CN105069190A - Tower material strength evaluation and calculation method for providing criterion for evaluation of tower structure - Google Patents

Abstract

The invention discloses a tower material strength evaluation and calculation method for providing a criterion for evaluation of a tower structure. The method comprises the following steps of applying a rough set theory to carry out reduction on an index set; adopting a method of combining subjective weight and objective weight to determine an evaluation index weight; and applying a fuzzy mathematical method to comprehensively evaluate and calculate the actual strength of a tower material according to the established evaluation grade set in order to solve a typical uncertain problem of unclear validity of a factor that affects the tower material strength in an evolutionary process. The method can provide an important science criterion for security evaluation of the tower structure.

Description

Tower Material Strength Evaluation and Calculation Method Provide Criterion for Steel Tower Structural Evaluation

This application is a divisional application with application number: 201410423096.2, application date: 2014.8.26, and title "A method for evaluating and calculating the strength of power transmission tower materials based on rough fuzzy sets".

technical field

The invention relates to the field of structural strength evaluation and calculation of transmission poles and towers, and is particularly suitable for the structural safety evaluation of transmission poles and towers operating for a long time in complex natural environments.

Background technique

The essence of line structure safety is an evolution process of an uncertain state space, and the state evolution (transition) process is random, in which the characteristic information representing the actual mechanical strength of tower materials is inaccurate, and the validity of the influencing factors is also It is not clear, the definition and extension of the operating state are vague, and the expert knowledge for state evaluation is incomplete, so the evaluation and calculation of the strength of the line tower material is a complex problem of uncertainty.

Fuzzy comprehensive evaluation is based on fuzzy mathematics. Its basic idea is to use the principle of fuzzy transformation to transform qualitative evaluation into quantitative evaluation through the theory of membership degree, and to consider all relevant factors, and to carry out comprehensive evaluation from the lowest level of factors, and then up to The highest target layer, so as to make a relatively objective, correct, and realistic evaluation of things or objects that are restricted by various factors, and then solve practical problems with ambiguity. It has the characteristics of clear results and strong systematization, can better solve vague and difficult-to-quantify problems, and is suitable for solving various non-deterministic problems.

There are also some shortcomings in fuzzy comprehensive evaluation applied to the strength evaluation of tower materials: First, there are many factors that affect the strength of tower materials, while the traditional fuzzy comprehensive evaluation needs to calculate all evaluation indicators, so the calculation is more complicated, and may be due to various factors Severe distortion or multi-peak phenomenon caused by small weights; secondly, for the determination of index weights, due to the different evaluation standards of various experts, the final results will also be different, so the determination is relatively subjective. The invention reduces the index set based on the rough set theory, and performs comprehensive evaluation in combination with the analytic hierarchy process and the fuzzy set method after the reduction, avoiding some shortcomings of the traditional fuzzy comprehensive evaluation. The proposed method effectively solves the problem of structural safety evaluation of transmission towers operating in complex natural environments for a long time.

Contents of the invention

The purpose of the present invention is to propose a rough fuzzy set-based evaluation and calculation method for the strength of transmission towers and towers, which can directly provide necessary criteria for evaluating the safety evaluation of transmission line towers.

A method for evaluating the actual strength of transmission tower materials in long-term operation, characterized in that it includes the following steps:

Step 1: Index set reduction;

Step 2: Determine the weight of the factor set;

Step 3: determination of membership function;

Step 4: Actual strength assessment.

The index set in the step 1 is simplified, mainly for the index set composed of three major factors: meteorological zone conditions, sub-intensity damage, conductor stress and mechanical vibration, and the three types of influencing factor sets are expressed as U={U ₁ , U ₂ , U ₃ }. Among them: U ₁ ＝{u ₁₁ ,u ₁₂ ,u ₁₃ ,u ₁₄ ,u ₁₅ }, u ₁₁ is wind speed (maximum wind), u ₁₂ is atmospheric temperature (lowest temperature), u ₁₃ is annual average temperature, u ₁₄ is the thickness of ice (thickest ice), u ₁₅ is the number of days of annual thunderstorms. U ₂ = {u ₂₁ , u ₂₂ , u ₂₃ , u ₂₄ , u ₂₅ , u ₂₆ }, u ₂₁ is the running time, u ₂₂ is the number of bending repairs, u ₂₃ is the number of crack repairs, u ₂₄ is the lightning or fault current The number of damages, u ₂₅ is the number of repeated ice fatigue, and u ₂₆ is the average operating stress/maximum operating stress. U ₃ ＝{u ₃₁ ,u ₃₂ ,u ₃₃ ,u ₃₄ }, u ₃₁ is the number of wire splits, u ₃₂ is the wind direction and line angle, u ₃₃ is the roughness of the ground surface, and u ₃₄ is the amount of steel corrosion. Rough set is used to reduce the index set, and the attribute importance is defined as:

