CN108829857A - A kind of automatic O&M method based on O&M auditing system - Google Patents
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Abstract
The automatic O&M method based on O&M auditing system that the invention discloses a kind of, automatic O&M, including sequentially connected preprocessing module, characteristic matching module, track fitting module, intelligent O&M module are realized for the original Audit data collection in the log pond by O&M auditing system；By the way that there is the label or common label of attribute to the setting of original Audit data collection, and characteristic matching is carried out, fits O&M track, realize the intelligent O&M of automation.Automatic Fitting of the present invention goes out the daily O&M process of some high frequency times, avoids duplicate manpower intervention；Discrete O&M operation is sorted out, auxiliary carries out the analysis of anomalous event, achievees the purpose that O&M process closes rule；Repetition, abnormal information are filtered out, but the information filtered out can still be stored in original Audit data and concentrate, so that subsequent query is given for change；Operation/maintenance data uniform format of the invention, to analyze, to store.
Description
Technical field
The present invention relates to the automatic O&M technical fields of O&M auditing system, are a kind of based on O&M audit system specifically
The automatic O&M method of system.
Background technique
With the continuous development of Internet technology and universal, all kinds of terminal devices emerge one after another, and a large amount of terminal device gushes
Enter various units, enterprise, mechanism, how to ensure their intranet security and is effectively prevented the illegal external connection behavior of internal staff
It is a huge challenge.So more and more desktop terminals and the privately owned terminal access corporate intranet of diversification have become not
Come the inexorable trend developed, in order to ensure that one of the key of corporate intranet safety seeks to effectively monitor and record O&M operation
Process, then just have O&M auditing system.
But the O&M module of current O&M auditing system is only capable of by writing fixed script manually, and the modes such as hyperlink are real
Now part, simple automation O&M operation, and be isolated, not manageability.It, can be certainly after newly-increased intelligence O&M module
The dynamic daily O&M process for fitting some high frequency times, avoids duplicate manpower intervention, while can also grasp to discrete O&M
Sorted out, auxiliary carries out anomalous event analysis, achievees the purpose that O&M process closes rule.
Summary of the invention
The automatic O&M method based on O&M auditing system that the purpose of the present invention is to provide a kind of, by original audit
Data set is pre-processed, and cleaning filters out invalid data, and label is arranged to original Audit data collection and carries out characteristic matching, intends
O&M track is closed out, realizes the intelligent O&M of automation.Make operation/maintenance data uniform format, to analyze, to store, and filters out weight
Multiple, abnormal information.
The present invention is achieved through the following technical solutions：A kind of automatic O&M method based on O&M auditing system realizes fortune
The original automatic O&M of Audit data collection in the log pond of auditing system is tieed up, the O&M auditing system includes sequentially connected pre-
Processing module, characteristic matching module, track fitting module, intelligent O&M module；The automatic O&M method passes through first to be located in advance
Reason module is arranged the label with attribute or common label to original Audit data collection and carries out cleaning filtering to data, then special
It levies matching module and carries out characteristic matching to filtered data are cleaned, last track fitting module fits go out O&M track, in intelligence
The intelligent O&M of automation is realized in energy O&M module.
Further, in order to preferably realize the present invention, include the following steps：
Step P1：Extract the data that original Audit data is concentrated；
Step P2：Judge whether to need to carry out cleaning filtering to data；
Step P3：If desired cleaning filtering is carried out, then is inserted into preprocessing module, the preprocessing module, which joined, to be set with
Original Audit data is concentrated and is grasped by syntactic analysis, fuzzy matching technology and label with attribute by the label of attribute
Work, the related data content of state are excavated, subsequently into step P5；
Step P4：If not needing to carry out cleaning filtering, P5 is entered step；
Step P5：Common label is arranged to all data that original Audit data is concentrated；
Step P6：Characteristic matching is carried out to data according to common label；
Step P7：Data after characteristic matching are fitted, and calculate O&M track according to the data fitted；
Step P8：Realize automatic intelligent O&M.
Further, in order to preferably realize the present invention, the step P3 further includes that will clean the irrelevant messages filtered out
It resides in original Audit data to concentrate, so that subsequent query is given for change.
Further, in order to preferably realize the present invention, the step P6 specifically includes following steps：
Step P61：Unstructured data a large amount of in daily O&M is divided according to the feature of the common label of setting
Class and regularization；
Step P62：It will be in the user behaviors log library of the data deposit O&M auditing system after characteristic matching.
