
- Prior art keywords
- data
- monitoring
- data source
- source
- index
- 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.)
- Pending
Links
- 238000012544 monitoring process Methods 0.000 title claims abstract description 253
- 238000000034 method Methods 0.000 title claims abstract description 36
- 238000012423 maintenance Methods 0.000 claims abstract description 22
- 238000012545 processing Methods 0.000 claims abstract description 20
- 238000012806 monitoring device Methods 0.000 claims description 4
- 238000007689 inspection Methods 0.000 claims description 3
- 230000006978 adaptation Effects 0.000 abstract description 2
- 230000003044 adaptive effect Effects 0.000 abstract description 2
- 238000011161 development Methods 0.000 description 3
- 238000001514 detection method Methods 0.000 description 2
- 238000010586 diagram Methods 0.000 description 2
- 230000001960 triggered effect Effects 0.000 description 2
- 230000002159 abnormal effect Effects 0.000 description 1
- 238000004458 analytical method Methods 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000005540 biological transmission Effects 0.000 description 1
- 238000004364 calculation method Methods 0.000 description 1
- 238000007405 data analysis Methods 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 238000012360 testing method Methods 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F11/00—Error detection; Error correction; Monitoring
- G06F11/30—Monitoring
- G06F11/3055—Monitoring arrangements for monitoring the status of the computing system or of the computing system component, e.g. monitoring if the computing system is on, off, available, not available
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F11/00—Error detection; Error correction; Monitoring
- G06F11/30—Monitoring
- G06F11/3003—Monitoring arrangements specially adapted to the computing system or computing system component being monitored
- G06F11/3006—Monitoring arrangements specially adapted to the computing system or computing system component being monitored where the computing system is distributed, e.g. networked systems, clusters, multiprocessor systems
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F11/00—Error detection; Error correction; Monitoring
- G06F11/30—Monitoring
- G06F11/32—Monitoring with visual or acoustical indication of the functioning of the machine
- G06F11/323—Visualisation of programs or trace data
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F11/00—Error detection; Error correction; Monitoring
- G06F11/30—Monitoring
- G06F11/32—Monitoring with visual or acoustical indication of the functioning of the machine
- G06F11/324—Display of status information
- G06F11/327—Alarm or error message display
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F11/00—Error detection; Error correction; Monitoring
- G06F11/30—Monitoring
- G06F11/32—Monitoring with visual or acoustical indication of the functioning of the machine
- G06F11/324—Display of status information
- G06F11/328—Computer systems status display
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F2201/00—Indexing scheme relating to error detection, to error correction, and to monitoring
- G06F2201/80—Database-specific techniques
Landscapes
- Engineering & Computer Science (AREA)
- Theoretical Computer Science (AREA)
- Physics & Mathematics (AREA)
- General Engineering & Computer Science (AREA)
- Quality & Reliability (AREA)
- General Physics & Mathematics (AREA)
- Computing Systems (AREA)
- Mathematical Physics (AREA)
- Computer Hardware Design (AREA)
- Data Mining & Analysis (AREA)
- Debugging And Monitoring (AREA)
Abstract
The application provides a big data monitoring method, a device and a storage medium, wherein the method comprises the following steps: collecting one or more types of monitoring data sources; monitoring status information of each type of the monitoring data source; converting the format of the monitoring data source into the format of service data; monitoring at least one data index of the service data according to an operation and maintenance actual service scene or user setting; and generating a monitoring processing result according to the state information and the monitored data index. The invention realizes adaptive adaptation of the monitoring data source by converting the format of the monitoring data source, and realizes monitoring of different monitoring data sources on a unified monitoring platform.
Description
Technical Field
The invention relates to the field of data monitoring, in particular to a big data monitoring method, a big data monitoring device and a storage medium.
Background
Along with the development of big data technology, various kinds of big data are imported into the big data monitoring system platform, so that the big data monitoring system platform can provide storage management service for multi-source heterogeneous mass data, and along with the increase of the types and the quantity of software facilities, the types of monitoring data are improved, so that the operation and maintenance monitoring difficulty of the big data monitoring system platform is improved.
