CN115048274A - Operation and maintenance system based on big data - Google Patents

Operation and maintenance system based on big data Download PDF

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CN115048274A
CN115048274A CN202210959691.2A CN202210959691A CN115048274A CN 115048274 A CN115048274 A CN 115048274A CN 202210959691 A CN202210959691 A CN 202210959691A CN 115048274 A CN115048274 A CN 115048274A
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maintenance
data acquisition
node
big data
election
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CN115048274B (en
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秦健
熊海滨
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China Telecom Construction 3rd Engineering Co Ltd
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
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    • 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
    • G06F11/302—Monitoring arrangements specially adapted to the computing system or computing system component being monitored where the computing system component is a software system
    • 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
    • G06F11/3058—Monitoring arrangements for monitoring environmental properties or parameters of the computing system or of the computing system component, e.g. monitoring of power, currents, temperature, humidity, position, vibrations
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F11/00—Error detection; Error correction; Monitoring
    • G06F11/30—Monitoring
    • G06F11/3065—Monitoring arrangements determined by the means or processing involved in reporting the monitored data
    • G—PHYSICS
    • G08—SIGNALLING
    • G08B—SIGNALLING OR CALLING SYSTEMS; ORDER TELEGRAPHS; ALARM SYSTEMS
    • G08B21/00—Alarms responsive to a single specified undesired or abnormal condition and not otherwise provided for
    • G08B21/18—Status alarms
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W16/00—Network planning, e.g. coverage or traffic planning tools; Network deployment, e.g. resource partitioning or cells structures
    • H04W16/18—Network planning tools
    • H04W16/20—Network planning tools for indoor coverage or short range network deployment
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
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Abstract

The invention discloses an operation and maintenance system based on big data, which comprises a monitoring parameter acquisition module and a big data operation and maintenance module; the sensor module comprises a data acquisition node and a data gathering base station; the data acquisition node is used for acquiring monitoring parameters of the server room and transmitting the monitoring parameters to the big data operation and maintenance module; the big data operation and maintenance module is used for analyzing the monitoring parameters by adopting a big data technology to realize the operation and maintenance monitoring of the server room; the big data operation and maintenance module is also used for dividing the server room into a plurality of areas and determining the coordinates of the data acquisition nodes in each area by adopting a preset distribution algorithm; and the data acquisition nodes in each area generate cluster head nodes and member nodes in a self-adaptive election mode. The invention effectively prolongs the continuous working time of the data acquisition node and greatly reduces the influence of clustering on the acquisition efficiency of the monitoring parameters.

Description

Operation and maintenance system based on big data
Technical Field
The invention relates to the field of operation and maintenance, in particular to an operation and maintenance system based on big data.
Background
Server rooms are rooms designed for continuous operation of computer servers, usually equipped with air conditioning. Buildings or sites dedicated for this purpose are referred to as data centers. Computers in server rooms are typically operated remotely using headless computers through remote management software such as KVM switches or Secure Shell (ssh), VNC, remote desktop, etc.
In the prior art, the internet of things technology is generally used for operation and maintenance management of a server room, a large number of sensors are arranged in the server room to acquire monitoring parameters, and the monitoring parameters are transmitted to an operation and maintenance platform for operation and maintenance management.
However, in the existing operation and maintenance method based on the sensor, after the cluster head nodes are set, the cluster heads are uniformly reselected after a set time interval, but the power consumption levels of the cluster head nodes in different areas are not consistent, and the cluster head selection is performed on all the areas again, which not only wastes energy, but also affects the acquisition efficiency of the monitoring parameters.
Disclosure of Invention
The invention aims to disclose an operation and maintenance system based on big data, which solves the problems that in the prior art, when monitoring parameters of a server room are obtained, cluster heads are reselected for all areas at set time intervals, energy is wasted, and the obtaining efficiency of the monitoring parameters is influenced.
