CN111582718B - Cable channel fire risk assessment method and device based on network analytic hierarchy process - Google Patents

Cable channel fire risk assessment method and device based on network analytic hierarchy process Download PDF

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CN111582718B
CN111582718B CN202010380472.XA CN202010380472A CN111582718B CN 111582718 B CN111582718 B CN 111582718B CN 202010380472 A CN202010380472 A CN 202010380472A CN 111582718 B CN111582718 B CN 111582718B
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matrix
expert
fire
risk
evaluation
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CN111582718A (en
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张佳庆
陈潇
陆守香
余光辉
严波
范明豪
李森林
孙韬
郭可贵
叶良鹏
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University of Science and Technology of China USTC
State Grid Corp of China SGCC
Electric Power Research Institute of State Grid Anhui Electric Power Co Ltd
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University of Science and Technology of China USTC
State Grid Corp of China SGCC
Electric Power Research Institute of State Grid Anhui Electric Power Co Ltd
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G06Q10/063—Operations research, analysis or management
    • G06Q10/0639—Performance analysis of employees; Performance analysis of enterprise or organisation operations
    • G06Q10/06393—Score-carding, benchmarking or key performance indicator [KPI] analysis
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N7/00—Computing arrangements based on specific mathematical models
    • G06N7/02—Computing arrangements based on specific mathematical models using fuzzy logic
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G06Q10/063—Operations research, analysis or management
    • G06Q10/0635—Risk analysis of enterprise or organisation activities
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Systems or methods specially adapted for specific business sectors, e.g. utilities or tourism
    • G06Q50/10—Services
    • G06Q50/26—Government or public services
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
    • Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
    • Y04S10/00—Systems supporting electrical power generation, transmission or distribution
    • Y04S10/50—Systems or methods supporting the power network operation or management, involving a certain degree of interaction with the load-side end user applications

Abstract

The invention provides a cable channel fire risk assessment method and device based on a network analytic hierarchy process, comprising the following steps: establishing a fire risk assessment index system; assigning fire risk indexes, establishing a cable channel fire risk assessment index quantification standard, acquiring fire safety characteristics of power cables in the cable channel, and assigning scores to the indexes according to actual conditions; calculating index weight; and (5) risk assessment. The invention has the advantages that: aiming at each fire safety feature in the fire safety features, acquiring the mutual influence relation between the fire safety features relative to each fire safety feature, establishing expert matrixes for comparison in pairs according to the influence relation, and then carrying out consistency test on the expert matrixes to further acquire the target weight of the fire safety features; and the fire risk level evaluation for the cable channel is established according to the target weight, so that the fire risk level of the fire channel can be quantitatively evaluated under the condition of considering the dependency feedback relation among the fire safety features.

Description

Cable channel fire risk assessment method and device based on network analytic hierarchy process
Technical Field
The invention relates to a fire risk level assessment method and device, in particular to a cable channel fire risk level assessment method and device.
Background
The cable tunnel serves as a civil engineering facility for the passage of power cabling, in which a plurality of cables of different voltage classes are laid. Meanwhile, the cable channel also comprises a plurality of accessory devices, so that combustible materials are numerous, and the structure is complex. Once a fire disaster occurs in the cable channel, the cable channel is usually arranged underground and has a small space, so that the fire disaster spreads very rapidly, and when people find the fire disaster, the fire disaster possibly causes great loss and is unfavorable for fire fighters to carry out fire fighting and rescue. With the acceleration of the urban process, the construction of cable channels is more and more, so that the fire risk level of the cable channels is necessary to be accurately and effectively estimated.
At present, the fire protection design of the cable channel mainly adopts a standardized design thought based on specifications, and two methods for carrying out fire risk assessment on the cable channel are mainly adopted, one method is to adopt a safety check list method to check and evaluate the safety condition of the channel, and the known dangerous category and design defect in engineering and systems are judged and checked mainly according to relevant standards and specifications, so that corresponding improvement measures are provided; and the other is to obtain the weight of each influence factor by using a hierarchical analysis method according to the influence factors which are checked in the field, so as to establish an evaluation system and evaluate the fire risk level of the cable channel, thereby providing corresponding improvement measures. The method for evaluating the combustion performance of the cable based on the analytic hierarchy process is disclosed in Vol.21, no.4, which is a comprehensive index evaluation system of the combustion performance of the cable based on the analytic hierarchy process. Compared with the two methods, the second evaluation method using the analytic hierarchy process has more scientificity and accuracy, and can quantitatively evaluate the fire risk level of the cable channel.
In the past cable passage fire risk assessment system research, when the mutual influence relation of indexes is considered, only the second-level indexes under the same first-level indexes are considered, the mutual influence relation existing in the two-level indexes is less and can be approximately independent, and the mutual influence relation existing in the second-level indexes under different first-level indexes cannot be ignored.
The weight calculation of the evaluation method by using the analytic hierarchy process is the most accurate on the premise that all influence factors are independent of each other and no mutual influence exists. However, among the factors affecting the risk of fire in the cable duct, there are many factors that are not independent of each other. There is a mutual influence relation (feedback or dependence) between them, and if the weight is analyzed by using the analytic hierarchy process under the condition, a certain error is caused, so that the accuracy of fire risk assessment is affected.
The network analytic hierarchy process is used as an index analysis method to calculate the weight of each factor in consideration of the mutual influence among the influence factors. The method is applied to the aspects of performance evaluation of an aviation support system, comprehensive evaluation of submarine pipeline failure risk, evaluation of artificial wetland operation state, evaluation of comprehensive energy service level and the like.
However, network hierarchies have not been applied in the field of fire risk assessment of cable channels. There is a need for a method for quantitatively assessing the fire risk level of a cable duct using network hierarchies taking into account the interplay between the individual influencing factors.
Meanwhile, in consideration of uncertainty, ambiguity and unpredictability of fire risks of the cable channels, when the comprehensive weight is calculated by using a network analytic hierarchy process, the risks of the fire of the cable channels are difficult to accurately measure by general index assignment. In order to solve the technical problem, the fire risk of the cable channel needs to be evaluated by combining the gray system theory with a network analytic hierarchy process.