The existing big data monitoring system platform is built by each family, all families are in the same place, no unified standard exists, some data monitoring systems are built abnormally and complicated, a user is difficult to take over, some systems are very simple or even crude, and normal operation and maintenance requirements cannot be met. At present, a big data monitoring system platform lacks effective management of a monitoring data source, and mainly has the following defects:
1) enterprise-level monitoring requires that each enterprise is customized according to different data sources, and development and management resources are wasted; even in an enterprise, different monitoring models can be customized according to different data source structures, so that too many monitoring modules are caused, unified management is inconvenient, and the operation and maintenance cost is increased;
2) for small and medium-sized enterprises without the ability to develop a big data monitoring platform, big data monitoring platforms of other enterprises are purchased or used, and similarly, the big data monitoring platforms of other enterprises cannot be transplanted due to different data sources to be monitored, so that the purchased big data monitoring platforms are difficult to use.
Disclosure of Invention
The application provides a big data monitoring method, a big data monitoring device and a storage medium, and realizes monitoring, operation and maintenance of various data sources.
The technical scheme is as follows:
the invention provides a big data monitoring method, which comprises the following steps:
collecting one or more types of monitoring data sources;
monitoring status information of each type of the monitoring data source; converting the format of the monitoring data source into the format of service data;
monitoring at least one data index of the service data according to an operation and maintenance actual service scene or user setting;
and generating a monitoring processing result according to the state information and the monitored data index.
Preferably, the converting the format of the monitoring data source into the format of the service data includes:
determining one or more data fields of each monitoring data source and the meaning of each data field according to the data structure of each monitoring data source, and extracting the data of each data field;
and filling the extracted data of each data field into the data fields representing the corresponding meanings according to the data structure of the service data, wherein the data structure of the service data supports the data structures of various types of monitoring data sources.
Preferably, the converting the format of the monitoring data source into the format of the service data further includes:
determining whether the monitoring data source contains time sequence data information according to the data structure of each monitoring data source;
For a monitoring data source without time sequence data information, filling time of receiving data of the monitoring data source into a time sequence field of service data as timestamp information;
and for a monitoring data source with time sequence data information, filling timestamp information in the time sequence data information into a time sequence field of service data.
Preferably, the status information comprises at least one of: capacity occupancy; a network condition; and monitoring indexes by the hardware equipment.
Preferably, the operation and maintenance actual service scenario includes at least one of: a network condition monitoring scene, a cluster state monitoring scene, a high-load task monitoring scene, a daily inspection scene and a key monitoring scene.
Preferably, generating a monitoring processing result according to the state information and the monitored data index includes:
extracting data fields representing corresponding meanings from data fields of a data structure of the service data as data indexes according to an operation and maintenance actual service scene or user setting;
determining the operation and maintenance condition of the corresponding monitoring data source according to the state information of each monitoring data source;
determining a change of each of the data indicators over time, and/or;
And determining whether the corresponding monitoring data source has a fault according to the data index, and determining the fault type if the corresponding monitoring data source has the fault.
Preferably, determining whether the corresponding monitoring data source has a fault according to the data index includes:
when at least one data index exists in the monitoring data source or the variation of the at least one data index is larger than or equal to a preset danger coefficient threshold value, determining the corresponding monitoring data source as a high-risk data source; the danger coefficient threshold value is a preset fault critical value corresponding to the monitoring data index;
when at least one data index exists in the monitoring data source or the variation of the at least one data index is larger than or equal to a preset damage coefficient threshold value, determining the corresponding monitoring data source as a vulnerable data source; the damage coefficient threshold value is a preset damage critical value corresponding to the monitoring data index.
Preferably, the method further comprises, before:
and acquiring a data structure of the one or more types of monitoring data sources preset through the configuration interface.
Preferably, the method further comprises, before:
and setting access parameters of the one or more types of monitoring data sources, and distinguishing and identifying different types of monitoring data sources through the access parameters.
Preferably, the big data monitoring method further includes: displaying at least one of the following information in a display interface in a network topological graph mode or a table mode:
the state information of each type of monitoring data source, the change condition of each data index along with time and the fault type of the monitoring data source.