Further, in order to preferably realize the present invention, the step P8 specifically includes following steps：
Step P81：The O&M track come will be fitted to be pushed in intelligent O&M module, and keep data format unified；
Step P82：By artificial screening, realize that rapidly, automatically daily O&M operates.
Further, in order to preferably realize the present invention, the data of the raw data set include operation log, assets fortune
Tie up log and command record.
Further, in order to preferably realize that the present invention, the preprocessing module include data cleansing module, the data
Cleaning module joined the label for being set with attribute.
Working principle：
1. being docked with log pond and original Audit data collection, it is first determined whether needing to concentrate original Audit data
Data carry out cleaning filtering, if desired carry out cleaning filtering, be then inserted into preprocessing module, the preprocessing module, which joined, to be set
Surely there is the label of attribute, concentrated original Audit data by syntactic analysis, fuzzy matching technology and label with attribute
Data content related with Cao Zuo, state is excavated, and irrelevant messages are resided in original Audit data and are concentrated, are looked into so as to subsequent
Inquiry is given for change.
2. common label is arranged in all data that pair original Audit data is concentrated, to a large amount of unstructured in daily O&M
Data carry out classification and regularization according to the feature of the common label of setting, by the data deposit O&M audit system after characteristic matching
In the user behaviors log library of system；If not needing to carry out data cleaning filtering, the data root for directly concentrating original Audit data
Characteristic matching is carried out according to common label and is stored in user behaviors log library.
3. the data after characteristic matching are fitted, and O&M track is calculated according to the data fitted；It will fitting
O&M track out is pushed in intelligent O&M module, and keeps data format unified, by artificial screening, is realized quick
Ground, automatically daily O&M operation.
Compared with prior art, the present invention having the following advantages that and beneficial effect：
(1) automatic Fitting of the present invention goes out the daily O&M process of some high frequency times, avoids duplicate manpower intervention；
(2) present invention sorts out discrete O&M operation, and auxiliary carries out the analysis of anomalous event, reaches O&M process
Close the purpose of rule；
(3) present invention filters out repetition, abnormal information, but the information filtered out can still be stored in original Audit data
It concentrates, so that subsequent query is given for change；
(4) operation/maintenance data uniform format of the present invention, to analyze, to store.
Detailed description of the invention
Fig. 1 is flow chart of the invention；
Fig. 2 is module connection figure of the invention.
Specific embodiment
The present invention is described in further detail below with reference to embodiment, embodiments of the present invention are not limited thereto.
Embodiment 1：
The present invention is achieved through the following technical solutions, as Figure 1-Figure 2, a kind of automatic fortune based on O&M auditing system
Dimension method realizes the original automatic O&M of Audit data collection in the log pond of O&M auditing system, the O&M auditing system packet
Include sequentially connected preprocessing module, characteristic matching module, track fitting module, intelligent O&M module；The automatic O&M side
Method passes through preprocessing module first and the label with attribute or common label is arranged to original Audit data collection and carries out to data
Cleaning filtering, then characteristic matching module carries out characteristic matching, last track fitting module fits to filtered data are cleaned
The intelligent O&M of automation is realized in O&M track out in intelligent O&M module；
Include the following steps：
Step P1：Extract the data that original Audit data is concentrated；
Step P2：Judge whether to need to carry out cleaning filtering to data；
Step P3：If desired cleaning filtering is carried out, then is inserted into preprocessing module, the preprocessing module, which joined, to be set with
Original Audit data is concentrated and is grasped by syntactic analysis, fuzzy matching technology and label with attribute by the label of attribute
Work, the related data content of state are excavated, subsequently into step P5；
Step P4：If not needing to carry out cleaning filtering, P5 is entered step；
Step P5：Common label is arranged to all data that original Audit data is concentrated；
Step P6：Characteristic matching is carried out to data according to common label；
Step P7：Data after characteristic matching are fitted, and calculate O&M track according to the data fitted；
Step P8：Realize automatic intelligent O&M.
