CN111582718A - Cable channel fire risk assessment method and device based on network analytic hierarchy process - Google Patents
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Abstract
The invention provides a cable channel fire risk assessment method and device based on a network analytic hierarchy process, which comprises the following steps: establishing a fire risk evaluation index system; assigning fire risk indexes, establishing a cable channel fire risk assessment index quantitative standard, acquiring fire safety characteristics of a power cable in a cable channel, and assigning the indexes according to actual conditions; calculating index weight; and (4) 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 and each fire safety feature, establishing two-by-two compared expert matrixes according to the influence relation, and then carrying out consistency check on the expert matrixes to further obtain the target weight of the fire safety features; and the evaluation of the fire risk level of the cable channel is established according to the target weight, so that the fire risk level can be quantitatively evaluated for the fire channel under the condition of considering the dependence feedback relationship among all fire safety characteristics.
Description
Technical Field
The invention relates to a method and a device for evaluating a fire risk level, in particular to a method and a device for evaluating a cable channel fire risk level.
Background
The cable channel serves as a civil engineering facility for the passage of power cable lines, in which cables of various voltage classes are laid. Meanwhile, the cable channel also comprises a plurality of accessory equipment, so that combustible materials are numerous and the structure is complex. In case of a fire disaster in the cable channel, the cable channel is usually arranged underground and has a narrow space, so that the fire disaster is spread very quickly, and when people find the fire disaster, the fire disaster can be caused to be extremely lost and is not beneficial to fire fighters to carry out fire extinguishing rescue. With the acceleration of the urbanization process, the construction of the cable channel is more and more, so that it is necessary to accurately and effectively evaluate the fire risk level of the cable channel.
At present, aiming at the fire protection design of a cable channel, a standard design thought based on a standard is mainly adopted, and two methods for evaluating the fire risk of the cable channel are mainly adopted, wherein one method is to adopt a safety check table method to check and evaluate the safety condition of the channel, and judge and check the known danger types and design defects in engineering and systems mainly according to related standards and standards, so that corresponding improvement measures are provided; and the other method is that the weight of each influence factor is obtained by an analytic hierarchy process according to the influence factors found 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. For example, the "evaluation system of cable combustion performance comprehensive indexes based on an analytic hierarchy process" published in "fire science" vol.21, No.4 discloses an evaluation method of cable combustion performance based on an analytic hierarchy process. Compared with the two methods, the second evaluation method utilizing the analytic hierarchy process is more scientific and accurate, and can quantitatively evaluate the fire risk level of the cable channel.
In the past cable channel fire risk assessment system research, when the mutual influence relationship of indexes is considered, only the secondary indexes under the same primary index are considered, the mutual influence relationship of the two indexes under the same primary index is less and can be approximately mutually independent, but the mutual influence relationship of the secondary indexes under different primary indexes cannot be ignored.
The weight calculation of the evaluation method by using the analytic hierarchy process is most accurate on the premise that all the influence factors are independent and do not have mutual influence. However, among the factors that affect the risk of fire in the cable duct, there are many factors that are not independent of each other. There is an influence relationship (feedback or dependency) between them, and if the weight is analyzed by using the analytic hierarchy process under such conditions, a certain error is caused, and the accuracy of fire risk assessment is affected.
The network analytic hierarchy process as an index analysis method can calculate the weight of each factor under the condition of considering the mutual influence among the influencing factors. The method is applied to the aspects of performance evaluation of an aviation support system, comprehensive evaluation of failure risks of submarine pipelines, evaluation of operating states of constructed wetlands, evaluation of comprehensive energy service levels and the like.
However, the network analytic hierarchy process has no precedent to be applied in the field of fire risk assessment of cable channels. There is a need for a method for quantitative assessment of fire risk level of a cable channel using network analytic hierarchy process, taking into account the interaction between the various influencing factors.
Meanwhile, in consideration of uncertainty, ambiguity and unpredictability of the cable channel fire risk, when the comprehensive weight is calculated by using a network analytic hierarchy process, the risk of the cable channel fire is difficult to accurately measure by common index assignment. In order to solve the technical problem, the fire risk of the cable channel needs to be evaluated by combining a grey system theory and a network analytic hierarchy process.
Disclosure of Invention
The invention aims to solve the technical problem of how to more accurately evaluate the fire risk of a cable channel so as to reduce the harm caused by fire.
The invention solves the technical problems through 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 evaluation index system, selecting fire safety characteristics in a cable channel, and establishing a fire risk index system;
step 2: and assigning fire risk indexes, determining the evaluation gray class and whitening of the cable channel, establishing an expert scoring matrix, and establishing a gray fuzzy evaluation matrix.