U/R={{1,7},{2,4},{3,6,8},{5}}

U/(R-{u ₁₁ })={{1,3,5,7,8},{2,4}}

U/(R-{u ₁₂ })={{1,2,4,5,7},{3,6,8}}

U/(R-{u ₁₃ })={{1,7},{2,4},{3,6,8},{5}}

U/(R-{u ₁₄ })={{1,5,7},{2,4},{3,6,8}}

U/(R-{u ₁₅ })={{1,7},{2,4},{3,6,8},{5}}

U/(R-{u ₁₃ ,u ₁₅ })={{1,7},{2,4},{3,6,8},{5}}

U/R≠U/(R-{u ₁₁ })

U/R≠U/(R-{u ₁₂ })

U/R≠U/(R-{u ₁₄ })

U/R=U/(R-{u ₁₃ })=U/(R-{u ₁₅ })=U/(R-{u ₁₃ ,u ₁₅ })

After attribute importance reduction calculation, the u ₁₃ and u ₁₅ indexes are redundant. Similarly, attribute importance reduction is performed on the sub-strength damage factor, conductor stress and mechanical vibration factor respectively, and the final evaluation index is: U= {U ₁ , U ₂ , U ₃ }, where U ₁ ={u ₁₁ ,u ₁₂ ,u ₁₄ }, U ₂ ={u ₂₁ ,u ₂₂ ,u ₂₃ ,u ₂₄ ,u ₂₅ }, U ₃ ={ u ₃₁ , u ₃₂ , u ₃₃ , u ₃₄ };

The index weight of the factor set in the said step 2 is determined, and the method of combining subjective weight and objective weight is adopted to determine the evaluation index weight, and the combination of subjective and objective influence makes the strength evaluation result more scientific. The optimal combination algorithm of RS weight and MAHP weight for strength evaluation is as follows:

w=μw ₁ +(1-μ)w ₂

Among them, μ (0<μ<1) is the weight factor, which reflects the importance of RS weight and MAHP weight in the evaluation process. w is the weight value under the combination of RS and MAHP, w ₁ is the weight value under RS, and w ₂ is the weight value under MAHP.

1) Rough set model establishment:

An information system S can be expressed as a quadruple S={U _s , R _s , V _s , f _s }. Among them, U _s is the universe (a collection of objects, U _s ={x ₁ ,x ₂ ,...,x _m }); R _s is an attribute set; V _s is a collection of attribute values; f _s is derived from U _s × Information function of R _s to V _s . set property set U _s /IND(B)＝{x ₁ x ₂ …x _m }, then the information content of B is defined as:

$\mathrm{<span\; class="notranslate">\; I</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; B</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; =</span>\underset{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{\overset{\mathrm{<span\; class="notranslate">\; m</span>}}{<span\; class="notranslate">\; \&Sigma;</span>}}\frac{<span\; class="notranslate">\; |</span>{\mathrm{<span\; class="notranslate">\; x</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; |</span>}{<span\; class="notranslate">\; |</span>{\mathrm{<span\; class="notranslate">\; u</span>}}_{\mathrm{<span\; class="notranslate">\; 1</span>}}<span\; class="notranslate">\; |</span>}<span\; class="notranslate">\; \&lsqb;</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; -</span>\frac{<span\; class="notranslate">\; |</span>{\mathrm{<span\; class="notranslate">\; x</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; |</span>}{<span\; class="notranslate">\; |</span>{\mathrm{<span\; class="notranslate">\; u</span>}}_{\mathrm{<span\; class="notranslate">\; 1</span>}}<span\; class="notranslate">\; |</span>}<span\; class="notranslate">\; \&rsqb;</span><span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; -</span>\frac{\mathrm{<span\; class="notranslate">\; 1</span>}}{{<span\; class="notranslate">\; |</span>{\mathrm{<span\; class="notranslate">\; u</span>}}_{\mathrm{<span\; class="notranslate">\; 1</span>}}<span\; class="notranslate">\; |</span>}^{\mathrm{<span\; class="notranslate">\; 2</span>}}}\underset{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{\overset{\mathrm{<span\; class="notranslate">\; m</span>}}{<span\; class="notranslate">\; \&Sigma;</span>}}{<span\; class="notranslate">\; |</span>{\mathrm{<span\; class="notranslate">\; x</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; |</span>}^{\mathrm{<span\; class="notranslate">\; 2</span>}}$

Among them, |X _i | _represents the cardinality of the equivalence class set Xi.

