CN112347617B - Multi-factor-based fault troubleshooting strategy evaluation method and device - Google Patents
Multi-factor-based fault troubleshooting strategy evaluation method and device Download PDFInfo
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Abstract
The application discloses a fault troubleshooting strategy evaluation method and device based on multiple factors, wherein the method comprises the following steps: constructing a fault investigation model according to a preset fault investigation strategy, wherein the fault investigation model comprises a multi-stage system composition node, a plurality of fault nodes and at least one problem node; determining all problem nodes in the fault investigation model, and determining a processing path corresponding to each problem node and system composition nodes connected through a problem index path; determining a processing path and a fault node corresponding to a system composition node respectively, 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 a grading value according to a preset fault-factor association matrix and a factor weight vector, and evaluating a fault troubleshooting strategy according to the grading value to obtain an evaluation result. The application solves the technical problem of blank evaluation of the fault detection strategy in the prior art.
Description
Technical Field
The application relates to the technical field of fault investigation strategy evaluation, in particular to a fault investigation strategy evaluation method and device based on multiple factors.
Background
The fault checking strategy is to gradually narrow the fault checking range through environmental and condition cues, and finally locate the fault, so that the excellent fault checking strategy has fewer steps and more accurate location.
At present, various Fault troubleshooting strategies exist, such as a Fault tree analysis method (Fault TREE ANALYSIS, FTA), a problem-guided Fault troubleshooting method based on an ontology model and the like; the basic principle of the fault detection strategy is as follows: and establishing a fault checking model, and then evaluating the processing capacity of the problem through the fault checking model and carrying out normalization processing. And a plurality of different fault troubleshooting models can be established by adopting the same fault troubleshooting strategy, for example, when the fault troubleshooting model is established by using the problem-guided fault troubleshooting method based on the ontology model, different fault troubleshooting models can be generated due to different positions of the node hung by the problem. Therefore, how to determine what kind of troubleshooting model has higher fault locating efficiency and more comprehensive troubleshooting, so as to optimize the troubleshooting strategy, and no method for quantifying the evaluation of the troubleshooting strategy exists at present.
Disclosure of Invention
The application solves the technical problems that: aiming at the evaluation blank of the fault detection strategy in the prior art. In the scheme provided by the embodiment of the application, by establishing a fault troubleshooting model, all possible paths capable of troubleshooting are deduced, weighting calculation is carried out on all troubleshooting paths, whether the influence range and positioning capability of judging conditions under different weights are proper or not is measured, and finally, the score accumulation summation is carried out on the whole path, the whole troubleshooting path and even the whole troubleshooting strategy are evaluated, so that the quantitative evaluation method is carried out on the fault troubleshooting strategy, and the blank of the evaluation of the fault troubleshooting strategy is filled.
In a first aspect, an embodiment of the present application provides a method for evaluating a fault detection policy based on multiple factors, where the method includes:
Constructing a fault investigation model according to a preset fault investigation strategy, wherein the fault investigation model comprises a multi-stage system composition node, a plurality of fault nodes connected with a final-stage system composition node according to the sequence from top to bottom, and at least one problem node connected with the system composition node through a problem index path and a processing path;
determining all problem nodes in the fault troubleshooting model, and determining the processing path corresponding to each problem node and system composition nodes connected through the problem index path;
determining the processing path and the fault node corresponding to the system component node respectively, 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 a grading value according to a preset fault-factor association matrix and the factor weight vector, and evaluating the fault investigation strategy according to the grading value to obtain an evaluation result.
In the scheme provided by the embodiment of the application, a fault troubleshooting model is built according to a preset fault troubleshooting strategy, all problem nodes in the fault troubleshooting model are determined, the processing path corresponding to each problem node and system component nodes connected through the problem index path are determined, the processing path and the fault nodes corresponding to the system component nodes are respectively determined, the normalization processing is carried out on the preset multiple factor weight information corresponding to the fault nodes to obtain normalized factor weight vectors, finally, a grading value is calculated according to a preset fault-factor correlation matrix and the factor weight vectors, and the fault troubleshooting strategy is evaluated according to the grading value 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 capability of judging 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 a quantitative evaluation method on the troubleshooting strategy, and filling the blank of evaluation of the fault troubleshooting strategy.
