CN108306756B - Holographic evaluation system based on power data network and fault positioning method thereof - Google Patents
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Abstract
The invention relates to the technical field of power data network fault location, and discloses a holographic evaluation system based on a power data network and a fault location method thereof, wherein the method comprises the following steps: step 1: selecting a detection path to form a detection set; step 2: sending detection to detect faults; and step 3: analyzing the detection result of the fault detection; and 4, step 4: fault location is carried out based on active detection; the holographic evaluation system and the fault positioning method solve the technical problems that the time for finding the risk is long, the fault positioning technology needs to be judged by means of experience of maintenance personnel, and the efficiency is low in the conventional risk early warning method, can quickly complete detection and perform positioning when detection is needed, reduce the time for finding the risk, and improve the efficiency.
Description
Technical Field
The invention relates to the technical field of power data network fault positioning, in particular to a power data network-based holographic evaluation system and a fault positioning method thereof.
Background
With the continuous development of society and economy, the development of the power data network is very rapid, especially the development of the power data network fault positioning technology reaches a certain height, the existing technologies effectively avoid and reduce the loss caused by the power data network fault, provide reliable power utilization environment for people, and facilitate the life of people. The risk early warning timeliness required by the power data network is higher than that of a common network, the required fault location time is also shorter, the passive network data acquisition method adopted by the traditional network management technology cannot meet the requirements of the power data network, and how to realize active monitoring and real-time monitoring of network service quality by utilizing various detection modes such as active and passive probes and the like needs to be researched so as to improve the risk early warning timeliness.
The invention provides a holographic evaluation system based on a power data network and a fault positioning method thereof, aiming at the technical problems that the time for finding a risk is long, the fault positioning technology needs to be judged by means of experience of maintenance personnel, and the efficiency is low in the conventional risk early warning method.
Disclosure of Invention
Aiming at the problems in the prior art, the invention provides a power data network-based holographic evaluation system and a fault positioning method thereof, which are used for carrying out real-time tracking analysis on the network service quality, thereby realizing high-precision risk early warning and fault positioning of the power data network service quality.
The invention provides a holographic evaluation system based on a power data network, which comprises a data detection part, a data preprocessing part, a data analysis part, a data application part and a platform part, wherein the data detection part is used for completing the original acquisition of a multi-dimensional fine-grained network and service quality data by deploying various active and/or passive measurement probes, and comprises the acquisition of network and service performance data and flow data of a backbone network, an access network and a data center of the power data network;
the data preprocessing part completes preprocessing of the acquired data according to analysis requirements and puts the data into a warehouse; the data preprocessing part is used for realizing data preprocessing configuration, a data preprocessing process and data preprocessing management and control, and the data preprocessing configuration provides an operation basis for the data preprocessing process; the data preprocessing process comprises the steps of respectively finishing format conversion, data translation and data derivation functions through a format conversion unit, a data translation unit and a data derivation unit, and the data preprocessing management and control provides scheduling, monitoring, exception control and statistic functions for the preprocessing process;
the data analysis part comprises a multidimensional data fusion analysis unit which is used for carrying out multidimensional data fusion analysis on the preprocessed data; the multidimensional data fusion analysis is characterized in that a data packet captured by an intelligent probe is analyzed after being preprocessed, and network layer performance analysis, transmission layer performance analysis, application layer performance analysis and general service performance analysis are respectively completed through each unit module by means of information such as a data packet arrival sequence and state and the result of protocol identification;
the data application part is used for finishing various applications by utilizing the data output by the data analysis part, and comprises a risk early warning unit for finishing the risk early warning function of the power data network based on a risk tolerance model, a fault positioning unit for finishing a power data network fault positioning mechanism based on the risk tolerance model and a visualization unit for finishing the dynamic visualization of the service quality of the whole network;
the platform part is used for building a system platform and bearing all the functions, and the platform part is realized in a modularization and layering mode.
Preferably, the format conversion unit is configured to parse different data storage formats between the source data and the target data and determine a conversion rule between corresponding storage formats, and the data translation unit implements data translation according to a mapping rule from the source data to the target data; and the data derivation unit is used for analyzing the original information and deriving the original information by combining the business rules to obtain the information.
Preferably, the data analysis unit is further configured to implement application service tracing in the process of completing application layer service performance analysis, so as to dynamically trace the completion process of a specific application layer service in real time, record response time in each step, and then complete performance analysis on multi-dimensional fine-grained data according to specific characteristics of data, voice, video, multimedia and other power data network services.
Preferably, the risk early warning unit matches the network real-time data acquired from the probe with the network abnormal knowledge by learning the knowledge of network abnormal judgment and processing, such as parameters of an abnormal traffic model, and judges whether abnormal traffic occurs; if abnormal flow is found, performing related processing according to a preset processing rule, and giving an alarm; meanwhile, interacting with a database, updating parameters of the abnormal flow model, and generating a new rule; the risk early warning unit can automatically adjust an abnormal flow model according to historical flow information so as to realize the self-adaptive detection of abnormal flow.
Preferably, the visualization unit converts the data into a graph or an image to be displayed on a screen through a computer graph technology and an image processing technology, and performs interactive processing, so that the effective information implicit in the data is revealed through the image; the visualized analysis result can be embodied by various layout modes, including an orthogonal layout and a radiation layout.