In order to achieve the purpose, the invention adopts the following technical scheme:
an operation and maintenance system based on big data comprises a monitoring parameter acquisition module and a big data operation and maintenance module;
the sensor module comprises a data acquisition node and a data summarizing base station;
the data acquisition node is used for acquiring monitoring parameters of the server room and transmitting the monitoring parameters to the big data operation and maintenance module;
the big data operation and maintenance module is used for analyzing the monitoring parameters by adopting a big data technology to realize the operation and maintenance monitoring of the server room;
the big data operation and maintenance module is also used for dividing the server room into a plurality of areas and determining the coordinates of the data acquisition nodes in each area by adopting a preset allocation algorithm;
and the data acquisition nodes in each area generate cluster head nodes and member nodes in a self-adaptive election mode.
Preferably, the member node is configured to acquire a monitoring parameter of the server room, and send the monitoring parameter to a cluster head node in an area where the member node is located;
and the cluster head node is used for transmitting the monitoring parameters to the data summarizing base station.
Preferably, the monitoring parameters include temperature, humidity and smoke concentration of the machine room environment.
Preferably, the big data operation and maintenance module comprises an operation and maintenance server and an operation and maintenance terminal;
the operation and maintenance server is used for storing the monitoring parameters sent by the data summarizing base station, analyzing the monitoring parameters by adopting a big data technology to obtain operation and maintenance monitoring results, and sending the operation and maintenance monitoring results to the operation and maintenance terminal;
and the operation and maintenance terminal is used for receiving the operation and maintenance monitoring result.
Preferably, the operation and maintenance terminal comprises a local operation and maintenance device and a remote operation and maintenance device;
the local operation and maintenance equipment comprises a desktop computer arranged in an on-duty room of the server room;
the remote operation and maintenance equipment comprises a tablet computer, a smart phone and a notebook computer.
Preferably, the dividing the server room into a plurality of areas includes:
the total number of regions is calculated using the following formula:
Figure DEST_PATH_IMAGE002AA
wherein N represents the total number of regions,
Figure DEST_PATH_IMAGE004AA
representing the total area of the server room,
Figure DEST_PATH_IMAGE006AA
indicating the communication radius of the data acquisition node,
Figure DEST_PATH_IMAGE008AA
representing a preset control coefficient;
Figure DEST_PATH_IMAGE010AA
。
preferably, the cluster head node and the member node are generated by a self-adaptive election mode, and the cluster head node and the member node comprise;
when the holding time of the last election result is over, each data acquisition node respectively calculates the clustering parameter of the data acquisition node and mutually exchanges the clustering parameter with other data acquisition nodes in the same area;
the data acquisition node with the highest clustering parameter calculates the maintaining time of the election result, and sends a message including the maintaining time of the election result to other data acquisition nodes in the same region, wherein the message is used for informing the data acquisition node to be selected as a cluster head node;
in the same area, the data acquisition nodes except the cluster head node are used as member nodes.
Preferably, the holding time of the election result is calculated as follows:
by using
Figure DEST_PATH_IMAGE012AA
Indicating the hold time of the result of the v-th election,
if it is
Figure DEST_PATH_IMAGE014AA
Then, the cluster head node generated by the (v + 1) th election calculates the holding time of the (v + 1) th election result by using the following formula:
Figure DEST_PATH_IMAGE016AA
if it is
Figure DEST_PATH_IMAGE018AA
Then, the cluster head node generated by the v +1 th election calculates the holding time of the v +1 th election result by using the following formula:
Figure DEST_PATH_IMAGE020AA
wherein the content of the first and second substances,
Figure DEST_PATH_IMAGE022AA
indicates the hold time of the v +1 th election result,
Figure DEST_PATH_IMAGE024AA
indicating the working parameters of the member nodes in the region in the holding time of the result of the v-th election,
Figure DEST_PATH_IMAGE026AA
represents a preset contrast value of the working parameter,
Figure DEST_PATH_IMAGE028AA
representing a preset time interval.