The size of the change in the amount of information caused by removing {b _i } in B is defined as the attribute importance of {b _i }, and the importance of {b _i } can be expressed as:

${\mathrm{<span\; class="notranslate">\; SIG</span>}}_{\mathrm{<span\; class="notranslate">\; B</span>}<span\; class="notranslate">\; -</span><span\; class="notranslate">\; \{</span>{\mathrm{<span\; class="notranslate">\; b</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; \}</span>}<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; b</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; I</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; B</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; I</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; B</span>}<span\; class="notranslate">\; -</span><span\; class="notranslate">\; \{</span>{\mathrm{<span\; class="notranslate">\; b</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; \}</span><span\; class="notranslate">\; )</span>$

The weight of attribute {b _i } is defined as:

${\mathrm{<span\; class="notranslate">\; w</span>}}_{{\mathrm{<span\; class="notranslate">\; b</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}}<span\; class="notranslate">\; =</span>\frac{{\mathrm{<span\; class="notranslate">\; SIG</span>}}_{\mathrm{<span\; class="notranslate">\; B</span>}<span\; class="notranslate">\; -</span><span\; class="notranslate">\; \{</span>{\mathrm{<span\; class="notranslate">\; b</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; \}</span>}<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; b</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; )</span>}{\underset{\mathrm{<span\; class="notranslate">\; j</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{\overset{\mathrm{<span\; class="notranslate">\; m</span>}}{<span\; class="notranslate">\; \&Sigma;</span>}}{\mathrm{<span\; class="notranslate">\; SGF</span>}}_{\mathrm{<span\; class="notranslate">\; B</span>}<span\; class="notranslate">\; -</span><span\; class="notranslate">\; \{</span>{\mathrm{<span\; class="notranslate">\; b</span>}}_{\mathrm{<span\; class="notranslate">\; j</span>}}<span\; class="notranslate">\; \}</span>}<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; b</span>}}_{\mathrm{<span\; class="notranslate">\; j</span>}}<span\; class="notranslate">\; )</span>}<span\; class="notranslate">\; =</span>\frac{\mathrm{<span\; class="notranslate">\; I</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; B</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; I</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; B</span>}<span\; class="notranslate">\; -</span><span\; class="notranslate">\; \{</span>{\mathrm{<span\; class="notranslate">\; b</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; \}</span><span\; class="notranslate">\; )</span>}{\mathrm{<span\; class="notranslate">\; m</span>}\mathrm{<span\; class="notranslate">\; I</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; B</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; -</span>\underset{\mathrm{<span\; class="notranslate">\; j</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{\overset{\mathrm{<span\; class="notranslate">\; m</span>}}{<span\; class="notranslate">\; \&Sigma;</span>}}\mathrm{<span\; class="notranslate">\; I</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; B</span>}<span\; class="notranslate">\; -</span><span\; class="notranslate">\; \{</span>{\mathrm{<span\; class="notranslate">\; b</span>}}_{\mathrm{<span\; class="notranslate">\; j</span>}}<span\; class="notranslate">\; \}</span><span\; class="notranslate">\; )</span>}$

2) MAHP weight determination:

(1) Establish a hierarchical structure through the above formula;

(2) Construct a judgment matrix;

Judgment matrix P determination method: p _i , p _j (i,j=1,2,…,n) represent the evaluation index of the same level, compare the two indexes, p _ij represents the relative importance value of p _i to p _j ;

(3) Calculate the maximum eigenvalue λ _max of the judgment matrix P and its corresponding eigenvector W, the normalization of W is the weight w of the factor of this level, and calculate the maximum eigenvalue λ _max = 3.0053 and the corresponding features from the judgment matrix P Vector W=(0.4185,0.5500,0.7228), the weight of each factor obtained by normalization is w=(0.2474,0.3252,0.4274);

(4) Consistency check. The steps of consistency check are as follows:

a) Calculate the consistency index CI, $CI=\frac{{\mathrm{\&lambda;}}_{max}-\mathrm{no}}{\mathrm{no}-1}=\frac{3.0053-3}{3-1}=0.00265;$

b) Obtain the average random consistency index RI=0.58 according to n=3 look-up table;

c) Calculate the consistency ratio (If CR<0.10, it is considered that the consistency of the judgment matrix is acceptable, otherwise the judgment matrix should be corrected appropriately):

(5) Repeat the above steps to obtain the weights of each primary index and secondary index.

