CN117041018A - Remote intelligent operation and maintenance management method for data center and related equipment - Google Patents
Remote intelligent operation and maintenance management method for data center and related equipment Download PDFInfo
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- CN117041018A CN117041018A CN202311295891.3A CN202311295891A CN117041018A CN 117041018 A CN117041018 A CN 117041018A CN 202311295891 A CN202311295891 A CN 202311295891A CN 117041018 A CN117041018 A CN 117041018A
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- 238000012423 maintenance Methods 0.000 title claims abstract description 34
- 238000007726 management method Methods 0.000 title claims abstract description 31
- 238000000034 method Methods 0.000 claims abstract description 45
- 230000007246 mechanism Effects 0.000 claims abstract description 29
- 230000005540 biological transmission Effects 0.000 claims abstract description 27
- 230000008569 process Effects 0.000 claims abstract description 24
- 230000002159 abnormal effect Effects 0.000 claims description 68
- 238000001514 detection method Methods 0.000 claims description 47
- 230000005856 abnormality Effects 0.000 claims description 26
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L41/00—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks
- H04L41/06—Management of faults, events, alarms or notifications
- H04L41/0631—Management of faults, events, alarms or notifications using root cause analysis; using analysis of correlation between notifications, alarms or events based on decision criteria, e.g. hierarchy, tree or time analysis
- H04L41/064—Management of faults, events, alarms or notifications using root cause analysis; using analysis of correlation between notifications, alarms or events based on decision criteria, e.g. hierarchy, tree or time analysis involving time analysis
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L41/00—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks
- H04L41/06—Management of faults, events, alarms or notifications
- H04L41/0677—Localisation of faults
Abstract
The embodiment of the application discloses a remote intelligent operation and maintenance management method for a data center and related equipment, which are convenient for the data management center to manage various types of equipment and carry out subsequent overhaul work. The method comprises the steps of obtaining subscription mechanism information of various types of target equipment, wherein the subscription mechanism information comprises equipment information of the various types of target equipment, and the equipment information comprises a supported data structure; establishing a subscription relation with the various types of target equipment according to the subscription mechanism information, wherein the subscription relation comprises a data transmission protocol, and the data transmission protocol is generated according to a supported data structure; and during the effective period of the subscription relation, acquiring equipment data information of various types of target equipment based on the data transmission protocol, wherein the equipment data information comprises equipment information of the target equipment and data streams generated by the target equipment in the working process, and the data streams comprise a plurality of data points corresponding to the target equipment.
Description
Technical Field
The embodiment of the application relates to the technical field of data operation and maintenance processing, in particular to a remote intelligent operation and maintenance management method for a data center and related equipment.
Background
The data monitoring center needs to monitor various kinds of devices in real time, monitors the running state of each device in real time according to the data fed back by each device, and at present, the data monitoring center further analyzes and processes the fed back data after acquiring the data fed back by the device to judge whether the data is abnormal or not, but at present, the data monitoring center uniformly analyzes and processes the acquired device data, and may cause other normal devices to be misjudged as abnormal devices when analyzing the device data. When the equipment is judged to be abnormal equipment, the data supervision center needs to make corresponding instructions, when the misjudged equipment is more, the scheduling difficulty of the data supervision center can be increased, under the multi-process environment, the data supervision center needs to schedule the processes according to the priority, the scheduling algorithm and the like, and under the condition that the scheduling events are more, certain processes of the data supervision center can be blocked or even forced to be withdrawn, so that the management and subsequent overhaul work of the equipment by the data supervision center are inconvenient.
Disclosure of Invention
The embodiment of the application provides a remote intelligent operation and maintenance management method for a data center and related equipment, which can reduce or avoid misjudgment of other equipment as abnormal equipment through a target abnormality detection template, effectively reduce scheduling events of the data supervision center, solve the problem that certain processes of the data supervision center are blocked or even forcedly retired, quickly find out sources of abnormal data when determining that abnormal equipment data information occurs, locate the equipment with abnormal operation state through the abnormal data, and ensure that the equipment with abnormal data can be quickly overhauled, thereby being convenient for the data management center to manage various types of equipment and carry out subsequent overhauling work.