And step 3: calculating index weight, namely acquiring the mutual influence relationship between the influence factor and other characteristic influence factors, namely the association condition of the index, aiming at the association condition of the fire safety characteristic, acquiring an expert matrix with relative importance, performing consistency verification on the expert matrix, calculating a limit hypermatrix according to the expert matrix after the expert matrix passes the consistency verification, acquiring a weight vector according to the limit hypermatrix, and acquiring target weight aiming at each characteristic influence factor according to the weight vector;
and 4, step 4: risk assessment, namely establishing an assessment model of the fire risk level of the cable channel according to the target weight to assess the fire risk level of the cable channel, and according to an assessment result, if the risk is acceptable, ending the assessment process; and if the risk is not acceptable, giving an adjustment and modification suggestion according to the result, returning to the step 2 after the adjustment and modification, and continuing the evaluation process until the risk is reduced to be 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 evaluation matrix, determining the evaluation gray class and whitening of the cable channel, and constructing a gray fuzzy evaluation matrix. Aiming at each fire safety feature in the fire safety features, acquiring the mutual influence relationship (feedback or dependency) of the fire safety features relative to each fire safety feature, establishing pairwise comparison expert matrixes according to the influence relationship, then carrying out consistency check on the expert matrixes, constructing an unweighted hypermatrix by using the inspected expert matrixes, normalizing each row of the unweighted hypermatrix, converting the normalized unweighted hypermatrix into a weighted hypermatrix by using the weighted matrix constructed by the expert matrixes, and calculating a limit hypermatrix by using the weighted hypermatrix to obtain the target weight of the fire safety features; and an evaluation equation for the fire risk level of the cable channel is established according to the target weight and the grey 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 dependence feedback relationship among all fire safety characteristics.
As a further specific technical scheme, in the step 2, establishing an expert scoring matrix, and determining the evaluation ash and whitening of the cable channel comprises the following specific processes: n experts are arranged to participate in the evaluation of the cable channel fire risk, and the grade evaluation value of the kth expert on the ith evaluation index is arrangedIs dik. Dividing the fire risk of the fire safety feature of the cable channel into n ash classes, and endowing the nth class of fire risk to an ash threshold value snThen, the whitening processing is carried out, and the whitening weight function is set as follows:
In the step 2, the specific process of constructing the gray fuzzy evaluation matrix comprises the following steps: obtaining the gray weight value r of the ith index corresponding to the nth evaluationin. Using formulas
Calculating rin. The gray weight values r of all the evaluation gray classes of each evaluation indexin. And then representing the nth grey class grade according to the nth column, representing the ith evaluation index according to the ith row, and compiling a grey fuzzy evaluation matrix R:
as a further specific technical solution, in the step 3, for each characteristic influence factor in the fire safety characteristics, a specific process of obtaining an influence relationship between the influence factor and other characteristic influence factors includes: and establishing an expert evaluation table aiming at the mutual influence relationship of the fire safety characteristics of the cable channel according to the fire safety characteristics, and acquiring the mutual influence relationship of the influence factors and other characteristic influence factors.
As a further specific technical scheme, in the expert matrix, an expert matrix representing the importance relationship among the first-level indexes is called an expert weighting matrix, and an expert matrix representing the importance relationship among the second-level indexes is called an expert judgment matrix;
in the step 3, the process of performing consistency verification on the expert matrix is as follows:
calculating the expertMaximum eigenvalue lambda of the decision matrix and the expert weighting matrixmaxAnd according to the maximum characteristic value, using a formula,
n is the order of the matrix, 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 less 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 consistency verification.
As a further specific technical solution, after the expert matrix passes the consistency verification, a specific process of calculating a limit hypermatrix according to the expert matrix is as follows:
and constructing an unweighted super matrix W according to the expert judgment matrix. Wherein W ═ Wij),WijAnd the matrix represents the influence relation of the ith factor on the jth factor. WijThe column vector is the sequencing vector obtained by the characteristic 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 row of the unweighted supermatrix, and converting the normalized unweighted supermatrix W into a weighted supermatrix by using a weighted matrix A
Wherein
aijIs the element in ith row and j column in the weighting matrix a.
By means of the formula (I) and (II),
to weight a super matrix
Normalized limit supermatrix
k is the number of squarings required for the weighted super-matrix to be fixed.
As a further specific technical solution, the process of obtaining a weight vector according to the limit supermatrix, and obtaining a target weight for each characteristic influence factor according to the weight vector is as follows:
according to limit supermatrixes
Obtaining weight vectors
A target weight for the fire safety feature is obtained. Wherein the weight vector is a limit supermatrix
The column vector of (2).
As a further specific technical solution, the risk assessment in step 4 specifically comprises:
using the calculated grey fuzzy evaluation matrix R and the weight vector
And the ash threshold value snMatrix operation is carried out on the formed vectors to obtain a fire risk level score U of the cable channel:
and finally calculating the fire risk level score of the cable channel to be evaluated according to the calculated fire risk level score, 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 using the definition of the risk intervals so as to qualitatively reflect the fire risk of the cable channel.
