CN112347617A - Fault troubleshooting strategy evaluation method and device based on multiple factors - Google Patents
Fault troubleshooting strategy evaluation method and device based on multiple factors Download PDFInfo
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Abstract
The application discloses a multi-factor-based troubleshooting strategy evaluation method and device, and the method comprises the following steps: constructing a troubleshooting model according to a preset troubleshooting strategy, wherein the troubleshooting model comprises a multi-level system composition node, a plurality of fault nodes and at least one problem node; determining all problem nodes in the troubleshooting model, determining a processing path corresponding to each problem node and system composition nodes connected through a problem index path; respectively determining a processing path and a fault node corresponding to a system composition node, and carrying out normalization processing on a plurality of preset factor weight information corresponding to the fault node to obtain a normalized factor weight vector; and calculating to obtain a score value according to a preset fault-factor incidence matrix and a factor weight vector, and evaluating the fault troubleshooting strategy according to the score value to obtain an evaluation result. The method and the device solve the technical problem of blank evaluation of the troubleshooting strategy in the prior art.
Description
Technical Field
The application relates to the technical field of troubleshooting strategy evaluation, in particular to a troubleshooting strategy evaluation method and device based on multiple factors.
Background
The troubleshooting strategy is characterized in that the troubleshooting range is gradually reduced through clues such as environment and conditions, and finally the fault is positioned, so that the excellent troubleshooting strategy has fewer steps and is more accurate in positioning.
At present, a plurality of troubleshooting strategies are available, such as a Fault Tree Analysis (FTA) method, a problem-guided troubleshooting method based on a body model, and the like; the basic principle of the troubleshooting strategy is as follows: and establishing a troubleshooting model, evaluating the processing capacity of the problem through the troubleshooting model, and performing normalization processing. And the same troubleshooting strategy can be adopted to establish a plurality of different troubleshooting models, for example, when a troubleshooting model is established by using a problem-guided troubleshooting method based on a body model, different troubleshooting models can be generated due to different positions of nodes articulated by problems. Therefore, how to determine which troubleshooting model has higher fault positioning efficiency and more comprehensive troubleshooting so as to optimize the troubleshooting strategy, no method exists at present for quantifying the evaluation of the troubleshooting strategy.
Disclosure of Invention
The technical problem that this application was solved is: aiming at the evaluation blank of the troubleshooting strategy in the prior art. In the scheme provided by the embodiment of the application, all possible paths capable of troubleshooting are deduced by establishing a troubleshooting model, all troubleshooting paths are subjected to weighted calculation, whether the influence range and the positioning capacity of judgment conditions under different weights are appropriate or not is measured, finally, scores of the whole paths are summed up in an accumulated mode, the whole troubleshooting path and even the whole troubleshooting strategy are evaluated, and further, the troubleshooting strategy is subjected to a quantitative evaluation method, and the blank of troubleshooting strategy evaluation is filled.
In a first aspect, an embodiment of the present application provides a troubleshooting policy evaluation method based on multiple factors, where the method includes:
constructing a troubleshooting model according to a preset troubleshooting strategy, wherein the troubleshooting model comprises a plurality of levels of system composition nodes, a plurality of failure nodes connected with the last level of system composition nodes in the sequence from top to bottom, and at least one problem node connected with the system composition nodes through a problem index path and a processing path;
determining all problem nodes in the troubleshooting model, determining the processing path corresponding to each problem node and system composition nodes connected through the problem index path;
respectively determining the processing path and the fault node corresponding to the system composition node, and normalizing preset multiple factor weight information corresponding to the fault node to obtain a normalized factor weight vector;
and calculating to obtain a score value according to a preset fault-factor incidence matrix and the factor weight vector, and evaluating the fault troubleshooting strategy according to the score value to obtain an evaluation result.
In the scheme provided by the embodiment of the application, a troubleshooting model is built according to a preset troubleshooting strategy, then all problem nodes in the troubleshooting model are determined, the processing path corresponding to each problem node and the system composition nodes connected through the problem index path are determined, then the processing path and the system composition nodes corresponding to the fault nodes are respectively determined, a plurality of preset factor weight information corresponding to the fault nodes are subjected to normalization processing to obtain normalized factor weight vectors, finally, the value of the score is obtained according to a preset fault-factor incidence matrix and the factor weight vectors, and the troubleshooting strategy is evaluated according to the value of the score to obtain an evaluation result. The method comprises the steps of establishing a fault troubleshooting model, deducing all possible paths capable of troubleshooting, carrying out weighted calculation on all troubleshooting paths, measuring whether the influence range and the positioning capacity of judgment conditions under different weights are proper or not, finally carrying out score accumulation summation on the whole path, evaluating the whole troubleshooting path and even the whole troubleshooting strategy, further carrying out quantitative evaluation on the troubleshooting strategy, and filling the blank of troubleshooting strategy evaluation.
