CN108306756A - One kind being based on electric power data network holography assessment system and its Fault Locating Method - Google Patents
One kind being based on electric power data network holography assessment system and its Fault Locating Method Download PDFInfo
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Classifications
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- H—ELECTRICITY
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- 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
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- H02J13/0013—
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- H04—ELECTRIC COMMUNICATION TECHNIQUE
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- 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
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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
- H04L41/14—Network analysis or design
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- H—ELECTRICITY
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- H04L43/04—Processing captured monitoring data, e.g. for logfile generation
- H04L43/045—Processing captured monitoring data, e.g. for logfile generation for graphical visualisation of monitoring data
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- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L43/00—Arrangements for monitoring or testing data switching networks
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
- Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
- Y04S10/00—Systems supporting electrical power generation, transmission or distribution
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
- Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
- Y04S10/00—Systems supporting electrical power generation, transmission or distribution
- Y04S10/50—Systems or methods supporting the power network operation or management, involving a certain degree of interaction with the load-side end user applications
- Y04S10/52—Outage or fault management, e.g. fault detection or location
Abstract
The present invention relates to electric power data network fault location technology fields, disclose one kind and being based on electric power data network holography assessment system and its Fault Locating Method, the described method comprises the following steps：Step 1：Detective path is selected to form detection collection；Step 2：It sends detection and carries out fault detect；Step 3：The result of detection of fault detect is analyzed；Step 4：Fault location is carried out based on active probe；The present invention proposes that solving existing method for prewarning risk by the holography assessment system and Fault Locating Method finds that risk is time-consuming longer, fault location technology needs the micro-judgment by maintenance personnel, less efficient technical problem, when needing detection, detection can be quickly completed, and positioned, the duration for finding risk is reduced, efficiency is improved.
Description
Technical field
The present invention relates to electric power data network fault location technology fields, more particularly to one kind to be commented based on electric power data network holography
Estimate system and its Fault Locating Method.
Background technology
With the continuous development of society and economy, the development of electric power data network is also extremely rapid, especially electric power data network
The development of fault location technology has reached certain altitude, these existing technologies effectively avoid and reduce electric power data network failure
The loss brought provides reliable power utilization environment for people, facilitates people’s lives.The Risk-warning that electric power data network requires
For timeliness compared with general network higher, required fault location time is also shorter, is passively adopted used by traditional network management technology
The method of collection network data can no longer meet the demand of electric power data network, need research how a variety of using main passive probe etc.
Detection mode realizes the actively monitoring and real time monitoring of network servicequality, to improve the timeliness of Risk-warning.
It is longer to find that risk takes for existing method for prewarning risk, fault location technology is needed by maintenance personnel's
Micro-judgment, less efficient technical problem, the present invention propose one kind based on electric power data network holography assessment system and its event
Hinder localization method.
Invention content
For problems of the prior art, the present invention propose it is a kind of based on electric power data network holography assessment system and
Its Fault Locating Method carries out real-time tracking analysis, to realize the height of electric power data network quality of service to network servicequality
Precision Risk-warning and fault location.
The present invention provides one kind being based on electric power data network holography assessment system, and the system comprises data snooping portion, numbers
Data preprocess portion, data analysis portion, data application portion and platform part, wherein the data snooping portion is used for each by deployment
Class actively and/or passively measures probe, completes the acquired original to various dimensions fine granularity network and business quality data, including right
Electric power data network backbone network, access network and the network of data center and the acquisition of service feature data and data on flows；
The data prediction portion completes pretreatment to institute's gathered data according to analysis demand, and by data loading；Institute
Data prediction portion is stated, for realizing data prediction configuration, process of data preprocessing and data prediction management and control, the data
Pretreatment is configured to process of data preprocessing and provides operation foundation；The process of data preprocessing includes passing through format conversion list
Member, data translation unit and data derived units are respectively completed format conversion, data translation and data derivation function, the data
Pretreatment management and control provides scheduling, monitoring, exception control and statistical function for preprocessing process；
The data analysis portion, including multidimensional data convergence analysis unit, for more to being carried out by pretreated data
Dimension data convergence analysis；The multidimensional data convergence analysis, it is laggard by pre-processing by the data packet captured to intelligent probe
Row analysis, and the information such as sequence and state are reached by means of data packet, using protocol identification as a result, passing through each unit mould respectively
Block completes network layer performance analysis, transport layer performance analysis, application layer performance analysis and general service performance evaluation；
The data application portion, for completing various applications, the data using the data of data analysis portion output
Application section, including the Risk-warning unit of the electric power data network Risk-warning function based on risk tolerance model is completed, it completes
The failure location unit and completion the whole network quality of service of electric power data network fault localization mechanism based on risk tolerance model are dynamic
The visual visualization of state；
The platform part, for building a system platform, the above-mentioned institute of carrying is functional, and the platform part uses modularization
It is realized with layered mode.
Preferably, the format conversion unit stores lattice for parsing the different data between source data and target data
Transformation rule between formula and determining corresponding storage format, the data translation unit, by from source data to number of targets
According to mapping ruler, realize data translation；The data derived units, for raw information is analyzed and combine business rule into
Row is derivative to obtain information.
Preferably, the data analysis portion will also realize during completing the analysis of application layer service feature and apply industry
Business is traced to the source, dynamically to trace the complete process of some concrete application layer service in real time, when recording the response in each step
Between, and then for data, the concrete property of voice, video, multimedia and other electric power data network services, complete to various dimensions
The performance evaluation of fine-grained data works.
Preferably, the Risk-warning unit judges the knowledge with processing by learning network, such as abnormal flow mould extremely
The network real time data obtained from probe is matched with Network Abnormal knowledge, judges whether to generate different by the parameter of type, realization
Normal flow；If it find that abnormal flow, then carry out relevant treatment, and propose to alert according to the processing rule being previously set；Meanwhile
It is interacted with database, the parameter of update abnormal discharge model, generates new rule；Wherein, the Risk-warning unit can
According to historical traffic information adjust automatically abnormal flow model, to realize the self-adapting detecting of abnormal flow.