The present invention also provides a big data monitoring apparatus, comprising:
the acquisition module is used for acquiring one or more types of monitoring data sources;
the compatible module is used for monitoring the state information of each type of monitoring data source and converting the format of the monitoring data source into the format of service data;
the monitoring module is arranged for monitoring at least one data index of the service data according to an operation and maintenance actual service scene or user setting;
and the processing module is configured to generate a monitoring processing result according to the state information and the monitored data index.
The invention also provides a computer-readable storage medium, which stores computer-executable instructions for executing the big data monitoring method.
The invention also provides a device for realizing big data monitoring, which comprises a memory and a processor, wherein the memory stores the big data monitoring program, and the processor is used for executing the big data monitoring method when the big data monitoring program is read.
The application has the following beneficial effects:
the invention realizes adaptive adaptation of the monitoring data source by converting the format of the monitoring data source, and realizes monitoring of different monitoring data sources on a unified monitoring platform;
in an exemplary embodiment, the access of a self-adaptive monitoring data source can be realized, the big data abnormity monitoring is rapidly carried out, the use efficiency of a platform is improved through the management of different monitoring data sources, the user participation degree in the process is reduced, meanwhile, the flexibility and the intelligence of abnormity detection are improved, and the enterprise cost and the dependence on resources are reduced;
in an exemplary embodiment, the method can also be adapted to big data monitoring of different monitoring data sources, can be directly applied to monitoring systems of various data sources without additional secondary development work, can further improve productivity and refresh operation and maintenance levels of enterprises, and is an innovative method for improving productivity of a big data intelligent monitoring platform.
Drawings
FIG. 1 is a flow chart of a big data monitoring method according to an embodiment of the present invention;
fig. 2 is a schematic structural diagram of a big data monitoring apparatus according to an embodiment of the present invention.
Detailed Description
The technical solutions of the present application will be described in more detail below with reference to the accompanying drawings and embodiments.
It should be noted that, if not conflicted, the embodiments and the features of the embodiments can be combined with each other and are within the scope of protection of the present application. Additionally, while a logical order is shown in the flow diagrams, in some cases, the steps shown or described may be performed in an order different than here.
As shown in fig. 1, an embodiment of the present invention provides a big data monitoring method, including:
101. collecting one or more types of monitoring data sources;
102. monitoring status information of each type of the monitoring data source; converting the format of the monitoring data source into the format of service data;
103. monitoring at least one data index of the service data according to an operation and maintenance actual service scene or user setting;
and S104, generating a monitoring processing result according to the state information and the monitored data index.
The monitoring data source of the embodiment of the invention can comprise various software such as WeChat, short message, nail and the like, and equipment such as background server, cloud server and the like of various software or services.
In this embodiment of the present invention, the converting the format of the monitoring data source into the format of the service data in step 102 includes:
Determining one or more data fields of each monitoring data source and the meaning of each data field according to the data structure of each monitoring data source, and extracting the data of each data field;
and filling the extracted data of each data field into the data fields representing the corresponding meanings according to the data structure of the service data, wherein the data structure of the service data supports the data structures of various types of monitoring data sources.
In the embodiment of the present invention, the data structure information of the monitoring data source is obtained after access tests of various monitoring data sources are performed in advance, and the embodiment of the present invention may be used to bear data structures of various monitoring data sources, for example: mainstream promethues data, MySQL and mainstream time sequence database TSDB access.
In the embodiment of the present invention, the data structure of the service data supports data structures of multiple types of monitoring data sources, and for data fields with one or more meanings contained in any monitoring data source not existing in the data structure of the service data, the data fields with the corresponding meanings can be supplemented by increasing the field length of the service data or the data fields with the corresponding meanings can be added in blank fields of the service data.
In the embodiment of the invention, when monitoring data is remotely accessed, a user keys in access parameters of different data types, and various data sources are converted into an internal uniform service data structure (TSDB structure) to realize monitoring of various data sources.
In this embodiment of the present invention, the converting the format of the monitoring data source into the format of the service data in step 102 further includes:
determining whether the monitoring data source contains time sequence data information according to the data structure of each monitoring data source;
for a monitoring data source without time sequence data information, filling time of receiving data of the monitoring data source into a time sequence field of service data as timestamp information;
and for a monitoring data source with time sequence data information, filling timestamp information in the time sequence data information into a time sequence field of service data.