The invention also provides a cable channel fire risk assessment device based on the network analytic hierarchy process, which comprises the following modules:
the fire risk assessment index system establishing module is used for selecting fire safety characteristics in the cable channel and establishing a fire risk index system;
the fire risk index assigning module is used for determining the evaluation gray class and whitening of the cable channel, establishing an expert scoring matrix and establishing a gray fuzzy evaluation matrix;
the index weight calculation module is used for acquiring the mutual influence relationship 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, performing consistency verification on the expert matrix, calculating a limit hypermatrix according to the expert matrix after the expert matrix passes the consistency verification, acquiring weight vectors according to the limit hypermatrix, and acquiring target weights aiming at the characteristic influence factors according to the weight vectors;
the risk evaluation module is used for establishing an evaluation 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, and according to an evaluation result, if the risk is acceptable, the evaluation process is ended; and if the risk is not acceptable, giving an adjustment and modification suggestion according to the result, returning to the step 2 after the adjustment and modification, and continuing the evaluation process until the risk is reduced to be within an acceptable range.
As a further specific technical scheme, in the index assigning module, establishing an expert scoring matrix, and determining the evaluation ash and whitening of the cable channel comprises the following specific processes: n experts are arranged to participate in the evaluation of the cable channel fire risk, and the grade evaluation value of the kth expert on the ith evaluation index is dik. Classifying fire risks of cable channel fire safety features asn grey class grades and endowing the nth class fire risk to a grey class threshold value snThen, whitening processing is carried out, and the whitening weight function is set as follows:
In the index assigning module, the specific process of constructing the gray fuzzy evaluation matrix is as follows: obtaining the gray weight value r of the ith index corresponding to the nth evaluationin. Using formulas
Calculating rin. The gray weight values r of all the evaluation gray classes of each evaluation indexin. Then, the nth grey class grade is represented according to the nth column, the ith line represents the ith evaluation index, and grey fuzzy evaluation is compiled
The price matrix R:
as a further specific technical scheme, in the index weight calculation module, for each characteristic influence factor in the fire safety characteristics, the specific process of obtaining 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 relationship of the fire safety characteristics of the cable channel according to the fire safety characteristics, and acquiring the mutual influence relationship of the influence factors and other characteristic influence factors;
in the expert matrix, the expert matrix representing the importance relation among the first-level indexes is called an expert weighting matrix, and the expert matrix representing the importance relation among the second-level indexes is called an expert judgment matrix;
in the index weight calculation module, the process of performing consistency verification on the expert matrix is as follows:
calculating the maximum eigenvalue lambda of the expert judgment matrix and the expert weighting matrixmaxAnd according to the maximum characteristic value, using a formula,
n is the order of the matrix, 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 less 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 consistency verification.
As a further specific technical solution, in the index weight calculation module, after the expert matrix passes the consistency verification, a specific process of calculating the limit hypermatrix according to the expert matrix is as follows:
and constructing an unweighted super matrix W according to the expert judgment matrix. Wherein W ═ Wij),WijAnd the matrix represents the influence relation of the ith factor on the jth factor. WijThe column vector is the sequencing vector obtained by the characteristic 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 row of the unweighted supermatrix, and converting the normalized unweighted supermatrix W into a weighted supermatrix by using a weighted matrix A
Wherein
aijIs the element in ith row and j column in the weighting matrix a.
By means of the formula (I) and (II),
to weight a super matrix
Normalized limit supermatrix
k is the number of squarings required for fixing the weighted super matrix;
obtaining a weight vector according to the limit supermatrix, and obtaining the target weight aiming at each characteristic influence factor according to the weight vector comprises the following steps:
obtaining a weight vector from a limit supermatrix
A target weight for the fire safety feature is obtained. Wherein the weight vector is a limit supermatrix
The column vector of (2).
As a further specific technical solution, in the risk assessment module:
using the calculated grey fuzzy evaluation matrix R and the weight vector
And the ash threshold value snMatrix operation is carried out on the formed vectors to obtain a fire risk level score U of the cable channel:
and finally calculating the fire risk level score of the cable channel to be evaluated according to the calculated fire risk level score, 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 using the definition of the risk intervals so as to qualitatively reflect the fire risk of the cable channel.
The invention has the advantages that: with the diversity of the influence factors of cable fires, the evaluation requirement of the cable fire risk level cannot be met by using the existing analytic hierarchy process, the network level construction is very complicated, and no report that the cable fire risk level evaluation is carried out by using the network analytic hierarchy process exists at present. Aiming at the selected fire safety features, acquiring the mutual influence relationship (feedback or dependence) of the fire safety features relative to each fire safety feature, establishing an expert matrix for pairwise comparison according to the influence relationship, and then carrying out consistency check on the expert matrix. Constructing an unweighted hypermatrix by using an expert judgment matrix passing the inspection, normalizing each row of the unweighted hypermatrix, converting the normalized unweighted hypermatrix into a weighted hypermatrix by using a weighting matrix constructed by the expert weighting matrix, and calculating an extreme hypermatrix by using the weighted hypermatrix to obtain the target weight of the fire safety characteristic; and an evaluation equation for the fire risk level of the cable channel is established according to the target weight and the grey 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 dependence feedback relationship among all fire safety characteristics.
