CN112347617B - A multi-factor based fault detection strategy evaluation method and device - Google Patents

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

A multi-factor based fault detection strategy evaluation method and device

Technical Field

The present application relates to the technical field of troubleshooting strategy evaluation, and in particular to a method and device for evaluating a troubleshooting strategy based on multiple factors.

Background technique

A troubleshooting strategy is to gradually narrow the scope of troubleshooting through clues such as the environment and conditions, and finally locate the fault. An excellent troubleshooting strategy has fewer steps and more accurate positioning.

At present, there are many troubleshooting strategies, such as Fault Tree Analysis (FTA), problem-guided troubleshooting method based on ontology model, etc. The basic principle of troubleshooting strategy is to establish a troubleshooting model, and then evaluate the problem handling capability through the troubleshooting model and perform normalization. The same troubleshooting strategy can be used to establish a variety of different troubleshooting models. For example, when using the problem-guided troubleshooting method based on ontology model to establish a troubleshooting model, different troubleshooting models will be generated when the node position to which the problem is attached is different. Therefore, how to determine which troubleshooting model has higher fault location efficiency and more comprehensive troubleshooting in order to optimize the troubleshooting strategy, there is currently no method to quantify the evaluation of troubleshooting strategies.

Summary of the invention

The technical problem solved by the present application is: to address the gap in the evaluation of troubleshooting strategies in the prior art. The present application provides a method and device for evaluating troubleshooting strategies based on multiple factors. In the solution provided by the embodiments of the present application, by establishing a troubleshooting model, all possible paths that can troubleshoot the fault are derived, and all troubleshooting paths are weighted to measure whether the influence range and positioning capabilities of the judgment conditions under different weights are appropriate. Finally, the scores of the entire path are accumulated and summed, and the entire troubleshooting path and even the entire troubleshooting strategy are evaluated, thereby performing a quantitative evaluation method for the troubleshooting strategy, filling the gap in the evaluation of the troubleshooting strategy.

In a first aspect, an embodiment of the present application provides a method for evaluating a troubleshooting strategy based on multiple factors, the method comprising:

Constructing a fault troubleshooting model according to a preset fault troubleshooting strategy, wherein the fault troubleshooting model includes a multi-level system component node, a plurality of fault nodes connected to the last level of system component nodes in a top-to-bottom order, and at least one problem node connected to the system component node through a problem index path and a processing path;

Determine all problem nodes in the troubleshooting model, determine the processing path corresponding to each problem node and the system component nodes connected by the problem index path;

Determine the processing path and the fault node corresponding to the system component node respectively, and normalize the preset multiple factor weight information corresponding to the fault node to obtain a normalized factor weight vector;

A score value is calculated based on a preset fault-factor association matrix and the factor weight vector, and the fault troubleshooting strategy is evaluated based on the score value to obtain an evaluation result.

In the scheme provided in the embodiment of the present application, a fault troubleshooting model is constructed according to a preset fault troubleshooting strategy, and then all the problem nodes in the fault troubleshooting model are determined, the processing path corresponding to each problem node and the system component nodes connected by the problem index path are determined, and then the processing path and the fault node corresponding to the system component node are respectively determined, and the preset multiple factor weight information corresponding to the fault node is normalized to obtain a normalized factor weight vector, and finally a score value is calculated according to the preset fault-factor association matrix and the factor weight vector, and the fault troubleshooting strategy is evaluated according to the score value to obtain an evaluation result. That is, by establishing a fault troubleshooting model, all possible paths that can troubleshoot the fault are derived, and all troubleshooting paths are weighted to measure whether the influence range and positioning ability of the judgment conditions under different weights are appropriate, and finally the score of the entire path is accumulated and summed, and the entire troubleshooting path and even the entire troubleshooting strategy are evaluated, and then a quantitative evaluation method for the troubleshooting strategy is performed, filling the gap in the evaluation of the troubleshooting strategy.

Optionally, normalizing the preset multiple factor weight information corresponding to the faulty node to obtain a normalized factor weight vector includes:

Determine 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, the minimum value and the weight value corresponding to any factor;

The normalized factor weight vector is obtained 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 any factor includes:

The normalized weight value corresponding to any of the factors is calculated by the following formula:

Among them, X _norm represents the normalized weight value corresponding to any factor; X represents the weight value corresponding to any factor; X _min represents the minimum weight value in the preset factor weight information; X _max represents the maximum weight value in the preset factor weight information.