Preferably, the visualization converts the data into figure by computer graphics techniques and image processing techniques
Shape or image are shown on the screen, and carry out interaction process, to realize by image come disclose in data imply have
Imitate information；Its visual analyzing result can be embodied by various layout type, including orthogonal packing and radiation are laid out.
Preferably, the platform part is electric power data network intelligent detecting and holographic Evaluation Platform, including：Physical layer, acquisition
Layer, pretreatment layer, management level, analysis layer and presentation layer, wherein physical layer is basic facility layer, which disposes electric power data network
Various kinds of equipment and link, belong to the object being managed；Acquisition layer is data collection layer, and it is passive which disposes all kinds of masters
Probe is measured, completes the acquired original to various dimensions fine granularity network and business quality data, including to electric power data network backbone
Network, access network and the network of data center and the acquisition of service feature data and data on flows；Pretreatment layer according to point
Analysis demand completes pretreatment to institute's gathered data, and by data loading；Management level are detection control functional layer, which completes to visit
The functions such as needle tubing reason, monitoring task scheduling, measurement strategies management；Analysis layer is analysis and evaluation function layer, which completes each layer
The functions such as flow and protocal analysis, performance evaluation, performance test and service traffics signature analysis, and complete Risk-warning, event
Barrier detection and positioning function；Presentation layer is interface presentation layer, this layer completes configuration management and the showing interface of entire plateform system
Function；Accumulation layer is completed to enter library storage to disparate networks and quality of service gathered data and correlation analysis data；Interface layer is complete
At the interactive interfacing between data network integrated network management system；Between the layers, unified secure communication mechanism will be used,
Realize certification, compression and the safe transmission function when exchanging visits between each layer function.
In addition to this, being applied to the above-mentioned failure based on electric power data network holography assessment system the invention also discloses a kind of
Localization method the described method comprises the following steps：
Step 1：Detective path is selected to form detection collection；
Step 2：It sends detection and carries out fault detect；
Step 3：The result of detection of fault detect is analyzed；
Step 4：Fault location is carried out based on active probe；
Wherein, the detective path is from detection site to path caused by other nodes, and selected detective path needs
Meet all nodes in overlay network, and the detective path is minimum detective path；
The transmission detection carries out fault detect and includes the following steps：
Step 21：Detection collection is combined into sky when initializing for the first time, no to then follow the steps 22；
Step 22：Matrix modeling is relied on based on detection, calculates the weights of the corresponding column vector of each node；
Step 23：It obtains the detection number by each node, finds out the node passed through by minimum detection；
Step 24：The row vector weights of all detections of the above-mentioned node passed through by minimum detection are calculated, and carry out descending
The detection of the not yet capped node of cover-most is found out in arrangement；
The result of detection to fault detect carries out analysis and includes the following steps：
Step 31：Result of detection is collected, is analyzed and determined；
Step 32：If detecting successfully, then it is assumed that all nodes that successful probe passes through are normal node, and these are saved
Normal node set is added in point；
Step 33：If being judged as suspect node before having node, it is deleted from suspect node set；
Step 34：If detection failure, unsuccessfully all nodes for not being judged as normal node before are equal on detective path
For suspect node, and suspect node set is added；
Step 35：If being only suspect node there are one node in all nodes that unsuccessfully detection is passed through, other sections
Point is all passed through by certain successful probes, then the suspect node is malfunctioning node, and is added into malfunctioning node set.
Preferably, described to be included the following steps based on active probe progress fault location：
Step 41：The detection met so that suspect node number reduces to the greatest extent is chosen from alternative detection set the inside；
Step 42：Matrix modeling is relied on based on detection, fault detect is analyzed again；
Step 43：If the probing test result for only covering a suspect node returns to failure, show that the suspect node is
Malfunctioning node；
Step 44：If the probing test result of covering suspect node returns successfully, show that these suspect nodes are normal
Node；
Wherein, the alternative detection collection is combined into not by the detection set as detection, the fault detect stage indicate in addition to
Available detection set other than fault detect set is indicated in fault location stage in addition to the prior fault of fault detect set sum is fixed
Available detection set other than the detection of position.
Preferably, in the step 44, it is also necessary to suspect node be deleted from suspect node set, and normal section is added
Point set；Judgement is re-started to the detection set of detection failure before all later.
Preferably, the method for the detection includes information inquiry and the detection of connectivity triggered to related network device, and
It is detected in conjunction with network equipment information automatic trigger Active Networks performance.
The technical solution of the embodiment of the present invention provides a kind of fixed based on electric power data network holography assessment system and its failure
The technical solution of position method, the embodiment of the present invention has following remarkable result：
The present invention proposes one kind and being based on electric power data network holography assessment system and its Fault Locating Method, passes through the holography
Assessment system and Fault Locating Method solve existing method for prewarning risk and find that risk is time-consuming longer, and fault location technology needs
Detection can be quickly completed when needing detection by the micro-judgment of maintenance personnel, less efficient technical problem, and
It is positioned, reduces the duration for finding risk, improve efficiency.
Description of the drawings
Fig. 1 is the electric power data network intelligent detecting of the embodiment of the present invention one and holographic assessment system figure；
Fig. 2 is two multidimensional data convergence analysis process schematic of the embodiment of the present invention；
Fig. 3 is the flow diagram of electric power data network fault positioning method of the present invention；
Fig. 4 is the flow diagram of fault detect in electric power data network fault positioning method of the present invention；
Fig. 5 is the flow diagram analyzed result of detection in electric power data network fault positioning method of the present invention；
Fig. 6 is the flow diagram of fault location in electric power data network fault positioning method of the present invention；
Specific implementation mode
In order to make the invention's purpose, features and advantages of the invention more obvious and easy to understand, below in conjunction with the present invention
Attached drawing in embodiment, technical scheme in the embodiment of the invention is clearly and completely described, it is clear that described reality
It is only a part of the embodiment of the present invention to apply example, and not all embodiments.Based on the embodiments of the present invention, people in the art
The every other embodiment that member is obtained without making creative work, shall fall within the protection scope of the present invention.