Drawings
FIG. 1 is a schematic diagram of a method for assessing a fire risk level of a cable channel according to an embodiment of the present invention;
fig. 2 is a schematic structural diagram of an apparatus for evaluating a fire risk level of a cable channel according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the embodiments of the present invention, and it is obvious that the described embodiments are some embodiments of the present invention, but not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Example one
Fig. 1 is a schematic diagram illustrating a cable channel fire risk assessment method based on a network analytic hierarchy process according to an embodiment of the present invention, as shown in fig. 1, the method includes:
step 1: and establishing a fire risk assessment index system (EFRIS), selecting fire safety features in the cable channel, and establishing the fire risk index system. Wherein, the fire safety feature includes: cable burn attributes, cable run status, cable channel environmental characteristics, and cable channel fire prevention configurations.
Illustratively, the fire safety characteristics of the cable channel are selected according to the modes of literature reference, field investigation and the like, and a fire risk index system is established. Table 1 shows selected combinations of fire safety features provided in the examples of the present invention.
TABLE 1 fire safety characteristic selection combination table
Step 2: and Fire Risk Indicator Scoring (FRIS), determining the evaluation gray class and whitening of the cable channel, establishing an expert scoring matrix, and establishing 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 channel can be divided into 5 grey classes of low risk, medium risk and high risk. Corresponding grey threshold value snSequentially 2, 4, 5.5, 6.5 and 8.
Illustratively, Table 2 is a questionnaire with 5 experts assigned fire safety features.
TABLE 2 expert assignment questionnaire
And establishing an expert scoring matrix according to the expert awarding questionnaire. Using formulas
Calculating rin. The gray weight values r of all the evaluation gray classes of each evaluation indexin. And then representing the nth grey class grade according to the nth column, representing the ith evaluation index according to the ith row, and compiling a grey fuzzy evaluation matrix R:
and step 3: and Index Weight Calculation (FRIWC) for acquiring the mutual influence relationship (feedback or dependency) of the influence factor and other characteristic influence factors, namely the correlation condition of the index, aiming at each characteristic influence factor in the fire safety characteristics. And acquiring an expert matrix of relative importance aiming at the correlation 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 hypermatrix according to the expert matrix; and acquiring a weight vector according to the limit supermatrix. And acquiring target weights aiming at the characteristic influence factors according to the weight vector.
Specifically, an expert evaluation table for the mutual influence relationship of the fire safety characteristics of the cable channel can be established according to the fire safety characteristics, and the mutual influence relationship (feedback or dependency) between the influence factors and other characteristic influence factors can be 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 a required expert matrix according to a case of drawing a tick on the table. If the second-level indexes under the first-level indexes have mutual influence relations (the number is more than 1), an expert weighting matrix needs to be established among the first-level indexes, and an expert judgment matrix needs to be established among each second-level index having the mutual influence relation.
Table 3 first round expert scoring table
Survey shows that: the top element is the risk factor that is affected and the left column is the factor that may cause the top risk factor. Please mark a square 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 for facilitating filling out of the design.
TABLE 4C 1 index importance questionnaire
Survey shows that: the top is the weight assignment, the left column is the comparison index. Please type "+" or "-" in the corresponding space of the left column of the 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 a C1 index importance questionnaire.
TABLE 5C 1 related expert matrix Table
| C1 | B1 | B2 | B3 | B4 |
| B1 | ||||
| B2 | ||||
| B3 | ||||
| B4 | ||||
| C1 | D1 | D2 | D3 | |
| D1 | ||||
| D2 | ||||
| D3 |
Illustratively, the expert matrixes of all the fire safety features are obtained, wherein the expert matrix representing the importance relationship among the first-level indexes is called an expert weighting matrix, and the expert matrix representing the importance relationship among the second-level indexes is called an expert judgment matrix.
The process of carrying out consistency verification on 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 matrixmaxAnd according to the maximum characteristic value, using a formula,
n is the order of the matrix, 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 less 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 consistency verification.
After the expert matrix passes the consistency verification, the specific process of calculating the limit hypermatrix according to the expert matrix comprises the following steps:
and constructing an unweighted super matrix W according to the expert judgment matrix. Wherein W ═ Wij),,WijAnd the matrix represents the influence relation of the ith factor on the jth factor. WijThe column vector is the sequencing vector obtained by the characteristic 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 row of the unweighted super matrix W, and converting the normalized unweighted super matrix W into a weighted super matrix by using a weighted matrix A
Wherein
aijIs the element in ith row and j column in the weighting matrix a. By means of the formula (I) and (II),
to weight a super matrix
Normalized limit supermatrix
k is the number of squarings required for the weighted super-matrix to be fixed. The super matrix achieves the purpose of matrix fixing through matrix squaring. The matrix will change when it is multiplied, but the super matrix will not change after it has been multiplied a certain number of times. However, this squaring number is generally large and tends to be infinite.