The embodiment of the invention can realize monitoring management on the time sequence data source and the non-time sequence data source, and the non-time sequence database can also convert the time sequence data source and the non-time sequence data source into a structure of internal unified service data for unified processing.
At present, monitoring data sources are mainly divided into a Time sequence DataBase TSDB (Time-Series DataBase) and a non-Time sequence DataBase, namely, a common DataBase records information of the Time sequence DataBase, a far original data structure of monitoring data is extracted through the common DataBase similar to MySQL, MongoDB, Oracle and the like, and Time stamp information recorded by data is added to form Time sequence data information.
In this embodiment of the present invention, the status information includes at least one of the following: monitoring the capacity occupation condition of a data source; monitoring a network condition of a data source; and monitoring the hardware equipment monitoring index of the data source.
In this embodiment of the present invention, the operation and maintenance actual service scenario includes at least one of the following: a network condition monitoring scene, a cluster state monitoring scene, a high-load task monitoring scene, a daily inspection scene and a key monitoring scene.
In this embodiment of the present invention, the generating a monitoring processing result according to the state information and the monitored data index in step 104 includes:
generating a monitoring processing result according to the state information and the monitored data index comprises:
extracting data fields representing corresponding meanings from data fields of a data structure of the service data as data indexes according to an operation and maintenance actual service scene or user setting;
determining the operation and maintenance condition of the corresponding monitoring data source according to the state information of each monitoring data source;
determining a change of each of the data indicators over time, and/or;
and determining whether the corresponding monitoring data source has a fault according to the data index, and determining the fault type if the corresponding monitoring data source has the fault.
In the embodiment of the invention, the operation and maintenance conditions of the corresponding monitoring data source can be determined according to the state information of each monitoring data source, including the capacity occupation condition of the monitoring data source; monitoring a network condition of a data source; and monitoring the hardware equipment monitoring index of the data source. The increase rate or fluctuation change of the data index can be determined by monitoring the numerical value of the data index within a period of time; and further determining whether the monitoring data source has a fault according to the change mode of the data index.
Specifically, determining whether a corresponding monitoring data source has a fault according to the change mode of the data index includes:
when at least one data index exists in the monitoring data source and/or the variation of the at least one data index is larger than or equal to a preset danger coefficient threshold value, determining the corresponding monitoring data source as a high-risk data source; the danger coefficient threshold value is a preset fault critical value corresponding to the monitoring data index;
when at least one data index exists in the monitoring data source and/or the variation of the at least one data index is larger than or equal to a preset damage coefficient threshold value, determining the corresponding monitoring data source as a vulnerable data source; the damage coefficient threshold value is a preset damage critical value corresponding to the monitoring data index.
In the embodiment of the invention, the data analysis, calculation and prediction of the (converted) service data can be realized, and an alarm or a prompt can be given after certain threshold values or set rules are reached.
The rules of the alarm or prompt may be manually entered by the user, for example, in a manner of "triggering the alarm after some indexes are greater than a threshold", and after receiving the manually entered rules, the data comparison operation is performed, and when the alarm condition is reached, the alarm is triggered.
The alarm or prompt rule can be that trend analysis is carried out according to the data of the monitoring data source, data is calculated through an algorithm, and an alarm is triggered after the data are found to be mismatched.
In the embodiment of the present invention, the method further includes:
and acquiring a data structure of the one or more types of monitoring data sources preset through the configuration interface.
In the embodiment of the present invention, the method further includes:
and setting access parameters of the one or more types of monitoring data sources, and distinguishing and identifying different types of monitoring data sources through the access parameters.
The embodiment of the invention keys in the data structures of different data sources through the access pages of the different data sources and converts the data structures into the unified business data structure. And realizing compatible processing of all monitoring data sources through an internal unified service data structure.
The embodiment of the invention can realize the monitoring of different types of data sources by adding the data information of the monitoring data source, and realize the automatic loading of data of different types of data sources for monitoring processing.
In the embodiment of the invention, the method further comprises the following steps: further comprising: displaying at least one of the following information in a display interface in a network topological graph mode or a table mode:
the state information of each type of monitoring data source, the change condition of each data index along with time and the fault type of the monitoring data source.
