CN105187255A

CN105187255A - Fault analysis method, fault analysis device and server

Info

Publication number: CN105187255A
Application number: CN201510634107.6A
Authority: CN
Inventors: 吴伟; 于璠; 樊瑞
Original assignee: Huawei Technologies Co Ltd
Current assignee: Shenzhen Shangge Intellectual Property Service Co ltd; Zhao Xiuwen
Priority date: 2015-09-29
Filing date: 2015-09-29
Publication date: 2015-12-23
Anticipated expiration: 2035-09-29
Also published as: CN105187255B

Abstract

The embodiment of the invention provides a fault analysis method, a fault analysis device and a server. The fault analysis method provided by the invention may comprise the following steps of: determining a network fault with the highest fault probability in a network; and performing fault analysis on the network fault with the highest fault probability. According to the embodiment of the invention, efficiency of analysis on network survivability may be improved.

Description

Fault analysis method, fault analysis device and server

Technical Field

The present invention relates to communications technologies, and in particular, to a fault analysis method, a fault analysis apparatus, and a server.

Background

In recent years, optical transport network technology has advanced greatly, and both transport technology and network deployment and application are becoming mature. With the maturity of optical transport network technology, the degree of network intelligence and automation is higher and higher, which makes the optical network gradually develop from the traditional optical network to the intelligent optical network.

In order to ensure the reliability of the intelligent optical network, network survivability analysis may be performed by the controller in the intelligent optical network. The controller analyzes network survivability, and can determine the influence of network faults on the service by analyzing the network faults, thereby providing fault protection and fault recovery for the service, ensuring normal operation of the service and ensuring network reliability. A commonly used network survivability analysis may be performed for all network failures.

However, analyzing all network failures can make the failure analysis process longer, so that the network survivability analysis process is longer and less efficient.

Disclosure of Invention

The embodiment of the invention provides a fault analysis method, a fault analysis device and a server, which are used for improving the efficiency of network survivability analysis.

In a first aspect, an embodiment of the present invention provides a fault analysis method, including:

determining the network fault with the maximum fault probability in the network;

and carrying out fault analysis on the network fault with the maximum fault probability.

According to the first aspect, in a first possible implementation manner of the first aspect, before the determining a network failure with a largest failure probability in the network, the method further includes:

a failure probability for each network failure in the network is determined.

In a second possible implementation manner, the determining a failure probability of each network failure in the network according to the first possible implementation manner of the first aspect includes:

determining the fault probability of each network object according to the historical fault information of each network object in the network; the network object includes: a node and/or link;

determining the failure probability of each network object as the failure probability of each network failure.

In a third possible implementation manner, according to the second possible implementation manner of the first aspect, the determining the failure probability of each network object according to the historical failure information of each network object includes:

determining the fault probability of each network object at the time t by adopting a formula 1 according to the historical fault information of each network object; the historical fault information comprises historical fault times and the generation time of each historical fault;

p_{e_{i}} (t) 1 - e^{- \frac{k}{t_{k}^{e_{i}} - t_{1}^{e_{i}}} t}

.... equation 1;

wherein e is_iThe method comprises the following steps of (1) setting an ith network object as an ith network object, wherein i is any one element of 1,2 and 3.. N is the number of network objects in the network;is e_iProbability of failure at time t;is e_iTime series of k historical failures;is e_iThe generation time of the 1 st history fault.

In a fourth possible implementation manner, before determining the failure probability of each network object according to the historical failure information of each network object in the network, the method further includes:

and receiving the historical fault information of each network object sent by the controller.

In a fifth possible implementation manner, according to the third possible implementation manner of the first aspect, the determining a network fault with a highest fault probability in the network includes:

determining at least one fault combination with the maximum sum of the fault probabilities; the network fault number in each fault combination is equal to a preset network fault number; the preset network fault number is the number of network faults occurring simultaneously;

and determining the network fault in the at least one fault combination as the network fault with the maximum fault probability.

In a sixth possible implementation manner, according to the fifth possible implementation manner of the first aspect, the determining at least one fault combination with the largest sum of the fault probabilities includes:

determining all fault combinations corresponding to the preset network fault number;

determining the sum of the fault probabilities of all the fault combinations as a total fault probability;

determining the at least one fault combination of which the sum of the fault probabilities is greater than or equal to a preset probability threshold in all the fault combinations; the preset probability threshold is less than the total failure probability.

In a sixth possible implementation manner of the first aspect, in a seventh possible implementation manner, before the determining a sum of the failure probabilities of all the failure combinations as a total failure probability, the method further includes:

and determining the fault probability of each fault combination in all fault combinations.

According to a seventh possible implementation manner of the first aspect, in an eighth possible implementation manner, if the preset number of network failures is 1, the determining the failure probability of each failure combination of all the failure combinations includes:

and determining the fault probability of one network fault in each fault combination as the fault probability of each fault combination.