Preferably, the platform part is a power data network intelligent detection and holographic evaluation platform, including: the system comprises a physical layer, an acquisition layer, a preprocessing layer, a management layer, an analysis layer and a display layer, wherein the physical layer is an infrastructure layer, and various devices and links of a power data network are deployed on the layer and belong to a monitored and managed object; the acquisition layer is a data acquisition layer, and various active and passive measurement probes are deployed on the data acquisition layer to finish the original acquisition of the multidimensional fine-grained network and the service quality data, including the acquisition of network and service performance data and flow data of a power data network backbone network, an access network and a data center; the preprocessing layer completes preprocessing of the acquired data according to analysis requirements and puts the data into a warehouse; the management layer is a detection control function layer and completes the functions of probe management, monitoring task scheduling, measurement strategy management and the like; the analysis layer is an analysis and evaluation functional layer, and the layer completes the functions of flow and protocol analysis, performance test, service flow characteristic analysis and the like of each layer and completes the functions of risk early warning, fault detection and positioning; the display layer is an interface display layer and completes the configuration management and interface display functions of the whole platform system; the storage layer finishes the storage of the data collected by various networks and service quality and the related analysis data; the interface layer completes interface interaction with the data network integrated network management system; and a uniform safety communication mechanism is adopted among the layers to realize the functions of authentication, compression and safety transmission when the functions of the layers are mutually accessed.
In addition, the invention also discloses a fault positioning method applied to the power data network-based holographic evaluation system, which comprises the following steps:
step 1: selecting a detection path to form a detection set;
step 2: sending detection to detect faults;
and step 3: analyzing the detection result of the fault detection;
and 4, step 4: fault location is carried out based on active detection;
the detection path is a path generated from a detection station to other nodes, the selected detection path needs to satisfy all nodes in the overlay network, and the detection path is the minimum detection path;
the sending detection for fault detection comprises the following steps:
step 21: the detection set is empty during the first initialization, otherwise step 22 is executed;
step 22: based on the detection dependency matrix modeling, calculating the weight of the column vector corresponding to each node;
step 23: obtaining the detection number of each node, and finding out the node which is detected at least;
step 24: calculating the row vector weights of all the detections of the nodes which are passed by the least detection, and performing descending order to find out the detection which covers the nodes which are not covered at most;
the analysis of the detection result of the fault detection comprises the following steps:
step 31: collecting detection results, and analyzing and judging;
step 32: if the detection is successful, all nodes which are successfully detected are considered to be normal nodes, and the nodes are added into a normal node set;
step 33: if the node is judged to be a suspicious node before, deleting the node from the suspicious node set;
step 34: if the detection fails, all nodes which are not judged as normal nodes before on the failure detection path are suspicious nodes, and are added into a suspicious node set;
step 35: if only one node in all the nodes passed by the failed detection is a suspicious node and other nodes are passed by some successful detections, the suspicious node is a failed node and is added into the failed node set.
Preferably, the fault location based on active detection comprises the following steps:
step 41: selecting the detection which meets the requirement that the number of suspicious nodes is reduced as much as possible from the alternative detection set;
step 42: modeling based on the detection dependency matrix, and analyzing the fault detection again;
step 43: if the detection test result covering only one suspicious node fails to return, the suspicious node is indicated as a fault node;
step 44: if the detection test result covering the suspicious nodes returns successfully, the suspicious nodes are indicated to be normal nodes;
wherein the alternative detection set is a detection set which is not used as detection, and represents an available detection set except the fault detection set in the fault detection stage, and represents an available detection set except the fault detection set and the previous fault location detection in the fault location stage.
Preferably, in step 44, the suspicious node is further deleted from the suspicious node set and added to the normal node set; and then all the detection sets with the failure detection in the previous detection are judged again.
Preferably, the method for detecting includes triggering information query and connectivity detection on related network devices, and automatically triggering active network performance detection in combination with network device information.
The technical scheme of the embodiment of the invention provides a holographic evaluation system based on a power data network and a fault positioning method thereof, and the technical scheme of the embodiment of the invention has the following remarkable effects:
the invention provides a holographic evaluation system based on a power data network and a fault positioning method thereof, and the holographic evaluation system and the fault positioning method solve the technical problems that the time for finding risks is long, the fault positioning technology needs to be judged by the experience of maintenance personnel, and the efficiency is low in the conventional risk early warning method.
Drawings
Fig. 1 is a diagram of an intelligent detection and holographic evaluation system for a power data network according to a first embodiment of the present invention;
FIG. 2 is a schematic diagram of a two-dimensional data fusion analysis process according to an embodiment of the present invention;
FIG. 3 is a schematic flow chart of a method for locating a fault in a power data network according to the present invention;
FIG. 4 is a schematic diagram illustrating a flow of fault detection in the power data network fault location method according to the present invention;
FIG. 5 is a schematic flow chart illustrating analysis of the detection result in the method for locating a fault in a power data network according to the present invention;
FIG. 6 is a schematic flow chart of fault location in the power data network fault location method according to the present invention;
Detailed Description
In order to make the objects, features and advantages of the present invention more obvious and understandable, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention, and it is apparent that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The method has the technical problems that the time consumed for finding the risk is long in the conventional risk early warning and fault positioning method, the fault positioning technology needs to be judged by virtue of the experience of maintenance personnel, and the efficiency is low; aiming at the problems, the invention provides a holographic evaluation system based on a power data network and a fault positioning method thereof.