Disclosure of Invention
The invention aims to solve the technical problem of more accurately evaluating the fire risk of the cable passage, thereby reducing the hazard caused by fire.
The invention solves the technical problems by the following technical means: a cable channel fire risk assessment method based on a network analytic hierarchy process comprises the following steps:
step 1: establishing a fire risk assessment index system, selecting fire safety features in a cable channel, and establishing the fire risk index system;
Step 2: and (3) assigning fire risk indexes, determining the evaluation ash class and whitening of the cable channel, establishing an expert scoring matrix, and constructing a gray fuzzy evaluation matrix.
Step 3: index weight calculation, namely, acquiring the mutual influence relation between the influence factors and other characteristic influence factors, namely, the association condition of indexes, aiming at the association condition of the fire safety characteristics, acquiring an expert matrix of relative importance, carrying out consistency verification on the expert matrix, after the expert matrix passes the consistency verification, calculating a limit supermatrix according to the expert matrix, acquiring a weight vector according to the limit supermatrix, and acquiring target weights aiming at all characteristic influence factors according to the weight vector;
step 4: the risk assessment is carried out, an assessment model of the fire risk level of the cable channel is established according to the target weight so as to carry out fire risk level assessment on the cable channel, and according to the assessment result, if the risk is acceptable, the assessment flow is ended; if the risk is not acceptable, giving a rectifying and modifying suggestion according to the result, returning to the step 2 after rectifying and modifying, and continuing the evaluation flow until the risk is within an acceptable range.
By adopting the technical scheme, the fire safety characteristics of the power cable in the cable channel are obtained. And establishing an expert scoring matrix, determining the evaluation gray class and whitening of the cable channel, and constructing a gray fuzzy evaluation matrix. For each fire safety feature in the fire safety features, acquiring an interaction relation (feedback or dependence) between the fire safety features relative to each fire safety feature, establishing expert matrixes for comparison every two according to the influence relation, then carrying out consistency test on the expert matrixes, constructing an unweighted super matrix by using the tested expert matrixes, normalizing each column of the unweighted super matrix, converting the normalized unweighted super matrix into a weighted super matrix by using a weighting matrix constructed by the expert matrixes, and calculating a limit super matrix by using the weighted super matrix to obtain the target weight of the fire safety features; and an evaluation equation of the fire risk level of the cable channel is established according to the target weight and the gray fuzzy evaluation matrix, so that the fire risk level of the cable channel is evaluated, and the fire risk level of the fire channel can be quantitatively evaluated under the condition of considering the dependency feedback relation among all fire safety features.
As a further specific technical scheme, in the step 2, an expert scoring matrix is established to determine the specific procedures of evaluating ash class and whitening of the cable channelThe method comprises the following steps: n experts are arranged to participate in the assessment of the fire risk of the cable channel, and the grade assessment value of the kth expert on the ith assessment index is d ik . Dividing fire risks of fire safety features of cable channels into n ash classes, and endowing the n-th class fire risks with ash thresholds s n Then, whitening processing is performed, and a whitening weight function is set as follows:
function f n (d ik ) A whitening weight function representing an i-th indicator of the kth expert's nth class risk.
In the step 2, the specific process of constructing the gray fuzzy evaluation matrix is as follows: obtaining gray weight value r of the nth evaluation corresponding to the ith index in . Using the formulaCalculating r in . The gray weight value r of all the evaluation gray classes of each evaluation index in . Then according to the nth column representing the nth gray class level and the ith row representing the ith evaluation index, a gray fuzzy evaluation matrix R is compiled:
as a further specific technical solution, in the step 3, for each feature influence factor in the fire safety feature, the specific process of obtaining the mutual influence relationship between the influence factor and other feature influence factors is as follows: and establishing an expert evaluation table aiming at the mutual influence relation of the fire safety characteristics of the cable channel according to the fire safety characteristics, and acquiring the mutual influence relation of the influence factors and other characteristic influence factors.
As a further specific technical scheme, among the expert matrices, the expert matrix representing the importance relationship between the first-level indexes is referred to as an expert weighting matrix, and the expert matrix representing the importance relationship between the second-level indexes is referred to as an expert judgment matrix;
in the step 3, the consistency verification process for the expert matrix is as follows:
calculating the maximum eigenvalue lambda of the expert judgment matrix and the expert weighting matrix max And according to the maximum characteristic value, using a formula,n is the matrix order, and the consistency check index of the expert judgment matrix and the expert weighting matrix is calculated;
when the consistency check index is smaller than a preset threshold value, judging that the expert judgment matrix and the expert weighting matrix pass consistency verification;
and when the consistency check index is not smaller than a preset threshold value, judging that the expert judgment matrix and the expert weighting matrix do not pass consistency verification, and adjusting the values of elements in the expert judgment matrix and the expert weighting matrix until the expert judgment matrix and the expert weighting matrix pass the consistency verification.
As a further specific technical scheme, after the expert matrix passes the consistency verification, the specific process of calculating the limit supermatrix according to the expert matrix is as follows:
And constructing an unweighted super matrix W according to the expert judgment matrix. Wherein w= (W ij ),W ij Representing a matrix of influence relationships of the ith factor to the jth factor. W (W) ij The column vectors of (2) are the sorting vectors obtained by the feature root method of the corresponding expert judgment matrix.
And constructing a weighting matrix A according to the expert weighting matrix. The column vector of A is the sorting vector obtained by the characteristic root method of each expert weighting matrix.
Normalizing each column of the unweighted super matrix, and converting the normalized unweighted super matrix W into a weighted super matrix by using a weighting matrix AWherein->a ij Is the element of row i and column j in the weighting matrix a.
By means of the formula (i),the weighting super matrix->Is a limit super matrix->k is the number of squaring times required for the weight super matrix fixation.