In existing Risk-warning and Fault Locating Method find risk take it is longer, fault location technology need by
The micro-judgment of maintenance personnel, less efficient technical problem；In view of the above-mentioned problems, the present invention, which proposes one kind, being based on electric power number
According to the holographic assessment system of net and its Fault Locating Method.
Embodiment one, electric power data network intelligent detecting and holographic assessment system
As shown in Figure 1, the electric power data network intelligent detecting for the embodiment of the present invention one and holographic assessment system figure, the present invention
It provides one kind and being based on electric power data network holography assessment system, the system comprises data snooping portion 1, data prediction portion 2, numbers
According to analysis portion 3, data application portion 4 and platform part 5, wherein the data snooping portion 1 be used for by dispose all kinds of actives and/
Or passive measurement probe, the acquired original to various dimensions fine granularity network and business quality data is completed, including to electric power data
Net backbone network, access network and the network of data center and the acquisition of service feature data and data on flows.
The data prediction portion 2 completes pretreatment to institute's gathered data according to analysis demand, and by data loading；
The data prediction portion 2, it is described for realizing data prediction configuration, process of data preprocessing and data prediction management and control
Data prediction is configured to process of data preprocessing and provides operation foundation, including preprocessing rule configuration and pretreatment mode are matched
It sets；The contents such as rule, mode, the frequency that the process of data preprocessing can be provided according to configuration feature carry out concrete operations,
It includes being respectively completed format conversion, data by format conversion unit 21, data translation unit 22 and data derived units 23
Translation and data derivation function, the data prediction management and control provide scheduling, monitoring, exception control and statistics for preprocessing process
Function；
The data analysis portion 3, including multidimensional data convergence analysis unit 31, for being carried out by pretreated data
Multidimensional data convergence analysis；The multidimensional data convergence analysis, by the data packet that is captured to intelligent probe after pretreatment
It is analyzed, and the information such as sequence and state is reached by means of data packet, using protocol identification as a result, passing through each unit respectively
Module completes network layer performance analysis, transport layer performance analysis, application layer performance analysis and general service performance evaluation function；
The data application portion 4, for completing various applications, the data using the data of data analysis portion output
Application section 4, including the Risk-warning unit 41 of the electric power data network Risk-warning function based on risk tolerance model is completed, it is complete
At the failure location unit 42 and completion the whole network business matter of the electric power data network fault localization mechanism based on risk tolerance model
Measure the visualization 43 of dynamic and visual；
The platform part 5, for building a system platform, the above-mentioned institute of carrying is functional, and the platform part uses module
Change and layered mode is realized.
Preferably, the format conversion unit 21, for parsing the storage of the different data between source data and target data
Transformation rule between format and determining corresponding storage format, the format conversion include data type conversion, data essence
Degree conversion, null value judgement processing, string processing, date format processing；The data translation unit, by from source data to mesh
The mapping ruler of data is marked, realizes data translation；The data derived units, for analyzing raw information and combining business to advise
Then carry out derivative acquisition information.
Preferably, the data analysis portion 2 will also realize and apply during completing the analysis of application layer service feature
Business is traced to the source, and dynamically to trace the complete process of some concrete application layer service in real time, records the response in each step
Time, and then for data, the concrete property of voice, video, multimedia and other electric power data network services, complete to multidimensional
Spend the performance evaluation work of fine-grained data.
Preferably, the Risk-warning unit 41 judges the knowledge with processing, such as abnormal flow by learning network extremely
The network real time data obtained from probe is matched with Network Abnormal knowledge, judges whether to generate by the parameter of model, realization
Abnormal flow；If it find that abnormal flow, then carry out relevant treatment, and propose to alert according to the processing rule being previously set；Together
When, it is interacted with database, the parameter of update abnormal discharge model, generates new rule；Wherein, the Risk-warning unit 41
It can be according to historical traffic information adjust automatically abnormal flow model, to realize the self-adapting detecting of abnormal flow.
Preferably, the visualization 43 is converted the data by computer graphics techniques and image processing techniques
Figure or image are shown on the screen, and carry out interaction process, to realize by image come disclose in data imply
Effective information；Its visual analyzing result can be embodied by various layout type, including orthogonal packing and radiation are laid out.
(1) orthogonal packing visualization technique
Orthogonal packing is required to be aligned according to direction either vertically or horizontally when layout selects position and be put,
Layout type is regular, matches with human vision custom.
The figure can be used to carry out for topological hierarchical relationship, the service feature classification etc. between data network network node intuitive
Show, can be checked by mouse rollover operation when data volume is big.
(2) radiation layout visualization technique
The position for being mainly characterized by root node and being located at the center of circle of radiant type layout, level of other nodes at it
It is respectively placed in above the concentric circles of different radii, disclosure satisfy that the characteristics of increasing as graphical nodes increase with level.Needle
It is straight to the radiation layout can be used to carry out according to the scene for statistical analysis to data traffic such as applicating category, protocol class
Sight shows.