Optionally, normalizing the preset multiple factor weight information corresponding to the fault node to obtain a normalized factor weight vector, which includes:
determining the maximum value and the minimum value of the weight in the weight information;
Calculating a normalized weight value corresponding to any factor according to the maximum value and the minimum value and the weight value corresponding to the 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 the weight value corresponding to the any factor, including:
calculating a normalized weight value corresponding to any factor by the following formula:
Wherein, X norm represents the normalized weight value corresponding to any factor; x represents a weight value corresponding to any factor; x min represents a weight minimum value in the preset factor weight information; x max represents the maximum value of the weight in the preset factor weight information.
Optionally, the fault-factor correlation matrix is represented by:
wherein A represents a fault-factor correlation matrix, the number of columns of the fault-factor correlation matrix is the number of fault points (g 1,…,gm), and the number of rows of the fault-factor correlation matrix is the number of factors (s 1,…,sn); a mn represents the extent to which fault g m is affected by factor s n.
Optionally, the processing path includes a fault localization path and a fault removal path;
The fault nodes comprise a first fault node corresponding to the fault locating path, a second fault node corresponding to the fault removing path and a third fault node corresponding to the system component node;
the factor weight vector comprises a first factor weight vector corresponding to the first fault node, a second factor weight vector corresponding to the second fault node and a third factor weight vector corresponding to the third fault node.
Optionally, calculating a scoring value according to the fault-factor association matrix and the factor weight vector includes:
calculating the product of the fault-factor association matrix and the first factor weight vector, taking the modulus of the product result to obtain a first grading value, and marking the first grading value as S1;
calculating the product of the fault-factor association matrix and the second factor weight vector, taking the modulus of the product result to obtain a second grading value, and marking the second grading value as S2;
And calculating the product of the fault-factor association matrix and the third factor weight vector, taking the modulus of the product result to obtain a third grading value, and marking the third grading value as W.
Optionally, evaluating the troubleshooting policy according to the grading value to obtain an evaluation result includes:
Comparing the first grading value S1 with the second grading value S2, determining the maximum value of the S1 and the S2, and marking the maximum value as S;
Calculating the ratio S/W of the S to the W, and evaluating the fault investigation strategy according to the ratio to obtain an evaluation result.
In the scheme provided by the embodiment of the application, in the process of evaluating the fault troubleshooting strategy, the required association information is low, and the problem evaluation can be performed without other nodes only by the problem node to be evaluated and the subordinate node formed by the affiliated system. The coverage function processing flow is the same, multiplexing can be realized, and the algorithm calculation complexity is further reduced.
In a second aspect, an embodiment of the present application provides a device for evaluating a fault troubleshooting policy based on multiple factors, where the device includes:
The system comprises a construction unit, a processing unit and a processing unit, wherein the construction unit is used for constructing a fault troubleshooting model according to a preset fault troubleshooting strategy, and the fault troubleshooting model comprises a multi-stage system component node, a plurality of fault nodes connected with a final-stage system component node according to the sequence from top to bottom, and at least one problem node connected with the system component node through a problem index path and a processing path;
the determining unit is used for determining all problem nodes in the fault troubleshooting model, and 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 paths and the fault nodes corresponding to the system component nodes, and carrying out normalization processing on the preset multiple factor weight information corresponding to the fault nodes to obtain normalized factor weight vectors;
And the calculation evaluation unit is used for calculating the evaluation value according to a preset fault-factor association matrix and the factor weight vector, and evaluating the fault investigation strategy according to the evaluation 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 normalized weight value corresponding to any factor according to the maximum value and the minimum value and the weight value corresponding to the 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 factor by the following formula:
Wherein, X norm represents the normalized weight value corresponding to any factor; x represents a weight value corresponding to any factor; x min represents a weight minimum value in the preset factor weight information; x max represents the maximum value of the weight in the preset factor weight information.