The degree of membership function in the step 3 can obtain the degree of influence of certain factors on the actual strength of the tower material, and clarify the uncertain factors. The degree of membership function of the actual strength evaluation of the tower material proposed by the present invention adopts Gaussian The model is as follows:

${\mathrm{<span\; class="notranslate">\; r</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}\mathrm{<span\; class="notranslate">\; j</span>}}<span\; class="notranslate">\; =</span>\left\{\begin{array}{cc}\mathrm{<span\; class="notranslate">\; 1</span>}& \mathrm{<span\; class="notranslate">\; x</span>}<span\; class="notranslate">\; \&le;</span>{\mathrm{<span\; class="notranslate">\; d</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}\mathrm{<span\; class="notranslate">\; 1</span>}}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; x</span>}<span\; class="notranslate">\; \&Greater\; Equal;</span>{\mathrm{<span\; class="notranslate">\; d</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}\mathrm{<span\; class="notranslate">\; no</span>}}\\ \mathrm{<span\; class="notranslate">\; exp</span>}<span\; class="notranslate">\; (</span><span\; class="notranslate">\; -</span>\frac{{<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; x</span>}<span\; class="notranslate">\; -</span>{\mathrm{<span\; class="notranslate">\; d</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; j</span>}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; )</span>}<span\; class="notranslate">\; )</span>}^{\mathrm{<span\; class="notranslate">\; 2</span>}}}{\mathrm{<span\; class="notranslate">\; 2</span>}{<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; d</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}\mathrm{<span\; class="notranslate">\; j</span>}}<span\; class="notranslate">\; -</span>{\mathrm{<span\; class="notranslate">\; d</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; j</span>}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; )</span>}<span\; class="notranslate">\; )</span>}^{\mathrm{<span\; class="notranslate">\; 2</span>}}}<span\; class="notranslate">\; )</span>& {\mathrm{<span\; class="notranslate">\; d</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; j</span>}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; )</span>}<span\; class="notranslate">\; <</span>\mathrm{<span\; class="notranslate">\; x</span>}<span\; class="notranslate">\; <</span>{\mathrm{<span\; class="notranslate">\; d</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}\mathrm{<span\; class="notranslate">\; j</span>}}\\ \mathrm{<span\; class="notranslate">\; exp</span>}<span\; class="notranslate">\; (</span><span\; class="notranslate">\; -</span>\frac{{<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; d</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}\mathrm{<span\; class="notranslate">\; j</span>}}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; x</span>}<span\; class="notranslate">\; )</span>}^{\mathrm{<span\; class="notranslate">\; 2</span>}}}{\mathrm{<span\; class="notranslate">\; 2</span>}{<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; d</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; j</span>}<span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; )</span>}<span\; class="notranslate">\; -</span>{\mathrm{<span\; class="notranslate">\; d</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}\mathrm{<span\; class="notranslate">\; j</span>}}<span\; class="notranslate">\; )</span>}^{\mathrm{<span\; class="notranslate">\; 2</span>}}}<span\; class="notranslate">\; )</span>& {\mathrm{<span\; class="notranslate">\; d</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}\mathrm{<span\; class="notranslate">\; j</span>}}<span\; class="notranslate">\; <</span>\mathrm{<span\; class="notranslate">\; x</span>}<span\; class="notranslate">\; <</span>{\mathrm{<span\; class="notranslate">\; d</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; j</span>}<span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; )</span>}\\ \mathrm{<span\; class="notranslate">\; 0</span>}& \mathrm{<span\; class="notranslate">\; x</span>}<span\; class="notranslate">\; \&le;</span>{\mathrm{<span\; class="notranslate">\; d</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; j</span>}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; )</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; x</span>}<span\; class="notranslate">\; \&Greater\; Equal;</span>{\mathrm{<span\; class="notranslate">\; d</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; j</span>}<span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; )</span>}\end{array}\right.$

In the formula: r _ij represents the membership degree of the i-th factor to the j-level evaluation grade; x _i represents the actual occurrence value of the i-th factor; d _ij represents the standard value of the i-th factor in the j-level evaluation standard; i= 1,2,...,m; j=2,3,...,n-1.

In the actual strength evaluation in the step 4, according to the established evaluation grade set, the actual strength value of the tower material calculated by using the fuzzy comprehensive evaluation, the fuzzy comprehensive evaluation method of the actual strength of the tower material proposed by the present invention is as follows:

a) First-level fuzzy comprehensive evaluation:

The evaluation fuzzy relationship R _i of factor U _i is obtained from the membership function, and r _kj(i) represents the membership degree of the kth second-level factor in the factor U _i to the j-th-level evaluation standard, and the first-level fuzzy relationship is performed on each factor Comprehensive evaluation, the evaluation result is recorded as B _i , namely:

Wherein: b _i1 =a _i1 ·r _11(i) +a _i2 ·r _21(i) +...+a _im ·r _m1(i) .