Preferably, the operating parameter is calculated by:
Figure DEST_PATH_IMAGE030AA
wherein the content of the first and second substances,
Figure DEST_PATH_IMAGE032AA
、
Figure DEST_PATH_IMAGE034AA
which represents a pre-set scale parameter that is,
Figure DEST_PATH_IMAGE036AA
，
Figure DEST_PATH_IMAGE038AAAA
represents a collection of member nodes in the area,
Figure DEST_PATH_IMAGE040AA
indicating the electric quantity percentage of the member node u after the holding time of the result of the nth election is finished,
Figure DEST_PATH_IMAGE042AA
to represent
Figure DEST_PATH_IMAGE044AA
The total number of member nodes contained in it,
Figure DEST_PATH_IMAGE046AA
representing a set comparison value of the variance of the percentage of the electric quantity,
Figure DEST_PATH_IMAGE048AA
indicating the length of the data transmitted to the cluster head node by the member node u in the holding time of the result of the v-th election,
Figure DEST_PATH_IMAGE050AA
indicating the set data length comparison value.
Preferably, the clustering parameter is calculated as follows:
Figure DEST_PATH_IMAGE052AA
wherein the content of the first and second substances,
Figure DEST_PATH_IMAGE054AA
a clustering parameter representing a data acquisition node z,
Figure DEST_PATH_IMAGE056AA
representing the communication delay between the data acquisition node z and the data summarization base station,
Figure DEST_PATH_IMAGE058AA
representing the average distance between data acquisition node z and other data acquisition nodes in the same area,
Figure DEST_PATH_IMAGE060AA
representing the remaining capacity of the data acquisition node z,
Figure DEST_PATH_IMAGE062AA
representing the total number of other data acquisition nodes having a distance z less than Q, Q representing a predetermined distance parameter, S 1 Representing a preset comparison value of communication delays, S 2 Represents a preset average distance comparison value,
Figure DEST_PATH_IMAGE064AA
represents a preset comparison value of the remaining power amount,
Figure DEST_PATH_IMAGE066AA
representing a preset quantity comparison value.
According to the invention, the server room is divided into a plurality of areas, and then each area independently selects the cluster head node, so that the situation that all areas are reselected by adopting a set time interval is effectively avoided, the energy of the data acquisition nodes is effectively saved, the continuous working time of the data acquisition nodes is prolonged, and as each area independently selects the cluster head node, when one or more areas select the cluster head nodes, the data acquisition nodes in other areas also acquire the monitoring parameters, thereby greatly reducing the influence on the acquisition efficiency of the monitoring parameters.
Drawings
The invention is further illustrated by means of the attached drawings, but the embodiments in the drawings do not constitute any limitation to the invention, and for a person skilled in the art, other drawings can be obtained on the basis of the following drawings without inventive effort.
Fig. 1 is a diagram of an embodiment of a big data-based operation and maintenance system according to the present invention.
Fig. 2 is a diagram illustrating an embodiment of generating cluster head nodes and member nodes by adaptive election according to the present invention.
Detailed Description
Reference will now be made in detail to embodiments of the present invention, examples of which are illustrated in the accompanying drawings, wherein like or similar reference numerals refer to the same or similar elements or elements having the same or similar function throughout. The embodiments described below with reference to the accompanying drawings are illustrative only for the purpose of explaining the present invention, and are not to be construed as limiting the present invention.
As shown in fig. 1, in an embodiment, the present invention provides an operation and maintenance system based on big data, which includes a monitoring parameter obtaining module and a big data operation and maintenance module;
the sensor module comprises a data acquisition node and a data summarizing base station;
the data acquisition node is used for acquiring monitoring parameters of the server room and transmitting the monitoring parameters to the big data operation and maintenance module;
the big data operation and maintenance module is used for analyzing the monitoring parameters by adopting a big data technology to realize the operation and maintenance monitoring of the server room;
the big data operation and maintenance module is also used for dividing the server room into a plurality of areas and determining the coordinates of the data acquisition nodes in each area by adopting a preset allocation algorithm;
and the data acquisition nodes in each area generate cluster head nodes and member nodes in a self-adaptive election mode.
According to the invention, the server room is divided into a plurality of areas, and then each area independently selects the cluster head node, so that the situation that all areas are reselected by adopting a set time interval is effectively avoided, the energy of the data acquisition nodes is effectively saved, the continuous working time of the data acquisition nodes is prolonged, and as each area independently selects the cluster head node, when one or more areas select the cluster head nodes, the data acquisition nodes in other areas also acquire the monitoring parameters, thereby greatly reducing the influence on the acquisition efficiency of the monitoring parameters.