Optionally, normalizing the preset multiple factor weight information corresponding to the fault node to obtain a normalized factor weight vector, including:
determining the maximum value and the minimum value of the weight in the weight information;
calculating a normalization weight value corresponding to any factor according to the maximum value, the minimum value and the weight value corresponding to any factor;
and obtaining the normalized factor weight vector according to the normalized weight value.
Optionally, calculating a normalized weight value corresponding to any factor according to the maximum value, the minimum value, and a weight value corresponding to any factor, includes:
calculating a normalized weight value corresponding to any one factor by the following formula:
wherein, XnormRepresenting a normalized weight value corresponding to the any factor; x represents a weight value corresponding to any factor; xminRepresenting the weight minimum value in the preset factor weight information; xmaxAnd representing the weight maximum value in the preset factor weight information.
Optionally, the fault-factor correlation matrix is represented by:
wherein A represents a fault-factor correlation matrix, and the number of columns of the fault-factor correlation matrix is a fault point (g)1,…,gm) Number, number of rows of fault-factor correlation matrix1,…,sn) The number of the particles; a ismnIndicates a fault gmAt a factor snDegree of influence under influence.
Optionally, the processing path includes a fault location path and a fault clearance path;
the fault nodes comprise a first fault node corresponding to the fault positioning path, a second fault node corresponding to the fault removing path and a third fault node corresponding to the system composition node;
the factor weight vector comprises a first factor weight vector corresponding to the first failure node, a second factor weight vector corresponding to the second failure node and a third factor weight vector corresponding to the third failure node.
Optionally, calculating a score value according to the fault-factor correlation matrix and the factor weight vector, including:
calculating the product of the fault-factor incidence matrix and the first factor weight vector, taking the modulus of the product result to obtain a first score value, and recording the first score value as S1;
calculating the product of the fault-factor incidence matrix and the second factor weight vector, taking the modulus of the product result to obtain a second score value, and recording the second score value as S2;
and calculating the product of the fault-factor incidence matrix and the third factor weight vector, taking the modulus of the product result to obtain a third scoring value, and marking the third scoring value as W.
Optionally, evaluating the troubleshooting strategy according to the score to obtain an evaluation result, including:
comparing the first score value S1 with the second score value S2, determining the maximum of the S1 and the S2, denoted as S;
and calculating the ratio S/W of the S to the W, and evaluating the troubleshooting strategy according to the ratio to obtain an evaluation result.
In the scheme provided by the embodiment of the application, the required associated information is low in the troubleshooting strategy evaluation process, the problem node to be evaluated and the attached system are only required to form the subordinate node, and the problem evaluation can be carried out without other nodes. The processing flows of the coverage range functions are the same, multiplexing can be realized, and the calculation complexity of the algorithm is further reduced.
In a second aspect, an embodiment of the present application provides a troubleshooting policy evaluation device based on multiple factors, where the troubleshooting policy evaluation device includes:
the system comprises a construction unit and a processing unit, wherein the construction unit is used for constructing a troubleshooting model according to a preset troubleshooting strategy, and the troubleshooting model comprises a plurality of levels of system composition nodes, a plurality of failure nodes connected with the last level of system composition nodes in the sequence from top to bottom, and at least one problem node connected with the system composition nodes through a problem index path and a processing path;
the determining unit is used for determining all problem nodes in the troubleshooting model, determining the processing path corresponding to each problem node and system composition nodes connected through the problem index path;
the processing unit is used for respectively determining the processing path and the fault node corresponding to the system composition node, and normalizing the preset multiple factor weight information corresponding to the fault node to obtain a normalized factor weight vector;
and the calculation evaluation unit is used for calculating to obtain a score value according to a preset fault-factor incidence matrix and the factor weight vector, and evaluating the fault troubleshooting strategy according to the score value to obtain an evaluation result.
Optionally, the processing unit is specifically configured to:
determining the maximum value and the minimum value of the weight in the weight information;
calculating a normalization weight value corresponding to any factor according to the maximum value, the minimum value and the weight value corresponding to any factor;
and obtaining the normalized factor weight vector according to the normalized weight value.