Embodiment two, multidimensional data convergence analysis
It is illustrated in figure 2 two multidimensional data convergence analysis process schematic of the embodiment of the present invention；The multidimensional data fusion
Analytic unit 31, for carrying out multidimensional data convergence analysis by pretreated data；The multidimensional data convergence analysis leads to
It crosses and the data packet that intelligent probe captures is analyzed after pretreatment, and the letters such as sequence and state are reached by means of data packet
Breath, using protocol identification as a result, completing network layer performance analysis by each unit module respectively, transport layer performance is analyzed, is answered
With layer performance evaluation and general service performance evaluation function；
It is specific as follows：Data packet or flow table enter after the processing of network layer performance analytic unit 32, pass through transport layer successively
Performance analysis unit 33, application layer performance analytic unit 34 and general service performance evaluation 35 carry out analyzing processing, then export
It is analyzed to different service feature analytic units, includes mainly：Data service performance analysis unit 301, speech business
It can analytic unit 302, video traffic performance analysis unit 303, multimedia service performance analysis unit 304, performance of activating business
Analytic unit 305；Wherein, further include 38 output protocol result A of protocol identification module and decoder module 37 export decoding result B to
Trace to the source unit 34 using layer service, and flow table maintenance module 36 export respectively flow data C and D analyzed respectively to transport layer performance it is single
Member 33 and network layer service performance analysis unit 32, meanwhile, network layer performance analytic unit 32, transport layer performance analytic unit
33, application layer performance analytic unit 34 and general service performance evaluation 35 export respectively result to monitoring result management module 39 into
Row management, to complete multidimensional data analysis process.
Embodiment three, data prediction
Data prediction function includes mainly data prediction configuration and process of data preprocessing (ETL), data prediction
Management and control three parts, data prediction are configured to process of data preprocessing and provide operation foundation, and process of data preprocessing can be with
The contents such as rule, mode, the frequency provided according to configuration feature carry out concrete operations, and data prediction management and control is preprocessing process
Scheduling, monitoring, exception control and statistics are provided.
A) data prediction configures
Data prediction configuration is the important realization link of Data processing, is related to multiple systemic origins and data processing
Technology needs to be directed to separate sources, and the source data of different quality formulates flexible processing strategy, to support flexible data to locate in advance
Reason, including preprocessing rule configuration and pretreatment mode configuration.
Preprocessing rule is constraint and requirement of each stage to data processing, to data in process of data preprocessing
Source and operation clearly require the regulation followed, the configuration of data prediction rule includes following content：
The cleaning rule of data configures：For accuracy existing for source data and integrity issue, commented according to the quality of data
Estimate method, formulate corresponding filtering rule, to filter out undesirable data, data quality problem is recorded, for day
After carry out query statistic.The accuracy of data refers to the determinant attribute of each object type data or information is to meet rule custom value,
Meet defined enumerated value, defined character length, defined character string type, defined value range including value.Data
Integrality refer to each object type data determinant attribute or information it is whether complete.The determinant attribute or information evaluated are not
For sky, then it is considered as complete；The determinant attribute or information evaluated are sky, then are considered as imperfect.
The rule configuration of data conversion：The Data Format Transform rule for setting source data, to the initial data and mesh of detection
Mapping and Converting relationship is established between mark Fusion Model.By the guide of rule, original source data is converted to unified number of targets
According to format.
The incidence relation of data configures：Since each subject area is dispersed in different business systems, between reinforcement data
Associate feature identifies activity condition of the same target in different themes domain, can be built by the incidence relation between subject area
Vertical relationship between data and data, to obtain the complete reflection of object.By taking equipment as an example, equipment is in communication resource data mould
There are equipment essential information, equipment bearer service information, device configuration informations in type；The equipment is by asset number, in account
There are physical attribute information, assets record informations in data model；The equipment is by asset number, the energy in data model
The essential information of engineering, engineering process information where finding equipment；Equipment can find equipment in event data by asset number
The work orders data such as the defects of model, alarm, failure.It, can be organic for one by dispersion data contact by the above incidence relation
It is whole, enhance the globality of object.
B) process of data preprocessing
When carrying out data prediction according to preprocessing rule, to undesirable data, the result that will determine that passes through pre-
Processing task management and control forms data and checks task.Undesirable data include mainly incomplete data, the data of mistake
With the data three categories repeated.Wherein：Deficiency of data refer to due to some due loss of learning or the incidence relation of data,
Bearing relation is imperfect；The data of mistake refer to since parsing mistake or artificial incorrect operation cause, and do not have when wrong data inputs
It carries out judging to write direct database；The data repeated refer to due to data model Shortcomings, and major key or constraint are defective, or
Because there is duplicate data caused by mistake in message preprocessing process.
After data fit checks, need to carry out conversion pretreatment to data, data conversion includes format conversion, number
According to translation, data derivative, data aggregate etc..In most cases, process of data preprocessing mainly completes format conversion, data are turned over
It translates, data derivative, and complicated data aggregate and other complicated calculations is mainly realized in data summarization.Data converting function
It should support the definition of data, the conversion process of data structure and wrong data.Data conversion includes mainly following components：
Format conversion：Due to data source systems with communicate between big data system in data model, data format etc.
There may be the data deficiency regularities that larger difference or data source systems itself provide, in order to enable pretreatment follow-up link
Can by it is simple it is consistent in a manner of handled, need to parse the different data storage format between source data and target data with
And the transformation rule between determining corresponding storage format.Format conversion includes data type conversion, data precision conversion, null value
Judgement is handled, string processing, date format processing etc..
Data translation：It is processing procedure most complicated in data prediction, must be deep during data translation
Understand and understanding source data information, identification abnormal data situation establish the mapping ruler from source data to target data.It is mapping
During, some information can directly be obtained from source data, such as 0 represent gender female, 1 represents gender man etc..And some
It can not be directly obtained from source data, need to carry out the translating operations such as certain calculating, merging, fractionation to source data.For every
A data source and datum target will be that each data entity of communication big data is determined from data source to the conversion of purpose table
(translation) rule, this partial content include corresponding to target data specific to which field of one or more table of source system
Which field of one or multiple tables and corresponding transformation rule are as how.