Embodiment I, intelligent detection and holographic evaluation system of power data network
As shown in fig. 1, the invention provides a holographic evaluation system based on a power data network, which is an intelligent detection and holographic evaluation system diagram of a power data network in an embodiment of the invention, and the system includes a data detection part 1, a data preprocessing part 2, a data analysis part 3, a data application part 4 and a platform part 5, wherein the data detection part 1 is used for completing original collection of multidimensional fine-grained network and service quality data by deploying various active and/or passive measurement probes, including collection of network and service performance data and traffic data of a backbone network, an access network and a data center of the power data network.
The data preprocessing part 2 finishes preprocessing the acquired data according to the analysis requirement and puts the data into a warehouse; the data preprocessing part 2 is used for realizing data preprocessing configuration, a data preprocessing process and data preprocessing management and control, and the data preprocessing configuration provides operation basis for the data preprocessing process and comprises preprocessing rule configuration and preprocessing mode configuration; the data preprocessing process can perform specific operations according to the rules, modes, frequencies and other contents provided by the configuration function, and comprises the steps of respectively completing format conversion, data translation and data derivation functions through a format conversion unit 21, a data translation unit 22 and a data derivation unit 23, wherein the data preprocessing management and control provides scheduling, monitoring, exception control and statistical functions for the preprocessing process;
the data analysis part 3 comprises a multidimensional data fusion analysis unit 31 for carrying out multidimensional data fusion analysis on the preprocessed data; the multidimensional data fusion analysis is characterized in that a data packet captured by the intelligent probe is analyzed after being preprocessed, and network layer performance analysis, transmission layer performance analysis, application layer performance analysis and general service performance analysis functions are respectively completed through each unit module by means of information such as a data packet arrival sequence and state and the result of protocol identification;
the data application part 4 is configured to complete various applications by using the data output by the data analysis part, and the data application part 4 includes a risk early warning unit 41 that completes a risk early warning function of the power data network based on a risk tolerance model, a fault positioning unit 42 that completes a fault positioning mechanism of the power data network based on the risk tolerance model, and a visualization unit 43 that completes dynamic visualization of the service quality of the whole network;
the platform part 5 is used for building a system platform and bearing all the functions, and the platform part is realized in a modularization and layering mode.
Preferably, the format conversion unit 21 is configured to parse different data storage formats between the source data and the target data and determine a conversion rule between corresponding storage formats, where the format conversion includes data type conversion, data precision conversion, null value judgment processing, character string processing, and date format processing; the data translation unit realizes data translation through a mapping rule from source data to target data; and the data derivation unit is used for analyzing the original information and deriving the original information by combining the business rules to obtain the information.
Preferably, the data analysis part 2 also realizes application service tracing in the process of completing application layer service performance analysis, so as to dynamically trace the completion process of a specific application layer service in real time, record the response time in each step, and then complete performance analysis work on multi-dimensional fine-grained data according to specific characteristics of data, voice, video, multimedia and other electric power data network services.
Preferably, the risk early warning unit 41 matches the network real-time data acquired from the probe with the network abnormal knowledge by learning the knowledge of network abnormal judgment and processing, such as parameters of an abnormal traffic model, and judges whether abnormal traffic occurs; if abnormal flow is found, performing related processing according to a preset processing rule, and giving an alarm; meanwhile, interacting with a database, updating parameters of the abnormal flow model, and generating a new rule; the risk early warning unit 41 can automatically adjust an abnormal flow model according to historical flow information to achieve adaptive detection of abnormal flow.
Preferably, the visualization unit 43 converts the data into a graph or an image to be displayed on a screen through a computer graphics technology and an image processing technology, and performs interactive processing, so as to reveal the effective information implied in the data through the image; the visualized analysis result can be embodied by various layout modes, including an orthogonal layout and a radiation layout.
(1) Orthogonal layout visualization techniques
The orthogonal layout requires that the layout is aligned and placed in the vertical or horizontal direction when the position is selected, and the layout mode is regular and matched with the human visual habit.
The graph can be used for visually showing the topological hierarchical relation, the service characteristic classification and the like among the network nodes of the data network, and can be checked through mouse rolling operation when the data volume is large.
(2) Radiation layout visualization techniques
The radiation type layout is mainly characterized in that the root node is positioned at the center of a circle, and other nodes are respectively placed on concentric circles with different radiuses according to the levels where the other nodes are positioned, so that the characteristic that the nodes of the graph increase along with the increase of the levels can be met. The radiation layout diagram can be used for visually showing scenes for carrying out statistical analysis on data traffic according to application categories, protocol categories and the like.