Preferably, the member node is configured to acquire a monitoring parameter of the server room, and send the monitoring parameter to a cluster head node in an area where the member node is located;
the cluster head node is used for transmitting the monitoring parameters to the data summarizing base station.
By means of clustering, the communication electric quantity consumption of the data acquisition nodes can be further reduced, and therefore the continuous working time of the data acquisition nodes is prolonged.
Preferably, the monitoring parameters include temperature, humidity and smoke concentration of the machine room environment.
Preferably, the monitoring parameters may further include operating parameters of the air conditioning equipment, operating parameters of the UPS, operating and checking of the power distribution system, and the like. The data acquisition node acquires the corresponding parameters by communicating with the devices.
Preferably, the big data operation and maintenance module comprises an operation and maintenance server and an operation and maintenance terminal;
the operation and maintenance server is used for storing the monitoring parameters sent by the data summarizing base station, analyzing the monitoring parameters by adopting a big data technology to obtain operation and maintenance monitoring results, and sending the operation and maintenance monitoring results to the operation and maintenance terminal;
and the operation and maintenance terminal is used for receiving the operation and maintenance monitoring result.
Preferably, the analyzing the monitoring parameters by using a big data technology to obtain the operation and maintenance monitoring result includes:
for a set Ut of monitoring parameters with the type t in a period of time, dividing the Ut into a training set and a testing set;
training the prediction model by using a training set, and testing the training model by using a test set to obtain a prediction model with symbol requirements;
inputting a set Uts including the first monitoring parameter of type t into the trained predictive model, predicting future values of the monitoring parameter of type t;
and judging whether the future value meets the set operation and maintenance monitoring condition, if not, generating an operation and maintenance monitoring result comprising alarm information, and if so, generating an operation and maintenance monitoring result indicating that the state is normal.
In addition to monitoring the operation and maintenance of the prediction results, the present invention also sets corresponding conditions to monitor the implemented monitoring parameters, for example, by comparing with corresponding thresholds.
Preferably, the operation and maintenance terminal comprises a local operation and maintenance device and a remote operation and maintenance device;
the local operation and maintenance equipment comprises a desktop computer arranged in an on-duty room of the server room;
the remote operation and maintenance equipment comprises a tablet computer, a smart phone and a notebook computer.
The operation and maintenance terminal can send a prompt to related workers when the operation and maintenance monitoring result contains alarm information.
Preferably, the dividing the server room into a plurality of areas includes:
the total number of regions is calculated using the following formula:
Figure DEST_PATH_IMAGE068AA
wherein N represents the total number of regions,
Figure DEST_PATH_IMAGE070A
representing the total area of the server room,
Figure DEST_PATH_IMAGE072A
indicating the communication radius of the data acquisition node,
Figure DEST_PATH_IMAGE074AAAA
representing a preset control coefficient;
Figure DEST_PATH_IMAGE076A
。
the number of the areas is not specified in advance, but the number of the areas is calculated according to actual values of parameters such as the total area of the server room, the communication radius of the nodes and the like, so that the arrangement mode effectively improves the adaptability of the invention in field arrangement, and if the number of the areas is specified in advance, the problem of overlarge areas or undersize areas can be encountered, and the two situations are not beneficial to saving the electric quantity of the data acquisition nodes.
Preferably, as shown in fig. 2, the generating of the cluster head node and the member node by means of adaptive election includes;
when the holding time of the last election result is over, each data acquisition node respectively calculates the clustering parameter of the data acquisition node and mutually exchanges the clustering parameter with other data acquisition nodes in the same area;
the data acquisition node with the highest clustering parameter calculates the maintaining time of the election result, and sends a message including the maintaining time of the election result to other data acquisition nodes in the same region, wherein the message is used for informing the data acquisition node to be selected as a cluster head node;
in the same area, the data acquisition nodes except the cluster head node are used as member nodes.
In the invention, each area elects the cluster head node independently, and the maintaining time of each election result of each area is calculated in a self-adaptive mode and is related to the actual operation condition in the area. The method and the device can timely adjust the partial area when the electric quantity is unbalanced, and avoid the influence on the coverage range of the invention caused by the fact that the data acquisition node consumes the electric quantity too early.