Data derive：Production and operation analysis application of the data in big data system around enterprise is communicated, therefore is existed big
The contextual information of amount, the data needs of source system extract and could be used by communication big data system.Communicate big data
Contextual information refinement be to carry out the processing of the derivation information of data after the standardization (cleaning) of data.Communication
Required data do not exist directly in production system in big data analysis application, but need by raw information point
It analyses and business rule is combined to derive the information obtained, it is therefore desirable to which derivation information processing is carried out to raw information.
Finally, by pretreated data loading, in order to subsequent data mining and analysis work.
Example IV, electric power data network intelligent detecting and holographic Evaluation Platform
(1) plateform system structure
The platform part 5 is electric power data network intelligent detecting and holographic Evaluation Platform 51, including：It is physical layer, acquisition layer, pre-
Process layer, management level, analysis layer and presentation layer, wherein physical layer is basic facility layer, this layer disposes all kinds of of electric power data network
Equipment and link belong to the object being managed；Acquisition layer is data collection layer, and all kinds of main passive measurements of layer deployment are visited
Needle is completed to the acquired original of various dimensions fine granularity network and business quality data, including to electric power data network backbone network, connect
Enter the network of network and data center and the acquisition of service feature data and data on flows；Pretreatment layer is according to analysis demand
Complete pretreatment to institute's gathered data, and by data loading；Management level are detection control functional layer, which completes probe tube
The functions such as reason, monitoring task scheduling, measurement strategies management；Analysis layer is analysis and evaluation function layer, which completes each laminar flow amount
With the functions such as protocal analysis, performance evaluation, performance test and service traffics signature analysis, and Risk-warning, failure inspection are completed
Survey and positioning function；Presentation layer is interface presentation layer, this layer completes configuration management and the showing interface function of entire plateform system；
Accumulation layer is completed to enter library storage to disparate networks and quality of service gathered data and correlation analysis data；Interface layer is completed and number
According to the interactive interfacing between net integrated network management system；Between the layers, unified secure communication mechanism, realization pair will be used
Certification, compression and safe transmission function when exchanging visits between each layer function.
(2) plateform system basic function and expanded function
Electric power data network intelligent detecting and holographic Evaluation Platform system, main basic function include：
Probe management：Complete the United Dispatching and management function to the passive probe of master；
Monitor task management：Complete management to main passive detection task, including the establishment of detection mission, deletion, inquiry,
The functions such as modification, stopping；
Tactical management：Complete the functions such as the management to main passive measurement strategy, including tactful formulation, modification, inquiry；
Flow analysis：Complete the multi-level multi dimensional analysis function to all kinds of service traffics；
Quality of service is analyzed：Complete the multi-angle difference analysis function to all kinds of quality of services；
Risk-warning：The Risk-warning function to quality of service is completed according to detection data；
Fault detection and location：According to the detection of the complete paired data net network failure of detection data and positioning function.In addition,
The partial function of data network network management will be also realized in this system, including：
Resource management function：Complete the acquisition to electric power data network resource data and basic management function.
VPN management functions：VPN traffic information inspection function and VPN traffic discovery feature are completed, is obtained according to periodically automatic
Each PE equipment on RT information matched, to automatically find three layers of MPLS VPN informations, obtain MPLS VPN in network
Topology situation.
Analysis for routing protocols function：Realize comprehensive prison to electric power data network backbone pe router equipment dynamic routing protocol
It surveys, finds the working condition of the whole network Routing Protocol in real time, automatically, and important routing-events are tracked, simulate.
It is docked with communications management system (TMS) and data sharing function：This system will be realized and communications management system (TMS)
Docking, provide real-time data on flows to communications management system TMS by standard interface, and according to the configuration of TMS, complete stream
Measure the functions such as setting is synchronous with data on flows of collection period.
(3) parallel optimization technique of intelligent detecting and Evaluation Platform
In platform R＆D process, in order to improve processing capacity and real-time response energy of the system platform towards electric power data network
Power, this project is also by the parallel optimization technique of research platform system.Thread-level parallelism based on multi processor platform is to carry
The preferred option of high computer system performance.Multiple processor structure can be divided into according to the difference of interconnection mode between processor：Loose coupling
Close multiple processor structure and close coupling multiple processor structure.The former is common in for example all kinds of group systems of occasion of each mass computing.
This project more pays close attention to the latter, and especially with the close coupling multiple processor structure that CMP (multi-core processor) is representative, this is a kind of
With respect to lightweight but do not lose the solution of high efficiency.CMP is considered as a SoC chip for being integrated with multiple cores.Each
Core has the Ll level data caching (cache) and instruction buffer (cache) of oneself；The shared L2 grades of cachings of each core have oneself independent
L2 grades caching, synchronized by hardware realization.
As multi-core processor and multi processor platform technology are gradually ripe, become by software multithread improving performance
Current research hotspot.Hardware resource can be made more fully to be utilized using multithreading, meter is improved to reach
Calculate the purpose of performance.Thread (thread) is the discrete series of related instructions.The execution phase of thread and other instruction sequences
It is mutually independent.Each program includes at least a thread, i.e. main thread.Main thread is responsible for the initial work of program, and executes
Initial order.Then, other threads can be respectively created to execute various different tasks in main thread.From hardware level,
Thread is one and other mutually independent execution routes of hardware thread execution route.The work of operating system is by software thread
Hardware is mapped to execute in resource.Because multithreading supports multiple operations to be performed simultaneously, program can be significantly improved
Performance.But multithreading, simultaneously but also application behavior becomes more complicated, basic reason is：Program can be sent out simultaneously
Raw multiple actions.These simultaneous actions and the interaction between them are managed and need to consider following four side
Face：Synchronous, communication, load balancing and scalability.