Optionally, the fault-factor correlation matrix is represented by:
wherein A represents a fault-factor correlation matrix, the number of columns of the fault-factor correlation matrix is the number of fault points (g 1,…,gm), and the number of rows of the fault-factor correlation matrix is the number of factors (s 1,…,sn); a mn represents the extent to which fault g m is affected by factor s n.
Optionally, the processing path includes a fault localization path and a fault removal path;
The fault nodes comprise a first fault node corresponding to the fault locating path, a second fault node corresponding to the fault removing path and a third fault node corresponding to the system component node;
the factor weight vector comprises a first factor weight vector corresponding to the first fault node, a second factor weight vector corresponding to the second fault node and a third factor weight vector corresponding to the third fault node.
Optionally, the computing and evaluating unit is specifically configured to:
calculating the product of the fault-factor association matrix and the first factor weight vector, taking the modulus of the product result to obtain a first grading value, and marking the first grading value as S1;
calculating the product of the fault-factor association matrix and the second factor weight vector, taking the modulus of the product result to obtain a second grading value, and marking the second grading value as S2;
And calculating the product of the fault-factor association matrix and the third factor weight vector, taking the modulus of the product result to obtain a third grading value, and marking the third grading value as W.
Optionally, the computing and evaluating unit is specifically configured to:
Comparing the first grading value S1 with the second grading value S2, determining the maximum value of the S1 and the S2, and marking the maximum value as S;
Calculating the ratio S/W of the S to the W, and evaluating the fault investigation strategy according to the ratio to obtain an evaluation result.
Optionally, the computing and evaluating unit 304 is specifically configured to:
calculating the product of the fault-factor association matrix and the first factor weight vector, taking the modulus of the product result to obtain a first grading value, and marking the first grading value as S1;
calculating the product of the fault-factor association matrix and the second factor weight vector, taking the modulus of the product result to obtain a second grading value, and marking the second grading value as S2;
And calculating the product of the fault-factor association matrix and the third factor weight vector, taking the modulus of the product result to obtain a third grading value, and marking the third grading value as W.
Drawings
FIG. 1 is a schematic flow chart of a multi-factor-based troubleshooting strategy evaluation method according to an embodiment of the present application;
FIG. 2 is a schematic structural diagram of a troubleshooting model according to an embodiment of the present application;
Fig. 3 is a schematic structural diagram of a fault detection policy evaluation device based on multiple factors according to an embodiment of the present application.
Detailed Description
In the solutions provided by the embodiments of the present application, the described embodiments are only some embodiments of the present application, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.
The following describes in further detail a multi-factor-based fault troubleshooting policy evaluation method provided by the embodiment of the present application with reference to the accompanying drawings, and a specific implementation manner of the method may include the following steps (the method flow is shown in fig. 1):
step 101, constructing a fault troubleshooting model according to a preset fault troubleshooting strategy, wherein the fault troubleshooting model comprises a multi-stage system component node, a plurality of fault nodes connected with a final-stage system component node according to the sequence from top to bottom, and at least one problem node connected with the system component node through a problem index path and a processing path.
Specifically, in the scheme provided by the embodiment of the application, the system component node refers to a node describing a software module and/or a hardware module and the like, for example, the system component node comprises a software node, a hardware node or a memory node, the multi-stage system component nodes are connected through a path formed by a real structure of the fault checking system, and are divided from thick to thin from top to bottom according to the division granularity, for example, the memory node is a child node of the hardware node, and the memory loosening node is a child node of the memory node; the failed node is used for describing the occurred failure, for example, the content contained in the failed node comprises memory looseness, insufficient disk space and the like; the problem node is used for representing the problems of the system caused by the faults, wherein the problem node is related to the system composition node through a problem index path, and the faults causing the problems are determined according to a processing path to be subjected to troubleshooting, and the processing path comprises a fault locating path and a fault removing path.
In order to facilitate understanding of the above troubleshooting model, the troubleshooting model will be briefly described below by way of example. Referring to fig. 2, a schematic structural diagram of a troubleshooting model is provided in an embodiment of the present application.