Example two, multidimensional data fusion analysis
FIG. 2 is a schematic diagram of a two-dimensional data fusion analysis process according to an embodiment of the present invention; the multidimensional data fusion analysis unit 31 is configured to perform multidimensional data fusion analysis on the preprocessed data; the multidimensional data fusion analysis is characterized in that a data packet captured by the intelligent probe is analyzed after being preprocessed, and network layer performance analysis, transmission layer performance analysis, application layer performance analysis and general service performance analysis functions are respectively completed through each unit module by means of information such as a data packet arrival sequence and state and the result of protocol identification;
the method comprises the following specific steps: after entering the network layer performance analysis unit 32 for processing, the data packet or the flow table sequentially passes through the transmission layer performance analysis unit 33, the application layer performance analysis unit 34 and the general service performance analysis unit 35 for analysis and processing, and then is output to different service performance analysis units for analysis, which mainly includes: a data service performance analysis unit 301, a voice service performance analysis unit 302, a video service performance analysis unit 303, a multimedia service performance analysis unit 304, and an extended service performance analysis unit 305; the method further comprises the steps that a protocol recognition module 38 outputs a protocol result A and a decoding module 37 outputs a decoding result B to an application layer service tracing unit 34, a flow table maintenance module 36 respectively outputs flow data C and D to a transmission layer performance analysis unit 33 and a network layer service performance analysis unit 32, and meanwhile, the network layer performance analysis unit 32, the transmission layer performance analysis unit 33, the application layer performance analysis unit 34 and a general service performance analysis unit 35 respectively output results to a monitoring result management module 39 for management, so that a multidimensional data analysis process is completed.
EXAMPLE III data Pre-processing
The data preprocessing function mainly comprises three parts, namely data preprocessing configuration, a data preprocessing process (ETL) and data preprocessing management and control, the data preprocessing configuration provides an operation basis for the data preprocessing process, the data preprocessing process can carry out specific operation according to the contents such as rules, modes, frequencies and the like provided by the configuration function, and the data preprocessing management and control provides scheduling, monitoring, exception control and statistics for the preprocessing process.
a) Data pre-processing configuration
The data preprocessing configuration is an important implementation link in data processing, relates to a plurality of system sources and data processing technologies, and needs to make a flexible processing strategy aiming at source data with different sources and different qualities so as to support flexible data preprocessing, including preprocessing rule configuration and preprocessing mode configuration.
The preprocessing rule is the constraint and requirement of each stage on data processing and the regulation which is definitely required to be followed for the source and operation of the data in the data preprocessing process, and the configuration of the data preprocessing rule comprises the following contents:
and (3) configuring a cleaning rule of the data: aiming at the problems of accuracy and integrity of source data, a corresponding filtering rule is formulated according to a data quality evaluation method so as to screen out data which do not meet requirements, and the data quality problem is recorded for query statistics in the future. The accuracy of the data means that the key attribute or information of each object type data is a compliance value, and comprises an enumeration value, a specified character length, a specified character string type and a specified value range, wherein the value of the key attribute or information conforms to the specification. The integrity of the data refers to whether key attributes or information of each object type data are complete. If the key attribute or information of the evaluation is not null, the evaluation is regarded as complete; if the key attribute or information to be evaluated is empty, it is considered to be incomplete.
Rule configuration of data conversion: and setting a data format conversion rule of the source data, and establishing a mapping conversion relation between the detected original data and the target fusion model. And converting the original source data into a uniform target data format through the guidance of the rule.
And (3) configuration of association relationship of data: as each theme domain is dispersed in different business systems, in order to strengthen the association characteristic between data and identify the activity condition of the same object in different theme domains, the relationship between the data and the data can be established through the association relationship between the theme domains, thereby obtaining the complete reflection of the object. Taking the device as an example, the device has device basic information, device bearer service information and device configuration information in a communication resource data model; the equipment has physical attribute information and asset resume information in a ledger data model through asset numbers; the equipment can find the basic information of the project where the equipment is located and the project flow information in the project data model through the asset number; and the equipment can find the work order data such as defects, alarms, faults and the like of the equipment in the event data model through the asset number. Through the incidence relation, the dispersed data can be connected into an organic whole, and the integrity of the object is strengthened.
b) Data preprocessing process
And when data preprocessing is carried out according to the preprocessing rule, for data which do not meet the requirements, the judged result is controlled through the preprocessing task to form a data checking task. Unsatisfactory data mainly comprises three major categories of incomplete data, erroneous data and repeated data. Wherein: incomplete data refers to that some information should be missing or the association and bearing relationships of the data are incomplete; the wrong data refers to that the data is directly written into the database without judgment when the wrong data is input because of analysis errors or manual misoperation; duplicate data refers to duplicate data due to an insufficient data model, a defective primary key or constraint, or an error in the message preprocessing process.
After data conformance inspection, data conversion preprocessing is required, and the data conversion includes format conversion, data translation, data derivation, data aggregation and the like. In most cases, the data preprocessing process is mainly to complete format conversion, data translation and data derivation, and the complex data aggregation and other complex calculations are mainly implemented during data summarization. The data conversion function should support the definition of data, data structure, and conversion process of error data. The data conversion mainly comprises the following parts:
format conversion: because there may be a large difference between the data source system and the communication big data system in terms of data model, data format, etc., or the data provided by the data source system itself lacks regularity, in order to enable the subsequent links of the preprocessing to be processed in a simple and consistent manner, it is necessary to analyze different data storage formats between the source data and the target data and determine a conversion rule between the corresponding storage formats. The format conversion includes data type conversion, data precision conversion, null value judgment processing, character string processing, date format processing, and the like.