It should be noted that by above-mentioned improvement, the sequentially connected preprocessing module, characteristic matching module, track
Fitting module, intelligent O&M module are docked with the original Audit data collection in log pond, it is first determined whether needing to original
The data information that beginning Audit data is concentrated carries out cleaning filtering, and the cleaning filtering is exactly to carry out regular processing to information, will weigh
Multiple, invalid, abnormal information filtering is fallen.Carry out cleaning filtering if necessary, then the preprocessing module being inserted into data information into
Row cleaning and filtering.The preprocessing module has been set the label with attribute in advance, and preprocessing module is according to these marks
Label and some syntactic analyses, fuzzy matching technology concentrate data information content related with Cao Zuo, state to original Audit data
It excavates.Then common label is arranged to original Audit data collection, characteristic matching module carries out data according to common label
Characteristic matching.When not needing to carry out data cleaning filtering, then characteristic matching is directly carried out.After system can be to characteristic matching
Data are fitted, and according to fit come data calculate O&M track, realize the intelligent O&M of automation.Mistake of the present invention
Filter repetition, abnormal data information, avoid artificial repetition intervention and alleviate artificial operation, at the same can also to from
Scattered O&M operation carries out automatic clustering, makes operation/maintenance data uniform format, to analyze and to store, and assists point of anomalous event
Analysis achievees the purpose that O&M process closes rule.
The other parts of the present embodiment are same as the previously described embodiments, and so it will not be repeated.
Embodiment 2：
The present embodiment advanced optimizes on the basis of the above embodiments, and as Figure 1-Figure 2, the step P3 is also wrapped
It includes and the irrelevant messages that cleaning filters out is resided in into original Audit data concentration, so that subsequent query is given for change.
It should be noted that preprocessing module carries out the data information that original Audit data is concentrated by above-mentioned improvement
Cleaning and filtering are believed data related with operation and state by the label of setting, syntactic analysis and fuzzy matching technology
Breath content mining comes out, and unrelated data information is still stored at original Audit data and is concentrated, any data will not be lost,
So that subsequent query is given for change, can also be analyzed for event scenarios.
The other parts of the present embodiment are same as the previously described embodiments, and so it will not be repeated.
Embodiment 3：
The present embodiment advanced optimizes on the basis of the above embodiments, and as Figure 1-Figure 2, the step P6 is specific
Include the following steps：
Step P61：Unstructured data a large amount of in daily O&M is divided according to the feature of the common label of setting
Class and regularization；
Step P62：It will be in the user behaviors log library of the data deposit O&M auditing system after characteristic matching；
Step P7：Data after characteristic matching are fitted, and calculate O&M track according to the data fitted；
The step P8 specifically includes following steps：
Step P81：The O&M track come will be fitted to be pushed in intelligent O&M module, and keep data format unified；
Step P82：By artificial screening, realize that rapidly, automatically daily O&M operates.
It should be noted that by above-mentioned improvement, a large amount of data information is very complicated in daily O&M, if by former
The artificial treatment of beginning, work can be very big, and the present invention, which summarizes, has characteristic common label in daily O&M, according to this
A little labels carry out classification and regular to data, then in the log library of data deposit O&M auditing system.Again in log library
These data are fitted, and then foundation fits the data come and calculates O&M track, and intelligent O&M is sent into O&M track
In module.Last staff screens the data of these uniform formats, realize rapidly, the daily O&M of automation behaviour
Make.
The other parts of the present embodiment are same as the previously described embodiments, and so it will not be repeated.
Embodiment 4：
The present embodiment advanced optimizes on the basis of the above embodiments, and the data of the raw data set include operation
Log, the log of assets O&M and command record.
It should be noted that the data of the raw data set include operation log, assets O&M day by above-mentioned improvement
Will and command record, but more than these described data.
The other parts of the present embodiment are same as the previously described embodiments, and so it will not be repeated.
Embodiment 5：
The present embodiment is highly preferred embodiment of the present invention, as Figure 1-Figure 2, a kind of based on the automatic of O&M auditing system
O&M method realizes the original automatic O&M of Audit data collection in the log pond of O&M auditing system, the O&M auditing system
Including sequentially connected preprocessing module, characteristic matching module, track fitting module, intelligent O&M module；The automatic O&M
Method pass through first preprocessing module to original Audit data collection setting have attribute label or common label and to data into
Row cleaning filtering, then characteristic matching module carries out characteristic matching to filtered data are cleaned, and last track fitting module is quasi-
O&M track is closed out, the intelligent O&M of automation is realized in intelligent O&M module.