The first aspect of the application provides a remote intelligent operation and maintenance management method for a data center, which comprises the following steps:
acquiring subscription mechanism information of various types of target devices, wherein the subscription mechanism information comprises device information of the various types of target devices, and the device information comprises a supported data structure;
establishing a subscription relation with the various types of target equipment according to the subscription mechanism information, wherein the subscription relation comprises a data transmission protocol, and the data transmission protocol is generated according to a supported data structure;
acquiring equipment data information of various types of target equipment based on the data transmission protocol during the effective period of the subscription relation, wherein the equipment data information comprises equipment information of the target equipment and data streams generated by the target equipment in the working process, and the data streams comprise a plurality of data points corresponding to the target equipment;
determining a target abnormality detection template in a preset template library according to equipment information of target equipment, wherein the template library stores abnormality detection templates corresponding to various trained equipment;
setting a plurality of sampling points for a target time period, and intercepting an initial data set in a time sequence corresponding to the plurality of sampling points;
calculating the average value of each data point of an initial data set in the same time sequence to obtain a data sequence to be detected corresponding to each time sequence;
respectively analyzing the data sequence to be detected corresponding to the time sequence by utilizing the abnormality detection template to obtain an analysis result;
and determining abnormal equipment data information from the equipment data information according to the analysis result, and positioning abnormal equipment corresponding to the abnormal equipment data information.
Optionally, the analyzing the data sequence to be detected corresponding to the time sequence by using the anomaly detection template respectively, to obtain an analysis result includes:
respectively extracting all data points in all the data sequences to be detected to obtain a data point sequence;
extracting the characteristics of the data point sequence to obtain data point characteristics;
and inputting the data point characteristics into the abnormality detection template for comparison, so as to obtain an analysis result.
Optionally, the feature extracting the data point sequence to obtain a data point feature includes:
calculating a mean value within the sequence of data points;
calculating a difference between each data point in the sequence of data points and a mean;
square operation is carried out on the difference value between each data point and the mean value, and a difference value square value corresponding to the data point is obtained;
and calculating a difference average value of all difference square values in the data point sequence.
Optionally, inputting the data point feature into the anomaly detection template for comparison, so as to obtain an analysis result includes:
calculating the difference value average value corresponding to all the data point sequences to form a difference value average value sequence;
inputting the difference value average value sequence into an anomaly detection template for comparison, wherein the anomaly detection template stores a template sequence, and the comparison process comprises the following steps:
calculating the difference value between the difference value average value sequence and the template sequence to obtain a difference value sequence;
and determining whether the corresponding data point is abnormal according to the difference value sequence.
Optionally, the establishing a subscription relationship with the various types of target devices according to the subscription mechanism information includes:
sending requests for establishing data connection to various types of target equipment according to the subscription mechanism information;
receiving data connection responses sent by various types of target devices;
and establishing a subscription relation with the target devices of various types according to the data connection response.
Optionally, the acquiring device data information of the target devices of various types based on the data transmission protocol includes:
generating a target control instruction according to the subscription relation, wherein the target control instruction is an instruction for calling equipment data information of various types of target equipment;
and sending the target control instruction to the various types of target equipment so as to send equipment data information of the equipment to a data supervision center when the various types of target equipment detect the data types supported by the equipment from the target control instruction.
Optionally, after sending the device data information of the present device to the data supervision center, the method further includes:
and classifying and storing the device data information of the various types of target devices.
The second aspect of the present application provides a remote intelligent operation and maintenance management system for a data center, comprising:
a first acquisition unit, configured to acquire subscription mechanism information of various types of target devices, where the subscription mechanism information includes device information of the various types of target devices, and the device information includes a supported data structure;
the establishing unit is used for establishing a subscription relation with the various types of target equipment according to the subscription mechanism information, wherein the subscription relation comprises a data transmission protocol, and the data transmission protocol is generated according to a supported data structure;
the second acquisition unit is used for acquiring equipment data information of various types of target equipment based on the data transmission protocol during the effective period of the subscription relation, wherein the equipment data information comprises equipment information of the target equipment and data streams generated by the target equipment in the working process, and the data streams comprise a plurality of data points corresponding to the target equipment;
a determining unit, configured to determine a target abnormality detection template in a preset template library according to device information of a target device, where the template library stores abnormality detection templates corresponding to various types of trained devices;
the intercepting unit is used for setting a plurality of sampling points for a target time period and intercepting an initial data set in a time sequence corresponding to the sampling points;
the computing unit is used for computing the average value of each data point of the initial data set in the same time sequence to obtain a data sequence to be detected corresponding to each time sequence;
the analysis unit is used for respectively analyzing the data sequences to be detected corresponding to the time sequences by utilizing the abnormal detection templates to obtain analysis results;
and the positioning unit is used for determining abnormal equipment data information from the equipment data information according to the analysis result and positioning abnormal equipment corresponding to the abnormal equipment data information.