Displaying the status information and the monitoring processing result may include at least one of:
displaying the network resource operation state, the network resource operation capacity and the network fault information of the monitoring data source;
displaying the software running state, the software running capability and the storage resource of the monitoring data source;
displaying a monitoring data source with a fault;
displaying the running state of the monitoring data source;
monitoring a display result of the state information of the data source;
displaying network equipment information; the network equipment information comprises network transmission speed, network occupancy rate and network packet loss;
displaying the calling condition between the software; the calling condition among the software comprises the total quantity of received requests, the total quantity of sent requests, the quantity of returned results and the success of the requests among the software;
Displaying input and output information of software; the software input and output information comprises network bandwidth occupied by software, hard disk throughput and hard disk read-write data volume;
displaying shared monitoring information among different data sources;
displaying various distributed file system cluster monitoring indexes;
displaying various monitoring indexes of the memory database system;
displaying cluster monitoring indexes of the storage system;
displaying various cluster monitoring indexes of the distributed database system;
displaying the capacity occupation condition of the monitoring data source;
displaying the network condition of the monitoring data source;
and displaying the monitoring index of the hardware equipment.
In the embodiment of the invention, the state information and the monitoring processing result can be displayed in a network topological graph mode; the status information and the monitoring processing result may also be presented in a table.
As shown in fig. 2, an embodiment of the present invention further provides a big data monitoring apparatus, including:
the acquisition module is used for acquiring one or more types of monitoring data sources;
the compatible module is used for monitoring the state information of each type of monitoring data source and converting the format of the monitoring data source into the format of service data;
the monitoring module is arranged for monitoring at least one data index of the service data according to an operation and maintenance actual service scene or user setting;
And the processing module is configured to generate a monitoring processing result according to the state information and the monitored data index.
The big data monitoring device of the embodiment of the invention determines the type of the monitoring data source after accessing the device according to the data source of the business side of the enterprise user, reads the data of the monitoring data source, converts the data and judges the data to realize abnormal detection and/or alarm.
The embodiment of the invention also provides a computer-readable storage medium, which stores computer-executable instructions, wherein the computer-executable instructions are used for executing the big data monitoring method.
The embodiment of the invention also provides a device for realizing big data monitoring, which comprises a memory and a processor, wherein the memory stores the big data monitoring program, and the processor is used for executing the big data monitoring method when the big data monitoring program is read.
Although the embodiments of the present invention have been described above, the contents thereof are merely embodiments adopted to facilitate understanding of the technical aspects of the present invention, and are not intended to limit the present invention. It will be apparent to persons skilled in the art that various changes in form and details may be made therein without departing from the spirit and scope of the invention as defined in the appended claims.
Claims (13)
1. A big data monitoring method is characterized by comprising the following steps:
collecting one or more types of monitoring data sources;
monitoring status information of each type of the monitoring data source; converting the format of the monitoring data source into the format of service data;
monitoring at least one data index of the service data according to an operation and maintenance actual service scene or user setting;
and generating a monitoring processing result according to the state information and the monitored data index.
2. The big data monitoring method of claim 1, wherein: the converting the format of the monitoring data source into the format of the service data comprises:
determining one or more data fields of each monitoring data source and the meaning of each data field according to the data structure of each monitoring data source, and extracting the data of each data field;
and filling the extracted data of each data field into the data fields representing the corresponding meanings according to the data structure of the service data, wherein the data structure of the service data supports the data structures of various types of monitoring data sources.
3. The big data monitoring method of claim 2, wherein: the converting the format of the monitoring data source into the format of the service data further comprises:
Determining whether the monitoring data source contains time sequence data information according to the data structure of each monitoring data source;
for a monitoring data source without time sequence data information, filling time of receiving data of the monitoring data source into a time sequence field of service data as timestamp information;
and for a monitoring data source with time sequence data information, filling timestamp information in the time sequence data information into a time sequence field of service data.
4. The big data monitoring method of claim 1, wherein: the status information includes at least one of: capacity occupancy; a network condition; and monitoring indexes by the hardware equipment.