According to a seventh possible implementation manner of the first aspect, in a ninth possible implementation manner, if the preset number of network failures is greater than or equal to 2, the determining the failure probability of each failure combination of all the failure combinations includes:

determining the fault probability of each fault combination by adopting a formula 2;

P_mp (1) × p (2|1) … p (k '| k' -1) … … formula 2

Wherein M is any one element of 1,2, 3 … M; m is the number of fault combinations in all the fault combinations, andk 'is the preset network fault number, and k' is more than or equal to 2; p₁～P_MSequentially representing the probability of each fault combination from large to small in all the fault combinations; p (1) is the maximum failure probability in each failure combination; p (k '| k' -1) is the fault probability of the kth 'network fault under the condition that the kth' -1 network fault with the fault probability from large to small in each fault combination fails;

wherein,y is the historical failure times of the kth to 1 th network failure in each failure combination;a time series of y historical failures for the k' -1 th network failure; x is the historical failure times of the kth network failure in each failure combination;a time series of x historical failures for the k' -1 th network failure; t is t_rMean time to failure repair.

In an eighth possible implementation manner or a ninth possible implementation manner of the first aspect, in a tenth possible implementation manner, the determining that the at least one fault combination, of which the sum of the fault probabilities in all the fault combinations is greater than or equal to a preset probability threshold value, includes:

determining the at least one fault combination by adopting a formula 3 according to the total fault probability and preset analysis precision;

<math> <mrow> <mfrac> <mrow> <munderover> <mo>Σ</mo> <mn>1</mn> <mi>N</mi> </munderover> <msub> <mi>P</mi> <msup> <mi>n</mi> <mo>′</mo> </msup> </msub> </mrow> <mrow> <munderover> <mo>Σ</mo> <mn>1</mn> <mi>M</mi> </munderover> <msub> <mi>P</mi> <mi>m</mi> </msub> </mrow> </mfrac> <mo>&GreaterEqual;</mo> <mi>a</mi> </mrow> </math>

.... equation 3;

wherein,taking the total fault probability as a, and taking a as the preset analysis precision, wherein the preset probability threshold is the product of the total fault probability and the preset analysis precision; n 'is any one element of 1,2, 3.. N',is the sum of the failure probabilities of the at least one failure combination, P₁～P_N′Sequentially representing the probability of each fault combination from large to small in the at least one fault combination; p₁～P_N′The corresponding failure combination is the at least one failure combination.

In an eleventh possible implementation manner, the performing fault analysis on the network fault with the highest fault probability includes:

and analyzing the network faults in each fault combination in the at least one fault combination to determine the influence of each fault combination on the service.

In a twelfth possible implementation manner, according to the eleventh possible implementation manner of the first aspect, the performing fault analysis on the network fault in each fault combination of the at least one fault combination includes:

and determining the service influenced by each fault combination and/or the service not influenced by each fault combination according to the network object corresponding to each network fault in each fault combination and the bearing service information of the network object corresponding to each network fault.

In a second aspect, an embodiment of the present invention provides a fault analysis apparatus, including:

the determining module is used for determining the network fault with the maximum fault probability in the network;

and the analysis module is used for carrying out fault analysis on the network fault with the maximum fault probability.

In a first possible implementation manner of the second aspect, the determining module is further configured to determine a failure probability of each network failure in the network before determining the network failure with the highest failure probability in the network.

In a second possible implementation manner, the determining module is further configured to determine a failure probability of each network object according to historical failure information of each network object in the network, and determine the failure probability of each network object as the failure probability of each network failure; wherein the network object comprises: a node and/or link; .

According to a second possible implementation manner of the second aspect, in a third possible implementation manner, the determining module is further configured to determine, according to the historical failure information of each network object, a failure probability of each network object at time t by using formula 1; the historical fault information comprises historical fault times and the generation time of each historical fault;

p_{e_{i}} (t) = 1 - e^{- \frac{k}{t_{k}^{e_{i}} - t_{1}^{e_{i}}} t}

.... equation 1;

In a fourth possible implementation manner, according to the second or third possible implementation manner of the second aspect, the apparatus further includes:

and the receiving module is used for receiving the historical fault information of each network object sent by the controller.

In a fifth possible implementation manner, according to the third possible implementation manner of the second aspect, the determining module is further configured to determine at least one fault combination with a maximum sum of fault probabilities, and determine a network fault in the at least one fault combination as a network fault with the maximum fault probability; the network fault number in each fault combination is equal to a preset network fault number; the preset network fault number is the number of simultaneous network faults.

According to a fifth possible implementation manner of the second aspect, in a sixth possible implementation manner, the determining module is further configured to determine all fault combinations corresponding to the preset number of network faults; determining the sum of the fault probabilities of all the fault combinations as a total fault probability; determining the at least one fault combination of which the sum of the fault probabilities is greater than or equal to a preset probability threshold in all the fault combinations; the preset probability threshold is less than the total failure probability.