Data translation: the method is the most complex processing process in data preprocessing, and source data information must be deeply known and known in the data translation process, abnormal data conditions are identified, and a mapping rule from source data to target data is established. In the mapping process, some information is directly available from the source data, such as 0 for female gender, 1 for male gender, etc. Some of the data cannot be directly obtained from the source data, and certain translation operations such as calculation, merging and splitting are required to be performed on the source data. For each data source and data target, a conversion (translation) rule from the data source to the destination table is determined for each data entity communicating big data, which part of the content includes which fields of the table or tables specific to the source system correspond to which fields of the table or tables of the target data, and how the corresponding conversion rule is.
Data derivation: data in the communication big data system surrounds the production operation analysis application of an enterprise, so that a large amount of context information exists, and the data of a source system needs to be extracted to be used by the communication big data system. The refining of the context information of the communication big data is to perform the processing of the derivative information of the data after the standardization processing (cleaning) of the data. Data required in the communication big data analysis application does not directly exist in a production system, but information obtained by analyzing original information and combining business rules for derivation is required, and therefore derived information processing is required for the original information.
And finally, storing the preprocessed data into a database so as to facilitate subsequent data mining and analysis work.
Fourth embodiment, electric power data network intelligent detection and holographic evaluation platform
(1) Platform system structure
Platform portion 5 is electric power data network intelligent detection and holographic evaluation platform 51, includes: the system comprises a physical layer, an acquisition layer, a preprocessing layer, a management layer, an analysis layer and a display layer, wherein the physical layer is an infrastructure layer, and various devices and links of a power data network are deployed on the layer and belong to a monitored and managed object; the acquisition layer is a data acquisition layer, and various active and passive measurement probes are deployed on the data acquisition layer to finish the original acquisition of the multidimensional fine-grained network and the service quality data, including the acquisition of network and service performance data and flow data of a power data network backbone network, an access network and a data center; the preprocessing layer completes preprocessing of the acquired data according to analysis requirements and puts the data into a warehouse; the management layer is a detection control function layer and completes the functions of probe management, monitoring task scheduling, measurement strategy management and the like; the analysis layer is an analysis and evaluation functional layer, and the layer completes the functions of flow and protocol analysis, performance test, service flow characteristic analysis and the like of each layer and completes the functions of risk early warning, fault detection and positioning; the display layer is an interface display layer and completes the configuration management and interface display functions of the whole platform system; the storage layer finishes the storage of the data collected by various networks and service quality and the related analysis data; the interface layer completes interface interaction with the data network integrated network management system; and a uniform safety communication mechanism is adopted among the layers to realize the functions of authentication, compression and safety transmission when the functions of the layers are mutually accessed.
(2) Platform system basic function and extended function
Electric power data network intelligent detection and holographic evaluation platform system, main essential function includes:
and (3) probe management: the unified scheduling and management functions of the active and passive probes are completed;
monitoring task management: the management of active and passive detection tasks is completed, and the functions of the detection tasks such as creation, deletion, query, modification, stop and the like are included;
and (3) policy management: the management of the active and passive measurement strategies is completed, and the management comprises the functions of making, modifying, inquiring and the like of the strategies;
flow analysis: the multi-level and multi-dimensional analysis function of various service flows is completed;
analyzing the service quality: the multi-angle differential analysis function of various service qualities is completed;
risk early warning: completing a risk early warning function for the service quality according to the detection data;
fault detection and location: and completing the functions of detecting and positioning the network fault of the data network according to the detection data. In addition, the system also realizes partial functions of data network management, including:
resource management function: and the functions of acquiring the resource data of the power data network and basically managing the resource data are completed.
VPN management function: and finishing a VPN service information checking function and a VPN service discovery function, and matching according to the RT information on each PE device which is automatically acquired at regular intervals, so as to automatically discover the three-layer MPLS VPN information and obtain the topology condition of the MPLS VPN in the network.
Routing protocol analysis function: the dynamic routing protocol of the backbone PE router equipment of the power data network is comprehensively monitored, the working condition of the routing protocol of the whole network is automatically found in real time, and important routing events are tracked and simulated.
And (3) docking with a communication management system (TMS) and data sharing functions: the system realizes the butt joint with a communication management system (TMS), provides real-time flow data to the TMS through a standard interface, and completes the functions of setting a flow acquisition period, synchronizing the flow data and the like according to the configuration of the TMS.
(3) Parallel optimization technology of intelligent detection and evaluation platform
In the process of platform research and development, in order to improve the processing capacity and real-time response capacity of a system platform facing to a power data network, the subject also researches a parallel optimization technology of the platform system. Thread-level parallelism based on multiprocessor platforms is the preferred solution to improve computer system performance. Multiprocessor architectures can be classified into: loosely coupled multiprocessor architectures and tightly coupled multiprocessor architectures. The former is often used in large-scale computing occasions such as various cluster systems. The latter is of great interest, especially in tightly coupled multiprocessor architectures, represented by CMP (multi-core processor), which is a relatively lightweight solution without losing efficiency. The CMP may be regarded as an SoC chip integrating a plurality of cores. Each core has its own Ll level data cache (cache) and instruction cache (cache); the cores share the L2 level cache or have independent L2 level cache, and the synchronization is realized through hardware.