The third aspect of the present application provides a remote intelligent operation and maintenance management device for a data center, comprising:
a processor, a memory, an input-output unit, and a bus;
the processor, the memory and the input/output unit are respectively connected with the bus;
the memory holds a program that the processor invokes to perform the data center remote intelligent operation and maintenance management method according to any one of the first aspect and the first aspect.
A fourth aspect of the present application provides a computer readable storage medium having a program stored thereon, which when executed on a computer performs the data center remote intelligent operation and maintenance management method according to any one of the first aspect and the first aspect.
From the above technical solutions, the embodiment of the present application has the following advantages:
in the remote intelligent operation and maintenance management method of the data center, after a subscription relation is established between the data supervision center and various types of target equipment, equipment data information of the various types of target equipment is acquired based on a data transmission protocol during the effective period of the subscription relation, wherein the equipment data information comprises equipment information of the target equipment and data streams generated by the target equipment in the working process, and the data streams comprise a plurality of data points corresponding to the target equipment; determining a target abnormality detection template in a preset template library according to equipment information of target equipment, wherein the template library stores abnormality detection templates corresponding to various trained equipment; setting a plurality of sampling points for a target time period, and intercepting an initial data set in a time sequence corresponding to the plurality of sampling points; calculating the average value of each data point of an initial data set in the same time sequence to obtain a data sequence to be detected corresponding to each time sequence; respectively analyzing the data sequence to be detected corresponding to the time sequence by using an anomaly detection template to obtain an analysis result; and determining abnormal equipment data information in the equipment data information according to the analysis result, and positioning abnormal equipment corresponding to the abnormal equipment data information.
And then, according to the application, the corresponding target abnormality detection template is selected from the template library according to different types of target devices, thereby reducing or avoiding other devices from being misjudged as abnormal devices, effectively reducing the scheduling event of the data supervision center, and solving the problems that certain processes of the data supervision center are blocked and even forcedly retired. And when the abnormal data information of the equipment is determined, the source of the abnormal data can be quickly found, the abnormal data is positioned to the equipment with abnormal running state, so that the equipment with abnormal data can be quickly overhauled, and the data management center can conveniently manage various types of equipment and carry out subsequent overhauling work.
Drawings
In order to more clearly illustrate the technical solutions of the present application, the drawings that are needed in the description of the embodiments will be briefly described below, it being obvious that the drawings in the following description are only some embodiments of the present application, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a schematic diagram of a flow structure of a remote intelligent operation and maintenance management method of a data center according to the present application;
FIG. 2 is a schematic flow chart of an embodiment of analyzing a data sequence to be detected in the method for managing remote intelligent operation and maintenance of a data center according to the present application;
FIG. 3 is a schematic diagram of a flow structure of a remote intelligent operation and maintenance management system of a data center according to the present application;
fig. 4 is a schematic flow structure diagram of the remote intelligent operation and maintenance management device of the data center.
Detailed Description
At present, after the data fed back by the device is obtained, the data supervision center further analyzes and processes the fed back data to determine whether the data is abnormal, but at present, the data supervision center uniformly analyzes and processes the obtained device data, and when analyzing the device data, other normal devices may be misjudged as abnormal devices. When the equipment is judged to be abnormal equipment, the data supervision center needs to make corresponding instructions, when the misjudged equipment is more, the scheduling difficulty of the data supervision center can be increased, under the multi-process environment, the data supervision center needs to schedule the processes according to the priority, the scheduling algorithm and the like, and under the condition that the scheduling events are more, certain processes of the data supervision center can be blocked or even forced to be withdrawn, so that the management and subsequent overhaul work of the equipment by the data supervision center are inconvenient.