Then normalize B _i to get B' _i , record B' _i =[b' _i1 b' _i2 …b' _in ], where:

b) Second-level fuzzy comprehensive evaluation:

On the basis of the first-level fuzzy comprehensive evaluation, the evaluation vector B' _i is synthesized into R, where R is the fuzzy relationship matrix from the factor set U to the evaluation set Y, that is, the comprehensive evaluation transformation matrix. Carry out a second-level fuzzy comprehensive evaluation, denoted as B, namely:

Wherein: b _i =A ₁ ·r _1i +A ₂ ·r _2i +...+A _m ·r _mi .

Then B is normalized to obtain the final fuzzy evaluation vector B', B' is recorded as B'=[b' ₁ b' ₂ ... b' _n ], where: $b_i^{\&prime;}=b_i/\underset{j=1}{\overset{\mathrm{no}}{\&Sigma;}}{b}_j,(i=1,2,...,\mathrm{no}).$

Technical effect of the present invention:

Compared with the traditional method, this application not only simplifies the indicators, but also makes the weight distribution of each factor more reasonable, the evaluation results are clearly differentiated, and the results are more realistic. It adopts the method of combining subjective weight and objective weight , so that the evaluation indicators can reflect the evaluation results more effectively.

Description of drawings

Figure 1 is a schematic diagram of the actual strength calculation method for long-term operation transmission line tower materials.

Figure 2 is a graph of actual strength calculation results.

Fig. 3 is a graph showing strength evaluation results.

Detailed ways

A kind of actual strength calculation method of long-term operation transmission line tower material of the present invention is as shown in Figure 1, and this method mainly comprises the following steps:

(1) Basic index system establishment:

Through research in power supply enterprises, according to the evaluation indicators of various factors affecting the strength of tower materials given by relevant technical personnel, combined with a large number of actual data, the basic index system for the evaluation of the strength of 110kV voltage level line iron towers is obtained. The basic index system is mainly composed of three major factors: meteorological region conditions, sub-intensity damage, wire stress and mechanical vibration.

The factor set is: U={U ₁ , U ₂ , U ₃ }, where U ₁ ={u ₁₁ ,u ₁₂ ,u ₁₃ ,u ₁₄ ,u ₁₅ }, U ₂ ={u ₂₁ ,u ₂₂ ,u ₂₃ ,u ₂₄ ,u ₂₅ ,u ₂₆ }, U ₃ ={u ₃₁ ,u ₃₂ ,u ₃₃ ,u ₃₄ }.

1) Weather zone condition U ₁ :

Wind speed (maximum wind) u ₁₁ , atmospheric temperature (lowest temperature) u ₁₂ , annual average temperature u ₁₃ , ice thickness (thickest ice cover) u ₁₄ , annual number of thunderstorm days u ₁₅ .

2) Sub-intensity damage U ₂ :

Running time u ₂₁ , bending repair times u ₂₂ , crack repair times u ₂₃ , lightning or fault current damage times u ₂₄ , repeated ice fatigue times u ₂₅ , average operating stress/maximum operating stress u ₂₆ .

3) Wire stress and mechanical vibration U ₃ :

The wire splitting number u ₃₁ , the wind direction and line angle u ₃₂ , the roughness of the ground surface u ₃₃ , and the amount of steel corrosion u ₃₄ .

Since the values of each factor are different and complex, for the convenience of subsequent evaluation and calculation, the basic indicators are standardized here. According to the degree of influence of evaluation factors on the strength of tower materials, it is divided into five grades, namely Ⅰ, Ⅱ, Ⅲ, Ⅳ, and Ⅴ. Grade Ⅰ means that it has little influence on the actual strength of tower materials, and grade Ⅱ means that it has little influence on the actual strength of tower materials. Smaller, grade Ⅲ means that it has a moderate influence on the actual strength of the tower material, grade Ⅳ means that it has a greater influence on the actual strength of the tower material, and grade Ⅴ means that it has a great influence on the actual strength of the tower material. The meteorological area is taken as an example to establish the quantitative standard for the evaluation of the meteorological area.

The evaluation criteria of meteorological region conditions are established from the national typical meteorological region database, and the quantitative standard of meteorological region evaluation is obtained, as shown in Table 1.

Table 1 Quantitative standards for meteorological region evaluation

(2) Rough set for attribute reduction:

There are many factors in the basic index system, and there may be redundant factors. On the basis of not affecting the evaluation results, in order not to complicate the evaluation process, the basic index system will be reduced. The sample test data is shown in Table 2. In order to facilitate the calculation and simplification, the index value of II is used as the threshold value of each initial evaluation index. If the index value of II is satisfied, it is 1, otherwise it is 0, and the data in Table 2 and Table 3 are used. By discretizing the data, the initial evaluation index information can be obtained, as shown in Table 3.