With the gradual maturity of multi-core processor and multi-processor platform technologies, it is a hot spot of research at present to improve performance through software multithreading. The multithreading technology can make full use of hardware resources, thereby achieving the purpose of improving the computing performance. A thread (thread) is a discrete sequence of related instructions. Threads are independent of the execution of other instruction sequences. Each program contains at least one thread, the main thread. The main thread is responsible for initialization work of the program and executes initial instructions. Subsequently, the main thread may create additional threads for performing various tasks, respectively. At the hardware level, a thread is an execution path that is independent of the execution paths of other hardware threads. The task of the operating system is to map software threads onto hardware execution resources. Because multithreading supports the simultaneous execution of multiple operations, program performance can be significantly improved. However, multithreading also complicates application behavior, primarily because: the program may take multiple actions simultaneously. Managing these simultaneous actions and their interactions requires the following four aspects to be considered: synchronization, communication, load balancing, and scalability.
Fifth embodiment, based on the holographic evaluation system of the power data network fault location
As shown in fig. 3, a schematic flow chart of a fault location method based on a holographic evaluation system of a power data network is shown, a fault detection and location mechanism of the power data network based on intelligent detection is mainly based on an active probe detection technology, and the method is divided into three parts: 1. selection of a probe deployment location; 2. selecting a detection path; 3. fault diagnosis based on active probing. The selection of the detection path is divided into two parts: selection of fault detection probing and selection of fault location probing.
(1) Active probe selection for fault detection
Fig. 4 is a schematic diagram illustrating a flow of fault detection in the fault location method based on the holographic evaluation system of the power data network according to the present invention; the purpose of fault detection is to detect whether a fault exists in the network, and the selected test path needs to satisfy all nodes in the overlay network. In the fault detection stage, a part of detection paths in the available detection sets are required to be selected as fault detection sets, and the selection of the fault detection sets meets the following conditions: the selected test path covers all nodes in the network; the number of test paths is as small as possible.
The detection selection problem of fault detection is a dichotomous coverage problem which is proved to be an NP complete problem, a common basic method is an approximate algorithm based on a greedy algorithm, and at present, two greedy strategy solving ideas exist: one is the greedy increase algorithm: setting the initialization detection set to be empty, and continuously selecting the detection which can cover the most uncovered nodes until all the nodes are covered; second, greedy reduction algorithm: the method comprises the steps of setting a full set of all probes as an initialization probe set, continuously trying to delete a certain probe, and only judging that the deletion of the probe cannot cause certain nodes to be uncovered until no redundant probe exists in the probe set.
Although the probe sets selected by the two algorithms can cover all nodes in the network, the selected probe set is not necessarily the smallest probe set. Compared with a greedy increase algorithm, the greedy decrease algorithm has more uncertainty, and the subject aims to improve the greedy increase algorithm so as to improve the performance of the algorithm.
When the improved algorithm starts to select, the probes covering the most nodes are not selected firstly, but the nodes covered by the least probes are selected firstly, and then the probes covering the most nodes are selected from the probes which can cover the nodes. In the detection dependency matrix, each column vector corresponds to a state that a node is detected to pass through, the weight of the column vector corresponding to each node is calculated, the number of detections passing through each node can be obtained, after the node which is detected to pass through the least is found out, the detection which covers the most uncovered nodes can be found out by calculating the weights of all detected row vectors passing through the node and performing descending order, and the steps are repeated until all the nodes are covered.
(2) Active probe selection for fault localization
FIG. 5 is a schematic view of a process of analyzing a detection result in the method for positioning a fault based on a holographic evaluation system of a power data network according to the present invention; after the work in the fault detection stage is completed, the detection result needs to be analyzed, if the existence of the fault is detected, the detection selection in the fault location stage is performed to locate the root cause of the fault, and in order to achieve the purpose, appropriate detection needs to be sent to acquire more information.
First, the detection result of the fault detection is analyzed. If the detection is successful, all nodes which are successfully detected are considered to be normal nodes, and the nodes are added into a normal node set Nnn(Normal Node), if there is a Node which is judged as a suspicious Node before, deleting the Node from the suspicious Node set; if the detection fails, all nodes which are not judged as normal nodes before on the failure detection path are suspicious nodes, and a suspicious Node set N step N is added (step u step piciou step Node); if only one node in all the nodes passed by the failed detection is a suspicious node and other nodes are passed by some successful detections, the suspicious node is a failure node and is added into the failure node set Nfn(Fault Node)。
Then, by analyzing the detection return result in the fault detection stage, a normal node set, a suspicious node set and a fault node set can be obtained. Fig. 6 is a schematic diagram illustrating a fault location process in the fault location method based on the holographic evaluation system of the power data network according to the present invention; the suspicious node set is used as a node set with uncertain state and becomes a detection target in a fault positioning stage. The current detection selection methods in the fault location stage have two main categories: pre-selecting a detection mode and interactively selecting a detection mode. The former selects all fault location detection sets at one time, sends the fault location detection sets to the network and receives detection results, and the pre-selection mode applies fixed load to the network; the interactive detection mode is to adaptively select the next detection according to the last detection result each time, so that the number of required detections to be executed can be effectively reduced, and better timeliness and lower additional network load can be obtained, but the calculation process is often extremely complex. The subject is to design a fault positioning probe selection algorithm by selecting an interactive detection thought.