Embodiment five, the fault location based on electric power data network holography assessment system
It is illustrated in figure 3 the flow diagram of the Fault Locating Method based on electric power data network holography assessment system, is based on
The electric power data network fault detection and location mechanism of intelligent detecting is based primarily upon active probe Detection Techniques, which is divided into three
Part：1. the selection of probe deployment position；2. the selection of detective path；3. the fault diagnosis based on active probe.Wherein detect
Path selects to be divided into as two parts：The selection of the selection and fault location detection of fault detect detection.
(1) the active probe selection of fault-finding
As shown in figure 4, for the present invention is based on fault detects in electric power data network holography assessment system Fault Locating Method
Flow diagram；The purpose of fault detect is to whether there is failure in detection network, and selected test path needs to meet covering
All nodes in network.The detective path generated from detection site to other all nodes just forms all available detections
Collection needs to select a part of detective path as fault detect collection in available detection set in the fault detect detection phase
It closes, the selection of fault detect set need to meet the following conditions：All nodes in selected test path overlay network；Test road
Diameter is few as possible.
The detection select permeability of fault detect is two points of covering problems, has proved to be np complete problem, commonly
Basic skills is the approximate data based on greedy algorithm, and there are two types of Greedy strategy solution throughways common at present：First, greedy increasing
Computation system：If initialization detection collection is combined into sky, the detection for the not yet capped node for capableing of cover-most, Zhi Daosuo are constantly chosen
There is node all capped；Second is that greed reduces algorithm：The complete or collected works of all detections are set as initialization detection set, continuously attempt to delete
It except some detection, only need to judge that the deletion of the detection will not cause certain nodes to be uncovered, there is no this in detection is gathered
Until the redundancy detection of sample.
Although the detection set that both algorithms selections go out can cover all nodes in network, choose
The not necessarily minimum detection set of detection set.Increase algorithm relative to greed, greed reduces algorithm, and there is more
Uncertainty, this project is quasi- to be increased algorithm for greed and improves, to improve the performance of algorithm.
Innovatory algorithm does not select the detection of cover-most node, first picks out by most not instead of first when starting selection
The node of covering is detected less, then the detection of cover-most node is selected from the detection that can cover the node.It is relied in detection
In matrix, each column vector corresponds to the detected state passed through of a node, calculates the weights of the corresponding column vector of each node,
Can obtain the detection number by each node, find out at least detected by node after, can by calculate pass through it
All detections row vector weights, and carry out descending arrangement, to find out the detection of the not yet capped node of cover-most, such as
This cycle, until all nodes are capped.
(2) the active probe selection of fault location
Fig. 5 is that the present invention is based on analyzed result of detection in electric power data network holography assessment system Fault Locating Method
Flow diagram；It after the completion of the work in fault detect stage, needs to analyze result of detection, if detecting failure
Presence, then can carry out fault location stage detection selection, it is therefore an objective to the root of positioning failure needs to reach the purpose
Suitable detection is sent to obtain more information.
The result of detection of fault detect is analyzed first.If detecting successfully, then it is assumed that successful probe passes through all
Node is normal node, and normal node set N is added in these nodesnn(Normal Node), if being judged to before having node
It is set to suspect node, it is deleted from suspect node set；If detection failure, unsuccessfully on detective path it is all before not by
It is determined as that the node of normal node is suspect node, and suspect node set N steps n (step u step piciou steps is added
Node)；Only it is suspect node there are one node, other nodes are all by certain if in all nodes that unsuccessfully detection is passed through
A little successful probes pass through, then the suspect node is malfunctioning node, and are added into malfunctioning node set Nfn(Fault Node)。
Then, by the analysis returned the result to fault detect phase detection, we can obtain normal node set, can
Doubt node set and malfunctioning node set.Shown in Fig. 6, for the present invention is based on electric power data network holography assessment system fault location
The flow diagram of fault location in method；Suspect node set will determine as the uncertain node set of state as failure
The detection target in position stage.The detection selection method of fault location stage has two major classes at present：Detection mode and friendship is pre-selected
Mutual formula selects detection mode.The former disposably selects all fault location detection set, is sent in network and receives detection
As a result, the mode being pre-selected applies fixed load to network, although this mode calculating process is fairly simple, to institute
It is extremely inefficient that some detections, which execute such mode,；Interactive detection mode is every time adaptive according to last result of detection
Select next detection with answering, can effectively reduce in this way needed for execute detection quantity, to obtain better timeliness and
The load of lower additional networks, but calculating process often complex.This project is quasi- to select interactive probing thinking to design event
Hinder positioning probe selection algorithm.
The detection selection of fault location stage requires one new detection of selection, the detection that will make suspect node set
In number of nodes reduce to the greatest extent.The modeling of phase detection selection is also the spy that detection relies on matrix and fault-finding stage
It surveys unlike selection, the detection in this stage selects to be from alternative detection set (not by the detection set as detection, in failure
Detection-phase indicates the available detection set other than fault detect set, is indicated in addition to fault detect in fault location stage
Available detection set other than set and fault location detection before) the inside chooses and meets so that suspect node number reduces to the greatest extent
Detection, so the detection in this stage must cover suspect node.
There are following two situations for reducing suspect node：If a kind of situation is only to cover the detection survey of a suspect node
Test result returns to failure, then shows that the suspect node is malfunctioning node；Second is that if the probing test result of covering suspect node is returned
It returns successfully, then shows that these suspect nodes are normal node.It should be noted that when in face of the second situation, it is also contemplated that due to
These suspect nodes are deleted from suspect node set, are added after normal node set, result returns in test set before
The failure and detection for covering multiple suspect nodes may be now containing only there are one suspect node, needing to all then failures
Detection set re-start the judgement of situation one.
In a preferred embodiment, the Fault Locating Method is based on electric power data network evaluation the quality index system
And holographic assessment models carry out, and carry out combined detection to network and quality of service using the passive probe of master, and use multivariate joint probability
Analytical technology carries out real-time tracking analysis, to realize the high-precision wind of electric power data network quality of service to network servicequality
Dangerous early warning.