Obtaining a weight vector according to the limit supermatrix, and obtaining the target weight aiming at each characteristic influence factor according to the weight vector comprises the following steps:
extracting weight vectors obtained in a limit hypermatrix
Each element corresponding to a target weight for fire safety features. Wherein the weight vector is a limit supermatrix
The column vector of (2). The element arrangement sequence is the arrangement sequence in the first round of expert scoring table.
And 4, step 4: and a risk assessment (FRAR) for establishing an assessment model of the fire risk level of the cable channel according to the target weight to perform fire risk level assessment on the cable channel. According to the evaluation result, if the risk is acceptable, ending the evaluation process; if the risk is not acceptable, giving an adjustment and modification suggestion according to the result and performing adjustment and modification, and returning to the step 2 after the adjustment and modification: and assigning fire risk indexes, and continuing the evaluation process until the risk is reduced to an acceptable range.
Specifically, the calculated gray fuzzy evaluation matrix R and the weight vector are used
And the ash threshold value snMatrix operation is carried out on the formed vectors to obtain a fire risk level score U of the cable channel:
according to the finally calculated fire risk level score of the cable channel to be evaluated, the risk value is classified according to the set acceptable risk level, and the risk interval corresponding to different scores is determined, as shown in table 5. The fire risk level of the cable channel to be evaluated can be finally determined by using the definition of the risk interval, so that the fire risk of the cable channel is qualitatively reflected.
Table 6 shows fire risk level intervals of cable ducts according to embodiments of the present invention.
TABLE 6 fire risk level interval table
| Risk rating | Low risk | Low to medium risk | Middle risk | High and high risk | High risk |
| Value of risk | 0-3 | 3-5 | 5-6 | 6-7 | 7-10 |
With the embodiment of the invention shown in fig. 1, the fire safety features of the power cable in the cable channel are obtained. And establishing an expert evaluation matrix, determining the evaluation gray class and whitening of the cable channel, and constructing a gray fuzzy evaluation matrix. Aiming at each fire safety feature in the fire safety features, acquiring the mutual influence relationship (feedback or dependency) of the fire safety features relative to each fire safety feature, establishing pairwise comparison expert matrixes according to the influence relationship, then carrying out consistency check on the expert matrixes, constructing an unweighted hypermatrix by using an inspected expert judgment matrix, normalizing each row of the unweighted hypermatrix, converting the normalized unweighted hypermatrix into a weighted hypermatrix by using a weighting matrix constructed by the expert weighting matrix, and calculating a limit hypermatrix by using the weighted hypermatrix to obtain the target weight of the fire safety features; and an evaluation equation for the fire risk level of the cable channel is established according to the target weight, 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 dependence feedback relationship among all fire safety characteristics.
In the prior art, mutual influence relations (feedback or dependence) among various fire safety characteristic influence factors are not considered, a certain deviation exists between the calculated weight and the actual situation, and a large error exists in the actual application, so that the evaluation result is inaccurate and a certain defect exists. By applying the embodiment of the invention, the network analytic hierarchy process is used for obtaining the mutual influence relationship (feedback or dependence) between the fire safety features relative to each fire safety feature, and then the expert matrix is obtained according to the mutual influence relationship to further calculate the weight of each fire safety feature, and further calculate the fire risk level of the cable channel, so that the defects of the prior art can be overcome.
Example two
Corresponding to the first embodiment of the invention shown in fig. 1, the invention also provides a device for evaluating the fire risk level of the cable channel.
Fig. 2 is a schematic structural diagram of an apparatus for evaluating a fire risk level of a cable channel according to an embodiment of the present invention, as shown in fig. 2, the apparatus includes:
a fire risk assessment indicator system building module (EFRISM) for selecting fire safety features of cable channels and building a fire risk indicator system, wherein the fire safety features include: one or a combination of cable combustion attributes, cable operating conditions, cable channel environmental characteristics, cable channel fire protection configurations, and cable channel fire protection management;
the fire risk index assigning module (FRISM) is used for determining the evaluation gray class and whitening of a cable channel, establishing an expert scoring matrix and establishing a gray fuzzy evaluation matrix;
and the Fire Risk Index Weight Calculation Module (FRIWCM) is used for acquiring the mutual influence relationship (feedback or dependency) of the influence factor and other characteristic influence factors aiming at each characteristic influence factor in the fire safety characteristics, namely the correlation condition of the index. And acquiring an expert matrix of relative importance aiming at the correlation 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 hypermatrix according to the expert matrix; and acquiring a weight vector according to the limit supermatrix. Acquiring target weights aiming at all characteristic influence factors according to the weight vectors;
and a fire risk assessment module (FRARM) that establishes an assessment model of a fire risk level of the cable channel according to the target weight to perform a fire risk level assessment on the cable channel. According to the evaluation result, if the risk is acceptable, ending the evaluation flow and writing an evaluation report; and if the risk is not acceptable, giving an adjustment and modification suggestion according to the result, and continuing the evaluation process after the adjustment and modification until the risk is acceptable.