Include the following steps：
Step P1：Extract the data that original Audit data is concentrated；
Step P2：Judge whether that the data for concentrating original Audit data is needed to carry out cleaning filtering；
Step P3：If desired cleaning filtering is carried out, then is inserted into preprocessing module, the preprocessing module, which joined, to be set with
Original Audit data is concentrated and is grasped by syntactic analysis, fuzzy matching technology and label with attribute by the label of attribute
Work, the related data content of state are excavated, subsequently into step P5；
The step P3 further includes that the irrelevant messages that cleaning filters out are resided in original Audit data to concentrate, so as to subsequent
Inquiry is given for change.
Step P4：If not needing to carry out cleaning filtering, P5 is entered step；
Step P5：Common label is arranged to all data that original Audit data is concentrated；
Step P6：Characteristic matching is carried out to data according to common label；
The step P6 specifically includes following steps：
Step P61：Unstructured data a large amount of in daily O&M is divided according to the feature of the common label of setting
Class and regularization；
Step P62：It will be in the user behaviors log library of the data deposit O&M auditing system after characteristic matching.
Step P7：Data after characteristic matching are fitted, and calculate O&M track according to the data fitted；
Step P8：Realize automatic intelligent O&M.
The step P8 specifically includes following steps：
Step P81：The O&M track come will be fitted to be pushed in intelligent O&M module, and keep data format unified；
Step P82：By artificial screening, realize that rapidly, automatically daily O&M operates.
The data of the raw data set include operation log, the log of assets O&M and command record；The pretreatment
Module includes data cleansing module, and the data cleansing module joined the label for being set with attribute.
It should be noted that the present embodiment is preferred embodiment, the sequentially connected preprocessing module, feature
Matching module, track fitting module, intelligent O&M module are docked with the original Audit data collection in log pond, described original
The data of data set include operation log, the log of assets O&M and command record, but more than these described data.Sentence first
Whether disconnected that the data information concentrated to original Audit data is needed to carry out cleaning filtering, the cleaning filtering is exactly to carry out to information
Regular processing will repeat, invalid, abnormal information filtering is fallen.Cleaning filtering is carried out if necessary, then the preprocessing module being inserted into
Data information is cleared up and is filtered.The preprocessing module has been set the label with attribute in advance, pre-processes mould
Block concentrates original Audit data according to these labels and some syntactic analyses, fuzzy matching technology related with Cao Zuo, state
Data information content is excavated.Unrelated data information is still stored at original Audit data to concentrate, will not lose and appoint
What data can also be analyzed so that subsequent query is given for change for event scenarios.Then common mark is arranged to original Audit data collection
It signs, a large amount of data information is very complicated in daily O&M, if work can be very big by original artificial treatment, this
Invention summarize in daily O&M have characteristic common label, according to these labels to data carry out classification and it is regular, so
Afterwards in the log library of data deposit O&M auditing system.When not needing to carry out data cleaning filtering, then feature is directly carried out
In matching deposit log library.These data in log library are fitted again, then foundation fits the data come and calculates
O&M track is sent into intelligent O&M module by O&M track.Last staff sieves the data of these uniform formats
Choosing, realize rapidly, automation daily O&M operation.The present invention filters out repetition, abnormal data information, avoids artificial
Repetition intervention and alleviate artificial operation, while discrete O&M can also be operated and carry out automatic clustering, make O&M number
According to uniform format, to analyze and to store, and the analysis of anomalous event is assisted, achievees the purpose that O&M process closes rule.
The algorithm that track is fitted in step P7 is the trajectory clustering based on DBSCAN algorithm, and DBSCAN algorithm belongs to cluster and calculates
One kind of neighbours' grower in method, it " close enough " data point will be joined together to form a cluster each other, until all
Point is all classified.
It is fitted the algorithm flow brief introduction of track：
Input：One group of track
Output：
(1) one group of cluster
(2) one groups of exemplary trajectories
Algorithm steps explanation：
Step 01：For it is each each belong to the TR of I, according to MDL principle, i.e. Minimal Description Length Criterion.For giving
A fixed line segment, the standard of its characteristic point of algorithms selection are that the MDL expense that the point is characterized a little is less than and is not selected as
The MDL expense of characteristic point.Characteristic point is selected by gradually checking whether each point meets the condition.By the method by track
Be converted to the set of characteristic point.