Based on the method, the application provides a remote intelligent operation and maintenance management method for a data center and related equipment, other equipment can be reduced or avoided being misjudged as abnormal equipment through a target abnormality detection template, scheduling events of the data supervision center are effectively reduced, the problem that certain processes of the data supervision center are blocked or even forcedly withdrawn is solved, when abnormal equipment data information is determined, the source of abnormal data can be rapidly found out, the abnormal equipment with abnormal running state is positioned through the abnormal data, and the equipment with abnormal data can be rapidly overhauled, so that the data management center can manage various types of equipment and follow-up overhauling work conveniently.
It should be noted that the method for managing remote intelligent operation and maintenance of a data center provided by the application can be applied to a terminal, a system and a server, for example, the terminal can be a smart phone or a computer, a tablet computer, a smart television, a portable computer terminal and a fixed terminal such as a desktop computer. For convenience of explanation, the present application is exemplified by using the terminal as the execution subject.
Referring to fig. 1, fig. 1 is a flowchart of an embodiment of a remote intelligent operation and maintenance management method for a data center according to a first aspect of the present application, including:
101. the method comprises the steps that a terminal acquires subscription mechanism information of various types of target equipment, wherein the subscription mechanism information comprises equipment information of the various types of target equipment, and the equipment information comprises a supported data structure;
in the embodiment of the application, the terminal is mainly a data monitoring center, and in the following embodiment, the data monitoring center needs to monitor various types of target devices in real time, and judges whether any device in the various types of target devices is abnormal according to the data fed back by the various types of target devices, wherein the data monitoring center needs to establish a data connection relationship with the various types of target devices before acquiring the device data information of the various types of target devices, so that the device data information of the various types of target devices can be transmitted to the data monitoring center.
Specifically, the data supervision center obtains subscription mechanism information of various types of target devices, wherein the various types of target devices include, but are not limited to, the following devices: the data receiver type, the data transmitter type and the data analyzer type are selected from a plurality of corresponding target devices under each device type, and the number of each device type and the device types supervised by the data supervision center are not particularly limited in the application and can be set according to the situation. The subscription mechanism information contains device information of various types of target devices, and the device information contains data structures supported by the various types of target devices. For example: the data structure supported by the target device in the data receiver type is byte, the data structure supported by the target device in the data transmitter type is short, the data structure supported by the target device in the data analyzer type is int, etc., which are merely illustrative, and the specific supported data structure may be set according to the actual situation, and is not limited herein. When the data administration center acquires the data structures supported by the various types of target devices, step 102 is performed.
102. The terminal establishes a subscription relation with the various types of target equipment according to the subscription mechanism information, wherein the subscription relation comprises a data transmission protocol, and the data transmission protocol is generated according to a supported data structure;
in the embodiment of the application, after the data supervision center acquires the data structures supported by various types of target devices, an instruction for establishing a subscription relationship is sent to the various types of target devices, specifically, a request for establishing data connection is sent to the various types of target devices according to subscription mechanism information, and after a data connection response sent by the various types of target devices is received, the subscription relationship is established with the various types of target devices according to the data connection response.
Wherein the instruction to establish the subscription relationship is issued according to a data structure supported by various types of target devices, for example: the data structure supported by the data receiver type is byte, and the data structure in the instruction sent to the data receiver type by the data supervision center is byte; similarly, the data supervision center establishes a subscription relationship with various types of target devices according to the same manner, and the established subscription relationship comprises data transmission protocols which are generated according to the supported data structures.
103. The terminal obtains equipment data information of various types of target equipment based on the data transmission protocol during the effective period of the subscription relation, wherein the equipment data information comprises equipment information of the target equipment and data streams generated by the target equipment in the working process, and the data streams comprise a plurality of data points corresponding to the target equipment;
in the embodiment of the application, after the data supervision center establishes a subscription relation with various types of target equipment according to subscription mechanism information, a data transmission protocol is included in the subscription relation, equipment information of the various types of target equipment and data streams generated in the working process of the target equipment are obtained through the data transmission protocol, specifically, the data supervision center generates a target control instruction according to the subscription relation, the target control instruction is an instruction for retrieving the equipment data information of the various types of target equipment, and then the target control instruction is sent to the various types of target equipment, so that when the various types of target equipment detect the data types supported by the equipment from the target control instruction, the equipment data information of the equipment is sent to the data supervision center, and the data supervision center stores the equipment data information of the various types of target equipment in a classified manner.
