Cable fire risk assessment method and system based on analytic hierarchy process and fuzzy comprehensive evaluation

Abstract

The invention discloses a cable fire risk assessment method and system based on an analytic hierarchy process and fuzzy comprehensive evaluation, and the method comprises the steps: dividing the fire risk grade of a power cable through employing a centesimal evaluation scale, analyzing the ignition mechanism of a fire hazard in the power cable, determining an assessment index affecting the fire risk of the power cable, and carrying out the evaluation of the fire risk of the power cable. The evaluation indexes comprise a plurality of first-level evaluation indexes, the first-level evaluation indexes comprise a plurality of second-level evaluation indexes, and establishing a power cable fire risk level hierarchical structure model; determining a weight vector of each level of evaluation index by using an analytic hierarchy process, and determining a total weight of each level of evaluation index according to a hierarchical relationship to obtain a weight vector; determining a membership value of each secondary evaluation index by using fuzzy comprehensive evaluation to obtain a membership matrix; obtaining a judgment scale according to the weight vector and the membership matrix; and determining the fire risk grade of the power cable according to the evaluation scale. According to the invention, early warning of the fire risk of the power cable can be realized.

Description

Technical Field

[0001] The invention belongs to the technical field of power transmission and transformation, and relates to a cable fire risk assessment method and system based on hierarchical analysis method and fuzzy comprehensive evaluation. Background Art

[0002] With the continuous development of substation construction, a large number of power cables in the cable trenches in the substation are facing potential safety risks in terms of fire and safety. How to achieve early warning of power cable fire risks and take corresponding measures to ensure the safe, stable and efficient operation of the underground power grid in the substation is a problem that needs to be solved at present. Summary of the invention

[0003] Purpose of the invention: The purpose of the present invention is to provide a cable fire risk assessment method and system based on hierarchical analysis method and fuzzy comprehensive evaluation, so as to achieve early warning of fire risk of power cables.

[0004] Technical solution: The cable fire risk assessment method based on hierarchical analysis method and fuzzy comprehensive evaluation of the present invention comprises:

[0005] Establish a hierarchical structure model of power cable fire risk levels: use a percentage-based evaluation scale to divide the power cable fire risk levels; analyze the ignition mechanism of internal fires in power cables, and determine the evaluation indicators that affect the power cable fire risk, the evaluation indicators include several first-level evaluation indicators, and the first-level evaluation indicators include several second-level evaluation indicators;

[0006] The weight vectors of evaluation indicators at each level are determined by using the hierarchical analysis method, and the total weight of each secondary evaluation indicator is determined according to the hierarchical relationship to obtain the weight vector Y;

[0007] The membership value of each secondary evaluation index is determined by using fuzzy comprehensive evaluation to obtain the membership matrix M;

[0008] Obtain the judgment scale Z according to the weight vector Y and the membership matrix M;

[0009] The fire risk level of power cables is determined according to the evaluation scale Z.

[0010] Furthermore, the power cable fire risk level hierarchical structure model is divided into three levels, including a target level, an indicator level and a scheme level;

[0011] The target layer is divided into three first-level evaluation indicators: cable operation parameters, cable faults, and cable laying;

[0012] The indicator layer is divided into three categories: cable operation parameters, cable faults and cable laying, with a total of 8 secondary evaluation indicators. The cable operation parameters include 4 secondary evaluation indicators: conductor temperature, current carrying capacity, ambient temperature and ambient humidity. The cable faults include 2 secondary evaluation indicators: insulation aging fault and single-phase grounding fault. The cable laying includes 2 secondary evaluation indicators: laying method and fixing method.

[0013] The scheme layer divides the power cable fire risk level into four levels. The higher the level, the greater the possibility of fire and the scope of impact.

[0014] Further,

[0015]

[0016]

[0017] The power cable fire risk level and evaluation scale are determined by the above table.

[0018] Furthermore, the weight vectors of the evaluation indicators at each level are determined by using the hierarchical analysis method, and the total weight of each secondary evaluation indicator is determined according to the hierarchical relationship to obtain the weight vector Y, including:

[0019] Determine the judgment matrix A1 of the three first-level evaluation indicators of cable operation parameters, cable faults and cable laying;

[0020] Determine the judgment matrix A of the four secondary evaluation indicators of conductor temperature, current carrying capacity, ambient temperature, and ambient humidity 11 ;

[0021] Determine the judgment matrix A of two secondary evaluation indicators: insulation aging fault and single-phase grounding fault 12 ;

[0022] Determine the judgment matrix A of the two secondary evaluation indicators of laying method and fixing method 13 ;

[0023] The weight vectors of evaluation indicators at all levels are calculated according to the judgment matrix, and the consistency of the judgment matrix of evaluation indicators at all levels is tested; after the weight vectors of evaluation indicators at all levels are calculated, the total weight of each secondary evaluation indicator is finally obtained according to the hierarchical relationship, and the weight vector Y is obtained.