Optionally, the fault-factor association matrix is expressed by the following formula:

Wherein, A represents the fault-factor association matrix, the number of columns of the fault-factor association matrix is the number of fault points (g ₁ ,…,g _m ), the number of rows of the fault-factor association matrix is the number of factors (s ₁ ,…,s _n ); a _mn represents the influence degree of fault g _m under the influence of factor s _n .

Optionally, the processing path includes a fault location path and a fault elimination path;

The fault nodes include a first fault node corresponding to the fault location path, a second fault node corresponding to the fault elimination path, and a third fault node corresponding to the system component node;

The factor weight vector includes 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 score value according to the fault-factor association matrix and the factor weight vector includes:

Calculate the product of the fault-factor association matrix and the first factor weight vector, take the modulus of the product result to obtain a first score value, and record it as S1;

Calculate the product of the fault-factor association matrix and the second factor weight vector, take the modulus of the product result to obtain a second score value, and record it as S2;

The product of the fault-factor association matrix and the third factor weight vector is calculated, and the product result is modulo to obtain a third score value, which is recorded as W.

Optionally, evaluating the troubleshooting strategy according to the scoring value to obtain an evaluation result includes:

Compare the first score value S1 with the second score value S2, determine the maximum value between S1 and S2, and record it as S;

A ratio S/W of the S to the W is calculated, and the fault troubleshooting strategy is evaluated according to the ratio to obtain an evaluation result.

In the solution provided by the embodiment of the present application, in the process of evaluating the troubleshooting strategy, the required related information is relatively low, and only the problem node to be evaluated and the subordinate nodes of the attached system components are required, and the problem evaluation can be performed without other nodes. The coverage function processing flow is the same, and reuse can be achieved, further reducing the algorithm calculation complexity.

In a second aspect, an embodiment of the present application provides a multi-factor based troubleshooting strategy evaluation device, the device comprising:

A construction unit, configured to construct a fault troubleshooting model according to a preset fault troubleshooting strategy, wherein the fault troubleshooting model includes a multi-level system component node, a plurality of fault nodes connected to the last level of system component nodes in a top-to-bottom order, and at least one problem node connected to the system component node via a problem index path and a processing path;

A determination unit, used to determine all problem nodes in the troubleshooting model, determine the processing path corresponding to each problem node and the system component nodes connected through the problem index path;

A processing unit, used to respectively determine the processing path and the fault node corresponding to the system component node, and normalize the preset multiple factor weight information corresponding to the fault node to obtain a normalized factor weight vector;

The calculation and evaluation unit is used to calculate a score value according to a preset fault-factor association matrix and the factor weight vector, and evaluate the fault troubleshooting strategy according to the score value to obtain an evaluation result.

Optionally, the processing unit is specifically configured to:

Determine 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, the minimum value and the weight value corresponding to any factor;

The normalized factor weight vector is obtained according to the normalized weight value.

Optionally, the processing unit is specifically configured to:

The normalized weight value corresponding to any of the factors is calculated by the following formula:

Among them, X _norm represents the normalized weight value corresponding to any factor; X represents the weight value corresponding to any factor; X _min represents the minimum weight value in the preset factor weight information; X _max represents the maximum weight value in the preset factor weight information.

Optionally, the fault-factor association matrix is expressed by the following formula:

Wherein, A represents the fault-factor association matrix, the number of columns of the fault-factor association matrix is the number of fault points (g ₁ ,…,g _m ), the number of rows of the fault-factor association matrix is the number of factors (s ₁ ,…,s _n ); a _mn represents the influence degree of fault g _m under the influence of factor s _n .