The data stream comprises a plurality of data points corresponding to the target equipment, wherein the data points comprise information such as temperature, humidity, service life, maintenance times and the like of the target equipment. Specifically, since the various types of target devices establish a subscription relationship with the data monitoring center through the data structures supported by the target devices, the data monitoring center acquires device data information of the various types of target devices through a target instruction, specifically, the target instruction comprises data structure information supported by the various types of target devices establishing the subscription relationship with the data monitoring center, when the data monitoring center issues the target instruction to the various types of target devices, a data processor in the various types of target devices can identify the exclusive data structure information, and after the identification, the corresponding device data information is sent to the data monitoring center.
For example: after the data processor of the data receiver type receives the corresponding instruction, the data processor feeds back the running state data of the target equipment, namely the data receiver, to the data supervision center in real time.
104. The terminal determines a target abnormality detection template in a preset template library according to equipment information of target equipment, wherein the template library stores abnormality detection templates corresponding to various trained equipment;
in the embodiment of the application, after receiving the device data information of various types of target devices, the data supervision center then determines an abnormality detection template in a preset template library according to the device information of the target devices, wherein the abnormality detection template corresponding to the various types of trained devices is stored in the template library. For the generation of the abnormality detection template, the specific steps are as follows: the data supervision center establishes an initial equipment training model based on an anomaly detection algorithm One-class SVM of machine learning; the anomaly detection algorithm One-class SVM is a model which is trained by a support vector machine and has only positive samples, then whether new data points belong to anomalies is predicted by the model, specifically, the data supervision center calls standard equipment data information of various types of equipment, wherein the standard equipment data information comprises but is not limited to temperature, humidity, service life and maintenance times in the using process of the equipment, the standard equipment data information of the various types of equipment is input into an initial equipment training model, and the initial equipment training model trains the equipment data information of the various types of equipment to generate anomaly detection templates of the various types of equipment.
105. The terminal sets a plurality of sampling points for a target time period, and intercepts an initial data set in a time sequence corresponding to the sampling points;
in the embodiment of the application, the terminal intercepts the using time periods of various types of target devices, and selects the corresponding target time period from the using time periods, for example: the data receiver of the data receiver type is operated for 24 hours around the clock, and a time period of 9:00-21:00 is selected as a target time period in the 24 hours, and a plurality of sampling points are set in the target time period, for example: 3 sampling points are set in the target time period of 9:00-21:00, namely 9:00-13:00, 13:00-17:00 and 17:00-21:00 respectively, and initial data sets in corresponding time sequences are acquired in each sampling point, namely initial data sets corresponding to target equipment are acquired in different sampling points respectively. For example: acquiring data information of the temperature and the humidity of target equipment in the range of 9:00-13:00; acquiring data information of temperature and humidity of target equipment in a range of 13:00-17:00; and acquiring data information of the temperature and the humidity of the target equipment in 17:00-21:00.
106. The terminal calculates the average value of each data point for an initial data set in the same time sequence to obtain a data sequence to be detected corresponding to each time sequence;
in the embodiment of the application, after a plurality of sampling points are set and initial data sets in corresponding time sequences are intercepted from the plurality of sampling points, the average value of each data point is further calculated for the initial data sets in the same time sequence, so as to obtain a data sequence to be detected corresponding to each time sequence. Specifically, for example: in the range of 9:00-13:00, the target equipment can generate a plurality of groups of temperature and humidity data information, the terminal calculates the plurality of groups of temperature and humidity data information generated in the event section, takes out the average value, collects 8 groups of temperature data and 8 groups of humidity data every half an hour in the range of 9:00-13:00, then respectively takes the average value of the 8 groups of temperature data and the 8 groups of humidity data, thereby obtaining corresponding data sequences to be detected in the range of 9:00-13:00, and further, the target equipment of various types can obtain three data sequences to be detected in the three time sequences.
Various types of target devices can obtain the data sequence to be detected corresponding to each time sequence according to the method, and the description is omitted here.