Preferably, the holding time of the election result is calculated as follows:
by using
Figure DEST_PATH_IMAGE078A
Indicating the hold time of the result of the v-th election,
if it is
Figure DEST_PATH_IMAGE080A
Then, the cluster head node generated by the (v + 1) th election calculates the holding time of the (v + 1) th election result by using the following formula:
Figure DEST_PATH_IMAGE082AAA
if it is
Figure DEST_PATH_IMAGE084A
Then, the cluster head node generated by the (v + 1) th election calculates the holding time of the (v + 1) th election result by using the following formula:
Figure DEST_PATH_IMAGE086A
wherein the content of the first and second substances,
Figure DEST_PATH_IMAGE088
indicates the holding time of the result of the v +1 th election,
Figure DEST_PATH_IMAGE090
indicating the working parameters of the member nodes in the region in the holding time of the result of the v-th election,
Figure DEST_PATH_IMAGE092
represents a preset contrast value of the working parameter,
Figure DEST_PATH_IMAGE094
representing a preset time interval.
In the invention, the holding time of the election result is not fixed, but the holding time of the election result is associated with the holding time of the election result of the previous time, and the holding time of the election result is determined to be prolonged or shortened according to the size of the working parameter, so that the method has strong adaptability, is favorable for orderly consuming the electric quantity of the data acquisition nodes in the region, and further prolongs the continuous working time of the data acquisition nodes in the region.
Preferably, the operating parameter is calculated by:
Figure DEST_PATH_IMAGE096
wherein the content of the first and second substances,
Figure DEST_PATH_IMAGE098
、
Figure DEST_PATH_IMAGE100
which represents a pre-set scale parameter that is,
Figure DEST_PATH_IMAGE102
，
Figure DEST_PATH_IMAGE104
represents a collection of member nodes in the area,
Figure DEST_PATH_IMAGE106
indicating the electric quantity percentage of the member node u after the holding time of the result of the nth election is finished,
Figure DEST_PATH_IMAGE108
to represent
Figure DEST_PATH_IMAGE110
The total number of member nodes contained in it,
Figure DEST_PATH_IMAGE112
representing a set comparison value of the variance of the percentage of the electric quantity,
Figure DEST_PATH_IMAGE114
indicating the length of the data transmitted to the cluster head node by the member node u in the holding time of the result of the v-th election,
Figure DEST_PATH_IMAGE050AAA
indicating the set data length comparison value.
The working parameters are mainly related to the remaining electric quantity and the transmitted data quantity, the larger the data quantity is, the larger the electric quantity difference between the data acquisition nodes is, the larger the working parameters are, so that the maintaining time of the election result of the time is shortened when the maintaining time of the election result of the time is calculated, and otherwise, the maintaining time of the election result of the time is prolonged. So that the holding time of the election result changes as the operation state changes.
Preferably, the clustering parameter is calculated as follows:
Figure DEST_PATH_IMAGE116
wherein, the first and the second end of the pipe are connected with each other,
Figure DEST_PATH_IMAGE118
representing the score of a data acquisition node zThe cluster parameters are used to determine the cluster parameters,
Figure DEST_PATH_IMAGE120
representing the communication delay between the data acquisition node z and the data summarization base station,
Figure DEST_PATH_IMAGE122
representing the average distance between data acquisition node z and other data acquisition nodes in the same area,
Figure DEST_PATH_IMAGE124
representing the remaining capacity of the data acquisition node z,
Figure DEST_PATH_IMAGE126
representing the total number of other data acquisition nodes having a distance z less than Q, Q representing a predetermined distance parameter, S 1 Representing a preset comparison value of communication delays, S 2 Represents a preset average distance comparison value,
Figure DEST_PATH_IMAGE128
represents a preset comparison value of the remaining power,
Figure DEST_PATH_IMAGE130
representing a preset quantity comparison value.
When the clustering parameters are calculated, the communication delay, the residual electric quantity, the average distance and the number of other data acquisition nodes in the specified distance range are mainly considered, so that the clustering parameters can comprehensively represent the state of the data acquisition nodes from multiple aspects, and the optimal data acquisition node can be selected as a cluster head node.