The probe selection in the fault location phase requires the selection of a new probe that will minimize the number of nodes in the suspect node set. The modeling of the detection selection at this stage is also a detection dependency matrix, and different from the detection selection at the fault detection stage, the detection selection at this stage is to select a detection which satisfies the requirement of reducing the number of suspicious nodes as much as possible from an alternative detection set (a detection set which is not used as a detection, an available detection set except for a fault detection set at the fault detection stage, and an available detection set except for the fault detection set and the previous fault location detection at the fault location stage), so the detection at this stage must cover the suspicious nodes.
There are two cases for reducing suspicious nodes: one situation is that if the detection test result covering only one suspicious node fails to return, the suspicious node is indicated as a fault node; and secondly, if the detection test result covering the suspicious nodes returns successfully, the suspicious nodes are indicated to be normal nodes. It should be noted that in the second case, it is also considered that, after the suspicious nodes are deleted from the suspicious node set and added to the normal node set, the probes that have failed to return results in the previous test set and cover multiple suspicious nodes may now only contain one suspicious node, and the case one determination needs to be performed again on all the probe sets that then fail.
In a preferred embodiment, the fault positioning method is performed based on a power data network service quality evaluation index system and a holographic evaluation model, joint detection is performed on the network and the service quality by adopting an active and passive probe, and real-time tracking analysis is performed on the network service quality by adopting a multi-dimensional joint analysis technology, so that high-precision risk early warning of the power data network service quality is realized.
Preferably, the method of active probing includes triggering information query and connectivity detection for relevant network devices.
Preferably, the method of active probing includes triggering information query and connectivity detection on related network devices, and automatically triggering active network performance probing in combination with network device information.
Preferably, after the power data network fault is located, fault diagnosis is performed based on active detection, and risk early warning is performed.
Preferably, the power data network fault location is performed based on a power data network intelligent detection and holographic evaluation platform.
In a preferred embodiment, the risk early warning technology for the power data network should be capable of learning network anomaly judgment and processing knowledge, such as parameters of an abnormal traffic model, matching network real-time data acquired from a probe with the network anomaly knowledge, and judging whether abnormal traffic occurs. If the abnormal flow is found, the relevant processing is carried out according to the preset processing rule, and the alarm is raised. Meanwhile, the method interacts with a database, updates the parameters of the abnormal flow model and generates a new rule.
The risk prediction model can automatically adjust the abnormal flow model according to the historical flow information so as to realize the self-adaptive detection of the abnormal flow. However, setting the abnormal traffic determination threshold value requires a long time of learning or study of the traffic characteristics of the monitored network to determine the optimum threshold value.
There are many possible sources of abnormal traffic, including new applications and business coming online, computer viruses, hacker intrusions, network worms, denial of network service, use of illegal software, network device failures, illegal occupation of network bandwidth, etc. The detection method of the network flow abnormity comprises four types: statistical anomaly detection methods, anomaly detection methods based on machine learning, anomaly detection methods based on data mining, anomaly detection methods based on neural networks, and the like. The method is to study the time correlation of detection data on the basis of a time sequence model, then set a confidence interval by combining variance calculation, and predict the change of a network state by analyzing a state transition matrix by using a Markov process model, thereby realizing the risk early warning of the power data network and the service quality.
The above description is only exemplary of the present invention and should not be taken as limiting the scope of the present invention, and any modifications, equivalents, improvements, etc. that are within the spirit and principle of the present invention should be included in the present invention.
Claims (5)
1. A holographic evaluation system based on a power data network is characterized by comprising a data detection part, a data preprocessing part, a data analysis part, a data application part and a platform part, wherein the data detection part is used for completing the original acquisition of multidimensional fine-grained network and service quality data by deploying various active and/or passive measurement probes, and comprises the acquisition of network and service performance data and flow data of a backbone network, an access network and a data center of the power data network; the data preprocessing part completes preprocessing of the acquired data according to analysis requirements and puts the data into a warehouse; the data preprocessing part is used for realizing data preprocessing configuration, a data preprocessing process and data preprocessing management and control, and the data preprocessing configuration provides an operation basis for the data preprocessing process; the data preprocessing process comprises the steps of respectively finishing format conversion, data translation and data derivation functions through a format conversion unit, a data translation unit and a data derivation unit, and the data preprocessing management and control provides scheduling, monitoring, exception control and statistic functions for the preprocessing process; the data analysis part comprises a multidimensional data fusion analysis unit which is used for carrying out multidimensional data fusion analysis on the preprocessed data; the multidimensional data fusion analysis is characterized in that a data packet captured by an intelligent probe is analyzed after being preprocessed, and network layer performance analysis, transmission layer performance analysis, application layer performance analysis and general service performance analysis are respectively completed through each unit module by means of information such as a data packet arrival sequence and state and the result of protocol identification; the data application part is used for finishing various applications by utilizing the data output by the data analysis part, and comprises a risk early warning unit for finishing the risk early warning function of the power data network based on a risk tolerance model, a fault positioning unit for finishing a power data network fault positioning mechanism based on the risk tolerance model and a visualization unit for finishing the dynamic visualization of the service quality of the whole network; the platform part is used for building a system platform and bearing all the functions, and the platform part is realized in a modular and layered mode;