Optionally, the processing unit is specifically configured to:
calculating a normalized weight value corresponding to any one factor by the following formula:
wherein, XnormRepresenting a normalized weight value corresponding to the any factor; x represents a weight value corresponding to any factor; xminRepresenting the weight minimum value in the preset factor weight information; xmaxAnd representing the weight maximum value in the preset factor weight information.
Optionally, the fault-factor correlation matrix is represented by:
wherein A represents a fault-factor correlation matrix, and the number of columns of the fault-factor correlation matrix is a fault point (g)1,…,gm) Number, number of rows of fault-factor correlation matrix1,…,sn) The number of the particles; a ismnIndicates a fault gmAt a factor snDegree of influence under influence.
Optionally, the processing path includes a fault location path and a fault clearance path;
the fault nodes comprise a first fault node corresponding to the fault positioning path, a second fault node corresponding to the fault removing path and a third fault node corresponding to the system composition node;
the factor weight vector comprises a first factor weight vector corresponding to the first failure node, a second factor weight vector corresponding to the second failure node and a third factor weight vector corresponding to the third failure node.
Optionally, the calculation and evaluation unit is specifically configured to:
calculating the product of the fault-factor incidence matrix and the first factor weight vector, taking the modulus of the product result to obtain a first score value, and recording the first score value as S1;
calculating the product of the fault-factor incidence matrix and the second factor weight vector, taking the modulus of the product result to obtain a second score value, and recording the second score value as S2;
and calculating the product of the fault-factor incidence matrix and the third factor weight vector, taking the modulus of the product result to obtain a third scoring value, and marking the third scoring value as W.
Optionally, the calculation and evaluation unit is specifically configured to:
comparing the first score value S1 with the second score value S2, determining the maximum of the S1 and the S2, denoted as S;
and calculating the ratio S/W of the S to the W, and evaluating the troubleshooting strategy according to the ratio to obtain an evaluation result.
Optionally, the calculation and evaluation unit 304 is specifically configured to:
calculating the product of the fault-factor incidence matrix and the first factor weight vector, taking the modulus of the product result to obtain a first score value, and recording the first score value as S1;
calculating the product of the fault-factor incidence matrix and the second factor weight vector, taking the modulus of the product result to obtain a second score value, and recording the second score value as S2;
and calculating the product of the fault-factor incidence matrix and the third factor weight vector, taking the modulus of the product result to obtain a third scoring value, and marking the third scoring value as W.
Drawings
Fig. 1 is a schematic flowchart of a multi-factor-based troubleshooting policy evaluation method according to an embodiment of the present application;
fig. 2 is a schematic structural diagram of a troubleshooting model provided in an embodiment of the present application;
fig. 3 is a schematic structural diagram of a troubleshooting policy evaluation device based on multiple factors according to an embodiment of the present application.
Detailed Description
In the solutions provided in the embodiments of the present application, the described embodiments are only a part of the embodiments of the present application, 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 application.
The method for evaluating the multi-factor-based troubleshooting strategy provided by the embodiment of the present application is further described in detail below with reference to the drawings of the specification, and a specific implementation manner of the method may include the following steps (a method flow is shown in fig. 1):
step 101, a troubleshooting model is built according to a preset troubleshooting strategy, wherein the troubleshooting model comprises a plurality of levels of system composition nodes, a plurality of failure nodes connected with the last level of system composition nodes in the sequence from top to bottom, and at least one problem node connected with the system composition nodes through a problem index path and a processing path.
Specifically, in the solution provided in this embodiment of the present application, a system component node refers to a node describing a software module and/or a hardware module, for example, the system component node includes a software node, a hardware node, or a memory node, and multiple levels of system component nodes are connected through a path formed by a real structure of a troubleshooting system, and are divided from coarse to fine from top to bottom according to a division granularity, for example, the memory node is a child node of the hardware node, and the memory loose node is a child node of the memory node; the fault node is used for describing the fault, for example, the content included in the fault node includes memory looseness or insufficient disk space; the problem node is used for representing the problem of the system caused by the fault, wherein the problem node is associated with the system composition node through a problem index path, and the fault causing the problem is determined according to a processing path for troubleshooting, wherein the processing path comprises a fault positioning path and a fault troubleshooting path.
To facilitate understanding of the above troubleshooting models, the troubleshooting models are briefly described below by way of example. Referring to fig. 2, a schematic structural diagram of a troubleshooting model provided in an embodiment of the present application is shown.