[0024] Furthermore, the judgment matrix A compares the evaluation indicators of the same level pairwise and quantitatively expresses the importance of one evaluation indicator over another evaluation indicator:

[0025]

[0026] Among them, n is the number of indicators; a ijis a scale, which indicates the scale of index i relative to index j, and a fuzzy scale is given using the 1 to 9 scaling method.

[0027] Furthermore, the row vector of the judgment matrix A is normalized to approximately obtain the weight vector U of the judgment matrix, including:

[0028]

[0029] Among them, u represents the weight, k represents the number of weights, and a kj Represents the scale of index k and index j.

[0030] Furthermore, a consistency check is required to determine whether the obtained weight vector U is reasonable;

[0031] Using consistency index C R To check the consistency of the judgment matrix A:

[0032]

[0033] Where m is the matrix order; R I is the average random consistency index; max is the maximum eigenvalue of the judgment matrix;

[0034]

[0035] When C R When <0.1, it is considered that the judgment matrix A meets the consistency test standard.

[0036] Furthermore, the membership values ​​of the secondary evaluation indicators are determined by using fuzzy comprehensive evaluation to obtain a membership matrix M, including:

[0037] The evaluation indicators at each level are divided into four grading ranges. According to the different situations of the evaluation indicators, their membership degrees are 0.25, 0.50, 0.75, and 1.00 respectively; the fuzzy grading range and membership degree of each secondary evaluation indicator are listed as follows:

[0038]

[0039] According to the fuzzy classification range given in the table and the monitoring data of each secondary evaluation indicator, the membership matrix M is obtained:

[0040]

[0041] Among them, M is the membership matrix of the secondary evaluation index; m i is the membership value of the i-th secondary evaluation indicator.

[0042] Furthermore, the evaluation scale Z = 100 × Y T M.

[0043] The cable fire risk assessment system based on hierarchical analysis method and fuzzy comprehensive evaluation described in the present invention includes a processor and a memory, wherein the memory stores computer instructions, and the processor is used to execute the computer instructions stored in the memory. When the computer instructions are executed by the processor, the electronic device implements the steps of the cable fire risk assessment method based on hierarchical analysis method and fuzzy comprehensive evaluation.

[0044] Beneficial effects: Compared with the prior art, the present invention has the following significant advantages:

[0045] (1) The present invention establishes a hierarchical structure model of power cable fire risk levels, comprehensively considers the relative importance of indicators and factors in multiple levels in power cable fire risk assessment, and also solves uncertainty through fuzzy comprehensive evaluation to improve the accuracy and reliability of power cable fire risk assessment results. The present invention has high practicality and operability, provides a scientific basis for power cable fire risk prevention and control decisions, and can clearly understand how various factors affect fire risks, so as to formulate more effective prevention and control measures and emergency plans.

[0046] (2) The power cable fire risk level hierarchical structure model provided by the present invention is highly flexible and adaptable. The evaluation index system, weight distribution and fuzzy membership function can be adjusted according to specific circumstances, and can meet different scenarios and requirements, thereby enhancing its practicality and effectiveness. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] Figure 1 It is a flow chart of a cable fire risk assessment method based on hierarchical analysis method and fuzzy comprehensive evaluation provided by an embodiment of the present invention;

[0048] Figure 2 It is a hierarchical structure model of power cable fire risk levels in an embodiment of the present invention. DETAILED DESCRIPTION

[0049] The present invention will be further described below in conjunction with the accompanying drawings.

[0050] Example 1

[0051] like Figure 1 As shown, this embodiment 1 provides a cable fire risk assessment method based on hierarchical analysis method and fuzzy comprehensive evaluation, comprising the following steps:

[0052] (1) Establish a hierarchical model of power cable fire risk levels.