In a seventh possible implementation manner, according to the sixth possible implementation manner of the second aspect, the determining module is further configured to determine a failure probability of each failure combination in all the failure combinations.

According to a seventh possible implementation manner of the second aspect, in an eighth possible implementation manner, if the preset number of network faults is 1, the determining module is further configured to determine a fault probability of one network fault in each fault combination as the fault probability of each fault combination.

According to a seventh possible implementation manner of the second aspect, in a ninth possible implementation manner, if the preset number of network failures is greater than or equal to 2, the determining module is further configured to determine the failure probability of each failure combination by using formula 2;

P_mequation 2.. p (1) × p (2|1) … p (k '| k' -1)

According to an eighth possible implementation manner or a ninth possible implementation manner of the second aspect, in a tenth possible implementation manner, the determining module is further configured to determine the at least one fault combination by using a formula 3 according to the total fault probability and a preset analysis precision;

.... equation 3;

In an eleventh possible implementation manner, according to any one of the fifth to tenth possible implementation manners of the second aspect, the analysis module is further configured to perform fault analysis on a network fault in each fault combination of the at least one fault combination, and determine an influence of each fault combination on a service.

In an eleventh possible implementation manner of the second aspect, in a twelfth possible implementation manner, the analysis module is further configured to determine, according to the network object corresponding to each network fault in each fault combination and the bearer service information of the network object corresponding to each network fault, a service affected by each fault combination, and/or a service that is not affected by each fault combination.

In a third aspect, an embodiment of the present invention further provides a server, including: a fault analysis device as claimed in any one of the preceding claims.

According to the fault analysis method, the fault analysis device and the server provided by the embodiment of the invention, the network fault with the maximum fault probability in the network can be determined, and the network fault with the maximum fault probability is subjected to fault analysis without traversing all network faults in the network for analysis, so that the number of network faults of the fault analysis is reduced, the fault analysis process is shortened, the network survivability analysis process is shortened, and the survivability analysis efficiency is improved.

Drawings

In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings needed to be used in the description of the embodiments or the prior art will be briefly introduced below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to these drawings without creative efforts.

FIG. 1 is a schematic diagram of a network architecture in which embodiments of the present invention are implemented;

fig. 2 is a flowchart of a fault analysis method according to an embodiment of the present invention;

fig. 3 is a flowchart of a method for determining a failure probability of each network failure in the failure analysis method according to the second embodiment of the present invention;

fig. 4 is a flowchart of another fault analysis method according to a second embodiment of the present invention;

fig. 5 is a flowchart of another fault analysis method according to the second embodiment of the present invention;

fig. 6 is a flowchart of another fault analysis method according to the second embodiment of the present invention;

fig. 7 is a flowchart of a fault analysis method according to a third embodiment of the present invention;

fig. 8 is a schematic structural diagram of a fault analysis apparatus according to a fourth embodiment of the present invention;

fig. 9 is a schematic structural diagram of a server according to a fifth embodiment of the present invention.

Detailed Description

In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, but not all, embodiments of the present invention. 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 fault analysis method, the fault analysis device and the server provided by the embodiment of the invention can analyze the network survivability by analyzing the network fault. The survivability analysis of the network refers to the capability of the network to resist faults, namely, the influence of the network faults on the bearing services in the network. The fault analysis method can be used for survivability analysis of the intelligent optical network. The smart optical network may be, for example, a conventional smart optical network or a Software Defined Network (SDN) optical network. The conventional intelligent optical network may be, for example, a conventional Automatic Switching Optical Network (ASON). The SDN optical network may be, for example, an SDN-based ASON.

Fig. 1 is a schematic diagram of a network architecture applied in various embodiments of the present invention. The method of embodiments of the present invention may be performed by a fault analysis device, typically implemented in hardware and/or software, integrated on a server 101 as shown in fig. 1. The server 101 may be, for example, a data center server (DataCenter, DC server for short). An application (application) program corresponding to the failure analysis may be installed on a terminal 102 of an operator or a terminal 103 of a user in an Operation Support System (OSS), and the application (application) program accesses the server 101 to obtain a failure analysis result of the server 101 or issue an analysis instruction to the server 101. The controller 104 in fig. 1 may obtain historical failure information of the transmission network 105 through each router 106 in the transmission network 105, and send the historical failure information to the server 101, and the server 101 performs failure analysis on the transmission network according to the historical failure information of the transmission network 105, and then performs network survivability analysis. If the transport network 105 is an SDN optical network, the controller 104 may be an SDN controller. It should be noted that fig. 1 is only a schematic diagram, and therefore, the connection between the devices in fig. 1 may be a wired connection or a wireless connection.