With the embodiment of the invention shown in fig. 2, the fire safety features of the power cable in the cable channel are selected. And establishing an expert evaluation matrix, determining the evaluation gray class and whitening of the cable channel, and constructing a gray fuzzy evaluation matrix. Aiming at each fire safety feature in the fire safety features, acquiring the mutual influence relationship (feedback or dependence) of the fire safety features relative to the fire safety features, establishing expert matrixes for pairwise comparison according to the influence relationship, and then carrying out consistency check on the expert matrixes. In the expert matrix, the expert matrix representing the importance relationship among the first-level indexes is called an expert weighting matrix, and the expert matrix representing the importance relationship among the second-level indexes is called an expert judgment matrix. Constructing an unweighted hypermatrix by using an expert judgment matrix passing the inspection, normalizing each row of the unweighted hypermatrix, converting the normalized unweighted hypermatrix into a weighted hypermatrix by using a weighting matrix constructed by the expert weighting matrix, and calculating an extreme hypermatrix by using the weighted hypermatrix to obtain the target weight of the fire safety characteristic; and an evaluation equation for the fire risk level of the cable channel is established according to the target weight and the grey 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 dependence feedback relationship among all fire safety characteristics.
The following are exemplary:
the fire risk assessment indicator system set-up module (EFRISM) is configured to:
and selecting the fire safety characteristics of the cable channel according to the modes of literature reference, field investigation and the like, and establishing a fire risk index system. Table 1 as in example one selects combinations for fire safety features.
-said fire risk indicator assigning module (FRISM) for:
and determining the evaluation gray class and whitening of the cable channel, establishing an expert evaluation matrix, and establishing a gray fuzzy evaluation matrix.
The fire safety feature selected by the fire risk assessment indicator system building module (efRISM) may be used as an assessment indicator.
The fire risk of the fire safety feature of the cable channel can be divided into 5 grey classes of low risk, medium risk and high risk. Corresponding grey threshold value snSequentially 2, 4, 5.5, 6.5 and 8. The expert gives a questionnaire as in table 2 in example one.
And establishing an expert scoring matrix according to the expert awarding questionnaire. Using formulas
Calculating rin. The gray weight values r of all the evaluation gray classes of each evaluation indexin. And then representing the nth grey class grade according to the nth column, representing the ith evaluation index according to the ith row, and compiling a grey fuzzy evaluation matrix R:
and assigning scores to each index according to the input data and the quantization standard.
-said Fire Risk Indicator Weight Calculation Module (FRIWCM) for:
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 influence evaluation of the fire safety characteristics. Table 2 gives the first round expert a rating of the table as in example one.
And establishing an expert evaluation table aiming at the cable channel, and comparing every two of the expert evaluation tables when the expert evaluation tables have dependency and feedback relations with each other. And quantifying the expert scoring by using a scaling 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 process of carrying out consistency verification on 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 matrixmaxAnd according to the maximum characteristic value, using a formula,
n is the order of the matrix, 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 less 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 consistency verification.
After the expert judgment matrix and the expert weighting matrix pass the consistency verification, the process of calculating the limit hypermatrix according to the expert matrix comprises the following steps:
and constructing an unweighted super matrix W according to the expert judgment matrix. Wherein W ═ Wij),WijAnd the matrix represents the influence relation of the ith factor on the jth factor. WijThe column vector is the sequencing vector obtained by the characteristic 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 row of the unweighted super matrix W, and converting the normalized unweighted super matrix W into a weighted super matrix by using a weighted matrix A
Wherein
aijIs the element in ith row and j column in the weighting matrix a.
Using formulas，
To weight a super matrix
Normalized limit supermatrix
k is the number of squarings required for the weighted super-matrix to be fixed.
Obtaining a weight vector according to the limit supermatrix, and obtaining the target weight aiming at each characteristic influence factor according to the weight vector comprises the following steps:
extracting weight vectors obtained in a limit hypermatrix
Each element respectively corresponds to the target weight occupied by the fire safety feature, wherein the weight vector is a limit hypermatrix
The column vector of (2).
The fire risk assessment module (FRARM) configured to:
using the calculated grey fuzzy evaluation matrix R and the weight vector
And the ash threshold value snMatrix operation is carried out on the formed vectors 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 process; and if the risk is not acceptable, giving a modification suggestion according to the result, modifying, returning to the fire risk index assigning module after modification, and continuing the evaluation process until the risk is reduced to an acceptable range.
The above examples are only intended to illustrate the technical solution of the present invention, but not to limit it; although the present 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 solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.