As a further specific technical scheme, the process of obtaining the weight vector according to the limit super matrix and the target weight for each characteristic influence factor according to the weight vector is as follows:
according to the limit super matrixAcquiring weight vector->A target weight for the fire safety feature is obtained. Wherein the weight vector is a limit super matrix +.>Is a column vector of (a).
As a further specific technical solution, the risk assessment in step 4 is specifically:
Using the calculated gray fuzzy evaluation matrix R and weight vectorAnd the ash threshold s n The vector formed is subjected to matrix operation to obtain a fire risk level score U of the cable channel:
according to the fire risk level score of the cable channel to be evaluated, grading the risk value by referring to the set acceptable risk level, determining the risk intervals corresponding to different scores, and finally determining the fire risk level of the cable channel to be evaluated by utilizing the definition of the risk intervals, thereby qualitatively reflecting the fire risk of the cable channel.
The invention also provides a cable channel fire risk assessment device based on a network analytic hierarchy process, which comprises the following modules:
the fire risk assessment index system establishment module is used for selecting fire safety features in the cable channel and establishing a fire risk index system;
the fire risk index grading module determines the evaluation ash class and whitening of the cable channel, establishes an expert scoring matrix and constructs a gray fuzzy evaluation matrix;
the index weight calculation module is used for acquiring the mutual influence relation between the influence factors and other characteristic influence factors, namely the association condition of indexes, aiming at the association condition of the fire safety characteristics, acquiring an expert matrix with relative importance, carrying out consistency verification on the expert matrix, calculating a limit supermatrix according to the expert matrix after the expert matrix passes the consistency verification, acquiring a weight vector according to the limit supermatrix, and acquiring target weights aiming at all characteristic influence factors according to the weight vector;
The risk assessment module establishes an assessment model of the fire risk level of the cable channel according to the target weight so as to carry out fire risk level assessment on the cable channel, and if the risk is acceptable according to the assessment result, the assessment flow is ended; if the risk is not acceptable, giving a rectifying and modifying suggestion according to the result, returning to the step 2 after rectifying and modifying, and continuing the evaluation flow until the risk is within an acceptable range.
As a further specific technical scheme, in the index assigning module, an expert scoring matrix is established, and the specific process of determining the evaluation ash class and whitening of the cable channel is as follows: n experts are arranged to participate in the assessment of the fire risk of the cable channel, and the grade assessment value of the kth expert on the ith assessment index is d ik . Dividing fire risk of fire safety feature of cable duct into n gray class levels, and dividing nClass fire risk giving ash class threshold s n Then, whitening processing is performed, and the set whitening weight function is as follows:
function f n (d ik ) A whitening weight function representing an i-th indicator of the kth expert's nth class risk.
In the index scoring module, the specific process of constructing the gray fuzzy evaluation matrix is as follows: obtaining gray weight value r of the nth evaluation corresponding to the ith index in . Using the formulaCalculating r in . The gray weight value r of all the evaluation gray classes of each evaluation index in . Then according to the nth column representing the nth gray class level and the ith row representing the ith evaluation index, the gray fuzzy evaluation is compiled
Valence matrix R:
as a further specific technical solution, in the index weight calculation module, for each feature influence factor in the fire safety feature, a specific process of obtaining a mutual influence relationship between the influence factor and other feature influence factors is as follows: establishing an expert evaluation table aiming at the mutual influence relation of the fire safety features of the cable channel according to the fire safety features, and acquiring the mutual influence relation of the influence factors and other characteristic influence factors;
among the expert matrixes, the expert matrix representing the importance relationship between the first-level indexes is called an expert weighting matrix, and the expert matrix representing the importance relationship between the second-level indexes is called an expert judgment matrix;
in the index weight calculation module, the consistency verification process for the expert matrix is as follows:
calculating the maximum eigenvalue lambda of the expert judgment matrix and the expert weighting matrix max And according to the maximum characteristic value, using a formula, n is the matrix order, and the consistency check index of the expert judgment matrix and the expert weighting matrix is calculated;
when the consistency check index is smaller than a preset threshold value, judging that the expert judgment matrix and the expert weighting matrix pass consistency verification;
and when the consistency check index is not smaller than a preset threshold value, judging that the expert judgment matrix and the expert weighting matrix do not pass consistency verification, and adjusting the values of elements in the expert judgment matrix and the expert weighting matrix until the expert judgment matrix and the expert weighting matrix pass the consistency verification.
As a further specific technical scheme, in the index weight calculation module, after the expert matrix passes the consistency verification, a specific process of calculating a limit supermatrix according to the expert matrix is as follows:
and constructing an unweighted super matrix W according to the expert judgment matrix. Wherein w= (W ij ),W ij Representing a matrix of influence relationships of the ith factor to the jth factor. W (W) ij The column vectors of (2) are the sorting vectors obtained by the feature root method of the corresponding expert judgment matrix.
And constructing a weighting matrix A according to the expert weighting matrix. The column vector of A is the sorting vector obtained by the characteristic root method of each expert weighting matrix.
Normalizing each column of the unweighted super matrix, and converting the normalized unweighted super matrix W into a weighted super matrix by using a weighting matrix AWherein->a ij Is the element of row i and column j in the weighting matrix a.
By means of the formula (i),the weighting super matrix->Is a limit super matrix->k is the number of squaring times required by the fixation of the weighting hyper-matrix;
obtaining a weight vector according to a limit super matrix, and obtaining a target weight for each characteristic influence factor according to the weight vector, wherein the process of obtaining the target weight for each characteristic influence factor comprises the following steps:
obtaining weight vectors according to the limit super matrixA target weight for the fire safety feature is obtained. Wherein the weight vector is a limit super matrix +.>Is a column vector of (a).
As a further specific technical solution, in the risk assessment module:
using the calculated gray fuzzy evaluation matrix R and weight vectorAnd the ash threshold s n The vector formed is subjected to matrix operation to obtain a fire risk level score U of the cable channel:
according to the fire risk level score of the cable channel to be evaluated, grading the risk value by referring to the set acceptable risk level, determining the risk intervals corresponding to different scores, and finally determining the fire risk level of the cable channel to be evaluated by utilizing the definition of the risk intervals, thereby qualitatively reflecting the fire risk of the cable channel.