Optionally, the processing path includes a fault location path and a fault elimination path;

The fault nodes include a first fault node corresponding to the fault location path, a second fault node corresponding to the fault elimination path, and a third fault node corresponding to the system component node;

The factor weight vector includes 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 calculation and evaluation unit is specifically used to:

Calculate the product of the fault-factor association matrix and the first factor weight vector, take the modulus of the product result to obtain a first score value, and record it as S1;

Calculate the product of the fault-factor association matrix and the second factor weight vector, take the modulus of the product result to obtain a second score value, and record it as S2;

The product of the fault-factor association matrix and the third factor weight vector is calculated, and the product result is modulo to obtain a third score value, which is recorded as W.

Optionally, the calculation and evaluation unit is specifically used to:

Compare the first score value S1 with the second score value S2, determine the maximum value between S1 and S2, and record it as S;

A ratio S/W of the S to the W is calculated, and the fault troubleshooting strategy is evaluated according to the ratio to obtain an evaluation result.

Optionally, the calculation and evaluation unit 304 is specifically used to:

Calculate the product of the fault-factor association matrix and the first factor weight vector, take the modulus of the product result to obtain a first score value, and record it as S1;

Calculate the product of the fault-factor association matrix and the second factor weight vector, take the modulus of the product result to obtain a second score value, and record it as S2;

The product of the fault-factor association matrix and the third factor weight vector is calculated, and the product result is modulo to obtain a third score value, which is recorded as W.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG1 is a schematic diagram of a flow chart of a multi-factor based troubleshooting strategy evaluation method provided in an embodiment of the present application;

FIG2 is a schematic diagram of the structure of a fault troubleshooting model provided in an embodiment of the present application;

FIG3 is a schematic diagram of the structure of a multi-factor based troubleshooting strategy evaluation device provided in an embodiment of the present application.

Detailed ways

In the solutions provided in the embodiments of this application, the described embodiments are only part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

The following is a further detailed description of a multi-factor based troubleshooting strategy evaluation method provided by an embodiment of the present application in conjunction with the accompanying drawings. The specific implementation of the method may include the following steps (the method flow is shown in FIG1 ):

Step 101, construct a troubleshooting model according to a preset troubleshooting strategy, wherein the troubleshooting model includes a multi-level system component node, a plurality of fault nodes connected to the last level system component node in a top-to-bottom order, and at least one problem node connected to the system component node through a problem index path and a processing path.

Specifically, in the scheme provided in the embodiments of the present application, a system component node refers to a node that describes a software module and/or a hardware module, etc. For example, a system component node includes a software node, a hardware node or a memory node. The multi-level system component nodes are connected by a real structure component path of the troubleshooting system, and are divided from top to bottom from coarse to fine according to the division granularity. For example, a memory node is a child node of a hardware node, and a loose memory node is a child node of a memory node; a fault node is used to describe a fault that has occurred, for example, the content contained in a fault node includes loose memory or insufficient disk space, etc.; a problem node is used to characterize problems in the system caused by the fault, wherein the problem node is associated with the system component node through a problem index path, and the fault causing the problem is determined based on a processing path for troubleshooting, wherein the processing path includes a fault location path and a fault troubleshooting path.

In order to facilitate understanding of the above-mentioned troubleshooting model, the troubleshooting model is briefly introduced below in the form of an example. Referring to FIG2 , a schematic diagram of the structure of a troubleshooting model provided in an embodiment of the present application.

Specifically, in Figure 2, the fault troubleshooting model includes four levels of system component nodes, which are first-level system component nodes, second-level system component nodes, third-level system component nodes and fourth-level system component nodes in order from top to bottom in the fault troubleshooting model, wherein the first-level system component nodes include system component node 1; the second-level system component nodes include system component node 2 and system component node 3 connected to system component node 1; the third-level system component nodes include system component node 4, system component node 5 and system component node 6 connected to system component node 2, system component node 7 and system component node 8 connected to system component node 3; the fourth-level system component nodes include system component node 9 and system component node 10 connected to system component node 4, system component node 11 and system component node 12 connected to system component node 5, system component node 13 and system component node 14 connected to system component node 6, system component node 15 and system component node 16 connected to system component node 7, and system component node 17 and system component node 18 connected to system component node 8.

Furthermore, the fault troubleshooting model shown in Figure 2 also includes 11 fault nodes connected to the fourth-level system component nodes, namely 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.