Specifically, in fig. 2, the fault investigation model includes four-stage system constituent nodes, and in the fault investigation model, a first-stage system constituent node, a second-stage system constituent node, a third-stage system constituent node and a fourth-stage system constituent node are sequentially arranged in the order from top to bottom, where the first-stage system constituent node includes a system constituent node 1; the second-stage system constituent node comprises a system constituent node 2 connected with the system constituent node 1 and a system constituent node 3; the third-level system composition node comprises a system composition node 4, a system composition node 5 and a system composition node 6 which are connected with the system composition node 2, and a system composition node 7 and a system composition node 8 which are connected with the system composition node 3; the fourth-stage 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 fault troubleshooting model shown in fig. 2 further includes 11 fault nodes connected to the fourth-level system constituent nodes, and each of the 11 fault nodes is connected to the fault node 1, the fault node 2, the fault node 3, the fault node 4, the fault node 5, the fault node 6, the fault node 7, the fault node 8, the fault node 9, the fault node 10, and the fault node 11.
Further, the troubleshooting model shown in fig. 2 further includes 4 problem nodes, Q1, Q2, Q3, and Q4, respectively; the problem node Q1 and the problem node Q3 are connected with the system component node 1 through a problem index, the problem node Q1 is connected with the system component node 3 through a fault locating path and the system component node 6 through a fault removing path, and the problem node Q3 is connected with the system component node 7 through a fault removing path and is connected with the system component node 8 through a fault locating 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 locating path; problem node Q4 is connected to system component node 8 by a problem index, problem node Q4 is connected to system component node 16 by a fault-localization path, and is connected to system component node 18 by a fault-localization path.
Step 102, determining all problem nodes in the fault troubleshooting model, and determining the processing path corresponding to each problem node and system composition nodes connected through the problem index path.
Step 103, determining the processing path and the fault node corresponding to the system component node respectively, and normalizing the preset multiple factor weight information corresponding to the fault node to obtain a normalized factor weight vector.
In one possible implementation, the processing paths include a fault localization path and a fault removal path;
The fault nodes comprise a first fault node corresponding to the fault locating path, a second fault node corresponding to the fault removing path and a third fault node corresponding to the system component node;
the factor weight vector comprises a first factor weight vector corresponding to the first fault node, a second factor weight vector corresponding to the second fault node and a third factor weight vector corresponding to the third fault node.
For example, referring to fig. 2, a system constituent node to which a problem node Q1 is connected through a problem index path is a system constituent node 1, a fault node corresponding to a fault location path is a fault node 1, a fault node 2, a fault node 3, a fault node 4, a fault node 5, and a fault node 6, and a fault node corresponding to a fault removal path is a fault node 5 and a fault node 6; the fault nodes corresponding to the system component node 1 are fault node 1, fault node 2, fault node 3, fault node 4, fault node 5, fault node 6, fault node 7, fault node 8, fault node 9, fault node 10 and fault node 11.
Specifically, the weight information corresponding to each fault node is composed of weight values in different weighting directions, the sensitivity of the fault node in the corresponding direction is described in different weighting directions, and the weight values in different weighting directions in the system are scored and generated by a system administrator. In the solution provided by the embodiments of the present application, the different emphasis directions are characterized by "factors". The factor data selected by all fault points in the same system should be kept consistent, i.e. each fault point has n influence factors. The weight information corresponding to the fault point is expressed by the following formula:
X＝δ1+δ2+δ3+…+δn
Wherein X represents multi-factor weight information corresponding to the fault point; delta n represents the influence factor of the nth emphasis 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 is required to be performed on the preset multi-factor weight information corresponding to each fault node to obtain a normalized factor weight vector. Specifically, there are various ways to normalize the preset multiple factor weight information corresponding to each fault node to obtain a normalized factor weight vector, and a preferred way is described below as an example.
In a possible implementation manner, normalizing the preset multiple factor weight information corresponding to the fault 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 normalized weight value corresponding to any factor according to the maximum value and the minimum value and the weight value corresponding to the any factor; and obtaining the normalized factor weight vector according to the normalized weight value.
In one possible implementation manner, calculating a normalized weight value corresponding to any factor according to the maximum value, the minimum value and the weight value corresponding to the any factor includes:
calculating a normalized weight value corresponding to any factor by the following formula:
Wherein, X norm represents the normalized weight value corresponding to any factor; x represents a weight value corresponding to any factor; x min represents a weight minimum value in the preset factor weight information; x max represents the maximum value of the weight in the preset factor weight information.