Specifically, in fig. 2, the troubleshooting model includes four-level system constituent nodes, and a first-level system constituent node, a second-level system constituent node, a third-level system constituent node, and a fourth-level system constituent node are sequentially arranged in the troubleshooting model from top to bottom, where the first-level system constituent node includes a system constituent node 1; the second-level system component node comprises a system component node 2 and a system component node 3 which are connected with the system component node 1; the third-level system constituent node includes a system constituent node 4, a system constituent node 5, and a system constituent node 6 connected to the system constituent node 2, and a system constituent node 7 and a system constituent node 8 connected to the system constituent node 3; the fourth-level system constituent node includes a system constituent node 9 and a system constituent node 10 connected to the system constituent node 4, a system constituent node 11 and a system constituent node 12 connected to the system constituent node 5, a system constituent node 13 and a system constituent node 14 connected to the system constituent node 6, a system constituent node 15 and a system constituent node 16 connected to the system constituent node 7, and a system constituent node 17 and a system constituent node 18 connected to the system constituent node 8.
Further, the troubleshooting model shown in fig. 2 further includes 11 failure nodes connected to the fourth-level system component nodes, and the 11 failure nodes are respectively connected to the failure node 1, the failure node 2, the failure node 3, the failure node 4, the failure node 5, the failure node 6, the failure node 7, the failure node 8, the failure node 9, the failure node 10, and the failure node 11.
Further, the troubleshooting model shown in fig. 2 further includes 4 problem nodes, which are Q1, Q2, Q3, and Q4; the problem node Q1 and the problem node Q3 are connected to the system component node 1 through a problem index, the problem node Q1 is connected to the system component node 3 through a fault location path and the system component node 6 through a fault elimination path, and the problem node Q3 is connected to the system component node 7 through a fault elimination path and the system component node 8 through a fault location path; the problem node Q2 is connected with the system component node 2 through a problem index, and the problem node Q2 is connected with the system component node 4 through a fault location path; the problem node Q4 is connected to the system constituent node 8 through a problem index, and the problem node Q4 is connected to the system constituent node 16 through a troubleshooting path and to the system constituent node 18 through a fault locating path.
Step 102, determining all problem nodes in the troubleshooting model, determining the processing path corresponding to each problem node and the system composition nodes connected through the problem index path.
103, respectively determining the processing path and the fault node corresponding to the system component node, and performing normalization processing on a plurality of preset factor weight information corresponding to the fault node to obtain a normalized factor weight vector.
In one possible implementation, the processing path includes a fault locating path and a fault clearing path;
the fault nodes comprise a first fault node corresponding to the fault positioning path, a second fault node corresponding to the fault removing path and a third fault node corresponding to the system composition node;
the factor weight vector comprises a first factor weight vector corresponding to the first failure node, a second factor weight vector corresponding to the second failure node and a third factor weight vector corresponding to the third failure node.
For example, referring to fig. 2, the system component node connected by the problem node Q1 through the problem index path is the system component node 1, the fault nodes corresponding to the fault location path are the fault node 1, the fault node 2, the fault node 3, the fault node 4, the fault node 5, and the fault node 6, and the fault nodes corresponding to the fault elimination path are the fault node 5 and the fault node 6; the fault nodes corresponding to the system composition node 1 are a fault node 1, a fault node 2, a fault node 3, a fault node 4, a fault node 5, a fault node 6, a fault node 7, a fault node 8, a fault node 9, a fault node 10 and a fault node 11.
Specifically, the weight information corresponding to each fault node is composed of weight values in different weight directions, the different weight directions respectively describe the sensitivity of the fault node in the corresponding direction, and the weight values in the different weight directions in the system are generated by scoring of a system administrator. In the solution provided in the embodiments of the present application, the different direction of emphasis is characterized by a "factor". The factor data selected by all fault points in the same system should be kept consistent, that is, each fault point is composed of n influencing factors. The weight information corresponding to the fault point is represented by the following formula:
X＝δ1+δ2+δ3+…+δn
wherein X represents multi-factor weight information corresponding to the fault point; deltanRepresenting the impact factor of the nth weight direction.
Further, before the problem index path and the processing path of each problem node are evaluated through the multi-factor weight information corresponding to each fault node, normalization processing needs to be performed on a plurality of preset factor weight information corresponding to each fault node to obtain a normalized factor weight vector. Specifically, there are various ways of normalizing the preset multiple factor weight information corresponding to each failed node to obtain a normalized factor weight vector, and a preferred way is described below as an example.