The embodiment of the invention provides a fault analysis method. Fig. 2 is a flowchart of a fault analysis method according to an embodiment of the present invention. As shown in fig. 2, the method of this embodiment may include:

s201, determining the network fault with the maximum fault probability in the network.

In particular, the network may be an operator deployed transport backbone. If the signal transmission medium of the transmission backbone network is an optical fiber, the transmission backbone network may be an Optical Transport Network (OTN). The network fault with the highest probability may include: the network fails in at least one network with the highest probability of failure within the same preset time in the future. The network failure may include: node failure and/or link failure. The link failure may include, for example: Multi-Segment (MS) link failures, pipe (Duct) link failures, Cable link failures, and Shared Risk Link Group (SRLG) failures. The network failure with the highest probability of failure may be a partial network failure in the network.

S202, carrying out fault analysis on the network fault with the maximum fault probability.

Specifically, the fault analysis of the network fault with the maximum fault probability may be performed by analyzing through an analog subsystem in a synchronous digital hierarchy (synchronous digital hierarchy), so as to determine an influence of each fault on a service in the network fault with the maximum fault probability, output a fault analysis table, and determine, according to the fault analysis table, a network fault having a large influence on the service in the network fault with the maximum fault probability, and/or a bottleneck in the network. Bottlenecks in the network, which may be vulnerabilities of the network, may include, for example: and (3) network faults which have larger influence on the service and/or network faults with the largest fault probability.

It should be noted that, in the S202, the network fault with the largest fault probability is subjected to fault analysis, a network fault which has a large influence on the service is determined, and the normal operation of the service and the network reliability are ensured by providing fault protection and fault recovery for the service; and the vulnerability of the network is determined, and the vulnerability can be improved, so that the vulnerability of the network is reduced, and the reliability of the network is enhanced.

The fault analysis method provided by the embodiment of the invention can reduce the number of network faults of fault analysis and shorten the fault analysis process by determining the network fault with the maximum fault probability in the network and analyzing the network fault with the maximum fault probability without traversing all network faults in the network, thereby shortening the network survivability analysis process and improving the survivability analysis efficiency.

The second embodiment of the present invention provides a fault analysis method based on the fault analysis method of the first embodiment. Optionally, before determining the network fault with the maximum fault probability in the network in S201 in the method, the method may further include:

a failure probability for each network failure in the network is determined.

Fig. 3 is a flowchart of a method for determining a failure probability of each network failure in the failure analysis method according to the second embodiment of the present invention. As shown in fig. 3, the determining the failure probability of each network failure in the network may include:

s301, determining the fault probability of each network object according to the historical fault information of each network object in the network; the network object includes: nodes and/or links.

Specifically, the historical failure information of each network object may include historical failure information corresponding to at least one failure type of each network object. Therefore, the failure probability of each network object in the network is determined according to the historical failure information of each network object, and may be determined according to the historical failure information of each network object corresponding to each failure type.

S302, determining the fault probability of each network object as the fault probability of each network fault.

Specifically, the failure probability of each network object may be the failure probability of each network object corresponding to each failure type, and thus, the failure probability of each network failure may be the failure probability of each network object corresponding to each failure type.

Optionally, the second embodiment of the present invention further provides a fault analysis method. Fig. 4 is a flowchart of another fault analysis method according to the second embodiment of the present invention. Optionally, determining the failure probability of each network object according to the historical failure information of each network object in S301 as described above may include:

s401, determining the fault probability of each network object at the time t by adopting a formula 1 according to the historical fault information of each network object; the historical failure information includes the historical failure times and the generation time of each historical failure.

p_{e_{i}} (t) = 1 - e^{- \frac{k}{t_{k}^{e_{i}} - t_{1}^{e_{i}}} t}

.... equation 1;

wherein e is_iIs the ith network object, i is any element of 1,2, 3.Is e_iProbability of failure at time t;is e_iTime series of k historical failures;is e_iThe generation time of the 1 st history fault.

Optionally, before determining the failure probability of each network object according to the historical failure information of each network object in the above S301, the method may further include:

s401a, receiving the historical failure information of each network object sent by the controller.

In particular, the controller may be, for example, an SDN controller. The historical failure information of each network object may be, for example, historical failure information of each node and/or link in the transmission backbone network, which is obtained by the controller receiving network real-time information reported by the router in the transmission backbone network.

Optionally, on the basis of the method in the second embodiment, the second embodiment of the present invention further provides another fault analysis method. Fig. 5 is a flowchart of another fault analysis method according to the second embodiment of the present invention. As shown in fig. 5, the method for determining a network fault with the highest fault probability in the network in S201 in the above embodiment may include:

s501, determining at least one fault combination with the maximum fault probability sum; the network fault number in each fault combination is equal to a preset network fault number; the preset number of network failures is the number of simultaneous network failures.