107. The terminal respectively analyzes the data sequence to be detected corresponding to the time sequence by utilizing the abnormality detection template to obtain an analysis result;
in the embodiment of the present application, the detected data sequences in the corresponding time sequences are stored in the anomaly detection template, for example, in the above embodiment, the data receiver in the data receiver type acquires three data sequences to be detected in three time sequences in total, then the anomaly detection template also selects the standard data sequences in the corresponding three time sequences, and the three standard data sequences in the anomaly detection template and the three data sequences to be detected are compared and analyzed, and when the comparison is performed, each time sequence is compared correspondingly.
108. The terminal determines abnormal equipment data information from the equipment data information according to the analysis result, and positions abnormal equipment corresponding to the abnormal equipment data information;
in the embodiment of the application, after the data information of the abnormal equipment is determined in the equipment data information according to the analysis result, the data supervision center traces the source of the data information of the abnormal equipment, namely, the abnormal data is traced and sent by the target equipment of that type, after the equipment type is determined, the abnormal data sent by the target equipment in the equipment type is determined, the equipment sending the abnormal data is positioned, after the equipment with the abnormal is positioned, the data supervision center acquires the positioning information of the abnormal equipment, namely, the label information of the equipment is acquired, and the terminal sends the positioning information to the overhaul center, so that the overhaul center carries out overhaul treatment on the abnormal equipment according to the positioning information.
Referring to fig. 2, an embodiment of analyzing the data sequence to be detected corresponding to the time sequence by using the anomaly detection template to obtain an analysis result is provided below, where the embodiment includes:
201. the terminal extracts each data point in all the data sequences to be detected respectively to obtain a data point sequence;
in the implementation of the present application, a sequence is respectively extracted from each data point in all the data sequences to be detected, specifically, referring to three data sequences to be detected obtained in total in the three time sequences mentioned in the step 106, each data sequence to be detected has the same data point, and the three data sequences to be detected have temperature and humidity data, so that the temperature and humidity in the three data sequences to be detected are respectively extracted, thereby obtaining a temperature data point sequence and a humidity data point sequence, wherein the temperature data point sequence contains all the temperature data points in the three data sequences to be detected, and the humidity data point sequence contains all the humidity data points in the three data sequences to be detected.
202. The terminal performs feature extraction on the data point sequence to obtain data point features;
in the implementation of the application, specifically, firstly, the average value in the data point sequence is calculated, namely, the average value of the temperature data point sequence and the humidity data point sequence is calculated respectively, and then, the difference value between each data point in the temperature data point sequence and the humidity data point sequence and the average value is calculated; and carrying out square operation on the difference value of each data point and the average value to obtain a difference square value corresponding to the data point, and finally calculating the difference average value of all the difference square values in the data point sequence.
203. And the terminal inputs the data point characteristics into the abnormal detection template for comparison, so that an analysis result is obtained.
In the embodiment of the present application, the difference average value corresponding to all the data point sequences obtained in step 202 is calculated to form a difference average value sequence, and then the difference average value sequence is input into an anomaly detection template for comparison, wherein the anomaly detection template stores a template sequence, and the comparison process includes: calculating the difference between the difference mean value sequence and the template sequence to obtain a difference sequence, determining whether the corresponding data point is abnormal according to the difference sequence, namely checking whether the numerical value in the difference sequence is within a preset numerical range, if so, determining that the corresponding data point is normal, if not, determining that the corresponding data point is abnormal, and then tracing the source of the data information of the abnormal equipment by the data supervision center, namely tracing the abnormal data sent by the target equipment of that type, after determining the equipment type, further determining that the abnormal data sent by the target equipment in the equipment type, positioning the equipment sending the abnormal data, after positioning the equipment with the abnormality, acquiring the positioning information of the abnormal equipment, namely acquiring the label information of the equipment, and sending the positioning information to the overhaul center by the terminal so that the overhaul center carries out overhaul treatment on the abnormal equipment according to the positioning information.