Furthermore, the troubleshooting model shown in Figure 2 also includes four problem nodes, namely Q1, Q2, Q3 and Q4; wherein, problem node Q1 and problem node Q3 are connected to system component node 1 through a problem index, problem node Q1 is connected to system component node 3 through a fault location path and to system component node 6 through a fault elimination path, problem node Q3 is connected to system component node 7 through a fault elimination path and to system component node 8 through a fault location path; problem node Q2 is connected to system component node 2 through a problem index, problem node Q2 is connected to system component node 4 through a fault location path; problem node Q4 is connected to system component node 8 through a problem index, problem node Q4 is connected to system component node 16 through a fault elimination path and to system component node 18 through a fault location path.

Step 102, determining all problem nodes in the troubleshooting model, determining the processing path corresponding to each problem node and the system component nodes connected through the problem index path.

Step 103, respectively determining the processing path and the faulty node corresponding to the system component node, and normalizing the preset multiple factor weight information corresponding to the faulty node to obtain a normalized factor weight vector.

In a possible implementation, the processing path includes a fault location path and a fault elimination path;

The fault nodes include a first fault node corresponding to the fault location path, a second fault node corresponding to the fault elimination path, and a third fault node corresponding to the system component node;

The factor weight vector includes 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 Figure 2, the system component node connected to the problem node Q1 through the problem index path is system component node 1, the fault nodes corresponding to the fault location path are fault node 1, fault node 2, fault node 3, fault node 4, fault node 5 and fault node 6, and the fault nodes corresponding to the fault elimination path are fault node 5 and fault node 6; the fault nodes corresponding to 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 of different emphasis directions, and different emphasis directions respectively describe the sensitivity of the fault node in the corresponding direction. The weight values of different emphasis directions in the system are generated by the system administrator's scoring. In the scheme provided in the embodiment of the present application, different emphasis directions are characterized by "factors". The factor data selected for all fault points in the same system should be consistent, that is, each fault point is composed of n influencing factors. The weight information corresponding to the fault point is expressed by the following formula:

X＝δ ₁ +δ ₂ +δ ₃ +…+δ _n

Among them, X represents the multi-factor weight information corresponding to the fault point; _δn represents the influencing factor of the nth emphasis direction.

Furthermore, before evaluating the problem index path and processing path of each problem node through the multi-factor weight information corresponding to each fault node, it is necessary to normalize the preset multiple factor weight information corresponding to each fault node to obtain a normalized factor weight vector. Specifically, there are many 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 used as an example to illustrate below.

In one possible implementation, the preset multiple factor weight information corresponding to the fault node is normalized to obtain a normalized factor weight vector, including: determining the maximum and minimum values of the weights in the weight information; calculating the normalized weight value corresponding to any factor based on the maximum and minimum values and the weight value corresponding to any factor; and obtaining the normalized factor weight vector based on the normalized weight values.

In a 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 any factor includes:

The normalized weight value corresponding to any of the factors is calculated by the following formula:

Among them, X _norm represents the normalized weight value corresponding to any factor; X represents the weight value corresponding to any factor; X _min represents the minimum weight value in the preset factor weight information; X _max represents the maximum weight value in the preset factor weight information.

Furthermore, after normalizing the preset multiple factor weight information corresponding to each faulty node, a normalized factor weight vector corresponding to each faulty node is obtained. Specifically, the normalized factor weight vector corresponding to each faulty node is expressed by the following formula:

l ^T =(X ₁ ,X ₂ ,X ₃ …X _n )

Among them, l is a column vector; X ₁ , X ₂ , X ₃ …X _n are the normalized weight values of different factors.

Step 104: A score value is calculated based on a preset fault-factor association matrix and the factor weight vector, and the fault troubleshooting strategy is evaluated based on the score value to obtain an evaluation result.

Specifically, in a possible implementation, the fault-factor association matrix is expressed by the following formula:

Wherein, A represents the fault-factor association matrix, the number of columns of the fault-factor association matrix is the number of fault points (g ₁ ,…,g _m ), the number of rows of the fault-factor association matrix is the number of factors (s ₁ ,…,s _n ); a _mn represents the influence degree of fault g _m under the influence of factor s _n .