Specifically, the preset number of network failures may be, for example, a preset number of network failures occurring simultaneously in the network. The preset number of network failures may be greater than or equal to 1. For example, if the preset number of network failures is 1, the number of network failures in each failure combination may be 1; if the preset number of network failures is read, the number of network failures in each failure combination can be multiple.

And S502, determining the network fault in the at least one fault combination as the network fault with the maximum fault probability.

Optionally, on the basis of the method in the second embodiment, the second embodiment further provides another fault analysis method. Fig. 6 is a flowchart of another fault analysis method according to the second embodiment of the present invention. The determining at least one failure combination with the largest sum of the failure probabilities in S501 includes:

s601, determining all fault combinations corresponding to the preset network fault number.

Specifically, all the fault combinations corresponding to the preset network fault number may be determined by permutation and combination of all the network faults in the network according to the preset network fault number. All network faults in the network may be represented by identities of all network objects in the network, wherein a network fault may be represented by an identity of a network object, for example.

S602, determining the sum of the fault probabilities of all the fault combinations as a total fault probability.

Specifically, the sum of the failure probabilities of all the failure combinations may be obtained by summing the probabilities of each failure combination in all the failure combinations.

S603, determining the at least one fault combination of which the sum of the fault probabilities in all the fault combinations is greater than or equal to a preset probability threshold; the preset probability threshold is less than the total failure probability.

Optionally, before determining the sum of the failure probabilities of all the failure combinations as the total failure probability, the method further includes:

a failure probability is determined for each of the all failure combinations.

Optionally, if the preset number of network faults is 1, the determining the fault probability of each fault combination in all the fault combinations includes:

Alternatively, if the preset number of network faults is greater than or equal to 2, the determining the fault probability of each fault combination in all the fault combinations may include:

the failure probability of each failure combination is determined using equation 2.

P_mEquation 2.. p (1) × p (2|1) … p (k '| k' -1)

Wherein M is any one element of 1,2, 3 … M; m is the number of fault combinations in all the fault combinations, andk 'is the number of the preset network faults, and k' is more than or equal to 2; p₁～P_MSequentially representing the probability of each fault combination from large to small in all the fault combinations; p (1) is the maximum failure probability in each failure combination; p (k '| k' -1) is the failure probability of the kth 'network failure under the condition that the kth' -1 network failure with the failure probability from large to small in each failure combination fails.The number of combinations for permutation and combination of k' network faults may be selected from the N network faults.

Wherein,y is the historical failure times of the kth to 1 th network failure in each failure combination;a time series of y historical failures for the k' -1 th network failure; x is the historical failure times of the kth network failure in each failure combination;time series of x historical failures for the k' -1 th network failure; t is t_rMean time to failure repair.

Optionally, determining the at least one fault combination of which the sum of the fault probabilities in all the fault combinations is greater than or equal to the preset probability threshold in S603 described above may include:

and determining the at least one fault combination by adopting a formula 3 according to the total fault probability and the preset analysis precision.

.... equation 3

Wherein,taking the total fault probability as the preset analysis precision, wherein a is the total fault probability, and the preset probability threshold is the product of the total fault probability and the preset analysis precision; n 'is any one element of 1,2, 3.. N',is the sum of the failure probabilities of the at least one failure combination, P₁～P_N′Sequentially representing the probability of each fault combination from large to small in the at least one fault combination; p₁～P_N′The corresponding failure combination is the at least one failure combination.

Specifically, the predetermined probability threshold is determined according to the total failure probability and the predetermined analysis accuracy, then N' satisfying the formula 3 is determined according to the formula 3, and then P is determined₁～P_N′The corresponding failure combination is the at least one failure combination.

Optionally, performing fault analysis on the network fault with the maximum fault probability in S202 in the first embodiment may include:

Specifically, in this embodiment, the network fault in each fault combination of the at least one fault combination may be sequentially analyzed. Because different fault combinations are independent from each other, parallel fault analysis can be performed on each fault combination in the multiple fault combinations simultaneously in the embodiment, so that the analysis efficiency is improved.

Optionally, performing fault analysis on the network fault in each fault combination of the at least one fault combination includes:

The fault analysis method provided by the second embodiment of the invention can ensure the survivability analysis precision on the basis of reducing the fault analysis process and the network survivability analysis by providing a plurality of implementation methods for determining the network fault with the maximum fault probability in the network on the basis of the first embodiment.

The third embodiment of the invention also provides a fault analysis method. Fig. 7 is a flowchart of a fault analysis method according to a third embodiment of the present invention. As shown in fig. 7, the method may include:

s701, the controller sends historical fault information of each network object in the network to the server, wherein the historical fault information comprises: the number of historical failures for each network object, and the time of occurrence of each historical failure.

S702, the server determines the fault probability of each network object according to the historical fault frequency of each network object and the generation time of each historical fault.