Preferably, the method for the active probe includes information inquiry and the connectivity inspection triggered to related network device
It surveys.
Preferably, the method for the active probe includes information inquiry and the connectivity inspection triggered to related network device
It surveys, and network equipment information automatic trigger Active Networks performance is combined to detect.
Preferably, after the electric power data network fault location, fault diagnosis, sector-style of going forward side by side danger are carried out based on active probe
Early warning.
Preferably, the electric power data network fault location be based on electric power data network intelligent detecting and holographic Evaluation Platform into
Row.
In a preferred embodiment, electric power data network Risk-warning technology should be able to learning network judgement and place extremely
The parameter of the knowledge of reason such as abnormal flow model can carry out the network real time data obtained from probe and Network Abnormal knowledge
Matching judges whether to generate abnormal flow.If it find that abnormal flow, then carry out according to the processing rule being previously set at correlation
Reason, and propose to alert.Meanwhile being interacted with database, the parameter of update abnormal discharge model, generate new rule.
Risk forecast model can be according to historical traffic information adjust automatically abnormal flow model, to realize abnormal flow
Self-adapting detecting.But for the setting of abnormal flow decision threshold, it is also necessary to which the traffic characteristic to being monitored network is grown
The study or research of time, just can determine that optimal threshold.
Abnormal flow is there are many possible source, including new application system is reached the standard grade with business, computer virus, hacker enter
Invade, network worm, refusal network service, using illegal software, network equipment failure, illegally occupy network bandwidth etc..Network flow
The abnormal detection method of amount includes four classes：It counts abnormal detection method, the method for detecting abnormality based on machine learning, dug based on data
The abnormal detection method of pick and abnormal detection method etc. based on neural network.This project will be studied based on time series models
The temporal correlation of detection data calculates setting confidence interval, with Markov Process Model, by dividing then in conjunction with variance
Analysis state-transition matrix predicts the variation of network state, to realize to electric power data network network and quality of service
Risk-warning.
The foregoing is only a preferred embodiment of the present invention, is not intended to limit the scope of the present invention, it is all
All any modification, equivalent and improvement made by within the spirit and principles in the present invention etc. should be included in the protection of the present invention
Within the scope of.
Claims (10)
1. one kind being based on electric power data network holography assessment system, which is characterized in that the system comprises data snooping portion, data are pre-
Processing unit, data analysis portion, data application portion and platform part, wherein the data snooping portion is used for by disposing all kinds of masters
Dynamic and/or passive measurement probe completes the acquired original to various dimensions fine granularity network and business quality data, including to electric power
Data network backbone network, access network and the network of data center and the acquisition of service feature data and data on flows；
The data prediction portion completes pretreatment to institute's gathered data according to analysis demand, and by data loading；The number
Data preprocess portion locates in advance for realizing data prediction configuration, process of data preprocessing and data prediction management and control, the data
Reason is configured to process of data preprocessing and provides operation foundation；The process of data preprocessing include by format conversion unit,
Data translation unit and data derived units are respectively completed format conversion, data translation and data derivation function, and the data are pre-
Processing management and control provides scheduling, monitoring, exception control and statistical function for preprocessing process；
The data analysis portion, including multidimensional data convergence analysis unit, for carrying out multidimensional number by pretreated data
According to convergence analysis；The multidimensional data convergence analysis is divided by the data packet captured to intelligent probe after pretreatment
Analysis, and the information such as sequence and state are reached by means of data packet, using protocol identification as a result, complete by each unit module respectively
At network layer performance evaluation, transport layer performance analysis, application layer performance analysis and general service performance evaluation；
The data application portion, for completing various applications, the data application using the data of data analysis portion output
Portion, including the Risk-warning unit of the electric power data network Risk-warning function based on risk tolerance model is completed, it completes to be based on
The failure location unit and completion the whole network quality of service dynamic of the electric power data network fault localization mechanism of risk tolerance model can
Depending on the visualization of change；
The platform part, for building a system platform, the above-mentioned institute of carrying is functional, and the platform part is using modularization and divides
Layer mode is realized.
2. system according to claim 1, which is characterized in that the format conversion unit, for parsing source data and mesh
It marks the different data storage format between data and determines the transformation rule between corresponding storage format, the data translation
Unit realizes data translation by the mapping ruler from source data to target data；The data derived units, for original
Beginning information analysis simultaneously carries out derivative acquisition information in conjunction with business rule.
3. system according to claim 1, which is characterized in that the data analysis portion completes application layer service feature
During analysis, it will also realize that applied business is traced to the source, dynamically to trace the completion of some concrete application layer service in real time
Journey records the response time in each step, and then is directed to data, voice, video, multimedia and other electric power data networks
The concrete property of business completes the performance evaluation to various dimensions fine-grained data and works.
4. system according to claim 1, which is characterized in that the Risk-warning unit is sentenced extremely by learning network
The disconnected knowledge with processing realizes the network real time data and Network Abnormal that will be obtained from probe such as the parameter of abnormal flow model
Knowledge is matched, and judges whether to generate abnormal flow；If it find that abnormal flow, then according to the processing rule being previously set into
Row relevant treatment, and propose to alert；Meanwhile being interacted with database, the parameter of update abnormal discharge model, generate new rule
Then；Wherein, the Risk-warning unit can be according to historical traffic information adjust automatically abnormal flow model, to realize exception stream
The self-adapting detecting of amount.
5. system according to claim 1, which is characterized in that the visualization, by computer graphics techniques and
Image processing techniques, converts the data into figure or image is shown on the screen, and carries out interaction process, logical to realize
Image is crossed to disclose the effective information implied in data；Its visual analyzing result can be embodied by various layout type, packet
It includes orthogonal packing and radiation is laid out.