Referring to fig. 3, a second aspect of the present application provides a schematic structural diagram of a remote intelligent operation and maintenance management system for a data center, including:
a first obtaining unit 301, configured to obtain subscription mechanism information of various types of target devices, where the subscription mechanism information includes device information of the various types of target devices, and the device information includes a supported data structure;
an establishing unit 302, configured to establish a subscription relationship with the various types of target devices according to the subscription mechanism information, where the subscription relationship includes a data transmission protocol, and the data transmission protocol is generated according to a supported data structure;
a second obtaining unit 303, configured to obtain, during the period in which the subscription relationship takes effect, device data information of various types of target devices based on the data transmission protocol, where the device data information includes device information of the target device itself, and a data stream generated by the target device in a working process, where the data stream includes a plurality of data points corresponding to the target device;
a determining unit 304, configured to determine a target abnormality detection template in a preset template library according to device information of a target device, where abnormality detection templates corresponding to various types of trained devices are stored in the template library;
a clipping unit 305, configured to set a plurality of sampling points for a target time period, and clip an initial data set in a time sequence corresponding to the plurality of sampling points;
the calculating unit 306 is configured to calculate, for an initial data set in the same time sequence, a mean value of each data point, to obtain a data sequence to be detected corresponding to each time sequence;
an analysis unit 307, configured to analyze the data sequences to be detected corresponding to the time sequences by using the anomaly detection templates, so as to obtain analysis results;
and the positioning unit 308 is configured to determine abnormal equipment data information from the equipment data information according to the analysis result, and position an abnormal equipment corresponding to the abnormal equipment data information.
Referring to fig. 4, a third aspect of the present application provides a remote intelligent operation and maintenance management device for a data center, including:
a processor 401, a memory 402, an input/output unit 403, and a bus 404;
the processor 401, the memory 402, and the input-output unit 403 are connected to the bus 404, respectively;
the memory 402 holds a program, and the processor 401 calls the program to execute the data center remote intelligent operation and maintenance management method according to any one of the first aspect and the first aspect.
A fourth aspect of the present application provides a computer readable storage medium having a program stored thereon, which when executed on a computer performs the data center remote intelligent operation and maintenance management method according to any one of the first aspect and the first aspect.
It will be clear to those skilled in the art that, for convenience and brevity of description, specific working procedures of the above-described systems, apparatuses and units may refer to corresponding procedures in the foregoing method embodiments, which are not repeated herein.
In the several embodiments provided in the present application, it should be understood that the disclosed systems, devices, and methods may be implemented in other manners. For example, the apparatus embodiments described above are merely illustrative, e.g., the division of the units is merely a logical function division, and there may be additional divisions when actually implemented, e.g., multiple units or components may be combined or integrated into another system, or some features may be omitted or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be an indirect coupling or communication connection via some interfaces, devices or units, which may be in electrical, mechanical or other form.
The units described as separate units may or may not be physically separate, and units shown as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional unit in the embodiments of the present application may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit. The integrated units may be implemented in hardware or in software functional units.
The integrated units, if implemented in the form of software functional units and sold or used as stand-alone products, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present application may be embodied essentially or in part or all of the technical solution or in part in the form of a software product stored in a storage medium, including instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method according to the embodiments of the present application. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a read-only memory (ROM), a random access memory (RAM, random access memory), a magnetic disk, or an optical disk, or other various media capable of storing program codes.
Claims (10)
1. The remote intelligent operation and maintenance management method for the data center is characterized by comprising the following steps of:
acquiring subscription mechanism information of various types of target devices, wherein the subscription mechanism information comprises device information of the various types of target devices, and the device information comprises a supported data structure;
establishing a subscription relation with the various types of target equipment according to the subscription mechanism information, wherein the subscription relation comprises a data transmission protocol, and the data transmission protocol is generated according to a supported data structure;
acquiring equipment data information of various types of target equipment based on the data transmission protocol during the effective period of the subscription relation, wherein the equipment data information comprises equipment information of the target equipment and data streams generated by the target equipment in the working process, and the data streams comprise a plurality of data points corresponding to the target equipment;
determining a target abnormality detection template in a preset template library according to equipment information of target equipment, wherein the template library stores abnormality detection templates corresponding to various trained equipment;
setting a plurality of sampling points for a target time period, and intercepting an initial data set in a time sequence corresponding to the plurality of sampling points;
calculating the average value of each data point of an initial data set in the same time sequence to obtain a data sequence to be detected corresponding to each time sequence;
respectively analyzing the data sequence to be detected corresponding to the time sequence by utilizing the abnormality detection template to obtain an analysis result;
and determining abnormal equipment data information from the equipment data information according to the analysis result, and positioning abnormal equipment corresponding to the abnormal equipment data information.