This embodiment may be described by the network object being a link. If the link in the network includes: a. b, c and d. According to the above formula 1 in S702, it is determined that the failure probability of the link a may be 0.8, the failure probability of the link b may be 0.7, the failure probability of the link c may be 0.7, and the failure probability of the link d may be 0.2, for example.

S703, the server determines all fault combinations corresponding to the network fault number in the network according to the preset network fault number.

The network failure number may be a fiber break number, i.e., a link break number. The number of network failures may be 2, for example. All fault combinations corresponding to the number of network faults in the network may include: fault combinations a, b, fault combinations a, c, fault combinations a, d, fault combinations b, a, fault combinations b, c, fault combinations b, d, fault combinations c, a, fault combinations c, b, fault combinations c, d, fault combinations d, a, fault combinations d, b and fault combinations d, c.

S704, the server determines the failure probability of each failure combination.

The server may obtain the failure probability of each failure combination according to the failure probability of each network object by using the above formula 2. The probability P (a, b) of the fault combination a, b may be, for example, 0.9, the probability P (a, c) of the fault combination a, c is 0.1, the probability P (a, d) of the fault combination a, d is 0, the probability P (b, a) of the fault combination b, a is 0, the probability P (b, c) of the fault combination bc is 0.8, the probability P (b, d) of the fault combination b, d is 0, the probability P (c, a) of the fault combination c, b is 0, the probability P (c, b) of the fault combination c, d is 0, the probability P (d, a) of the fault combination d, a is 0, the probability P (d, b) of the fault combination d, b is 0, and the probability P (d, c) of the fault combination d, c is 0.

And sequencing all the fault combinations according to the probabilities of all the fault combinations to obtain a corresponding relation table of the fault combinations and the probabilities as shown below.

Fault combination	Probability of
		a,b	0.9
b,c	0.8
		a,c	0.1
a,d	0
		b,a	0
b,d	0
		c,a	0
c,b	0
		c,d	0
d,a	0
		d,b	0
d,c	0

S705, the server sums the probabilities of all the fault combinations to obtain the total fault probability.

The sum of the probabilities of all fault combinations may be P (a, b) + P (b, c) + P (a, c) equal to 1.8.

S706, the server determines at least one fault combination with the sum of the fault probabilities being greater than or equal to a preset probability threshold value in all the fault combinations according to the total fault probability and preset analysis accuracy.

The failure probability is 1.8, the predetermined analysis accuracy may be 0.9, for example, and the predetermined probability threshold may be 1.62, which is the product of 1.8 and 0.9. Of all the fault combinations, P (a, b) + P (b, c) is 1.7, which is greater than the preset probability threshold of 1.62, then the at least one fault combination may include: { a, bb, c }.

S707, the server determines, according to the network object corresponding to each network fault of each fault combination in the at least one fault combination and the bearer service information of the network object corresponding to each network fault, a service affected by each fault combination and/or a service not affected by each fault combination.

The embodiments of the present invention describe the fault analysis method of each of the above embodiments by specific examples, and the beneficial effects thereof are similar to those of the above embodiments and are not described herein again.

The fourth embodiment of the invention provides a fault analysis device. Fig. 8 is a schematic structural diagram of a fault analysis apparatus according to a fourth embodiment of the present invention. As shown in fig. 8, the fault analysis apparatus 800 may include: a determination module 801 and an analysis module 802.

A determining module 801, configured to determine a network fault with the highest fault probability in the network.

The analysis module 802 is configured to perform fault analysis on the network fault with the highest fault probability.

Optionally, the determining module 801 is further configured to determine a failure probability of each network failure in the network before determining the network failure with the highest failure probability in the network.

Optionally, the determining module 801 is further configured to determine a failure probability of each network object according to the historical failure information of each network object in the network, and determine the failure probability of each network object as the failure probability of each network failure; wherein the network object comprises: nodes and/or links.

Optionally, the determining module 801 is further configured to determine, according to the historical failure information of each network object, a failure probability of each network object at time t by using formula 1; the historical failure information includes the historical failure times and the generation time of each historical failure.

p_{e_{i}} (t) = 1 - e^{- \frac{k}{t_{k}^{e_{i}} - t_{1}^{e_{i}}} t}

.... equation 1;

Optionally, the fault analysis apparatus 800 may further include:

Optionally, the determining module 801 is further configured to determine at least one fault combination with the largest sum of the fault probabilities, and determine a network fault in the at least one fault combination as the network fault with the largest fault probability; the network fault number in each fault combination is equal to a preset network fault number; the preset number of network failures is the number of simultaneous network failures.

Optionally, the determining module 801 is further configured to determine all fault combinations corresponding to the preset number of network faults; determining the sum of the fault probabilities of all the fault combinations as a total fault probability; determining the at least one fault combination of which the sum of the fault probabilities is greater than or equal to a preset probability threshold in all the fault combinations; the preset probability threshold is less than the total failure probability.