the risk early warning unit is used for matching network real-time data acquired from the probe with network abnormal knowledge by learning the knowledge of network abnormal judgment and processing, such as parameters of an abnormal flow model, and judging whether abnormal flow is generated or not; if abnormal flow is found, performing related processing according to a preset processing rule, and giving an alarm; meanwhile, interacting with a database, updating parameters of the abnormal flow model, and generating a new rule; the risk early warning unit can automatically adjust an abnormal flow model according to historical flow information so as to realize the self-adaptive detection of abnormal flow;
the visualization unit converts data into graphs or images to be displayed on a screen through a computer graphics technology and an image processing technology and carries out interactive processing, so that effective information hidden in the data is revealed through the images; the visualized analysis result can be embodied by various layout modes, including an orthogonal layout and a radiation layout;
platform portion is electric power data net intelligent detection and holographic evaluation platform, includes: the system comprises a physical layer, an acquisition layer, a preprocessing layer, a management layer, an analysis layer and a display layer, wherein the physical layer is an infrastructure layer, and various devices and links of a power data network are deployed on the layer and belong to a monitored and managed object; the acquisition layer is a data acquisition layer, and various active and passive measurement probes are deployed on the data acquisition layer to finish the original acquisition of the multidimensional fine-grained network and the service quality data, including the acquisition of network and service performance data and flow data of a power data network backbone network, an access network and a data center; the preprocessing layer completes preprocessing of the acquired data according to analysis requirements and puts the data into a warehouse; the management layer is a detection control function layer and completes the functions of probe management, monitoring task scheduling, measurement strategy management and the like; the analysis layer is an analysis and evaluation functional layer, and the layer completes the functions of flow and protocol analysis, performance test, service flow characteristic analysis and the like of each layer and completes the functions of risk early warning, fault detection and positioning; the display layer is an interface display layer and completes the configuration management and interface display functions of the whole platform system; the storage layer finishes the storage of the data collected by various networks and service quality and the related analysis data; the interface layer completes interface interaction with the data network integrated network management system; between each layer, a uniform safety communication mechanism is adopted to realize the functions of authentication, compression and safety transmission when the functions of each layer are mutually accessed;
the fault positioning method based on the holographic evaluation system of the power data network is applied, and comprises the following steps: step 1: selecting a detection path to form a detection set; step 2: sending detection to detect faults; and step 3: analyzing the detection result of the fault detection; and 4, step 4: fault location is carried out based on active detection; the detection path is a path generated from a detection station to other nodes, the selected detection path needs to satisfy all nodes in the overlay network, and the detection path is the minimum detection path; the sending detection for fault detection comprises the following steps: step 21: the detection set is empty during the first initialization, otherwise step 22 is executed; step 22: based on the detection dependency matrix modeling, calculating the weight of the column vector corresponding to each node; step 23: obtaining the detection number of each node, and finding out the node which is detected at least; step 24: calculating the row vector weights of all the detections of the nodes which are passed by the least detection, and performing descending order to find out the detection which covers the nodes which are not covered at most; the analysis of the detection result of the fault detection comprises the following steps: step 31: collecting detection results, and analyzing and judging; step 32: if the detection is successful, all nodes which are successfully detected are considered to be normal nodes, and the nodes are added into a normal node set; step 33: if the node is judged to be a suspicious node before, deleting the node from the suspicious node set; step 34: if the detection fails, all nodes which are not judged as normal nodes before on the failure detection path are suspicious nodes, and are added into a suspicious node set; step 35: if only one node in all the nodes passed by the failed detection is a suspicious node and other nodes are passed by some successful detections, the suspicious node is a fault node and is added into a fault node set;
the fault location based on active detection comprises the following steps: step 41: selecting the detection which meets the requirement that the number of suspicious nodes is reduced as much as possible from the alternative detection set; step 42: modeling based on the detection dependency matrix, and analyzing the fault detection again; step 43: if the detection test result covering only one suspicious node fails to return, the suspicious node is indicated as a fault node; step 44: if the detection test result covering the suspicious nodes returns successfully, the suspicious nodes are indicated to be normal nodes; wherein the alternative detection set is a detection set which is not used as detection, and represents an available detection set except the fault detection set in the fault detection stage, and represents an available detection set except the fault detection set and the previous fault location detection in the fault location stage.
2. The system of claim 1, wherein the format conversion unit is configured to parse different data storage formats between the source data and the target data and determine a conversion rule between the corresponding storage formats, and the data translation unit is configured to perform data translation according to a mapping rule from the source data to the target data; and the data derivation unit is used for analyzing the original information and deriving the original information by combining the business rules to obtain the information.
3. The system according to claim 1, wherein the data analysis section further performs application service tracing in the process of completing the performance analysis of the application layer service, so as to dynamically trace the completion process of a specific application layer service in real time, record the response time in each step, and further complete the performance analysis of the multidimensional fine-grained data according to specific characteristics of data, voice, video, multimedia and other electric power data network services.
4. The system according to claim 1, wherein in step 44, the suspicious node is further deleted from the suspicious node set and added to the normal node set; and then all the detection sets with the failure detection in the previous detection are judged again.
5. The system of claim 1, wherein the probing method comprises triggering information query and connectivity detection for related network devices, and automatically triggering active network performance probing in conjunction with network device information.
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| CN106324428A (en) * | 2016-07-28 | 2017-01-11 | 东南大学 | Big data-based power cable monitoring system and monitoring method |
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