[0053] (1.1) In this embodiment, the power cable fire risk level is divided into four levels, namely green level, yellow level, orange level, and red level, thereby obtaining a power cable fire risk level assessment set E:

[0054] E = {green, yellow, orange, red} (1)

[0055] The higher the fire risk level, the greater the possibility of fire and the scope of impact. For the sake of clarity, the power cable fire risk level is divided into percentage-based evaluation scales, as shown in Table 1. Different measures should be taken according to different risk levels to timely reduce the probability of power cable fire.

[0056] Table 1 Classification of power cable fire risk levels

[0057]

[0058] (1.2) Analyze the ignition mechanism of internal fires in power cables and determine the assessment indicators that affect the risk of power cable fires.

[0059] The burning process of power cables can be divided into five stages: heating and melting, thermal decomposition, open flame, combustion and flame spread. The first two stages of this process are endothermic processes, which require the polymer to absorb a large amount of heat from the outside. For example, overload or fault current heating the cable will not cause the cable to burn. In addition, the cable joints will increase the resistance of the joints due to unreliable connection or moisture oxidation, resulting in local overheating of the cable. If the third stage of the cable fire is to enter the open flame, a flame source must appear to ignite the cable, and the cable's own flame source is generally a fault arc, such as a single-phase grounding arc and a short-circuit arc, which can make the temperature in the arc column reach 5000-15000℃, which is very easy to ignite the surrounding combustibles. Under the action of a single-phase grounding arc or a short-circuit arc when a single-phase grounding fault further develops into a phase-to-phase fault, the cable will explode over a large area, forming a "cable fire".

[0060] From the above analysis, it can be seen that the early characteristic of fire caused by power cable is overheating of cable core, which is generally caused by overload of cable line, poor heat dissipation or unreliable connection; and overheating of core will further lead to aging of cable insulation and degradation of insulation performance, decomposition by heat, and generation of flammable gas, leading to the formation of fault arc, and finally causing cable fire under the action of fault arc.

[0061] The above analysis of the ignition mechanism of internal fire in power cables provides a theoretical basis for the fire risk assessment of power cables. The assessment indicators that affect the fire risk of power cables are divided into three primary assessment indicators: cable operation parameters, cable faults and cable laying. Cable operation parameters include four secondary assessment indicators: conductor temperature, current carrying capacity, ambient temperature and ambient humidity. Cable faults include two secondary assessment indicators: insulation aging fault and single-phase grounding fault. Cable laying includes two secondary assessment indicators: laying method and fixing method. A total of eight secondary assessment indicators are included.

[0062] (1.3) The hierarchical analysis method divides the hierarchical structure model of power cable fire risk level into three levels: the highest level (target level) represents the purpose of decision-making and the problem to be solved; the middle level (indicator level) represents the factors to be considered and the indicators of decision-making; the lowest level (solution level) represents the decision-making solution.

[0063] Combined with the fire risk levels in Table 1, the following are established: Figure 2 The power cable fire risk level hierarchy model shown. Specifically:

[0064] The target layer is divided into three first-level evaluation indicators: cable operating parameters, cable faults and cable laying; the indicator layer is divided into three categories: cable operating parameters, cable faults and cable laying, with a total of 8 second-level evaluation indicators, among which cable operating parameters include four second-level evaluation indicators: conductor temperature, current carrying capacity, ambient temperature and ambient humidity; cable faults include two second-level evaluation indicators: insulation aging fault and single-phase grounding fault; cable laying includes two second-level evaluation indicators: laying method and fixing method; the scheme layer divides the fire risk level of power cables into the aforementioned four levels.

[0065] (2) The weight vectors of evaluation indicators at each level are determined by using the hierarchical analysis method. The total weight of each secondary evaluation indicator is determined based on the hierarchical relationship to obtain the weight vector Y.

[0066] (2.1) Establishing the fuzzy judgment matrix

[0067] In the hierarchical analysis, the evaluation indicators at the same level are compared pairwise, and the importance of one evaluation indicator over another is quantitatively expressed, as shown in formula (2):

[0068]

[0069] Among them, A is the judgment matrix, n is the number of indicators; a ij is a scale, which indicates the scale of index i relative to index j, and a fuzzy scale is given using the 1 to 9 scale method shown in Table 2.