Preferably, the determining the coordinates of the data acquisition nodes in each region by using a preset allocation algorithm includes:
establishing an equation set to be optimized:
Figure DEST_PATH_IMAGE132
in the formula (I), the compound is shown in the specification,
Figure DEST_PATH_IMAGE134
，
Figure DEST_PATH_IMAGE136
represents the d-th data acquisition node S d For the r-th region W r D represents the total number of data acquisition nodes,
Figure DEST_PATH_IMAGE138
a value of the integrated expected value is indicated,
Figure DEST_PATH_IMAGE140
represents the distance between the d-th data acquisition node and the r-th region if
Figure DEST_PATH_IMAGE140A
Greater than the maximum communication radius mcs of the data acquisition node
Figure DEST_PATH_IMAGE136A
Has a value of K, if
Figure DEST_PATH_IMAGE142
Then, then
Figure DEST_PATH_IMAGE143
Has a value of
Figure DEST_PATH_IMAGE145
And ics represents the optimal communication radius of the data acquisition node if
Figure DEST_PATH_IMAGE140AA
Less than ics, then
Figure DEST_PATH_IMAGE136AA
Is 0; mtbo represents an equation set to be optimized;
and (4) inputting the mtbo into a preset artificial fish swarm algorithm for optimizing, and obtaining the coordinate of each data acquisition node in each area.
In the invention, the arrangement positions of the data acquisition nodes are not randomly placed any more, but an equation set to be optimized is established, and then a group optimization algorithm is adopted to obtain an optimal coordinate result, so that the communication range of the data acquisition nodes in the invention can be maximally and redundantly covered in a server room, and the occurrence of operation and maintenance holes is effectively avoided.
The above examples are only intended to illustrate the technical solution of the present invention, but not to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, it should be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; such modifications and substitutions do not depart from the spirit and scope of the corresponding technical solutions.

Claims (10)

1. An operation and maintenance system based on big data is characterized by comprising a monitoring parameter acquisition module and a big data operation and maintenance module;
the sensor module comprises a data acquisition node and a data summarizing base station;
the data acquisition node is used for acquiring monitoring parameters of the server room and transmitting the monitoring parameters to the big data operation and maintenance module;
the big data operation and maintenance module is used for analyzing the monitoring parameters by adopting a big data technology to realize the operation and maintenance monitoring of the server room;
the big data operation and maintenance module is also used for dividing the server room into a plurality of areas and determining the coordinates of the data acquisition nodes in each area by adopting a preset allocation algorithm;
and the data acquisition nodes in each area generate cluster head nodes and member nodes in a self-adaptive election mode.
2. The operation and maintenance system based on big data as claimed in claim 1, wherein the member node is configured to obtain monitoring parameters of a server room, and send the monitoring parameters to the cluster head node in an area where the member node is located;
and the cluster head node is used for transmitting the monitoring parameters to the data summarizing base station.
3. The big data based operation and maintenance system according to claim 1, wherein the monitored parameters comprise temperature, humidity and smoke concentration of the machine room environment.
4. The big data based operation and maintenance system according to claim 1, wherein the big data operation and maintenance module comprises an operation and maintenance server and an operation and maintenance terminal;
the operation and maintenance server is used for storing the monitoring parameters sent by the data summarizing base station, analyzing the monitoring parameters by adopting a big data technology to obtain operation and maintenance monitoring results, and sending the operation and maintenance monitoring results to the operation and maintenance terminal;
and the operation and maintenance terminal is used for receiving the operation and maintenance monitoring result.
5. The big data based operation and maintenance system according to claim 4, wherein the operation and maintenance terminal comprises a local operation and maintenance device and a remote operation and maintenance device;
the local operation and maintenance equipment comprises a desktop computer arranged in an on-duty room of the server room;
the remote operation and maintenance equipment comprises a tablet computer, a smart phone and a notebook computer.