Further, in the solution provided in the embodiment of the present application, after normalizing the preset multiple factor weight information corresponding to the fault node to obtain a normalized factor weight vector, it is also necessary to calculate the score value based on the factor weight vector corresponding to the fault point and the preset fault-factor association matrix. Specifically, there are many ways to calculate the score value based on the factor weight vector corresponding to the fault point and the preset fault-factor association matrix. The following is an example of a preferred method.

In a possible implementation, the score value is calculated based on a preset fault-factor association matrix and the factor weight vector, including: calculating the product of the fault-factor association matrix and the first factor weight vector of the fault location path, taking the product modulo to obtain a first score value, and recording it as S1; calculating the product of the fault-factor association matrix and the second factor weight vector, taking the product modulo to obtain a second score value, and recording it as S2; calculating the product of the fault-factor association matrix and the third factor weight vector, taking the product modulo to obtain a third score value, and recording it as W.

Specifically, in the solution provided in the embodiment of the present application, the product of the fault-factor association matrix and the factor weight vector can be calculated by the following formula:

Among them, γ represents the fault-factor association matrix and the factor weight vector.

Furthermore, after calculating the product of the fault-factor association matrix and the factor weight vector, the product result is modulo by the following formula:

Among them, |γ| represents the modulus of the product result.

Further, in a possible implementation, the troubleshooting strategy is evaluated according to the scoring value to obtain an evaluation result, including: comparing the first scoring value S1 with the second scoring value S2, determining the maximum value between S1 and S2, and recording it as S; calculating the ratio S/W of S to W, and evaluating the troubleshooting strategy according to the ratio to obtain an evaluation result.

In order to facilitate understanding of the principle of the above-mentioned multi-factor-based troubleshooting strategy evaluation method, a brief introduction is given below in the form of an example.

Taking a certain information system as an example, a troubleshooting process is generated according to logical rules, and the method of the present invention is used to calculate the nodes and scores obtained. Some of the selected contents are shown in the following table:

In the scheme provided in the embodiment of the present application, a fault troubleshooting model is constructed according to a preset fault troubleshooting strategy, and then all the problem nodes in the fault troubleshooting model are determined, the processing path corresponding to each problem node and the system component nodes connected by the problem index path are determined, and then the processing path and the fault node corresponding to the system component node are respectively determined, and the preset multiple factor weight information corresponding to the fault node is normalized to obtain a normalized factor weight vector, and finally a score value is calculated according to the preset fault-factor association matrix and the factor weight vector, and the fault troubleshooting strategy is evaluated according to the score value to obtain an evaluation result. That is, by establishing a fault troubleshooting model, all possible paths that can troubleshoot the fault are derived, and all troubleshooting paths are weighted to measure whether the influence range and positioning ability of the judgment conditions under different weights are appropriate, and finally the score of the entire path is accumulated and summed, and the entire troubleshooting path and even the entire troubleshooting strategy are evaluated, and then a quantitative evaluation method for the troubleshooting strategy is performed, filling the gap in the evaluation of the troubleshooting strategy.

Furthermore, in the solution provided in the embodiment of the present application, in the process of evaluating the troubleshooting strategy, the required related information is relatively low, and only the problem node to be evaluated and the subordinate nodes of the attached system components are required, and the problem evaluation can be performed without other nodes. The coverage function processing flow is the same, and reuse can be achieved, further reducing the algorithm calculation complexity.

Based on the same inventive concept as the method shown in FIG1 , the embodiment of the present application provides a multi-factor based troubleshooting strategy evaluation device, referring to FIG3 , the device includes:

A construction unit 301 is used to construct a fault troubleshooting model according to a preset fault troubleshooting strategy, wherein the fault troubleshooting model includes a multi-level system component node, a plurality of fault nodes connected to the last level of system component nodes in a top-to-bottom order, and at least one problem node connected to the system component node through a problem index path and a processing path;

A determination unit 302 is used to determine all problem nodes in the troubleshooting model, determine the processing path corresponding to each problem node and the system component nodes connected through the problem index path;

The processing unit 303 is used to respectively determine the processing path and the fault node corresponding to the system component node, and normalize the preset multiple factor weight information corresponding to the fault node to obtain a normalized factor weight vector;

The calculation and evaluation unit 304 is used to calculate a score value according to a preset fault-factor association matrix and the factor weight vector, and evaluate the fault troubleshooting strategy according to the score value to obtain an evaluation result.