[0070] Table 2 Judgment matrix scale table

[0071] Scale meaning 1 Indicator i is equally important as indicator j. 3 Compared with indicator i, indicator j is slightly more important than indicator j. 5 Compared with indicator i, indicator j is obviously more important than indicator j. 7 Compared with indicator j, indicator i is much more important than indicator j. 9 Compared with indicator j, indicator i is extremely important than indicator j. 2,4,6,8 The middle value of the two adjacent scales mentioned above

[0072] Among them, a ii = 1 indicates that index i is equally important when compared with itself; if the scale between index i and index j is a ij , then the scale between index j and index i is 1 / a ij , that is, a ij has the following properties:

[0073]

[0074] (2.2) Calculation of the weight vector of the judgment matrix and consistency test

[0075] By normalizing the row vectors of the judgment matrix A using formula (4), the weight vector U of the judgment matrix A can be approximately obtained:

[0076]

[0077] Among them, u represents the weight, k represents the number of weights, and a kj represents the scale between index k and index j.

[0078] For whether the weight vector U obtained from formula (4) is reasonable, a consistency test is still required. Use the consistency index C R to test the consistency of the judgment matrix A, as shown in formula (5):

[0079]

[0080] Among them, m is the matrix order; R I is the average random consistency index, as shown in Table 3; λ max is the maximum eigenvalue of the judgment matrix;

[0081]

[0082] Table 3 Standard values of the average random consistency index

[0083] m 1 2 3 4 5 6 7 8 9 10 <![CDATA[R I ]]> 0 0 0.58 0.9 1.1 1.24 1.32 1.41 1.45 1.49

[0084] When C R < T, it is considered that the judgment matrix passes the consistency test. Generally, T = 0.1 is taken, so when C R < 0.1, it is considered that the judgment matrix A meets the standard of the consistency test.

[0085] For the solution of U and λ max , the [Y,D]=eig(A) function in MATLAB can also be used, where D is the eigenvalue and Y is the corresponding eigenvector. After normalizing the eigenvector Y, the weight vector U can be obtained.

[0086] According to experience and Table 2, the judgment matrix A1 of three first-level evaluation indicators of cable operation parameters, cable faults and cable laying can be obtained:

[0087]

[0088] According to formula (4), the weight vector of the first-level evaluation index is calculated as U1 = (0.485, 0.423, 0.092) T According to formula (6), the maximum eigenvalue λ of the judgment matrix A1 is calculated max =3.0183, then the consistency index C of the judgment matrix R =0.016<0.1, that is, the judgment matrix A1 is considered to have satisfactory consistency, so U1 is the weight vector of the first-level evaluation index.

[0089] Similarly, according to experience and Table 2, the judgment matrix A of the four secondary evaluation indicators of conductor temperature, current carrying capacity, ambient temperature and ambient humidity in the power cable fire risk level structure model can be obtained. 11 ; Judgment matrix A of two secondary evaluation indicators: insulation aging fault and single-phase grounding fault 12 ; Judgment matrix A of two secondary evaluation indicators: laying method and fixing method 13 .

[0090]

[0091] Calculate the weight vector U of the secondary evaluation index 11 =(0.564,0.258,0.109,0.069) T , U 12 =(0.500,0.500) T , U 13 =(0.833,0.167) T , and the judgment matrices of the secondary evaluation indicators all meet the consistency test.

[0092] After calculating the weight vectors of each level of evaluation indicators according to the judgment matrix, the total weight of the evaluation indicators is finally obtained according to the hierarchical relationship as shown in Table 4. From Table 4, we can get the weight vector of the secondary evaluation indicators for the target layer Y = (0.274, 0.125, 0.053, 0.033, 0.211, 0.212, 0.077, 0.015) T .

[0093] Table 4 Evaluation index weight table

[0094]

[0095]

[0096] (3) Use fuzzy comprehensive evaluation to determine the membership value of each secondary evaluation indicator and obtain the membership matrix M.

[0097] (3.1) Determine the membership matrix of the evaluation index

[0098] The membership of the evaluation index reflects the degree of influence of the index on the object itself. In order to better reflect the influence of each secondary evaluation index on the cable fire risk, each secondary evaluation index is divided into four classification ranges. According to the different evaluation indicators, the membership is 0.25, 0.50, 0.75, and 1.00 respectively. The fuzzy classification range and membership of each secondary evaluation index are listed as shown in Table 5.

[0099] Table 5 Fuzzy classification range of secondary evaluation indicators

[0100]

[0101] Among them, the grading range of the conductor temperature evaluation index refers to the ignition mechanism and pyrolysis temperature of the fire in the cable.

[0102] The insulation aging fault assessment index Δ(L0) can be calculated by formula (9) based on the actual operating life of the cable, taking into account the historical fault conditions, laying method, operating environment, service life and load factor.