Further, after normalization processing is carried out on the preset multiple 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 fault node is represented by the following formula:
lT＝(X1,X2,X3…Xn)
wherein l is a column vector; x 1,X2,X3…Xn is the normalized weight value of different factors.
And 104, calculating a grading value according to a preset fault-factor association matrix and the factor weight vector, and evaluating the fault troubleshooting strategy according to the grading value to obtain an evaluation result.
Specifically, in one possible implementation manner, the fault-factor correlation matrix is represented by the following formula:
wherein A represents a fault-factor correlation matrix, the number of columns of the fault-factor correlation matrix is the number of fault points (g 1,…,gm), and the number of rows of the fault-factor correlation matrix is the number of factors (s 1,…,sn); a mn represents the extent to which fault g m is affected by factor s n.
Further, in the solution provided by 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 is further required to be obtained by calculating according to the factor weight vector corresponding to the fault point and a preset fault-factor correlation matrix. Specifically, there are various ways of calculating the scoring value according to the factor weight vector corresponding to the fault point and the preset fault-factor correlation matrix, and a preferred way is described below.
In one possible implementation manner, calculating a scoring value according to a preset fault-factor association matrix and the factor weight vector includes: calculating the product of the fault-factor association matrix and the first factor weight vector of the fault locating path, taking the modulus of the product result to obtain a first grading value, and marking the first grading value as S1; calculating the product of the fault-factor association matrix and the second factor weight vector, taking the modulus of the product result to obtain a second grading value, and marking the second grading value as S2; and calculating the product of the fault-factor association matrix and the third factor weight vector, taking the modulus of the product result to obtain a third grading value, and marking the third grading value as W.
Specifically, in the scheme provided by the embodiment of the application, the product of the fault-factor correlation matrix and the factor weight vector can 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 modulo of the product result.
Further, in one possible implementation manner, evaluating the troubleshooting policy according to the evaluation value to obtain an evaluation result includes: comparing the first grading value S1 with the second grading value S2, determining the maximum value of the S1 and the S2, and marking the maximum value as S; calculating the ratio S/W of the S to the W, and evaluating the fault investigation strategy according to the ratio to obtain an evaluation result.
In order to facilitate understanding of the principle of the above-described multi-factor based troubleshooting policy evaluation method, it is briefly described below by way of example.
Taking a certain information system as an example, generating a fault checking flow according to logic rules, calculating by using the method of the invention to obtain nodes and scores, and displaying selected part of contents in the following table:
In the scheme provided by the embodiment of the application, a fault troubleshooting model is built according to a preset fault troubleshooting strategy, all problem nodes in the fault troubleshooting model are determined, the processing path corresponding to each problem node and system component nodes connected through the problem index path are determined, the processing path and the fault nodes corresponding to the system component nodes are respectively determined, the normalization processing is carried out on the preset multiple factor weight information corresponding to the fault nodes to obtain normalized factor weight vectors, finally, a grading value is calculated according to a preset fault-factor correlation matrix and the factor weight vectors, and the fault troubleshooting strategy is evaluated according to the grading value 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 capability of judging 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 a quantitative evaluation method on the troubleshooting strategy, and filling the blank of evaluation of the fault troubleshooting strategy.
Furthermore, in the scheme provided by the embodiment of the application, in the process of evaluating the fault checking strategy, the required association information is low, and the problem evaluation can be performed without other nodes only by the problem node to be evaluated and the subordinate nodes formed by the affiliated system. The coverage function processing flow is the same, multiplexing can be realized, and the algorithm calculation complexity is further reduced.