In a possible implementation manner, normalizing preset multiple pieces of factor weight information corresponding to the faulty node to obtain a normalized factor weight vector includes: determining the maximum value and the minimum value of the weight in the weight information; calculating a normalization weight value corresponding to any factor according to the maximum value, the minimum value and the weight value corresponding to any factor; and obtaining the normalized factor weight vector according to the normalized weight value.
In a possible implementation manner, calculating a normalized weight value corresponding to any factor according to the maximum value, the minimum value, and a weight value corresponding to the any factor includes:
calculating a normalized weight value corresponding to any one factor by the following formula:
wherein, XnormRepresenting a normalized weight value corresponding to the any factor; x represents a weight value corresponding to any factor; xminRepresenting the weight minimum value in the preset factor weight information; xmaxAnd representing the weight maximum value in the preset factor weight information.
Further, after normalization processing is performed on a plurality of preset factor weight information corresponding to each fault node, a normalized factor weight vector corresponding to each fault node is obtained. Specifically, the normalized factor weight vector corresponding to each failed node is represented by the following formula:
lT＝(X1,X2,X3…Xn)
wherein l is a column vector; x1,X2,X3…XnRespectively, the weight values after different factors are normalized.
And 104, calculating to obtain a score value according to a preset fault-factor incidence matrix and the factor weight vector, and evaluating the fault troubleshooting strategy according to the score value to obtain an evaluation result.
Specifically, in one possible implementation, the fault-factor correlation matrix is represented by the following formula:
wherein A represents a fault-factor correlation matrix, and the number of columns of the fault-factor correlation matrix is a fault point (g)1,…,gm) Number, number of rows of fault-factor correlation matrix1,…,sn) The number of the particles; a ismnIndicates a fault gmAt a factor snDegree of influence under influence.
Further, in the solution provided in the embodiment of the present application, after normalization processing is performed on the preset multiple factor weight information corresponding to the fault node to obtain a normalized factor weight vector, a score value needs to be calculated according to the factor weight vector corresponding to the fault node and a preset fault-factor association matrix. Specifically, the manner of calculating the score value according to the factor weight vector corresponding to the fault point and the preset fault-factor association matrix is described in various ways, taking a preferred manner as an example.
In a possible implementation manner, the calculating of the score value according to the preset fault-factor association matrix and the factor weight vector includes: calculating the product of the fault-factor incidence matrix and the first factor weight vector of the fault positioning path, taking the modulus of the product result to obtain a first score value, and recording the first score value as S1; calculating the product of the fault-factor incidence matrix and the second factor weight vector, taking the modulus of the product result to obtain a second score value, and recording the second score value as S2; and calculating the product of the fault-factor incidence matrix and the third factor weight vector, taking the modulus of the product result to obtain a third scoring value, and marking the third scoring value as W.
Specifically, in the solution provided in the embodiment of the present application, the product of the fault-factor correlation matrix and the factor weight vector may be calculated by the following formula:
where γ represents the fault-factor correlation matrix and the factor weight vector.
Further, after calculating the product of the fault-factor correlation matrix and the factor weight vector, the product result is modulo by:
where | γ | represents the product result modulo.
Further, in a possible implementation manner, evaluating the troubleshooting strategy according to the score to obtain an evaluation result, including: comparing the first score value S1 with the second score value S2, determining the maximum of the S1 and the S2, denoted as S; and calculating the ratio S/W of the S to the W, and evaluating the troubleshooting strategy according to the ratio to obtain an evaluation result.
To facilitate understanding of the principles of the above-described multi-factor based troubleshooting policy evaluation method, a brief description thereof is provided below by way of example.
Taking an information system as an example, generating a troubleshooting process according to a logic rule, calculating by using the method of the invention to obtain nodes and scores, and selecting part of contents to be shown in the following table:
in the scheme provided by the embodiment of the application, a troubleshooting model is built according to a preset troubleshooting strategy, then all problem nodes in the troubleshooting model are determined, the processing path corresponding to each problem node and the system composition nodes connected through the problem index path are determined, then the processing path and the system composition nodes corresponding to the fault nodes are respectively determined, a plurality of preset factor weight information corresponding to the fault nodes are subjected to normalization processing to obtain normalized factor weight vectors, finally, the value of the score is obtained according to a preset fault-factor incidence matrix and the factor weight vectors, and the troubleshooting strategy is evaluated according to the value of the score to obtain an evaluation result. The method comprises the steps of establishing a fault troubleshooting model, deducing all possible paths capable of troubleshooting, carrying out weighted calculation on all troubleshooting paths, measuring whether the influence range and the positioning capacity of judgment conditions under different weights are proper or not, finally carrying out score accumulation summation on the whole path, evaluating the whole troubleshooting path and even the whole troubleshooting strategy, further carrying out quantitative evaluation on the troubleshooting strategy, and filling the blank of troubleshooting strategy evaluation.