Claims (10)
1. A cable channel fire risk assessment method based on a network analytic hierarchy process is characterized by comprising the following steps: the method comprises the following steps:
step 1: establishing a fire risk evaluation index system, selecting fire safety characteristics in a cable channel, and establishing a fire risk index system;
step 2: assigning fire risk indexes, determining evaluation grey and whitening of a cable channel, establishing an expert scoring matrix, and establishing a grey fuzzy evaluation matrix;
and step 3: calculating index weight, namely acquiring the mutual influence relationship between the influence factor and other characteristic influence factors, namely the association condition of the index, aiming at the association condition of the fire safety characteristic, acquiring an expert matrix with relative importance, performing consistency verification on the expert matrix, calculating a limit hypermatrix according to the expert matrix after the expert matrix passes the consistency verification, acquiring a weight vector according to the limit hypermatrix, and acquiring target weight aiming at each characteristic influence factor according to the weight vector;
and 4, step 4: risk assessment, namely establishing an assessment model of the fire risk level of the cable channel according to the target weight to assess the fire risk level of the cable channel, and according to an assessment result, if the risk is acceptable, ending the assessment process; if the risk is not acceptable, giving a rectification suggestion according to the result and rectifying, returning to the step 2 after rectification, and continuing the evaluation process until the risk is reduced to an acceptable range;
the steps areIn step 2, establishing an expert scoring matrix, and determining the specific process of evaluating the grey and whitening of the cable channel comprises the following steps: n experts are arranged to participate in the evaluation of the cable channel fire risk, and the grade evaluation value of the kth expert on the ith evaluation index is dik. Dividing the fire risk of the fire safety feature of the cable channel into n ash classes, and endowing the nth class of fire risk to an ash threshold value snThen, the whitening processing is carried out, and the whitening weight function is set as follows:
in the step 2, the specific process of constructing the gray fuzzy evaluation matrix comprises the following steps: obtaining the gray weight value r of the ith index corresponding to the nth evaluationin. Using formulas
Calculating rin. The gray weight values r of all the evaluation gray classes of each evaluation indexin. And then representing the nth grey class grade according to the nth column, representing the ith evaluation index according to the ith row, and compiling a grey fuzzy evaluation matrix R:
2. the network analytic hierarchy process-based cable channel fire risk assessment method of claim 1, wherein: in the step 3, for each characteristic influence factor in the fire safety characteristics, the specific process of obtaining the mutual influence relationship between the influence factor and other characteristic influence factors is as follows: and establishing an expert evaluation table aiming at the mutual influence relationship of the fire safety characteristics of the cable channel according to the fire safety characteristics, and acquiring the mutual influence relationship of the influence factors and other characteristic influence factors.
3. The network analytic hierarchy process-based cable channel fire risk assessment method of claim 2, wherein: in the expert matrix, the expert matrix representing the importance relation among the first-level indexes is called an expert weighting matrix, and the expert matrix representing the importance relation among the second-level indexes is called an expert judgment matrix;
in the step 3, the process of performing consistency verification on the expert matrix is as follows:
calculating the maximum eigenvalue lambda of the expert judgment matrix and the expert weighting matrixmaxAnd according to the maximum characteristic value, using a formula,
n is the order of the matrix, 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 less 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 consistency verification.
4. The network analytic hierarchy process-based cable channel fire risk assessment method of claim 3, wherein: in the step 3, after the expert matrix passes the consistency verification, the specific process of calculating the limit hypermatrix according to the expert matrix is as follows:
constructing an unweighted hypermatrix W from the expert decision matrix, wherein W ═ Wij),WijA matrix, W, representing the influence of the ith factor on the jth factorijThe column vector is a sequencing vector obtained by an expert judgment matrix by a characteristic root method;
constructing a weighting matrix A according to the expert weighting matrix, wherein the column vector of A is a sequencing vector obtained by a characteristic root method of each expert weighting matrix;
normalizing each row of the unweighted super matrix W, and converting the normalized unweighted super matrix W into a weighted super matrix by using a weighted matrix A
Wherein
aijIs the element of ith row and j column in the weighting matrix A;
by means of the formula (I) and (II),
to weight a super matrix
Normalized limit supermatrix
k is the number of squarings required for the weighted super-matrix to be fixed.
5. The network analytic hierarchy process-based cable channel fire risk assessment method of claim 4, wherein:
obtaining a weight vector according to the limit supermatrix, and obtaining the target weight aiming at each characteristic influence factor according to the weight vector comprises the following steps:
extracting weight vectors obtained in a limit hypermatrix
Each element respectively corresponds to the target weight occupied by the fire safety feature, wherein the weight vector is a limit hypermatrix
A column vector of (a);
the risk assessment in the step 4 is specifically as follows:
using the calculated grey fuzzy evaluation matrix R and the weight vector
And the ash threshold value snMatrix operation is carried out on the formed vectors to obtain a fire risk level score U of the cable channel:
and finally determining the fire risk level of the cable channel to be evaluated by using the definition of the risk interval, thereby qualitatively reflecting the fire risk of the cable channel.