6. The big data based operation and maintenance system according to claim 1, wherein the dividing of the server room into a plurality of areas comprises:
the total number of regions is calculated using the following formula:
Figure DEST_PATH_IMAGE001
wherein N represents the total number of regions,
Figure 78869DEST_PATH_IMAGE002
the total area of the server room is represented,
Figure DEST_PATH_IMAGE003
indicating the communication radius of the data acquisition node,
Figure 194679DEST_PATH_IMAGE004
representing a preset control coefficient;
Figure DEST_PATH_IMAGE005
。
7. the big data based operation and maintenance system as claimed in claim 1, wherein the generating of the cluster head node and the member node by means of adaptive election comprises;
when the holding time of the last election result is over, each data acquisition node respectively calculates the clustering parameter of the data acquisition node and mutually exchanges the clustering parameter with other data acquisition nodes in the same area;
the data acquisition node with the highest clustering parameter calculates the maintaining time of the election result, and sends a message including the maintaining time of the election result to other data acquisition nodes in the same region, wherein the message is used for informing the data acquisition node to be selected as a cluster head node;
in the same area, the data acquisition nodes except the cluster head node are used as member nodes.
8. The big data-based operation and maintenance system according to claim 7, wherein the holding time of the election result is calculated as follows:
by using
Figure 630208DEST_PATH_IMAGE006
Indicating the hold time of the result of the v-th election,
if it is
Figure DEST_PATH_IMAGE007
Then, the cluster head node generated by the (v + 1) th election calculates the holding time of the (v + 1) th election result by using the following formula:
Figure 212368DEST_PATH_IMAGE008
if it is
Figure DEST_PATH_IMAGE009
Then, the cluster head node generated by the (v + 1) th election calculates the holding time of the (v + 1) th election result by using the following formula:
Figure 479270DEST_PATH_IMAGE010
wherein the content of the first and second substances,
Figure DEST_PATH_IMAGE011
indicates the holding time of the result of the v +1 th election,
Figure 223104DEST_PATH_IMAGE012
indicating the working parameters of the member nodes in the region in the holding time of the v-th election result,
Figure DEST_PATH_IMAGE013
represents a preset contrast value of the working parameter,
Figure 208247DEST_PATH_IMAGE014
representing a preset time interval.
9. The big data based operation and maintenance system according to claim 8, wherein the operation parameters are calculated by:
Figure DEST_PATH_IMAGE015
wherein the content of the first and second substances,
Figure 594098DEST_PATH_IMAGE016
、
Figure DEST_PATH_IMAGE017
which represents a pre-set scale parameter that is,
Figure 715506DEST_PATH_IMAGE018
，
Figure DEST_PATH_IMAGE019
represents a collection of member nodes in the area,
Figure 895821DEST_PATH_IMAGE020
indicating the electric quantity percentage of the member node u after the holding time of the result of the nth election is finished,
Figure DEST_PATH_IMAGE021
to represent
Figure 837101DEST_PATH_IMAGE022
The total number of member nodes contained in it,
Figure DEST_PATH_IMAGE023
representing a set comparison value of the variance of the percentage of the electric quantity,
Figure 761063DEST_PATH_IMAGE024
indicating the length of the data transmitted to the cluster head node by the member node u in the holding time of the result of the v-th election,
Figure DEST_PATH_IMAGE025
indicating the set data length comparison value.
10. The big data based operation and maintenance system according to claim 7, wherein the clustering parameter is calculated by:
Figure DEST_PATH_IMAGE027
wherein, the first and the second end of the pipe are connected with each other,
Figure 670069DEST_PATH_IMAGE028
a clustering parameter representing a data acquisition node z,
Figure DEST_PATH_IMAGE029
representing the communication delay between the data acquisition node z and the data summarization base station,
Figure 21284DEST_PATH_IMAGE030
representing the average distance between data acquisition node z and other data acquisition nodes in the same area,
Figure DEST_PATH_IMAGE031
representing the remaining capacity of the data acquisition node z,
Figure 184281DEST_PATH_IMAGE032
representing the total number of other data acquisition nodes having a distance z less than Q, Q representing a predetermined distance parameter, S 1 Representing a preset comparison value of communication delays, S 2 Represents a preset average distance comparison value,
Figure DEST_PATH_IMAGE033
represents a preset comparison value of the remaining power,
Figure 380776DEST_PATH_IMAGE034
representing a preset quantity comparison value.
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