Step 02：Pairing approximation track carries out subregion, and the set of one group of L line segment is obtained according to the result of step 01.It is described
Step 02 specifically includes following steps：
1. the distance between line segment LiLi and LjLj：
Dist (Li, Lj)=w ⊥ d ⊥ (Li, Lj)+w ∥ d ∥ (Li, Lj)+w θ d θ (Li, Lj) dist (Li, Lj)
=w ⊥ d ⊥ (Li, Lj)+w ∥ d ∥ (Li, Lj)+w θ d θ (Li, Lj)
2. epsilon neighborhood N ε (Li) the N ε (Li) of line segment:
N ε (Li)=Lj ∈ D | dist (Li, Lj)≤ε } N ε (Li)=Lj ∈ D | dist (Li, Lj)≤ε }
3. core line segment：Line segment Li (Li ∈ D) Li (Li ∈ D) is referred to as core line segment and if only if | N ε (Li) | >=
MinLns|Nε(Li)|≥MinLns；
4. the direct density of line segment is reachable：The line segment Li ∈ direct density of DLi ∈ D up to line segment Lj ∈ DLj ∈ D and if only if
Li ∈ N ε (Lj) Li ∈ N ε (Lj) and | N ε (Lj) | >=MinLns | N ε (Lj) | >=MinLns；
5. the density of line segment is reachable：Line segment Li ∈ DLi ∈ D density is reachable in line segment Lj ∈ DLj ∈ D, and if only if presence
One group of line segment Lj, Lj-1 ..., Li+1, Li ∈ DLj, Lj-1 ..., Li+1, Li ∈ D and the direct density of line segment LkLk is reachable
In line segment Lk+1Lk+1；
6. the density of line segment connects：Line segment Li ∈ DLi ∈ D density is connected in line segment Lj ∈ DLj ∈ D and if only if there are one
Line segment Lk ∈ DLk ∈ D makes line segment LiLi and the equal density of line segment LjLj up in line segment LkLk；
7. the density of line segment connects collection：One nonvoid subsetMeet referred to as density connection collection and if only if CC
Following two condition：
1) connectivity：Li density is connected in LjLj；
2) it maximizes：If Li ∈ CLi ∈ C and LjLj density up in LiLi,
There is Lj ∈ CLj ∈ C.
Step 03：L is added in set D, line segment L non-classified for each, algorithm calculates its epsilon neighborhood to sentence
Whether the line segment that breaks is core line segment.
The density for calculating core line segment, which is connected to collect to merge, to be added it in cluster of core line segment composition.If be newly added
Line segment is not classified, then it is added in queue Q to further expand, because the line segment may be core line segment；Ruo Xinjia
The line segment entered is not core line segment, then is not added in enqueue Q.The radix of each cluster is calculated, if its value is less than threshold value, algorithm will
The cluster is eliminated, because it is not intensive enough.
Step 04：Line segment cluster is executed to D, and obtains one group of results set O.
Step 05：To each line of linear scanning of one average trend perpendicular to cluster middle conductor of each C for belonging to O
Section will judge whether the number for intersecting line segment at this time is not less than when passing through the beginning or end of a line segment every time
MinLns.If so, calculating the equalization point of all intersection points and being stored in list, otherwise ignore.It ultimately generates
List is the node coordinate information of mean trajectory.
Step 06：Generate exemplary trajectory as a result.
The above is only presently preferred embodiments of the present invention, not does limitation in any form to the present invention, it is all according to
According to technical spirit any simple modification to the above embodiments of the invention, equivalent variations, protection of the invention is each fallen within
Within the scope of.
Claims (7)
1. a kind of automatic O&M method based on O&M auditing system realizes the original audit in the log pond of O&M auditing system
The automatic O&M of data set, it is characterised in that：The O&M auditing system includes sequentially connected preprocessing module, characteristic matching mould
Block, track fitting module, intelligent O&M module；The automatic O&M method passes through preprocessing module to original Audit data first
Label or common label of the collection setting with attribute simultaneously carry out cleaning filtering to data, and then characteristic matching module filters cleaning
Data afterwards carry out characteristic matching, and last track fitting module fits go out O&M track, realize in intelligent O&M module automatic
The intelligent O&M of change.