The invention has the advantages that: along with the diversity of influencing factors of cable fire, the existing analytic hierarchy process cannot meet the evaluation requirement of the cable fire risk level, network hierarchy construction is very complicated, no report of cable fire risk level evaluation is given by the network analytic hierarchy process at present, a detailed method for cable fire risk level evaluation by the network analytic hierarchy process is provided, an expert scoring matrix is established, evaluation ash and whitening of a cable channel are determined, and a gray fuzzy evaluation matrix is constructed. And aiming at the selected fire safety features, acquiring the mutual influence relation (feedback or dependence) between the fire safety features relative to each fire safety feature, establishing expert matrixes for pairwise comparison according to the influence relation, and then carrying out consistency test on the expert matrixes. Constructing an unweighted super matrix by using the expert judgment matrix passing the inspection, normalizing each column of the unweighted super matrix, converting the normalized unweighted super matrix into a weighted super matrix by using the weighting matrix constructed by the expert weighting matrix, and calculating a limit super matrix by using the weighted super matrix to obtain the target weight of the fire safety feature; and an evaluation equation of the fire risk level of the cable channel is established according to the target weight and the gray fuzzy evaluation matrix, so that the fire risk level of the cable channel is evaluated, and the fire risk level of the fire channel can be quantitatively evaluated under the condition of considering the dependency feedback relation among all fire safety features.
Drawings
Fig. 1 is a schematic diagram of an evaluation method for fire risk level of a cable duct according to an embodiment of the present invention;
fig. 2 is a schematic structural diagram of an apparatus for evaluating fire risk level of a cable duct according to an embodiment of the present invention.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present invention more apparent, the technical solutions in the embodiments of the present invention will be clearly and completely described in the following in conjunction with the embodiments of the present invention, and it is apparent that the described embodiments are some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Example 1
Fig. 1 is a schematic diagram of a cable duct fire risk assessment method based on a network analytic hierarchy process according to an embodiment of the present invention, as shown in fig. 1, where the method includes:
step 1: fire risk assessment index system Establishment (EFRIS), fire safety features in the cable channels are selected, and a fire risk index system is established. Wherein the fire safety feature comprises: cable burn attribute, cable run status, cable channel environmental characteristics, and cable channel fire fighting configuration.
Illustratively, the fire safety characteristics of the cable channels are selected according to the modes of literature review, on-site investigation and the like, and a fire risk index system is established. Table 1 shows selected combinations of fire safety features provided in embodiments of the present invention.
TABLE 1 fire safety feature selection combination table
Step 2: fire Risk Index Scoring (FRIS), determining the estimated gray class and whitening of the cable channel, and establishing an expert scoring matrix to construct a gray fuzzy evaluation matrix.
Illustratively, the fire safety feature selected according to step 1 may be used as an evaluation index.
The fire risk of the fire safety feature of the cable duct can be classified into 5 ash classes of low risk, medium and high risk, and high risk. Ash threshold value s corresponding to the ash threshold value n Sequentially 2, 4, 5.5, 6.5 and 8.
Illustratively, table 2 is a questionnaire with 5 experts assigned to the fire safety features.
Table 2 expert scoring questionnaire
And establishing an expert scoring matrix according to the expert scoring questionnaire. Using the formulaCalculating r in . The gray weight value r of all the evaluation gray classes of each evaluation index in . Then according to the nth column representing the nth gray class level and the ith row representing the ith evaluation index, a gray fuzzy evaluation matrix R is compiled: / >
Step 3: and (3) index weight calculation (FRIWC), and aiming at each characteristic influence factor in the fire safety characteristics, acquiring the mutual influence relation (feedback or dependence) between the influence factors and other characteristic influence factors, namely the association condition of indexes. And acquiring an expert matrix of relative importance according to the association condition of the fire safety features. And carrying out consistency verification on the expert matrix; after the expert matrix passes the consistency verification, calculating a limit super matrix according to the expert matrix; and obtaining a weight vector according to the limit super matrix. And acquiring target weights for the characteristic influence factors according to the weight vectors.
Specifically, an expert evaluation table for the mutual influence relation of the fire safety characteristics of the cable channel can be established according to the fire safety characteristics, and the mutual influence relation (feedback or dependence) of the influence factors and other characteristic influence factors is obtained. Table 3 is a first-round expert scoring table provided in the embodiment of the present invention, which is a table for determining whether there is a degree of association between each influencing factor, and then determining the required expert matrix according to the condition of scoring on the table. If the two-level indexes under the first-level index have an interaction relation (the number is greater than 1), an expert weighting matrix needs to be established between the first-level indexes, and an expert judgment matrix needs to be established between every two-level indexes with the interaction relation.
Table 3 first round expert scoring table
Investigation and description: the top element is the risk factor that is affected and the left is the factor that may cause the top risk factor. Please check "in the corresponding space where the left column factor affects the top factor.
Illustratively, the C1 index importance questionnaire as given in table 4 is a questionnaire designed to facilitate filling in.
Table 4 C1 index importance questionnaire
/>
Investigation and description: the top is assigned weight, and the left column is the comparison index. Please mark "+" or "-" in the corresponding space of the left column comparison index, wherein: "+" indicates a positive relationship, and "-" indicates a negative relationship.
Illustratively, the expert matrix associated with C1 is listed in Table 5 according to the C1 index importance questionnaire.
Table 5 C1 related expert matrix table
C1 B1 B2 B3 B4
B1
B2
B3
B4
C1 D1 D2 D3
D1
D2
D3
For example, among all the obtained expert matrixes of fire safety features, the expert matrix representing the importance relationship between the primary indexes is called an expert weighting matrix, and the expert matrix representing the importance relationship between the secondary indexes is called an expert judgment matrix.