Table 2 sample test data

Table 3 Discretization of sample data

According to the rough set theory, attribute importance reduction is performed on Table 4:

U/R={{1,7},{2,4},{3,6,8},{5}}

U/(R-{u ₁₁ })={{1,3,5,7,8},{2,4}}

U/(R-{u ₁₂ })={{1,2,4,5,7},{3,6,8}}

U/(R-{u ₁₃ })={{1,7},{2,4},{3,6,8},{5}}

U/(R-{u ₁₄ })={{1,5,7},{2,4},{3,6,8}}

U/(R-{u ₁₅ })={{1,7},{2,4},{3,6,8},{5}}

U/(R-{u ₁₃ ,u ₁₅ })={{1,7},{2,4},{3,6,8},{5}}

U/R≠U/(R-{u ₁₁ })

U/R≠U/(R-{u ₁₂ })

U/R≠U/(R-{u ₁₄ })

U/R=U/(R-{u ₁₃ })=U/(R-{u ₁₅ })=U/(R-{u ₁₃ ,u ₁₅ })

After attribute importance reduction, the calculation shows that the indicators u ₁₃ and u ₁₅ are redundant. Similarly, the attribute importance reduction is performed on the sub-strength damage factor, conductor stress and mechanical vibration factor respectively, and the final evaluation index is: U={U ₁ ,U ₂ ,U ₃ }, where U ₁ ={u ₁₁ , u ₁₂ , u ₁₄ }, U ₂ ={u ₂₁ , u ₂₂ , u ₂₃ , u ₂₄ , u ₂₅ }, U ₃ ={u ₃₁ , u ₃₂ , u ₃₃ , u ₃₄ }.

(3) Weight determination:

In fuzzy comprehensive evaluation, whether the weight is reasonable or not plays a vital role in the evaluation result. At present, the weight determination methods mainly include subjective weighting method and objective weighting method. The former is mainly determined by experts based on experience to determine the weight, so it has strong subjective randomness and poor objectivity; the latter mainly determines the weight based on the relationship between the original data and does not rely on human subjective judgment, but The calculation is highly affected by sample changes. This paper combines the advantages of the two methods and adopts the combined weighting method, that is, the weight is determined by combining the rough set method and the improved analytic hierarchy process, so that the evaluation result is closer to reality.

1) Rough set method:

An information system S can be expressed as a quadruple S={U _s , R _s , V _s , f _s }. Among them, U _s is the universe (a collection of objects, U _s ={x ₁ ,x ₂ ,...,x _m }); R _s is an attribute set; V _s is a collection of attribute values; f _s is derived from U _s × Information function of R _s to V _s . set property set U _s /IND(B)＝{x ₁ x ₂ …x _m }, then the information content of B is defined as:

$\mathrm{<span\; class="notranslate">\; I</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; B</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; =</span>\underset{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{\overset{\mathrm{<span\; class="notranslate">\; m</span>}}{<span\; class="notranslate">\; \&Sigma;</span>}}\frac{<span\; class="notranslate">\; |</span>{\mathrm{<span\; class="notranslate">\; x</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; |</span>}{<span\; class="notranslate">\; |</span>{\mathrm{<span\; class="notranslate">\; u</span>}}_{\mathrm{<span\; class="notranslate">\; 1</span>}}<span\; class="notranslate">\; |</span>}<span\; class="notranslate">\; \&lsqb;</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; -</span>\frac{<span\; class="notranslate">\; |</span>{\mathrm{<span\; class="notranslate">\; x</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; |</span>}{<span\; class="notranslate">\; |</span>{\mathrm{<span\; class="notranslate">\; u</span>}}_{\mathrm{<span\; class="notranslate">\; 1</span>}}<span\; class="notranslate">\; |</span>}<span\; class="notranslate">\; \&rsqb;</span><span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; -</span>\frac{\mathrm{<span\; class="notranslate">\; 1</span>}}{<span\; class="notranslate">\; |</span>{\mathrm{<span\; class="notranslate">\; u</span>}}_{\mathrm{<span\; class="notranslate">\; 1</span>}}{<span\; class="notranslate">\; |</span>}^{\mathrm{<span\; class="notranslate">\; 2</span>}}}\underset{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{\overset{\mathrm{<span\; class="notranslate">\; m</span>}}{<span\; class="notranslate">\; \&Sigma;</span>}}<span\; class="notranslate">\; |</span>{\mathrm{<span\; class="notranslate">\; x</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}{<span\; class="notranslate">\; |</span>}^{\mathrm{<span\; class="notranslate">\; 2</span>}}$

Among them, |X _i | _represents the cardinality of the equivalence class set Xi.