5. The big data monitoring method according to claim 1 or 4, wherein: the operation and maintenance actual service scene comprises at least one of the following: a network condition monitoring scene, a cluster state monitoring scene, a high-load task monitoring scene, a daily inspection scene and a key monitoring scene.
6. The big data monitoring method of claim 3, wherein: generating a monitoring processing result according to the state information and the monitored data index comprises:
extracting data fields representing corresponding meanings from data fields of a data structure of the service data as data indexes according to an operation and maintenance actual service scene or user setting;
Determining the operation and maintenance condition of the corresponding monitoring data source according to the state information of each monitoring data source;
determining a change of each of the data indicators over time, and/or;
and determining whether the corresponding monitoring data source has a fault according to the data index, and determining the fault type if the corresponding monitoring data source has the fault.
7. The big data monitoring method of claim 6, wherein: determining whether a corresponding monitoring data source has a fault according to the data index comprises:
when at least one data index exists in the monitoring data source or the variation of the at least one data index is larger than or equal to a preset danger coefficient threshold value, determining the corresponding monitoring data source as a high-risk data source; the danger coefficient threshold value is a preset fault critical value corresponding to the monitoring data index;
when at least one data index exists in the monitoring data source or the variation of the at least one data index is larger than or equal to a preset damage coefficient threshold value, determining the corresponding monitoring data source as a vulnerable data source; the damage coefficient threshold value is a preset damage critical value corresponding to the monitoring data index.
8. The big data monitoring method of claim 2, wherein: the method is also preceded by:
And acquiring a data structure of the one or more types of monitoring data sources preset through the configuration interface.
9. The big data monitoring method of claim 1, wherein: the method is also preceded by:
and setting access parameters of the one or more types of monitoring data sources, and distinguishing and identifying different types of monitoring data sources through the access parameters.
10. The big data monitoring method of claim 6, wherein: further comprising: displaying at least one of the following information in a display interface in a network topological graph mode or a table mode:
the state information of each type of monitoring data source, the change condition of each data index along with time and the fault type of the monitoring data source.
11. A big data monitoring device, comprising:
the acquisition module is used for acquiring one or more types of monitoring data sources;
the compatible module is used for monitoring the state information of each type of monitoring data source and converting the format of the monitoring data source into the format of service data;
the monitoring module is arranged for monitoring at least one data index of the service data according to an operation and maintenance actual service scene or user setting;
And the processing module is configured to generate a monitoring processing result according to the state information and the monitored data index.
12. A computer-readable storage medium storing computer-executable instructions for performing the big data monitoring method of any one of claims 1 to 10.
13. An apparatus for implementing big data monitoring, comprising a memory and a processor, wherein the memory stores a big data monitoring program, and the processor is configured to execute the big data monitoring method according to any one of claims 1 to 10 when the big data monitoring program is read.
Applications Claiming Priority (2)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910424518 | 2019-05-21 | ||
| CN2019104245180 | 2019-05-21 |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN111984495A true CN111984495A (en) | 2020-11-24 |
Family
ID=73436187
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201910725296.6A Pending CN111984495A (en) | 2019-05-21 | 2019-08-07 | Big data monitoring method and device and storage medium |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN111984495A (en) |
Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112650757A (en) * | 2020-12-30 | 2021-04-13 | 创业慧康科技股份有限公司 | Method, device, equipment and medium for storing structured and unstructured data |
| CN112667717A (en) * | 2020-12-23 | 2021-04-16 | 贵州电网有限责任公司电力科学研究院 | Transformer substation inspection information processing method and device, computer equipment and storage medium |