Optionally, the determining module 801 is further configured to determine a failure probability of each failure combination in all the failure combinations.

Optionally, if the preset number of network faults is 1, the determining module 801 is further configured to determine the fault probability of one network fault in each fault combination as the fault probability of each fault combination.

Alternatively, if the number of the preset network failures is greater than or equal to 2, the determining module 801 is further configured to determine the failure probability of each failure combination by using the formula 2.

P_m＝p(1)*p(2|1)…pEquation 2

Wherein M is any element of 1,2, 3.. M; m is the number of fault combinations in all the fault combinations, andk 'is the number of the preset network faults, and k' is more than or equal to 2; p₁～P_MSequentially representing the probability of each fault combination from large to small in all the fault combinations; p (1) is the maximum failure probability in each failure combination; p (k '| k' -1) is the failure probability of the kth 'network failure under the condition that the kth' -1 network failure with the failure probability from large to small in each failure combination fails.

Optionally, the determining module 801 is further configured to determine the at least one fault combination according to the total fault probability and the preset analysis accuracy by using formula 3.

.... equation 3;

Optionally, the analysis module 802 is further configured to perform fault analysis on the network fault in each fault combination of the at least one fault combination, and determine an influence of each fault combination on the service.

Optionally, the analysis module 802 is further configured to determine, according to the network object corresponding to each network fault in each fault combination and the bearer service information of the network object corresponding to each network fault, a service affected by each fault combination and/or a service that is not affected by each fault combination.

The fault analysis apparatus provided in the fourth embodiment of the present invention may be used in the fault analysis method described in any of the first embodiment or the second embodiment, and the beneficial effects thereof are similar to those of the first embodiment and are not described herein again.

The fifth embodiment of the invention also provides a server. Fig. 9 is a schematic structural diagram of a server according to a fifth embodiment of the present invention. As shown in fig. 9, the server 900 may include a failure analysis apparatus 901. The failure analysis device 901 may be the failure analysis device described in any one of the fourth embodiments.

The server provided in the fifth embodiment of the present invention may include the fault analysis apparatus of the fourth embodiment, so as to execute the fault analysis method provided in the first embodiment or the second embodiment, and the beneficial effects thereof are similar to those of the first embodiment, and are not described herein again.

Those of ordinary skill in the art will understand that: all or a portion of the steps of implementing the above-described method embodiments may be performed by hardware associated with program instructions. The program may be stored in a computer-readable storage medium. When executed, the program performs steps comprising the method embodiments described above; and the aforementioned storage medium includes: various media that can store program codes, such as ROM, RAM, magnetic or optical disks.

Finally, it should be noted that: the above embodiments are only used to illustrate the technical solution of the present invention, and not to limit the same; while the invention has been described in detail and with reference to the foregoing embodiments, it will be understood by those skilled in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some or all of the technical features may be equivalently replaced; and the modifications or the substitutions do not make the essence of the corresponding technical solutions depart from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method of fault analysis, comprising:

2. The method of claim 1, wherein prior to determining the network failure with the highest probability of failure in the network, the method further comprises:

a failure probability for each network failure in the network is determined.

3. The method of claim 2, wherein determining a failure probability for each network failure in the network comprises:

4. The method of claim 3, wherein determining the failure probability of each network object based on the historical failure information of each network object comprises:

p_{e_{i}} (t) = 1 - e^{- \frac{k}{t_{k}^{e_{i}} - t_{1}^{e_{i}}} t}

… … formula1；

Wherein e is_iThe number of the network objects in the network is the number of the network objects in the ith network object, wherein i is any one element of 1,2 and 3 … N;is e_iProbability of failure at time t;is e_iTime series of k historical failures;is e_iThe generation time of the 1 st history fault.

5. The method according to claim 3 or 4, wherein before determining the failure probability of each network object in the network according to the historical failure information of each network object, the method further comprises:

6. The method of claim 4, wherein determining the network failure with the highest probability of failure in the network comprises:

7. The method of claim 6, wherein determining at least one failure combination for which the sum of the failure probabilities is largest comprises:

8. The method of claim 7, wherein before determining the sum of the failure probabilities of all of the failure combinations as a total failure probability, the method further comprises:

9. The method according to claim 8, wherein if the preset number of network failures is 1, the determining the failure probability of each failure combination of all the failure combinations comprises:

10. The method of claim 8, wherein if the predetermined number of network failures is greater than or equal to 2, the determining the failure probability of each failure combination of the all failure combinations comprises:

P_mp (1) × p (2|1) … p (k '| k' -1) … … formula 2

Wherein M is any one element of 1,2, 3 … M; m is the number of fault combinations in all the fault combinations, andk 'is the preset network fault number, and k' is more than or equal to 2; p₁～P_MSequentially representing the larger to smaller of all said fault combinationsProbability; p (1) is the maximum failure probability in each failure combination; p (k '| k' -1) is the fault probability of the kth 'network fault under the condition that the kth' -1 network fault with the fault probability from large to small in each fault combination fails;