Optionally, the processing unit 303 is specifically configured to:

Determine 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, the minimum value and the weight value corresponding to any factor;

The normalized factor weight vector is obtained according to the normalized weight value.

Optionally, the processing unit 303 is specifically configured to:

The normalized weight value corresponding to any of the factors is calculated by the following formula:

Among them, X _norm represents the normalized weight value corresponding to any factor; X represents the weight value corresponding to any factor; X _min represents the minimum weight value in the preset factor weight information; X _max represents the maximum weight value in the preset factor weight information.

Optionally, the fault-factor association matrix is expressed by the following formula:

Wherein, A represents the fault-factor association matrix, the number of columns of the fault-factor association matrix is the number of fault points (g ₁ ,…,g _m ), the number of rows of the fault-factor association matrix is the number of factors (s ₁ ,…,s _n ); a _mn represents the influence degree of fault g _m under the influence of factor s _n .

Optionally, the processing path includes a fault location path and a fault elimination path;

The fault nodes include a first fault node corresponding to the fault location path, a second fault node corresponding to the fault elimination path, and a third fault node corresponding to the system component node;

The factor weight vector includes 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 calculation and evaluation unit 304 is specifically used to:

Calculate the product of the fault-factor association matrix and the first factor weight vector, take the modulus of the product result to obtain a first score value, and record it as S1;

Calculate the product of the fault-factor association matrix and the second factor weight vector, take the modulus of the product result to obtain a second score value, and record it as S2;

The product of the fault-factor association matrix and the third factor weight vector is calculated, and the product result is modulo to obtain a third score value, which is recorded as W.

Optionally, the calculation and evaluation unit 304 is specifically used to:

Compare the first score value S1 with the second score value S2, determine the maximum value between S1 and S2, and record it as S;

A ratio S/W of the S to the W is calculated, and the fault troubleshooting strategy is evaluated according to the ratio to obtain an evaluation result.

Optionally, the calculation and evaluation unit 304 is specifically used to:

Calculate the product of the fault-factor association matrix and the first factor weight vector, take the modulus of the product result to obtain a first score value, and record it as S1;

Calculate the product of the fault-factor association matrix and the second factor weight vector, take the modulus of the product result to obtain a second score value, and record it as S2;

The product of the fault-factor association matrix and the third factor weight vector is calculated, and the product result is modulo to obtain a third score value, which is recorded as W.

Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage and optical storage, etc.) that contain computer-usable program code.

The present application is described with reference to the flowchart and/or block diagram of the method, device (system) and computer program product according to the embodiment of the present application. It should be understood that each process and/or box in the flowchart and/or block diagram, and the combination of the process and/or box in the flowchart and/or block diagram can be realized by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device produce a device for realizing the function specified in one process or multiple processes in the flowchart and/or one box or multiple boxes in the block diagram.

These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer-readable memory produce a manufactured product including an instruction device that implements the functions specified in one or more processes in the flowchart and/or one or more boxes in the block diagram.

These computer program instructions may also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, whereby the instructions executed on the computer or other programmable device provide steps for implementing the functions specified in one or more processes in the flowchart and/or one or more boxes in the block diagram.

Obviously, those skilled in the art can make various changes and modifications to the present application without departing from the spirit and scope of the present application. Thus, if these 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 also intended to include these modifications and variations.

Claims (9)