The size of the change in the amount of information caused by removing {b _i } in B is defined as the attribute importance of {b _i }, and the importance of {b _i } can be expressed as:

${\mathrm{<span\; class="notranslate">\; SIG</span>}}_{\mathrm{<span\; class="notranslate">\; B</span>}<span\; class="notranslate">\; -</span><span\; class="notranslate">\; \{</span>{\mathrm{<span\; class="notranslate">\; b</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; \}</span>}<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; b</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; I</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; B</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; I</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; B</span>}<span\; class="notranslate">\; -</span><span\; class="notranslate">\; \{</span>{\mathrm{<span\; class="notranslate">\; b</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; \}</span><span\; class="notranslate">\; )</span>$

The weight of attribute {b _i } is defined as:

${\mathrm{<span\; class="notranslate">\; w</span>}}_{{\mathrm{<span\; class="notranslate">\; b</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}}<span\; class="notranslate">\; =</span>\frac{{\mathrm{<span\; class="notranslate">\; SIG</span>}}_{\mathrm{<span\; class="notranslate">\; B</span>}<span\; class="notranslate">\; -</span><span\; class="notranslate">\; \{</span>{\mathrm{<span\; class="notranslate">\; b</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; \}</span>}<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; b</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; )</span>}{\underset{\mathrm{<span\; class="notranslate">\; j</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{\overset{\mathrm{<span\; class="notranslate">\; m</span>}}{<span\; class="notranslate">\; \&Sigma;</span>}}{\mathrm{<span\; class="notranslate">\; SGF</span>}}_{\mathrm{<span\; class="notranslate">\; B</span>}<span\; class="notranslate">\; -</span><span\; class="notranslate">\; \{</span>{\mathrm{<span\; class="notranslate">\; b</span>}}_{\mathrm{<span\; class="notranslate">\; j</span>}}<span\; class="notranslate">\; \}</span>}<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; b</span>}}_{\mathrm{<span\; class="notranslate">\; j</span>}}<span\; class="notranslate">\; )</span>}<span\; class="notranslate">\; =</span>\frac{\mathrm{<span\; class="notranslate">\; I</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; B</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; I</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; B</span>}<span\; class="notranslate">\; -</span><span\; class="notranslate">\; \{</span>{\mathrm{<span\; class="notranslate">\; b</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; \}</span><span\; class="notranslate">\; )</span>}{\mathrm{<span\; class="notranslate">\; m</span>}\mathrm{<span\; class="notranslate">\; I</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; B</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; -</span>\underset{\mathrm{<span\; class="notranslate">\; j</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{\overset{\mathrm{<span\; class="notranslate">\; m</span>}}{<span\; class="notranslate">\; \&Sigma;</span>}}\mathrm{<span\; class="notranslate">\; I</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; B</span>}<span\; class="notranslate">\; -</span><span\; class="notranslate">\; \{</span>{\mathrm{<span\; class="notranslate">\; b</span>}}_{\mathrm{<span\; class="notranslate">\; j</span>}}<span\; class="notranslate">\; \}</span><span\; class="notranslate">\; )</span>}$

The attribute importance has been reduced for the indicator set, and then according to the formula, the weights of the primary and secondary indicators are obtained respectively, as shown in Table 5.

2) The present invention improves AHP:

1. Build a hierarchy. The evaluation index system and evaluation grades have been established.

2. Construct a judgment matrix. Constructing a judgment matrix is a process of quantifying people's comparative judgments, so it is greatly influenced by human subjective factors, and the determination of the judgment matrix is the basis for determining the weights, so constructing a judgment matrix is a very important step in the AHP. The traditional analytic hierarchy process uses a scale of 1-9. Due to its limitations, the results may not match the actual results.

Therefore, the present invention improves the analytic hierarchy process and proposes a new scaling method, which greatly reduces the subjective arbitrariness in the traditional weight determination process.

Judgment matrix P determination method: p _i , p _j (i, j=1, 2, ..., n) represent the evaluation index of the same level, compare the two indexes, p _ij represents the relative importance value of p _i to p _j , the value rules of p _ij are shown in Table 4.