11. The method according to claim 9 or 10, wherein said determining said at least one fault combination of said all fault combinations for which the sum of the fault probabilities is greater than or equal to a preset probability threshold comprises:

<math> <mrow> <mfrac> <mrow> <munderover> <mo>Σ</mo> <mn>1</mn> <msup> <mi>N</mi> <mo>′</mo> </msup> </munderover> <msub> <mi>P</mi> <msup> <mi>n</mi> <mo>′</mo> </msup> </msub> </mrow> <mrow> <munderover> <mo>Σ</mo> <mn>1</mn> <mi>M</mi> </munderover> <msub> <mi>P</mi> <mi>m</mi> </msub> </mrow> </mfrac> <mo>&GreaterEqual;</mo> <mi>a</mi> </mrow> </math>

… … equation 3;

wherein,taking the total fault probability as a, and taking a as the preset analysis precision, wherein the preset probability threshold is the product of the total fault probability and the preset analysis precision; n 'is any one element of 1,2 and 3 … N',is the sum of the failure probabilities of the at least one failure combination, P₁～P_N′Sequentially representing the probability of each fault combination from large to small in the at least one fault combination; p₁～P_N′The corresponding failure combination is the at least one failure combination.

12. The method according to any one of claims 6-11, wherein the performing fault analysis on the network fault with the highest fault probability comprises:

13. The method of claim 12, wherein the performing fault analysis on the network fault in each of the at least one fault combination comprises:

14. A fault analysis device, comprising:

15. The apparatus of claim 14,

the determining module is further configured to determine a failure probability of each network failure in the network before determining the network failure with the highest failure probability in the network.

16. The apparatus of claim 15,

the determining module is further configured to determine a failure probability of each network object according to historical failure information of each network object in the network, and determine the failure probability of each network object as the failure probability of each network failure; wherein the network object comprises: a node and/or link; .

17. The apparatus of claim 16,

the determining module is further configured to determine, according to the historical failure information of each network object, a failure probability of each network object at time t by using a formula 1; the historical fault information comprises historical fault times and the generation time of each historical fault;

p_{e_{i}} (t) = 1 - e^{- \frac{k}{t_{k}^{e_{i}} - t_{1}^{e_{i}}} t}

… … equation 1;

18. The apparatus of claim 16 or 17, further comprising:

19. The apparatus of claim 17,

the determining module is further configured to determine at least one fault combination with a maximum sum of fault probabilities, and determine a network fault in the at least one fault combination as a network fault with the maximum fault probability; the network fault number in each fault combination is equal to a preset network fault number; the preset network fault number is the number of simultaneous network faults.

20. The apparatus of claim 19,

the determining module is further configured to determine all fault combinations corresponding to the preset network fault number; determining the sum of the fault probabilities of all the fault combinations as a total fault probability; determining the at least one fault combination of which the sum of the fault probabilities is greater than or equal to a preset probability threshold in all the fault combinations; the preset probability threshold is less than the total failure probability.

21. The apparatus of claim 20,

the determining module is further configured to determine a failure probability of each failure combination of the all failure combinations.

22. The apparatus according to claim 21, wherein if the preset number of network failures is 1, the determining module is further configured to determine the failure probability of one network failure in each failure combination as the failure probability of each failure combination.

23. The apparatus according to claim 21, wherein if the predetermined number of network failures is greater than or equal to 2, the determining module is further configured to determine the failure probability of each failure combination by using formula 2;

P_mp (1) × p (2|1) … p (k '| k' -1) … … formula 2

Wherein M is any one element of 1,2, 3 … M; m is the number of fault combinations in all the fault combinations, andk 'is the preset network fault number, and k' is more than or equal to 2; p₁～P_MSequentially representing the probability of each fault combination from large to small in all the fault combinations; p (1) is eachMaximum failure probability in each failure combination; p (k '| k' -1) is the fault probability of the kth 'network fault under the condition that the kth' -1 network fault with the fault probability from large to small in each fault combination fails;

24. The apparatus of claim 22 or 23,

the determining module is further configured to determine the at least one fault combination by using a formula 3 according to the total fault probability and a preset analysis precision;

… … public affairsFormula 3;

25. The apparatus of any one of claims 19-24,

the analysis module is further configured to perform fault analysis on a network fault in each fault combination of the at least one fault combination, and determine an influence of each fault combination on a service.

26. The apparatus of claim 25,

the analysis module is further configured to determine, according to the network object corresponding to each network fault in each fault combination and the bearer service information of the network object corresponding to each network fault, a service affected by each fault combination and/or a service not affected by each fault combination.

27. A server, comprising: a fault analysis device as claimed in any one of claims 14 to 26.