Based on the same inventive concept as the method shown in fig. 1, an embodiment of the present application provides a multi-factor-based troubleshooting policy evaluation device, referring to fig. 3, including:
a construction unit 301, configured to construct a fault troubleshooting model according to a preset fault troubleshooting policy, where the fault troubleshooting model includes a multi-stage system component node, a plurality of fault nodes connected with a final-stage system component node in a sequence from top to bottom, and at least one problem node connected with the system component node through a problem index path and a processing path;
a determining unit 302, configured to determine all problem nodes in the troubleshooting model, and determine the processing path corresponding to each problem node and a system component node connected through the problem index path;
A processing unit 303, configured to determine the processing paths and the fault nodes corresponding to the system component nodes respectively, and normalize a plurality of preset factor weight information corresponding to the fault nodes to obtain normalized factor weight vectors;
and the calculation evaluation unit 304 is configured to calculate a score value according to a preset fault-factor association matrix and the factor weight vector, and evaluate the fault investigation policy 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 normalized weight value corresponding to any factor according to the maximum value and the minimum value and the weight value corresponding to the 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 factor by the following formula:
Wherein, X norm represents the normalized weight value corresponding to any factor; x represents a weight value corresponding to any factor; x min represents a weight minimum value in the preset factor weight information; x max represents the maximum value of the weight in the preset factor weight information.
Optionally, the fault-factor correlation matrix is represented by:
wherein A represents a fault-factor correlation matrix, the number of columns of the fault-factor correlation matrix is the number of fault points (g 1,…,gm), and the number of rows of the fault-factor correlation matrix is the number of factors (s 1,…,sn); a mn represents the extent to which fault g m is affected by factor s n.
Optionally, the processing path includes a fault localization path and a fault removal path;
The fault nodes comprise a first fault node corresponding to the fault locating path, a second fault node corresponding to the fault removing path and a third fault node corresponding to the system component node;
the factor weight vector comprises a first factor weight vector corresponding to the first fault node, a second factor weight vector corresponding to the second fault node and a third factor weight vector corresponding to the third fault node.
Optionally, the computing and evaluating unit 304 is specifically configured to:
calculating the product of the fault-factor association matrix and the first factor weight vector, taking the modulus of the product result to obtain a first grading value, and marking the first grading value as S1;
calculating the product of the fault-factor association matrix and the second factor weight vector, taking the modulus of the product result to obtain a second grading value, and marking the second grading value as S2;
And calculating the product of the fault-factor association matrix and the third factor weight vector, taking the modulus of the product result to obtain a third grading value, and marking the third grading value as W.
Optionally, the computing and evaluating unit 304 is specifically configured to:
Comparing the first grading value S1 with the second grading value S2, determining the maximum value of the S1 and the S2, and marking the maximum value as S;
Calculating the ratio S/W of the S to the W, and evaluating the fault investigation strategy according to the ratio to obtain an evaluation result.
Optionally, the computing and evaluating unit 304 is specifically configured to:
calculating the product of the fault-factor association matrix and the first factor weight vector, taking the modulus of the product result to obtain a first grading value, and marking the first grading value as S1;
calculating the product of the fault-factor association matrix and the second factor weight vector, taking the modulus of the product result to obtain a second grading value, and marking the second grading value as S2;
And calculating the product of the fault-factor association matrix and the third factor weight vector, taking the modulus of the product result to obtain a third grading value, and marking the third grading value as W.
It will be appreciated by those skilled in the art that 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, magnetic 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 flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations 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 modifications and variations can be made to the present application without departing from the spirit or scope of the application. Thus, it is intended that the present application also include such modifications and alterations insofar as they come within the scope of the appended claims or the equivalents thereof.
Claims (9)
1.A fault troubleshooting strategy evaluation method based on multiple factors is characterized by comprising the following steps:
Constructing a fault investigation model according to a preset fault investigation strategy, wherein the fault investigation model comprises a multi-stage system composition node, a plurality of fault nodes connected with a final-stage system composition node according to the sequence from top to bottom, and at least one problem node connected with the system composition node through a problem index path and a processing path;
determining all problem nodes in the fault troubleshooting model, and determining the processing path corresponding to each problem node and system composition nodes connected through the problem index path;
determining the processing path and the fault node corresponding to the system component node respectively, 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;
Calculating a grading value according to a preset fault-factor association matrix and the factor weight vector, and evaluating the fault investigation strategy according to the grading value to obtain an evaluation result;
The processing path comprises a fault locating path and a fault removing path;
The fault nodes comprise a first fault node corresponding to the fault locating path, a second fault node corresponding to the fault removing path and a third fault node corresponding to the system component node;
the factor weight vector comprises a first factor weight vector corresponding to the first fault node, a second factor weight vector corresponding to the second fault node and a third factor weight vector corresponding to the third fault node.