Further, in the scheme provided by the embodiment of the application, in the process of evaluating the troubleshooting strategy, the needed associated information is low, only the problem node to be evaluated and the attached system form a subordinate node, and the problem evaluation can be performed without other nodes. The processing flows of the coverage range functions are the same, multiplexing can be realized, and the calculation complexity of the algorithm is further reduced.
Based on the same inventive concept as the method shown in fig. 1, an embodiment of the present application provides a troubleshooting policy evaluation device based on multiple factors, and referring to fig. 3, the device includes:
a building unit 301, configured to build a troubleshooting model according to a preset troubleshooting strategy, where the troubleshooting model includes multiple levels of system constituent nodes, multiple failure nodes connected to a last level of system constituent node in an order from top to bottom, and at least one problem node connected to the system constituent nodes through a problem index path and a processing path;
a determining unit 302, configured to determine all problem nodes in the troubleshooting model, determine the processing path corresponding to each problem node, and determine system component nodes connected through the problem index path;
a processing unit 303, configured to determine the processing path and the fault node corresponding to the system component node, respectively, and perform normalization processing on preset multiple factor weight information corresponding to the fault node to obtain a normalized factor weight vector;
and the calculation evaluation unit 304 is configured to calculate a score value according to a preset fault-factor incidence matrix and the factor weight vector, and evaluate the troubleshooting strategy according to the score value to obtain an evaluation result.
Optionally, the processing unit 303 is specifically configured to:
determining the maximum value and the minimum value of the weight in the weight information;
calculating a normalization weight value corresponding to any factor according to the maximum value, the minimum value and the weight value corresponding to any factor;
and obtaining the normalized factor weight vector according to the normalized weight value.
Optionally, the processing unit 303 is specifically configured to:
calculating a normalized weight value corresponding to any one factor by the following formula:
wherein, XnormRepresenting a normalized weight value corresponding to the any factor; x represents a weight value corresponding to any factor; xminRepresenting the weight minimum value in the preset factor weight information; xmaxAnd representing the weight maximum value in the preset factor weight information.
Optionally, the fault-factor correlation matrix is represented by:
wherein A represents a fault-factor correlation matrix, and the number of columns of the fault-factor correlation matrix is a fault point (g)1,…,gm) Number, number of rows of fault-factor correlation matrix1,…,sn) The number of the particles; a ismnIndicates a fault gmAt a factor snDegree of influence under influence.
Optionally, the processing path includes a fault location path and a fault clearance path;
the fault nodes comprise a first fault node corresponding to the fault positioning path, a second fault node corresponding to the fault removing path and a third fault node corresponding to the system composition node;
the factor weight vector comprises a first factor weight vector corresponding to the first failure node, a second factor weight vector corresponding to the second failure node and a third factor weight vector corresponding to the third failure node.
Optionally, the calculation and evaluation unit 304 is specifically configured to:
calculating the product of the fault-factor incidence matrix and the first factor weight vector, taking the modulus of the product result to obtain a first score value, and recording the first score value as S1;
calculating the product of the fault-factor incidence matrix and the second factor weight vector, taking the modulus of the product result to obtain a second score value, and recording the second score value as S2;
and calculating the product of the fault-factor incidence matrix and the third factor weight vector, taking the modulus of the product result to obtain a third scoring value, and marking the third scoring value as W.
Optionally, the calculation and evaluation unit 304 is specifically configured to:
comparing the first score value S1 with the second score value S2, determining the maximum of the S1 and the S2, denoted as S;
and calculating the ratio S/W of the S to the W, and evaluating the troubleshooting strategy according to the ratio to obtain an evaluation result.
Optionally, the calculation and evaluation unit 304 is specifically configured to:
calculating the product of the fault-factor incidence matrix and the first factor weight vector, taking the modulus of the product result to obtain a first score value, and recording the first score value as S1;
calculating the product of the fault-factor incidence matrix and the second factor weight vector, taking the modulus of the product result to obtain a second score value, and recording the second score value as S2;
and calculating the product of the fault-factor incidence matrix and the third factor weight vector, taking the modulus of the product result to obtain a third scoring value, and marking the third scoring value as W.