The consistency verification process for the expert judgment matrix and the expert weighting matrix comprises the following steps:
calculating the maximum eigenvalue lambda of the expert judgment matrix and the expert weighting matrix max And according to the maximum characteristic value, using a formula,n is the matrix order, and the consistency check index of the expert judgment matrix and the expert weighting matrix is calculated;
when the consistency check index is smaller than a preset threshold value, judging that the expert judgment matrix and the expert weighting matrix pass consistency verification;
and when the consistency check index is not smaller than a preset threshold value, judging that the expert judgment matrix and the expert weighting matrix do not pass consistency verification, and adjusting the values of elements in the expert judgment matrix and the expert weighting matrix until the expert judgment matrix and the expert weighting matrix pass the consistency verification.
After the expert matrix passes the consistency verification, the specific process of calculating the limit supermatrix according to the expert matrix is as follows:
and constructing an unweighted super matrix W according to the expert judgment matrix. Wherein w= (W ij ),,W ij Representing a matrix of influence relationships of the ith factor to the jth factor. W (W) ij The column vectors of (2) are the sorting vectors obtained by the feature root method of the corresponding expert judgment matrix.
And constructing a weighting matrix A according to the expert weighting matrix. The column vector of A is the sorting vector obtained by the characteristic root method of each expert weighting matrix.
Normalizing each column of the unweighted super matrix W, and converting the normalized unweighted super matrix W into a weighted super matrix by using a weighting matrix AWherein->a ij Is the element of row i and column j in the weighting matrix a. By means of the formula->The weighting super matrix->Is a limit super matrix->k is the number of squaring times required for the weight super matrix fixation. The super matrix achieves the aim of matrix fixation through matrix squaring. The matrix will change when multiplying, but the supermatrix will not change after a certain number of times. However, the number of times of this ride is generally large, so that it approaches infinity.
Obtaining a weight vector according to a limit super matrix, and obtaining a target weight for each characteristic influence factor according to the weight vector, wherein the process of obtaining the target weight for each characteristic influence factor comprises the following steps:
extracting weight vector obtained from limit super matrixEach element corresponding to a target weight for the fire safety feature, respectively. Wherein the weight vector is a limit super matrix +.>Is a column vector of (a). The element arrangement sequence is the arrangement sequence in the first round expert scoring table.
Step 4: and (3) risk assessment (FRAR), and establishing an assessment model of the fire risk level of the cable channel according to the target weight so as to evaluate the fire risk level of the cable channel. According to the evaluation result, if the risk is acceptable, ending the evaluation flow; if the risk is not acceptable, giving a rectifying and modifying suggestion according to the result, and returning to the step 2 after rectifying and modifying: and assigning fire risk indexes, and continuing the evaluation process until the risk falls into an acceptable range.
Specifically, the calculated gray fuzzy evaluation matrix R and the weight vector are usedAnd the ash threshold s n The vector formed is subjected to matrix operation to obtain a fire risk level score U of the cable channel:
and (3) classifying the risk values according to the finally calculated fire risk level scores of the cable channels to be evaluated and referring to the set acceptable risk grades, and determining risk intervals corresponding to different scores, as shown in table 5. And finally determining the fire risk level of the cable channel to be evaluated by utilizing the definition of the risk interval, so as to qualitatively reflect the fire risk of the cable channel.
Table 6 shows fire risk level intervals for the cable plant in accordance with an embodiment of the present invention.
TABLE 6 fire risk level interval table
Risk level Low risk Low and medium risk Risk in Medium and high risk High risk
Risk value 0-3 3-5 5-6 6-7 7-10
The embodiment of the invention shown in fig. 1 is applied to obtain the fire safety characteristics of the power cable in the cable channel. And establishing an expert scoring matrix, determining the evaluation gray class and whitening of the cable channel, and constructing a gray fuzzy evaluation matrix. For each fire safety feature in the fire safety features, acquiring an interaction relation (feedback or dependence) between the fire safety features relative to each fire safety feature, establishing expert matrixes for comparison every two according to the influence relation, then carrying out consistency test on the expert matrixes, constructing an unweighted super matrix by using an expert judgment matrix passing the test, normalizing each column of the unweighted super matrix, converting the normalized unweighted super matrix into a weighted super matrix by using a weighting matrix constructed by an expert weighting matrix, and calculating a limit super matrix by using the weighted super matrix to obtain the target weight of the fire safety features; and an evaluation equation of the fire risk level for the cable channel is established according to the target weight, and then the fire risk level is evaluated for the cable channel, so that the fire risk level can be quantitatively evaluated for the fire channel under the condition of considering the dependency feedback relation among all fire safety features.
In the prior art, the mutual influence relation (feedback or dependence) among all fire safety characteristic influence factors is not considered, the calculated weight has certain deviation from the actual situation, and larger error exists in the actual application, so that the evaluation result is inaccurate and has certain defects. By applying the method, the mutual influence relation (feedback or dependence) of the fire safety features relative to each fire safety feature is obtained by using a network analytic hierarchy process, and then the expert matrix is obtained according to the mutual influence relation so as to calculate the weight of each fire safety feature, and then the fire risk level of the cable channel is calculated, so that the defects of the prior art can be overcome.
Example two
Corresponding to the first embodiment shown in fig. 1, the invention also provides an assessment device for the fire risk level of the cable channel.