| CN113556344A (en) * | 2021-07-21 | 2021-10-26 | 广州科腾信息技术有限公司 | General index monitoring billboard based on organizational performance scene |
| CN114140032A (en) * | 2022-01-29 | 2022-03-04 | 北京优特捷信息技术有限公司 | Facility running state monitoring method, device, equipment and storage medium |
| CN116582462A (en) * | 2023-07-14 | 2023-08-11 | 浪潮通信信息系统有限公司 | Converged service monitoring method and device |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104268173A (en) * | 2014-09-15 | 2015-01-07 | 中国工商银行股份有限公司 | Centralized data monitoring method, device and system |
| CN108880943A (en) * | 2018-07-26 | 2018-11-23 | 广东浪潮大数据研究有限公司 | A kind of monitoring system of isomery cloud platform |
| CN109522360A (en) * | 2018-11-16 | 2019-03-26 | 江苏物联网研究发展中心 | A kind of large data center monitoring data visualization system and method |
-
2019
- 2019-08-07 CN CN201910725296.6A patent/CN111984495A/en active Pending
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104268173A (en) * | 2014-09-15 | 2015-01-07 | 中国工商银行股份有限公司 | Centralized data monitoring method, device and system |
| CN108880943A (en) * | 2018-07-26 | 2018-11-23 | 广东浪潮大数据研究有限公司 | A kind of monitoring system of isomery cloud platform |
| CN109522360A (en) * | 2018-11-16 | 2019-03-26 | 江苏物联网研究发展中心 | A kind of large data center monitoring data visualization system and method |
Cited By (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112667717A (en) * | 2020-12-23 | 2021-04-16 | 贵州电网有限责任公司电力科学研究院 | Transformer substation inspection information processing method and device, computer equipment and storage medium |
| CN112667717B (en) * | 2020-12-23 | 2023-04-07 | 贵州电网有限责任公司电力科学研究院 | Transformer substation inspection information processing method and device, computer equipment and storage medium |
| CN112650757A (en) * | 2020-12-30 | 2021-04-13 | 创业慧康科技股份有限公司 | Method, device, equipment and medium for storing structured and unstructured data |
| CN113556344A (en) * | 2021-07-21 | 2021-10-26 | 广州科腾信息技术有限公司 | General index monitoring billboard based on organizational performance scene |
| CN114140032A (en) * | 2022-01-29 | 2022-03-04 | 北京优特捷信息技术有限公司 | Facility running state monitoring method, device, equipment and storage medium |
| CN116582462A (en) * | 2023-07-14 | 2023-08-11 | 浪潮通信信息系统有限公司 | Converged service monitoring method and device |
| CN116582462B (en) * | 2023-07-14 | 2023-09-22 | 浪潮通信信息系统有限公司 | Converged service monitoring method and device |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN111984495A (en) | Big data monitoring method and device and storage medium | |
| CN110661659B (en) | Alarm method, device and system and electronic equipment | |
| US20180365085A1 (en) | Method and apparatus for monitoring client applications | |
| CN112130999B (en) | Electric power heterogeneous data processing method based on edge calculation | |
| CN111813573B (en) | Communication method of management platform and robot software and related equipment thereof | |
| CN110147470B (en) | Cross-machine-room data comparison system and method | |
| CN113190423B (en) | Method, device and system for monitoring service data | |
| CN110727560A (en) | Cloud service alarm method and device | |
| CN109886631B (en) | Method, device, equipment and medium for supervising express delivery person dispatch behaviors | |
| CN112800061B (en) | Data storage method, device, server and storage medium | |
| CN110620699A (en) | Message arrival rate determination method, device, equipment and computer readable storage medium | |
| CN110806960A (en) | Information processing method and device and terminal equipment | |
| CN111221890B (en) | Automatic monitoring and early warning method and device for universal index class | |
| CN110727563A (en) | Cloud service alarm method and device for preset customer | |
| CN110677271B (en) | Big data alarm method, device, equipment and storage medium based on ELK | |
| CN112286930A (en) | Method, device, storage medium and electronic equipment for resource sharing of redis business side | |
| CN111427749A (en) | Monitoring tool and method for ironic service in openstack environment | |
| CN108234658B (en) | Method and device for sensing health condition of server cluster and server | |
| CN107193721B (en) | Method and device for generating log | |
| CN115718732A (en) | Disk file management method, device, equipment and storage medium | |
| CN115525392A (en) | Container monitoring method and device, electronic equipment and storage medium | |
| CN109829016B (en) | Data synchronization method and device | |
| CN109508356B (en) | Data abnormality early warning method, device, computer equipment and storage medium | |
| CN112988417A (en) | Message processing method and device, electronic equipment and computer readable medium | |
| CN111400156A (en) | Log analysis method and device |
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 |