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
It will be apparent to those skilled in the art that various changes and modifications may be made in the present application without departing from the spirit and scope of the application. Thus, if such modifications and variations of the present application fall within the scope of the claims of the present application and their equivalents, the present application is intended to include such modifications and variations as well.
Claims (10)
1. A multi-factor-based troubleshooting strategy evaluation method is characterized by comprising the following steps:
constructing a troubleshooting model according to a preset troubleshooting strategy, wherein the troubleshooting model comprises a plurality of levels of system composition nodes, a plurality of failure nodes connected with the last level of system composition nodes in the sequence from top to bottom, and at least one problem node connected with the system composition nodes through a problem index path and a processing path;
determining all problem nodes in the troubleshooting model, determining the processing path corresponding to each problem node and system composition nodes connected through the problem index path;
respectively determining the processing path and the fault node corresponding to the system composition node, and normalizing preset multiple factor weight information corresponding to the fault node to obtain a normalized factor weight vector;
and calculating to obtain a score value according to a preset fault-factor incidence matrix and the factor weight vector, and evaluating the fault troubleshooting strategy according to the score value to obtain an evaluation result.
2. The method of claim 1, wherein normalizing the preset multiple factor weight information corresponding to the failed node to obtain a normalized factor weight vector comprises:
determining the maximum value and the minimum value of the weight in the weight information;
calculating a normalization weight value corresponding to any factor according to the maximum value, the minimum value and the weight value corresponding to any factor;
and obtaining the normalized factor weight vector according to the normalized weight value.
3. The method of claim 2, wherein calculating a normalized weight value for any factor based on the maximum value and the minimum value and a weight value for the any factor comprises:
calculating a normalized weight value corresponding to any one factor by the following formula:
wherein, XnormRepresenting a normalized weight value corresponding to the any factor; x represents a weight value corresponding to any factor; xminRepresenting the weight minimum value in the preset factor weight information; xmaxAnd representing the weight maximum value in the preset factor weight information.
4. The method of claim 3, wherein the fault-factor correlation matrix is represented by:
wherein A represents a fault-factor correlation matrix, and the number of columns of the fault-factor correlation matrix is a fault point (g)1,…,gm) Number, number of rows of fault-factor correlation matrix1,…,sn) The number of the particles; a ismnIndicates a fault gmAt a factor snDegree of influence under influence.
5. The method of claim 4, wherein the processing path comprises a fault location path and a fault clearance path;
the fault nodes comprise a first fault node corresponding to the fault positioning path, a second fault node corresponding to the fault removing path and a third fault node corresponding to the system composition node;
the factor weight vector comprises a first factor weight vector corresponding to the first failure node, a second factor weight vector corresponding to the second failure node and a third factor weight vector corresponding to the third failure node.
6. The method of claim 5, wherein calculating a score value from the fault-factor correlation matrix and the factor weight vector comprises:
calculating the product of the fault-factor incidence matrix and the first factor weight vector, taking the modulus of the product result to obtain a first score value, and recording the first score value as S1;
calculating the product of the fault-factor incidence matrix and the second factor weight vector, taking the modulus of the product result to obtain a second score value, and recording the second score value as S2;
and calculating the product of the fault-factor incidence matrix and the third factor weight vector, taking the modulus of the product result to obtain a third scoring value, and marking the third scoring value as W.
7. The method of claim 6, wherein evaluating the troubleshooting strategy according to the score to obtain an evaluation result comprises:
comparing the first score value S1 with the second score value S2, determining the maximum of the S1 and the S2, denoted as S;
and calculating the ratio S/W of the S to the W, and evaluating the troubleshooting strategy according to the ratio to obtain an evaluation result.
8. A troubleshooting strategy evaluation device based on multiple factors is characterized by comprising the following components:
the system comprises a construction unit and a processing unit, wherein the construction unit is used for constructing a troubleshooting model according to a preset troubleshooting strategy, and the troubleshooting model comprises a plurality of levels of system composition nodes, a plurality of failure nodes connected with the last level of system composition nodes in the sequence from top to bottom, and at least one problem node connected with the system composition nodes through a problem index path and a processing path;
the determining unit is used for determining all problem nodes in the troubleshooting model, determining the processing path corresponding to each problem node and system composition nodes connected through the problem index path;
the processing unit is used for respectively determining the processing path and the fault node corresponding to the system composition node, and normalizing the preset multiple factor weight information corresponding to the fault node to obtain a normalized factor weight vector;
and the calculation evaluation unit is used for calculating to obtain a score value according to a preset fault-factor incidence matrix and the factor weight vector, and evaluating the fault troubleshooting strategy according to the score value to obtain an evaluation result.