6. The utility model provides a cable channel fire risk assessment device based on network analytic hierarchy process which characterized in that: the system comprises the following modules:
the fire risk assessment index system establishing module is used for selecting fire safety characteristics in the cable channel and establishing a fire risk index system;
the fire risk index assigning module is used for determining the evaluation gray class and whitening of the cable channel, establishing an expert scoring matrix and establishing a gray fuzzy evaluation matrix;
the index weight calculation module is used for acquiring the mutual influence relationship 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, performing consistency verification on the expert matrix, calculating a limit hypermatrix according to the expert matrix after the expert matrix passes the consistency verification, acquiring weight vectors according to the limit hypermatrix, and acquiring target weights aiming at the characteristic influence factors according to the weight vectors;
the risk evaluation module is used for establishing an evaluation 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, and according to an evaluation result, if the risk is acceptable, the evaluation process is ended; if the risk is not acceptable, giving a rectification suggestion according to the result and rectifying, returning to the step 2 after rectification, and continuing the evaluation process until the risk is reduced to an acceptable range;
in the fire risk index assigning module, an expert scoring matrix is established, and the specific process of determining the assessment ash and whitening of the cable channel is as follows: n experts are arranged to participate in the evaluation of the cable channel fire risk, and the grade evaluation value of the kth expert on the ith evaluation index is dik. Dividing the fire risk of the fire safety feature of the cable channel into n ash classes, and endowing the nth class of fire risk to an ash threshold value snThen, whitening processing is carried out, and the whitening weight function is set as follows:
in the index assigning module, the specific process of constructing the gray fuzzy evaluation matrix is as follows: obtaining the gray weight value r of the ith index corresponding to the nth evaluationin. Using formulas
Calculating rin. The gray weight values r of all the evaluation gray classes of each evaluation indexin. And then representing the nth grey class grade according to the nth column, representing the ith evaluation index according to the ith row, and compiling a grey fuzzy evaluation matrix R:
7. the network analytic hierarchy process-based cable channel fire risk assessment device of claim 6, wherein: in the index weight calculation module, for each characteristic influence factor in the fire safety characteristics, the specific process of obtaining 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 relationship of the fire safety characteristics of the cable channel according to the fire safety characteristics, and acquiring the mutual influence relationship of the influence factors and other characteristic influence factors;
in the expert matrix, the expert matrix representing the importance relation among the first-level indexes is called an expert weighting matrix, and the expert matrix representing the importance relation among the second-level indexes is called an expert judgment matrix;
8. the network analytic hierarchy process-based cable channel fire risk assessment device of claim 7, wherein:
in the index weight calculation module, the process of performing consistency verification on the expert matrix is as follows:
calculating the maximum eigenvalue lambda of the expert judgment matrix and the expert weighting matrixmaxAnd according to the maximum characteristic value, using a formula,
n is the order of the matrix, 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 less 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 consistency verification.
9. The network analytic hierarchy process-based cable channel fire risk assessment device of claim 8, wherein: in the index weight calculation module, after the expert matrix passes the consistency verification, the specific process of calculating the limit hypermatrix according to the expert matrix comprises the following steps:
constructing an unweighted hypermatrix W from the expert decision matrix, wherein W ═ Wij),WijA matrix, W, representing the influence of the ith factor on the jth factorijThe column vector is a sequencing vector obtained by an expert judgment matrix by a characteristic root method;
constructing a weighting matrix A according to the expert weighting matrix, wherein the column vector of A is a sequencing vector obtained by a characteristic root method of each expert weighting matrix;
normalizing each row of the unweighted super matrix W, and converting the normalized unweighted super matrix W into a weighted super matrix by using a weighted matrix A
Wherein
aijIs the element of ith row and j column in the weighting matrix A;
by means of the formula (I) and (II),
to weight a super matrix
Normalized limit supermatrix
k is the number of squarings required for fixing the weighted super matrix;
obtaining a weight vector according to the limit supermatrix, and obtaining the target weight aiming at each characteristic influence factor according to the weight vector comprises the following steps:
extracting weight vectors obtained in a limit hypermatrix
Each element respectively corresponds to the target weight occupied by the fire safety feature, wherein the weight vector is a limit hypermatrix
The column vector of (2).
10. The network analytic hierarchy process-based cable channel fire risk assessment device of claim 9, wherein: in the risk assessment module:
using the calculated grey fuzzy evaluation matrix R and the weight vector
And the ash threshold value snMatrix operation is carried out on the formed vectors to obtain a fire risk level score U of the cable channel:
and finally determining the fire risk level of the cable channel to be evaluated by using the definition of the risk interval, thereby qualitatively reflecting the fire risk of the cable channel.
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