[0103]

[0104] Among them, t0 is the operating years of the cable before the monitoring system was put into operation; K2 is the influencing factor of the laying method of each section of cable, and its value refers to Table 6;

[0105] Table 6 Influencing factors of laying method K2

[0106] Laying method Cable tunnel laying Cable trench laying Direct burial Pipe laying <![CDATA[Influencing factor K2]]> 1 1.1 1.2 1.3

[0107] K3 is the environmental impact factor. For cables that are in contact with soil and moisture for a long time, the value is 1.3, otherwise it is 1.

[0108] K 1j , K 4j are the historical fault influencing factors and service life influencing factors of the cable in the jth year of operation, and their values ​​refer to Table 7 and formula (10);

[0109] Table 7 Historical failure influencing factors K1

[0110]

[0111] K4=A1 exp(A2t0)(10)

[0112] Among them, A1 is the amplitude coefficient, usually A1=0.9531; A2 is the aging coefficient, usually A2=0.01917; t0 is the service life of the cable.

[0113] K Fj is the influencing factor of the annual load rate of the cable in the jth year, and its value is shown in Table 8;

[0114] Table 8 Load rate influencing factors K F

[0115] Load factor F F≤50％ 50％<F<70％ F≥70％ <![CDATA[Influencing factor K F > 1 1.2 1.4

[0116] V is the thermal aging rate of the cable at 70°C, that is, the benchmark thermal aging rate V = 1.6088 × 10 -6 .

[0117] According to the cable aging accumulation Δ(Ln), the cable aging status is divided into four levels: good, mild, moderate and severe. The grading range is shown in Table 9.

[0118] Table 9 Power cable aging status assessment level

[0119]

[0120]

[0121] The classification range of single-phase grounding fault assessment indicators refers to the single-phase grounding arc self-extinguishing judgment and fault location;

[0122] The grading ranges of other evaluation indicators are obtained based on daily experience.

[0123] According to the fuzzy classification range given in Table 5 and the monitoring data of each secondary evaluation indicator, the membership matrix M is obtained:

[0124]

[0125] Among them, M is the membership matrix of the secondary evaluation index; m i is the membership value of the i-th secondary evaluation indicator.

[0126] (4) Obtain the judgment scale Z based on the weight vector Y and the membership matrix M.

[0127] Z = 100 × Y T M(12)

[0128] Among them, Y is the weight vector of the secondary evaluation index to the target layer; M is the membership matrix of the secondary evaluation index.

[0129] (5) According to the evaluation scale Z and combined with Table 1, determine the power cable fire risk level.

[0130] Example 2

[0131] Example 2 provides a cable fire risk assessment system based on hierarchical analysis method and fuzzy comprehensive evaluation, including a processor and a memory, wherein the memory stores computer instructions, and the processor is used to execute the computer instructions stored in the memory. When the computer instructions are executed by the processor, the electronic device implements the steps of the cable fire risk assessment method based on hierarchical analysis method and fuzzy comprehensive evaluation as described above, and can achieve the same technical effect as the above method.

[0132] The memory may include computer system readable media in the form of volatile memory, such as random access memory (RAM) and / or cache memory. The device may further include other removable / non-removable, volatile / non-volatile computer system storage media. By way of example only, the memory may be used to read and write non-removable, non-volatile magnetic media (commonly referred to as a "hard drive"). A program / utility having a set (at least one) of program modules may be stored in, for example, the memory, such program modules including but not limited to an operating system, one or more application programs, other program modules, and program data, each of which or some combination may include the implementation of a network environment. The program modules typically perform the functions and / or methods in the embodiments described herein.

[0133] The processor executes various functional applications and data processing by running the programs stored in the memory, such as implementing the method provided in Embodiment 1 of the present invention.

Claims

1. A cable fire risk assessment method based on hierarchical analysis method and fuzzy comprehensive evaluation, characterized in that: include: Establish a hierarchical structure model of power cable fire risk levels: use a percentage-based evaluation scale to divide power cable fire risk levels; Analyze the ignition mechanism of internal fire of power cables and determine the evaluation indicators affecting the fire risk of power cables, wherein the evaluation indicators include several first-level evaluation indicators, and the first-level evaluation indicators include several second-level evaluation indicators; The weight vectors of evaluation indicators at each level are determined by using the hierarchical analysis method, and the total weight of each secondary evaluation indicator is determined according to the hierarchical relationship to obtain the weight vector Y; The membership value of each secondary evaluation index is determined by using fuzzy comprehensive evaluation to obtain the membership matrix M; Obtain the judgment scale Z according to the weight vector Y and the membership matrix M; The fire risk level of power cables is determined according to the evaluation scale Z.