Fig. 2 is a schematic structural diagram of an apparatus for evaluating fire risk level of a cable duct according to an embodiment of the present invention, as shown in fig. 2, where the apparatus includes:
a fire risk assessment indicator system creation module (EFRISM) for selecting a fire safety feature for a cable pathway, creating a fire risk indicator system, wherein the fire safety feature comprises: one or a combination of cable burn attribute, cable run status, cable channel environmental characteristics, cable channel fire fighting configuration, and cable channel fire fighting management;
A fire risk index grading module (FRISM) for determining the evaluation gray class and whitening of the cable channel, and establishing an expert scoring matrix to construct a gray fuzzy evaluation matrix;
and the Fire Risk Index Weight Calculation Module (FRIWCM) is used for acquiring the mutual influence relation (feedback or dependence) between the influence factors and other characteristic influence factors, namely the association condition of the index, aiming at each characteristic influence factor in the fire safety characteristics. And acquiring an expert matrix of relative importance according to the association condition of the fire safety features. And carrying out consistency verification on the expert matrix; after the expert matrix passes the consistency verification, calculating a limit super matrix according to the expert matrix; and obtaining a weight vector according to the limit super matrix. According to the weight vector, obtaining target weights aiming at all characteristic influence factors;
and a fire risk assessment module (FRARM) for establishing an assessment model of the fire risk level of the cable channel according to the target weight so as to assess the fire risk level of the cable channel. According to the evaluation result, if the risk is acceptable, ending the evaluation flow and writing an evaluation report; if the risk is not acceptable, giving a rectifying and modifying suggestion according to the result, and continuing the evaluation flow after rectifying and modifying until the risk is acceptable.
The fire safety features of the power cable in the cable channel are selected using the embodiment of the present invention shown in fig. 2. And establishing an expert scoring matrix, determining the evaluation gray class and whitening of the cable channel, and constructing a gray fuzzy evaluation matrix. And aiming at each fire safety feature in the fire safety features, acquiring an interaction relation (feedback or dependence) between the fire safety features relative to each fire safety feature, establishing expert matrixes for pairwise comparison according to the interaction relation, and then carrying out consistency test on the expert matrixes. Among the expert matrices, the expert matrix representing the importance relationship between the primary indexes is called an expert weighting matrix, and the expert matrix representing the importance relationship between the secondary indexes is called an expert judgment matrix. Constructing an unweighted super matrix by using the expert judgment matrix passing the inspection, normalizing each column of the unweighted super matrix, converting the normalized unweighted super matrix into a weighted super matrix by using the weighting matrix constructed by the expert weighting matrix, and calculating a limit super matrix by using the weighted super matrix to obtain the target weight of the fire safety feature; and an evaluation equation of the fire risk level of the cable channel is established according to the target weight and the gray fuzzy evaluation matrix, so that the fire risk level of the cable channel is evaluated, and the fire risk level of the fire channel can be quantitatively evaluated under the condition of considering the dependency feedback relation among all fire safety features.
Exemplary:
the fire risk assessment index system establishment module (EFRISM) is used for:
according to the reference, the fire safety characteristics of the cable channel are selected in the modes of on-site investigation and the like, and a fire risk index system is established. Table 1 as in embodiment one selects combinations for fire safety features.
The fire risk index assigning module (FRISM) is used for:
and determining the evaluation gray class and whitening of the cable channel, establishing an expert scoring matrix, and constructing a gray fuzzy evaluation matrix.
The fire safety feature may be selected as an evaluation index according to the fire risk assessment index system establishment module (EFRISM).
The fire risk of the fire safety feature of the cable duct can be classified into 5 ash classes of low risk, medium and high risk, and high risk. Ash threshold ash corresponding to the said methodThreshold s n Sequentially 2, 4, 5.5, 6.5 and 8. Expert assignment questionnaires were presented as table 2 in example one.
And establishing an expert scoring matrix according to the expert scoring questionnaire. Using the formulaCalculating r in . The gray weight value r of all the evaluation gray classes of each evaluation index in . Then according to the nth column representing the nth gray class level and the ith row representing the ith evaluation index, a gray fuzzy evaluation matrix R is compiled: / >
And assigning each index according to the input data and the quantization standard.
The Fire Risk Index Weight Calculation Module (FRIWCM) is configured to:
and establishing an expert evaluation table aiming at the mutual influence relation of the fire safety characteristics of the cable channel according to the fire safety characteristics, and acquiring fire safety characteristic influence evaluation. Table 2 as in embodiment one marks the table for the first round of expert.
And establishing an expert evaluation table aiming at the cable channel, wherein all the expert evaluation tables have dependency and feedback relation, and the expert evaluation tables are compared pairwise. And quantifying expert scoring by using a scale method to obtain the relative importance value of each characteristic influence factor of the fire safety characteristic and the relative importance value of the fire safety characteristic.
And constructing an expert judgment matrix and an expert weighting matrix of the cable channel according to the relative importance value of each characteristic influence factor of the fire safety characteristic and the relative importance value of the fire safety characteristic.
The consistency verification process for the expert judgment matrix and the expert weighting matrix comprises the following steps:
calculating the maximum eigenvalue lambda of the expert judgment matrix and the expert weighting matrix max And according to the maximum characteristic value, using a formula, n is the matrix order, and the consistency check index of the expert judgment matrix and the expert weighting matrix is calculated;
when the consistency check index is smaller than a preset threshold value, judging that the expert judgment matrix and the expert weighting matrix pass consistency verification;
and when the consistency check index is not smaller than a preset threshold value, judging that the expert judgment matrix and the expert weighting matrix do not pass consistency verification, and adjusting the values of elements in the expert judgment matrix and the expert weighting matrix until the expert judgment matrix and the expert weighting matrix pass the consistency verification.
After the expert judgment matrix and the expert weighting matrix pass the consistency verification, the process of calculating the limit supermatrix according to the expert matrix is as follows:
and constructing an unweighted super matrix W according to the expert judgment matrix. Wherein w= (W ij ),W ij Representing a matrix of influence relationships of the ith factor to the jth factor. W (W) ij The column vectors of (2) are the sorting vectors obtained by the feature root method of the corresponding expert judgment matrix.
And constructing a weighting matrix A according to the expert weighting matrix. The column vector of A is the sorting vector obtained by the characteristic root method of each expert weighting matrix.
Normalizing each column of the unweighted super matrix W, and converting the normalized unweighted super matrix W into a weighted super matrix by using a weighting matrix A Wherein->a ij Is the element of row i and column j in the weighting matrix a.
By means of the formula (i),the weighting super matrix->Is a limit super matrix->k is the number of squaring times required for the weight super matrix fixation.