9. The apparatus as claimed in claim 8, wherein said processing unit is specifically configured to:
determining the maximum value and the minimum value of the weight in the weight information;
calculating a normalization weight value corresponding to any factor according to the maximum value, the minimum value and the weight value corresponding to any factor;
and obtaining the normalized factor weight vector according to the normalized weight value.
10. The apparatus according to claim 9, wherein the computational evaluation unit is specifically configured to:
calculating the product of the fault-factor incidence matrix and the first factor weight vector, taking the modulus of the product result to obtain a first score value, and recording the first score value as S1;
calculating the product of the fault-factor incidence matrix and the second factor weight vector, taking the modulus of the product result to obtain a second score value, and recording the second score value as S2;
and calculating the product of the fault-factor incidence matrix and the third factor weight vector, taking the modulus of the product result to obtain a third scoring value, and marking the third scoring value as W.
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Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20120317058A1 (en) * | 2011-06-13 | 2012-12-13 | Abhulimen Kingsley E | Design of computer based risk and safety management system of complex production and multifunctional process facilities-application to fpso's |
| WO2014180400A1 (en) * | 2013-11-25 | 2014-11-13 | 中兴通讯股份有限公司 | Method and device for locating and processing problem |
| CN109150635A (en) * | 2018-10-26 | 2019-01-04 | 中国农业银行股份有限公司 | Failure effect analysis (FEA) method and device |
| CN110175745A (en) * | 2019-04-26 | 2019-08-27 | 中国电力科学研究院有限公司 | A kind of electric power telecommunication network risk assessment method and system based on fault modeling |
| CN110263856A (en) * | 2019-06-20 | 2019-09-20 | 北京实力伟业环保科技有限公司 | Fan trouble evaluation method, system and equipment based on Internet of Things |
| CN110401965A (en) * | 2018-04-24 | 2019-11-01 | 中国移动通信集团广东有限公司 | VoLTE voice quality testing analysis method and system |
| CN110544940A (en) * | 2019-09-04 | 2019-12-06 | 国电南瑞科技股份有限公司 | CPS (cyber physical System) security evaluation method and device considering information influence in fault recovery |
| CN111697566A (en) * | 2020-05-19 | 2020-09-22 | 浙江工业大学 | Reliability assessment method for active power distribution network information physical system considering information failure |
-
2020
- 2020-10-20 CN CN202011126016.9A patent/CN112347617B/en active Active
Patent Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20120317058A1 (en) * | 2011-06-13 | 2012-12-13 | Abhulimen Kingsley E | Design of computer based risk and safety management system of complex production and multifunctional process facilities-application to fpso's |
| WO2014180400A1 (en) * | 2013-11-25 | 2014-11-13 | 中兴通讯股份有限公司 | Method and device for locating and processing problem |
| CN110401965A (en) * | 2018-04-24 | 2019-11-01 | 中国移动通信集团广东有限公司 | VoLTE voice quality testing analysis method and system |
| CN109150635A (en) * | 2018-10-26 | 2019-01-04 | 中国农业银行股份有限公司 | Failure effect analysis (FEA) method and device |
| CN110175745A (en) * | 2019-04-26 | 2019-08-27 | 中国电力科学研究院有限公司 | A kind of electric power telecommunication network risk assessment method and system based on fault modeling |
| CN110263856A (en) * | 2019-06-20 | 2019-09-20 | 北京实力伟业环保科技有限公司 | Fan trouble evaluation method, system and equipment based on Internet of Things |
| CN110544940A (en) * | 2019-09-04 | 2019-12-06 | 国电南瑞科技股份有限公司 | CPS (cyber physical System) security evaluation method and device considering information influence in fault recovery |
| CN111697566A (en) * | 2020-05-19 | 2020-09-22 | 浙江工业大学 | Reliability assessment method for active power distribution network information physical system considering information failure |
Non-Patent Citations (1)
| Title |
|---|
| 陶秋香;涂继亮;程若发;潘洪亮;: "基于未确知集和模糊TOPSIS决策的故障模式风险评估", 计算机应用研究, no. 09, 28 August 2017 (2017-08-28), pages 122 - 125 * |
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