1. A multi-factor based troubleshooting strategy evaluation method, characterized by comprising: Constructing a fault troubleshooting model according to a preset fault troubleshooting strategy, wherein the fault troubleshooting model includes a multi-level system component node, a plurality of fault nodes connected to the last level of system component nodes in a top-to-bottom order, and at least one problem node connected to the system component node through a problem index path and a processing path; Determine all problem nodes in the troubleshooting model, determine the processing path corresponding to each problem node and the system component nodes connected by the problem index path; Determine the processing path and the fault node corresponding to the system component node respectively, and normalize the preset multiple factor weight information corresponding to the fault node to obtain a normalized factor weight vector; A scoring value is calculated according to a preset fault-factor association matrix and the factor weight vector, and the fault troubleshooting strategy is evaluated according to the scoring value to obtain an evaluation result; The processing path includes a fault location path and a fault elimination path; The fault nodes include a first fault node corresponding to the fault location path, a second fault node corresponding to the fault elimination path, and a third fault node corresponding to the system component node; The factor weight vector includes 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 according to claim 1, characterized in that normalizing the preset multiple factor weight information corresponding to the faulty node to obtain a normalized factor weight vector comprises: Determine 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, the minimum value and the weight value corresponding to any factor; The normalized factor weight vector is obtained according to the normalized weight value. 3. The method according to claim 2, characterized in that the normalized weight value corresponding to any factor is calculated according to the maximum value, the minimum value and the weight value corresponding to any factor, comprising: The normalized weight value corresponding to any of the factors is calculated by the following formula: Among them, X _norm represents the normalized weight value corresponding to any factor; X represents the weight value corresponding to any factor; X _min represents the minimum weight value in the preset factor weight information; X _max represents the maximum weight value in the preset factor weight information. 4. The method according to claim 3, characterized in that the fault-factor association matrix is represented by the following formula: Wherein, A represents the fault-factor association matrix, the number of columns of the fault-factor association matrix is the number of fault points (g ₁ ,…,g _m ), the number of rows of the fault-factor association matrix is the number of factors (s ₁ ,…,s _n ); a _mn represents the influence degree of fault g _m under the influence of factor s _n . 5. The method according to claim 1, characterized in that the scoring value is calculated according to the fault-factor association matrix and the factor weight vector, comprising: Calculate the product of the fault-factor association matrix and the first factor weight vector, take the modulus of the product result to obtain a first score value, and record it as S1; Calculate the product of the fault-factor association matrix and the second factor weight vector, take the modulus of the product result to obtain a second score value, and record it as S2; The product of the fault-factor association matrix and the third factor weight vector is calculated, and the product result is modulo to obtain a third score value, which is recorded as W. 6. The method according to claim 5, characterized in that evaluating the troubleshooting strategy according to the score value to obtain an evaluation result comprises: Compare the first score value S1 with the second score value S2, determine the maximum value between S1 and S2, and record it as S; A ratio S/W of the S to the W is calculated, and the fault troubleshooting strategy is evaluated according to the ratio to obtain an evaluation result. 7. A multi-factor based troubleshooting strategy evaluation device, comprising: A construction unit, configured to construct a fault troubleshooting model according to a preset fault troubleshooting strategy, wherein the fault troubleshooting model includes a multi-level system component node, a plurality of fault nodes connected to the last level of system component nodes in a top-to-bottom order, and at least one problem node connected to the system component node via a problem index path and a processing path; A determination unit, configured to determine all problem nodes in the troubleshooting model, determine the processing path corresponding to each problem node and the system component nodes connected via the problem index path; A processing unit, used to respectively determine the processing path and the fault node corresponding to the system component node, and normalize the preset multiple factor weight information corresponding to the fault node to obtain a normalized factor weight vector; A calculation and evaluation unit, used to calculate a score value according to a preset fault-factor association matrix and the factor weight vector, and evaluate the fault troubleshooting strategy according to the score value to obtain an evaluation result; The processing path includes a fault location path and a fault elimination path; The fault nodes include a first fault node corresponding to the fault location path, a second fault node corresponding to the fault elimination path, and a third fault node corresponding to the system component node; The factor weight vector includes 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 device according to claim 7, characterized in that the processing unit is specifically used for: Determine 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, the minimum value and the weight value corresponding to any factor; The normalized factor weight vector is obtained according to the normalized weight value. 9. The device according to claim 8, characterized in that the calculation and evaluation unit is specifically used for: Calculate the product of the fault-factor association matrix and the first factor weight vector, take the modulus of the product result to obtain a first score value, and record it as S1; Calculate the product of the fault-factor association matrix and the second factor weight vector, take the modulus of the product result to obtain a second score value, and record it as S2; The product of the fault-factor association matrix and the third factor weight vector is calculated, and the product result is modulo to obtain a third score value, which is recorded as W.