2. The cable fire risk assessment method according to claim 1, characterized in that: The power cable fire risk level hierarchical structure model is divided into three levels, including the target level, the indicator level and the scheme level; The target layer is divided into three first-level evaluation indicators: cable operation parameters, cable faults, and cable laying; The indicator layer is divided into three categories: cable operation parameters, cable faults and cable laying, with a total of 8 secondary evaluation indicators. The cable operation parameters include 4 secondary evaluation indicators: conductor temperature, current carrying capacity, ambient temperature and ambient humidity. The cable faults include 2 secondary evaluation indicators: insulation aging fault and single-phase grounding fault. The cable laying includes 2 secondary evaluation indicators: laying method and fixing method. The scheme layer divides the power cable fire risk level into four levels. The higher the level, the greater the possibility of fire and the scope of impact.

3. The cable fire risk assessment method according to claim 2, characterized in that: The power cable fire risk level and evaluation scale are determined by the above table.

4. The cable fire risk assessment method according to claim 2, characterized in that: The weight vectors of the evaluation indicators at each level are determined by using the hierarchical analysis method, and the total weight of each secondary evaluation indicator is determined according to the hierarchical relationship to obtain the weight vector Y, including: Determine the judgment matrix A1 of the three first-level evaluation indicators of cable operation parameters, cable faults and cable laying; Determine the judgment matrix A of the four secondary evaluation indicators of conductor temperature, current carrying capacity, ambient temperature, and ambient humidity 11 ; Determine the judgment matrix A of two secondary evaluation indicators: insulation aging fault and single-phase grounding fault 12 ; Determine the judgment matrix A of the two secondary evaluation indicators of laying method and fixing method 13 ; The weight vectors of evaluation indicators at all levels are calculated according to the judgment matrix, and the consistency of the judgment matrix of evaluation indicators at all levels is tested; after the weight vectors of evaluation indicators at all levels are calculated, the total weight of each secondary evaluation indicator is finally obtained according to the hierarchical relationship, and the weight vector Y is obtained.

5. The cable fire risk assessment method according to claim 4, characterized in that: The judgment matrix A compares the evaluation indicators of the same level in pairs and quantitatively expresses the importance of one evaluation indicator over another: Among them, n is the number of indicators; a ij is a scale, which indicates the scale of index i relative to index j, and a fuzzy scale is given using the 1 to 9 scaling method.

6. The cable fire risk assessment method according to claim 5, characterized in that: Normalize the row vector of the judgment matrix A to approximate the weight vector U of the judgment matrix, including: Among them, u represents the weight, k represents the number of weights, and a kj Represents the scale of index k and index j.

7. The cable fire risk assessment method according to claim 6, characterized in that: To determine whether the obtained weight vector U is reasonable, a consistency check is required; Using consistency index C R To check the consistency of the judgment matrix A: Where m is the matrix order; R I is the average random consistency index; max is the maximum eigenvalue of the judgment matrix; When C R When <0.1, it is considered that the judgment matrix A meets the consistency test standard.

8. The cable fire risk assessment method according to claim 1, characterized in that: The fuzzy comprehensive evaluation is used to determine the membership value of each secondary evaluation index to obtain a membership matrix M, including: The evaluation indicators at each level are divided into four grading ranges. According to the different situations of the evaluation indicators, their membership degrees are 0.25, 0.50, 0.75, and 1.00 respectively; the fuzzy grading range and membership degree of each secondary evaluation indicator are listed as follows: According to the fuzzy classification range given in the table and the monitoring data of each secondary evaluation indicator, the membership matrix M is obtained: Among them, M is the membership matrix of the secondary evaluation index; m i is the membership value of the i-th secondary evaluation indicator.

9. The cable fire risk assessment method according to claim 1, characterized in that: Evaluation scale Z = 100 × Y T M.

10. A cable fire risk assessment system based on hierarchical analysis method and fuzzy comprehensive evaluation, characterized in that: The electronic device comprises a processor and a memory, wherein the memory stores computer instructions, and the processor is used to execute the computer instructions stored in the memory. When the computer instructions are executed by the processor, the electronic device implements the steps of the cable fire risk assessment method based on hierarchical analysis method and fuzzy comprehensive evaluation as described in any one of claims 1 to 9.

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