Obtaining a weight vector according to a limit super matrix, and obtaining a target weight for each characteristic influence factor according to the weight vector, wherein the process of obtaining the target weight for each characteristic influence factor comprises the following steps:
extracting weight vector obtained from limit super matrixEach element corresponding to a target weight for fire safety features, wherein the weight vector is a limit supermatrix +.>Is a column vector of (a).
The fire risk assessment module (FRARM) is used for:
using the calculated gray fuzzy evaluation matrix R and the weight vectorAnd the ash threshold s n The vector formed is subjected to matrix operation to obtain a fire risk level score U of the cable channel:
according to the evaluation result, if the risk is acceptable, ending the evaluation flow; if the risk is not acceptable, giving a rectifying and modifying suggestion according to the result, returning to the fire risk index grading module after rectifying and modifying, and continuing the evaluation flow until the risk is reduced to be within an acceptable range.
The above embodiments are only for illustrating the technical solution of the present invention, and are not limiting; although the invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims (6)

1. A cable channel fire risk assessment method based on a network analytic hierarchy process is characterized by comprising the following steps of: comprising the following steps:
step 1: establishing a fire risk assessment index system, selecting fire safety features in a cable channel, and establishing the fire risk index system;
step 2: assigning scores to fire risk indexes, determining the evaluation gray class and whitening of a cable channel, establishing an expert scoring matrix, and constructing a gray fuzzy evaluation matrix;
step 3: index weight calculation, namely, acquiring the mutual influence relation between the influence factors and other characteristic influence factors, namely, the association condition of indexes, aiming at the association condition of the fire safety characteristics, acquiring an expert matrix of relative importance, carrying out consistency verification on the expert matrix, after the expert matrix passes the consistency verification, calculating a limit supermatrix according to the expert matrix, acquiring a weight vector according to the limit supermatrix, and acquiring target weights aiming at all characteristic influence factors according to the weight vector;
step 4: the risk assessment is carried out, an assessment model of the fire risk level of the cable channel is established according to the target weight so as to carry out fire risk level assessment on the cable channel, and according to the assessment result, if the risk is acceptable, the assessment flow is ended; if the risk is not acceptable, giving a rectifying and modifying suggestion according to the result, returning to the step 2 after rectifying and modifying, and continuing the evaluation flow until the risk is reduced to be within an acceptable range;
In the step 2, an expert scoring matrix is established, and the specific process of determining the evaluation ash class and whitening of the cable channel is as follows: n experts are arranged to participate in the assessment of the fire risk of the cable channel, and the grade assessment value of the kth expert on the ith assessment index is d ik Dividing fire risks of fire safety features of cable channels into n ash classes, and endowing the n-th class fire risks with ash thresholds s n Then proceed toThe whitening process is performed by setting the whitening weight function as follows:function f n (d ik ) A whitening weight function representing an i-th indicator of a kth expert nth class risk;
in the step 2, the specific process of constructing the gray fuzzy evaluation matrix is as follows: obtaining gray weight value r of the nth evaluation corresponding to the ith index in Using the formulaCalculating r in The gray weight value r of all the evaluation gray classes of each evaluation index in Then, according to the nth column representing the nth gray class level and the ith row representing the ith evaluation index, a gray fuzzy evaluation matrix R is compiled: />
In the step 3, for each characteristic influence factor in the fire safety characteristics, the specific process of acquiring the mutual influence relationship between the influence factor and other characteristic influence factors is as follows: establishing an expert evaluation table aiming at the mutual influence relation of the fire safety features of the cable channel according to the fire safety features, and acquiring the mutual influence relation of the influence factors and other characteristic influence factors;
Among the expert matrixes, the expert matrix representing the importance relationship between the first-level indexes is called an expert weighting matrix, and the expert matrix representing the importance relationship between the second-level indexes is called an expert judgment matrix;
in the step 3, the consistency verification process for the expert matrix is as follows:
calculating the maximum eigenvalue lambda of the expert judgment matrix and the expert weighting matrix max And according to the maximum characteristic value, using a formula,n is the matrix orderCalculating consistency check indexes of the expert judgment matrix and the expert weighting matrix;
when the consistency check index is smaller than a preset threshold value, judging that the expert judgment matrix and the expert weighting matrix pass consistency verification;
when the consistency check index is not smaller than a preset threshold value, judging that the expert judgment matrix and the expert weighting matrix do not pass consistency verification, and adjusting the values of elements in the expert judgment matrix and the expert weighting matrix until the expert judgment matrix and the expert weighting matrix pass the consistency verification;
in the step 3, after the expert matrix passes the consistency verification, the specific process of calculating the limit super matrix according to the expert matrix is as follows:
Constructing an unweighted super matrix W according to the expert judgment matrix, wherein w= (W) ij ),W ij A matrix representing the influence relation of the ith factor on the jth factor, W ij The column vector of (2) is the sorting vector obtained by the characteristic root method of the expert judgment matrix;
constructing a weighting matrix A according to the expert weighting matrix, wherein the column vector of A is the sorting vector obtained by a characteristic root method of each expert weighting matrix;
normalizing each column of the unweighted super matrix W, and converting the normalized unweighted super matrix W into a weighted super matrix by using a weighting matrix AWherein->a ij Is the element of the ith row and j columns in the weighting matrix A;
by means of the formula (i),the weighting super matrix->Is a limit super matrix->k is the number of squaring times required for the weight super matrix fixation.
2. The network-analytic-hierarchy-process-based cable channel fire risk assessment method of claim 1, wherein:
obtaining a weight vector according to a limit super matrix, and obtaining a target weight for each characteristic influence factor according to the weight vector, wherein the process of obtaining the target weight for each characteristic influence factor comprises the following steps:
extracting weight vector obtained from limit super matrixEach element corresponding to a target weight for fire safety features, wherein the weight vector is a limit supermatrix +. >Is a column vector of (a).