6. system according to claim 1, which is characterized in that the platform part is electric power data network intelligent detecting and holography
Evaluation Platform, including：Physical layer, acquisition layer, pretreatment layer, management level, analysis layer and presentation layer, wherein based on physical layer
Facility layer, this layer dispose the various kinds of equipment and link of electric power data network, belong to the object being managed；Acquisition layer is data
Acquisition layer, the layer dispose all kinds of main passive measurement probes, complete to the original of various dimensions fine granularity network and business quality data
Acquisition includes network and service feature data and flow to electric power data network backbone network, access network and data center
The acquisition of data；Pretreatment layer completes pretreatment to institute's gathered data according to analysis demand, and by data loading；Management level are
Control functional layer is detected, which completes the functions such as probe management, monitoring task scheduling, measurement strategies management；Analysis layer is analysis
With evaluation function layer, which completes each laminar flow amount and protocal analysis, performance evaluation, performance test and service traffics feature point
The functions such as analysis, and complete Risk-warning, fault detection and location function；Presentation layer is interface presentation layer, which completes entire flat
The configuration management of platform system and showing interface function；Accumulation layer is completed to disparate networks and quality of service gathered data and related point
Analysis data enter library storage；Interface layer completes the interactive interfacing between data network integrated network management system；Between the layers,
Unified secure communication mechanism will be used, realizes certification, compression and safe transmission function when exchanging visits between each layer function.
7. a kind of being applied to fault location sides of any one of the claim 1-6 based on electric power data network holography assessment system
Method, which is characterized in that the described method comprises the following steps：
Step 1：Detective path is selected to form detection collection；
Step 2：It sends detection and carries out fault detect；
Step 3：The result of detection of fault detect is analyzed；
Step 4：Fault location is carried out based on active probe；
Wherein, the detective path is from detection site to path caused by other nodes, and selected detective path needs full
All nodes in sufficient overlay network, and the detective path is minimum detective path；
The transmission detection carries out fault detect and includes the following steps：
Step 21：Detection collection is combined into sky when initializing for the first time, no to then follow the steps 22；
Step 22：Matrix modeling is relied on based on detection, calculates the weights of the corresponding column vector of each node；
Step 23：It obtains the detection number by each node, finds out the node passed through by minimum detection；
Step 24：The row vector weights of all detections of the above-mentioned node passed through by minimum detection are calculated, and carry out descending arrangement,
Find out the detection of the not yet capped node of cover-most；
The result of detection to fault detect carries out analysis and includes the following steps：
Step 31：Result of detection is collected, is analyzed and determined；
Step 32：If detecting successfully, then it is assumed that all nodes that successful probe passes through are normal node, and these nodes are added
Enter normal node set；
Step 33：If being judged as suspect node before having node, it is deleted from suspect node set；
Step 34：If detection failure, unsuccessfully on detective path it is all before be not judged as normal node nodes be can
Node is doubted, and suspect node set is added；
Step 35：Only it is suspect node there are one node, other nodes are all if in all nodes that unsuccessfully detection is passed through
Passed through by certain successful probes, then the suspect node is malfunctioning node, and is added into malfunctioning node set.
8. the method according to the description of claim 7 is characterized in that described be based on active probe to carry out fault location including following
Step：
Step 41：The detection met so that suspect node number reduces to the greatest extent is chosen from alternative detection set the inside；
Step 42：Matrix modeling is relied on based on detection, fault detect is analyzed again；
Step 43：If the probing test result for only covering a suspect node returns to failure, show that the suspect node is failure
Node；
Step 44：If the probing test result of covering suspect node returns successfully, show that these suspect nodes are normal node；
Wherein, the alternative detection collection is combined into not by the detection set as detection, is indicated in addition to failure in the fault detect stage
Available detection set other than detection set indicates that fault location is visited with before in addition to fault detect set in fault location stage
Available detection set other than survey.
9. according to the method described in claim 8, it is characterized in that, in the step 44, it is also necessary to suspect node from suspicious
It is deleted in node set, and normal node set is added；The detection set of detection failure before all is re-started later
Judgement.
10. according to the method described in any one of claim 7-9, which is characterized in that the method for the detection includes triggering
Information inquiry to related network device and detection of connectivity, and network equipment information automatic trigger Active Networks performance is combined to visit
It surveys.
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| CN112351024A (en) * | 2020-11-03 | 2021-02-09 | 广东电网有限责任公司 | Public network communication safety monitoring system and method |
| CN112994972B (en) * | 2021-02-02 | 2022-05-20 | 成都卓源网络科技有限公司 | Distributed probe monitoring platform |
| CN113259167A (en) * | 2021-05-28 | 2021-08-13 | 贵州电网有限责任公司 | Power distribution terminal data transmission method based on event trigger mechanism |
| CN113259167B (en) * | 2021-05-28 | 2023-07-18 | 贵州电网有限责任公司 | Power distribution terminal data transmission method based on event triggering mechanism |
| CN115550211A (en) * | 2021-06-29 | 2022-12-30 | 青岛海尔科技有限公司 | Method and device for detecting network connection quality, storage medium and electronic device |
| CN116089392A (en) * | 2022-09-17 | 2023-05-09 | 新疆维吾尔自治区信息中心 | Information system evaluation library building system and method |
| CN116089392B (en) * | 2022-09-17 | 2024-03-08 | 新疆维吾尔自治区信息中心 | Information system evaluation library building system and method |
| CN116962143A (en) * | 2023-09-18 | 2023-10-27 | 腾讯科技（深圳）有限公司 | Network fault detection method, device, computer equipment and storage medium |
| CN116962143B (en) * | 2023-09-18 | 2024-01-26 | 腾讯科技（深圳）有限公司 | Network fault detection method, device, computer equipment and storage medium |
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