2. The method of claim 1, wherein normalizing the preset plurality of factor weight information corresponding to the fault 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 normalized weight value corresponding to any factor according to the maximum value and the minimum value and the weight value corresponding to the 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 from the maximum and minimum values and the weight value for the any factor comprises:
calculating a normalized weight value corresponding to any factor by the following formula:
Wherein, X norm represents the normalized weight value corresponding to any factor; x represents a weight value corresponding to any factor; x min represents a weight minimum value in the preset factor weight information; x max represents the maximum value of the weight 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, the number of columns of the fault-factor correlation matrix is the number of fault points (g 1,…,gm), and the number of rows of the fault-factor correlation matrix is the number of factors (s 1,…,sn); a mn represents the extent to which fault g m is affected by factor s n.
5. The method of claim 1, wherein calculating a scoring value from the fault-factor correlation matrix and the factor weight vector comprises:
calculating the product of the fault-factor association matrix and the first factor weight vector, taking the modulus of the product result to obtain a first grading value, and marking the first grading value as S1;
calculating the product of the fault-factor association matrix and the second factor weight vector, taking the modulus of the product result to obtain a second grading value, and marking the second grading value as S2;
And calculating the product of the fault-factor association matrix and the third factor weight vector, taking the modulus of the product result to obtain a third grading value, and marking the third grading value as W.
6. The method of claim 5, wherein evaluating the troubleshooting policy based on the scoring value yields an evaluation result, comprising:
Comparing the first grading value S1 with the second grading value S2, determining the maximum value of the S1 and the S2, and marking the maximum value as S;
Calculating the ratio S/W of the S to the W, and evaluating the fault investigation strategy according to the ratio to obtain an evaluation result.
7. A multi-factor based troubleshooting policy evaluation device, comprising:
The system comprises a construction unit, a processing unit and a processing unit, wherein the construction unit is used for constructing a fault troubleshooting model according to a preset fault troubleshooting strategy, and the fault troubleshooting model comprises a multi-stage system component node, a plurality of fault nodes connected with a final-stage system component node according to the sequence from top to bottom, and at least one problem node connected with the system component node through a problem index path and a processing path;
the determining unit is used for determining all problem nodes in the fault troubleshooting model, and 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 paths and the fault nodes corresponding to the system component nodes, and carrying out normalization processing on the preset multiple factor weight information corresponding to the fault nodes to obtain normalized factor weight vectors;
the calculation evaluation unit is used for calculating a grading value according to a preset fault-factor association matrix and the factor weight vector, and evaluating the fault investigation strategy according to the grading value to obtain an evaluation result;
The processing path comprises a fault locating path and a fault removing path;
The fault nodes comprise a first fault node corresponding to the fault locating path, a second fault node corresponding to the fault removing path and a third fault node corresponding to the system component node;
the factor weight vector comprises a first factor weight vector corresponding to the first fault node, a second factor weight vector corresponding to the second fault node and a third factor weight vector corresponding to the third fault node.
8. The apparatus of claim 7, wherein the processing unit is configured to:
determining the maximum value and the minimum value of the weight in the weight information;
Calculating a normalized weight value corresponding to any factor according to the maximum value and the minimum value and the weight value corresponding to the any factor;
And obtaining the normalized factor weight vector according to the normalized weight value.
9. The apparatus of claim 8, wherein the computational evaluation unit is specifically configured to:
calculating the product of the fault-factor association matrix and the first factor weight vector, taking the modulus of the product result to obtain a first grading value, and marking the first grading value as S1;
calculating the product of the fault-factor association matrix and the second factor weight vector, taking the modulus of the product result to obtain a second grading value, and marking the second grading value as S2;
And calculating the product of the fault-factor association matrix and the third factor weight vector, taking the modulus of the product result to obtain a third grading value, and marking the third grading value as W.
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