3. The network-analytic-hierarchy-process-based cable channel fire risk assessment method of claim 2, wherein:
the risk assessment in step 4 is specifically:
using the calculated gray fuzzy evaluation matrix R and weight vectorAnd the ash threshold s n The vector formed is subjected to matrix operation to obtain a fire risk level score U of the cable channel:
and finally calculating fire risk level scores of the cable channels to be evaluated, grading the risk values with reference to the set acceptable risk levels, determining risk intervals corresponding to different scores, and finally determining the fire risk levels of the cable channels to be evaluated by utilizing the definition of the risk intervals, so as to qualitatively reflect the fire risks of the cable channels.
4. The utility model provides a cable passage fire risk assessment device based on network analytic hierarchy process which characterized in that: comprises the following modules:
the fire risk assessment index system establishment module is used for selecting fire safety features in the cable channel and establishing a fire risk index system;
the fire risk index grading module determines the evaluation ash class and whitening of the cable channel, establishes an expert scoring matrix and constructs a gray fuzzy evaluation matrix;
The index weight calculation module is used for acquiring the mutual influence relation between the influence factors and other characteristic influence factors, namely the association condition of indexes, aiming at the association condition of the fire safety characteristics, acquiring an expert matrix with relative importance, carrying out consistency verification on the expert matrix, calculating a limit supermatrix according to the expert matrix after the expert matrix passes the consistency verification, acquiring a weight vector according to the limit supermatrix, and acquiring target weights aiming at all characteristic influence factors according to the weight vector;
the risk assessment module establishes an assessment model of the fire risk level of the cable channel according to the target weight so as to carry out fire risk level assessment on the cable channel, and if the risk is acceptable according to the assessment result, the assessment flow is ended; if the risk is not acceptable, giving a rectifying and modifying suggestion according to the result, returning to the step 2 after rectifying and modifying, and continuing the evaluation flow until the risk is reduced to be within an acceptable range;
in the fire risk index scoring module, an expert scoring matrix is established, and the specific process of determining the evaluation ash class and whitening of the cable channel is as follows: n experts are arranged to participate in the assessment of the fire risk of the cable channel, and the kth expert is arranged to perform the ith The grade evaluation value of the evaluation index is d ik Dividing fire risks of fire safety features of cable channels into n ash classes, and endowing the n-th class fire risks with ash thresholds s n Then, whitening processing is performed, and the set whitening weight function is as follows:
function f n (d ik ) A whitening weight function representing an i-th indicator of a kth expert nth class risk;
in the index scoring module, the specific process of constructing the gray fuzzy evaluation matrix is as follows: obtaining gray weight value r of the nth evaluation corresponding to the ith index in Using the formulaCalculating r in The gray weight value r of all the evaluation gray classes of each evaluation index in Then, according to the nth column representing the nth gray class level and the ith row representing the ith evaluation index, a gray fuzzy evaluation matrix R is compiled: />
In the index weight calculation module, for each characteristic influence factor in the fire safety characteristics, the specific process of acquiring the mutual influence relationship between the influence factors and other characteristic influence factors is as follows: establishing an expert evaluation table aiming at the mutual influence relation of the fire safety features of the cable channel according to the fire safety features, and acquiring the mutual influence relation of the influence factors and other characteristic influence factors;
Among the expert matrixes, the expert matrix representing the importance relationship between the first-level indexes is called an expert weighting matrix, and the expert matrix representing the importance relationship between the second-level indexes is called an expert judgment matrix;
in the index weight calculation module, the consistency verification process for the expert matrix is as follows:
calculating the maximum eigenvalue lambda of the expert judgment matrix and the expert weighting matrix max And according to the maximum characteristic value, using a formula,n is the matrix order, and the consistency check index of the expert judgment matrix and the expert weighting matrix is calculated;
when the consistency check index is smaller than a preset threshold value, judging that the expert judgment matrix and the expert weighting matrix pass consistency verification;
when the consistency check index is not smaller than a preset threshold value, judging that the expert judgment matrix and the expert weighting matrix do not pass consistency verification, and adjusting the values of elements in the expert judgment matrix and the expert weighting matrix until the expert judgment matrix and the expert weighting matrix pass the consistency verification;
in the index weight calculation module, after the expert matrix passes the consistency verification, the specific process of calculating the limit super matrix according to the expert matrix is as follows:
Constructing an unweighted super matrix W according to the expert judgment matrix, wherein w= (W) ij ),W ij A matrix representing the influence relation of the ith factor on the jth factor, W ij The column vector of (2) is the sorting vector obtained by the characteristic root method of the expert judgment matrix;
constructing a weighting matrix A according to the expert weighting matrix, wherein the column vector of A is the sorting vector obtained by a characteristic root method of each expert weighting matrix;
normalizing each column of the unweighted super matrix W, and converting the normalized unweighted super matrix W into a weighted super matrix by using a weighting matrix AWherein->a ij Is the element of the ith row and j columns in the weighting matrix A;
by means of the formula (i),the weighting super matrix->Is a limit super matrix->k is the number of squaring times required for the weight super matrix fixation.
5. The network-analytic-hierarchy-process-based cable channel fire risk assessment device of claim 4, wherein:
obtaining a weight vector according to a limit super matrix, and obtaining a target weight for each characteristic influence factor according to the weight vector, wherein the process of obtaining the target weight for each characteristic influence factor comprises the following steps:
extracting weight vector obtained from limit super matrixEach element corresponding to a target weight for fire safety features, wherein the weight vector is a limit supermatrix +. >Is a column vector of (a).
6. The network-analytic-hierarchy-process-based cable channel fire risk assessment device of claim 5, wherein: risk assessment module:
using the calculated gray fuzzy evaluation matrix R and weight vectorAnd saidAsh threshold s n The vector formed is subjected to matrix operation to obtain a fire risk level score U of the cable channel:
and finally calculating fire risk level scores of the cable channels to be evaluated, grading the risk values with reference to the set acceptable risk levels, determining risk intervals corresponding to different scores, and finally determining the fire risk levels of the cable channels to be evaluated by utilizing the definition of the risk intervals, so as to qualitatively reflect the fire risks of the cable channels.
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