2. a kind of automatic O&M method based on O&M auditing system according to claim 1, it is characterised in that：Including with
Lower step：
Step P1：Extract the data that original Audit data is concentrated；
Step P2：Judge whether to need to carry out cleaning filtering to data；
Step P3：If desired cleaning filtering is carried out, then is inserted into preprocessing module, the preprocessing module, which joined, is set with attribute
Label, by syntactic analysis, fuzzy matching technology and label with attribute by original Audit data concentrate with operate, shape
The related data content of state is excavated, subsequently into step P5；
Step P4：If not needing to carry out cleaning filtering, P5 is entered step；
Step P5：Common label is arranged to all data that original Audit data is concentrated；
Step P6：Characteristic matching is carried out to data according to common label；
Step P7：Data after characteristic matching are fitted, and calculate O&M track according to the data fitted；
Step P8：Realize automatic intelligent O&M.
3. a kind of automatic O&M method based on O&M auditing system according to claim 2, it is characterised in that：The step
Rapid P3 further includes that the irrelevant messages that cleaning filters out are resided in original Audit data to concentrate, so that subsequent query is given for change.
4. a kind of automatic O&M method based on O&M auditing system according to claim 3, it is characterised in that：The step
Rapid P6 specifically includes following steps：
Step P61：To unstructured data a large amount of in daily O&M according to the feature of the common label of setting carry out classification with
Regularization；
Step P62：It will be in the user behaviors log library of the data deposit O&M auditing system after characteristic matching.
5. a kind of automatic O&M method based on O&M auditing system according to claim 4, it is characterised in that：The step
Rapid P8 specifically includes following steps：
Step P81：The O&M track come will be fitted to be pushed in intelligent O&M module, and keep data format unified；
Step P82：By artificial screening, realize that rapidly, automatically daily O&M operates.
6. a kind of automatic O&M method based on O&M auditing system according to claim 1-5, feature exist
In：The data of the raw data set include operation log, the log of assets O&M and command record.
7. a kind of automatic O&M method based on O&M auditing system according to claim 6, it is characterised in that：It is described pre-
Processing module includes data cleansing module, and the data cleansing module joined the label for being set with attribute.
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112633389A (en) * | 2020-12-28 | 2021-04-09 | 西北工业大学 | Method for calculating trend of hurricane motion track based on MDL and speed direction |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103514534A (en) * | 2013-09-18 | 2014-01-15 | 中国软件与技术服务股份有限公司 | Application supporting platform under hybrid terminal environment |
| CN104156439A (en) * | 2014-08-12 | 2014-11-19 | 华北电力大学句容研究中心 | Method for remote intelligent operation and maintenance audit |
| CN105049228A (en) * | 2015-06-12 | 2015-11-11 | 北京奇虎科技有限公司 | Method and apparatus for auditing operation and maintenance operation |
| CN106993271A (en) * | 2016-01-20 | 2017-07-28 | 中国移动通信集团福建有限公司 | A kind of operation management method and apparatus |
| CN107577588A (en) * | 2017-09-26 | 2018-01-12 | 北京中安智达科技有限公司 | A kind of massive logs data intelligence operational system |
-
2018
- 2018-06-21 CN CN201810646402.7A patent/CN108829857A/en active Pending
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103514534A (en) * | 2013-09-18 | 2014-01-15 | 中国软件与技术服务股份有限公司 | Application supporting platform under hybrid terminal environment |
| CN104156439A (en) * | 2014-08-12 | 2014-11-19 | 华北电力大学句容研究中心 | Method for remote intelligent operation and maintenance audit |
| CN105049228A (en) * | 2015-06-12 | 2015-11-11 | 北京奇虎科技有限公司 | Method and apparatus for auditing operation and maintenance operation |
| CN106993271A (en) * | 2016-01-20 | 2017-07-28 | 中国移动通信集团福建有限公司 | A kind of operation management method and apparatus |
| CN107577588A (en) * | 2017-09-26 | 2018-01-12 | 北京中安智达科技有限公司 | A kind of massive logs data intelligence operational system |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112633389A (en) * | 2020-12-28 | 2021-04-09 | 西北工业大学 | Method for calculating trend of hurricane motion track based on MDL and speed direction |
| CN112633389B (en) * | 2020-12-28 | 2024-01-23 | 西北工业大学 | Hurricane movement track trend calculation method based on MDL and speed direction |
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Application publication date: 20181116 |