2. The method for remote intelligent operation and maintenance management of a data center according to claim 1, wherein the analyzing the data sequence to be detected corresponding to the time sequence by using the anomaly detection template respectively includes:
respectively extracting all data points in all the data sequences to be detected to obtain a data point sequence;
extracting the characteristics of the data point sequence to obtain data point characteristics;
and inputting the data point characteristics into the abnormality detection template for comparison, so as to obtain an analysis result.
3. The method of claim 2, wherein the performing feature extraction on the sequence of data points to obtain data point features comprises:
calculating a mean value within the sequence of data points;
calculating a difference between each data point in the sequence of data points and a mean;
square operation is carried out on the difference value between each data point and the mean value, and a difference value square value corresponding to the data point is obtained;
and calculating a difference average value of all difference square values in the data point sequence.
4. The method for remote intelligent operation and maintenance management of a data center according to claim 3, wherein the step of inputting the data point features into the anomaly detection template for comparison, so as to obtain an analysis result comprises:
calculating the difference value average value corresponding to all the data point sequences to form a difference value average value sequence;
inputting the difference value average value sequence into an anomaly detection template for comparison, wherein the anomaly detection template stores a template sequence, and the comparison process comprises the following steps:
calculating the difference value between the difference value average value sequence and the template sequence to obtain a difference value sequence;
and determining whether the corresponding data point is abnormal according to the difference value sequence.
5. The method for remote intelligent operation and maintenance management of a data center according to claim 1, wherein the establishing a subscription relationship with the various types of target devices according to the subscription mechanism information comprises:
sending requests for establishing data connection to various types of target equipment according to the subscription mechanism information;
receiving data connection responses sent by various types of target devices;
and establishing a subscription relation with the target devices of various types according to the data connection response.
6. The method for remote intelligent operation and maintenance management of a data center according to claim 1, wherein the acquiring device data information of various types of target devices based on the data transmission protocol comprises:
generating a target control instruction according to the subscription relation, wherein the target control instruction is an instruction for calling equipment data information of various types of target equipment;
and sending the target control instruction to the various types of target equipment so as to send equipment data information of the equipment to a data supervision center when the various types of target equipment detect the data types supported by the equipment from the target control instruction.
7. The method of claim 6, wherein after sending the device data information of the device to the data administration center, the method further comprises:
and classifying and storing the device data information of the various types of target devices.
8. A data center remote intelligent operation and maintenance management system, comprising:
a first acquisition unit, configured to acquire subscription mechanism information of various types of target devices, where the subscription mechanism information includes device information of the various types of target devices, and the device information includes a supported data structure;
the establishing unit is used for establishing a subscription relation with the various types of target equipment according to the subscription mechanism information, wherein the subscription relation comprises a data transmission protocol, and the data transmission protocol is generated according to a supported data structure;
the second acquisition unit is used for acquiring equipment data information of various types of target equipment based on the data transmission protocol during the effective period of the subscription relation, wherein the equipment data information comprises equipment information of the target equipment and data streams generated by the target equipment in the working process, and the data streams comprise a plurality of data points corresponding to the target equipment;
a determining unit, configured to determine a target abnormality detection template in a preset template library according to device information of a target device, where the template library stores abnormality detection templates corresponding to various types of trained devices;
the intercepting unit is used for setting a plurality of sampling points for a target time period and intercepting an initial data set in a time sequence corresponding to the sampling points;
the computing unit is used for computing the average value of each data point of the initial data set in the same time sequence to obtain a data sequence to be detected corresponding to each time sequence;
the analysis unit is used for respectively analyzing the data sequences to be detected corresponding to the time sequences by utilizing the abnormal detection templates to obtain analysis results;
and the positioning unit is used for determining abnormal equipment data information from the equipment data information according to the analysis result and positioning abnormal equipment corresponding to the abnormal equipment data information.
9. A remote intelligent operation and maintenance management device for a data center, comprising:
a processor, a memory, an input-output unit, and a bus;
the processor, the memory and the input/output unit are respectively connected with the bus;
the memory holds a program that the processor invokes to perform the data center remote intelligent operation and maintenance management method according to any one of claims 1 to 7.
10. A computer-readable storage medium having a program stored thereon, which when executed on a computer performs the data center remote intelligent operation and maintenance management method according to any one of claims 1 to 7.
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