Table 4p _ij value rules

Taking the secondary index of meteorological area as an example, establish a judgment matrix $P=\left[\begin{array}{ccc}\frac{10}{10}& \frac{9}{11}& \frac{7}{13}\\ \frac{11}{9}& \frac{10}{10}& \frac{9}{11}\\ \frac{13}{7}& \frac{11}{9}& \frac{10}{10}\end{array}\right].$

3. Obtain the maximum eigenvalue λ _max of the judgment matrix P and its corresponding eigenvector W. The normalization of W is the weight w of the factor at this level. Calculate the maximum eigenvalue λ _max = 3.0053 and the corresponding eigenvector from the judgment matrix P W=(0.4185,0.5500,0.7228), the weight of each factor obtained by normalization is w=(0.2474,0.3252,0.4274);

4. Consistency check. The steps of consistency check are as follows:

5. First calculate the consistency index CI, $CI=\frac{{\mathrm{\&lambda;}}_{max}-\mathrm{no}}{\mathrm{no}-1}=\frac{3.0053-3}{3-1}=0.00265;$

a) Then look up the table according to n=3 to get the average random consistency index RI=0.58;

b) Finally calculate the consistency ratio (If CR<0.10, it is considered that the consistency of the judgment matrix is acceptable, otherwise the judgment matrix should be corrected appropriately).

6. Repeat the above steps to obtain the weights of each first-level indicator and second-level indicator, see Table 5.

The general calculation formulas for weights and weight optimization combinations are given as follows:

w=μw ₁ +(1-μ)w ₂

Among them, μ(0<μ<1) is the weight factor, reflecting the importance of RS weight and MAHP weight in the evaluation process, w is the weight value under the combination of RS and MAHP, w ₁ is the weight value under RS, w ₂ is Weight value under MAHP. According to different situations in different regions, it can be determined by the evaluators themselves. In this paper, 0.5 is taken, and the weights of each indicator are shown in Table 5.

Table 5 Weight of indicators

(4) The present invention determines the evaluation level set:

An evaluation level set is usually represented by Y={y ₁ ,y ₂ ,...,y _n }, where y _i (i=1, 2,...,n) represents n possible different levels. In this paper, n=5, that is, comprehensive consideration is divided into (very good, good, medium, poor, very poor) 5 grades, and the corresponding value of each grade is {95%, 85%, 75%, 65%, 55%}.

(5) Determination of membership function:

In the evaluation and calculation of tower material strength, the membership function is very important to the evaluation results. Through the membership function, the degree of influence of certain factors on the strength of the tower material can be obtained, and the uncertain factors are clarified. Through the analysis and research on the characteristics of tower materials in operation, and at the same time considering the relationship between the actual occurrence value of a certain factor and the standard values of this factor in the index quantification standard, the Gaussian function method is used to abstract the strength evaluation of tower materials in general The membership function model of the membership function adopts the Gaussian model as follows:

${\mathrm{<span\; class="notranslate">\; r</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}\mathrm{<span\; class="notranslate">\; j</span>}}<span\; class="notranslate">\; =</span>\left\{\begin{array}{cc}\mathrm{<span\; class="notranslate">\; 1</span>}& \mathrm{<span\; class="notranslate">\; x</span>}<span\; class="notranslate">\; \&le;</span>{\mathrm{<span\; class="notranslate">\; d</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}\mathrm{<span\; class="notranslate">\; 1</span>}}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; x</span>}<span\; class="notranslate">\; \&Greater\; Equal;</span>{\mathrm{<span\; class="notranslate">\; d</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}\mathrm{<span\; class="notranslate">\; no</span>}}\\ \mathrm{<span\; class="notranslate">\; exp</span>}<span\; class="notranslate">\; (</span><span\; class="notranslate">\; -</span>\frac{{<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; x</span>}<span\; class="notranslate">\; -</span>{\mathrm{<span\; class="notranslate">\; d</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; j</span>}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; )</span>}<span\; class="notranslate">\; )</span>}^{\mathrm{<span\; class="notranslate">\; 2</span>}}}{\mathrm{<span\; class="notranslate">\; 2</span>}{<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; d</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}\mathrm{<span\; class="notranslate">\; j</span>}}<span\; class="notranslate">\; -</span>{\mathrm{<span\; class="notranslate">\; d</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; j</span>}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; )</span>}<span\; class="notranslate">\; )</span>}^{\mathrm{<span\; class="notranslate">\; 2</span>}}}<span\; class="notranslate">\; )</span>& {\mathrm{<span\; class="notranslate">\; d</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; j</span>}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; )</span>}<span\; class="notranslate">\; <</span>\mathrm{<span\; class="notranslate">\; x</span>}<span\; class="notranslate">\; <</span>{\mathrm{<span\; class="notranslate">\; d</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}\mathrm{<span\; class="notranslate">\; j</span>}}\\ \mathrm{<span\; class="notranslate">\; exp</span>}<span\; class="notranslate">\; (</span><span\; class="notranslate">\; -</span>\frac{{<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; d</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}\mathrm{<span\; class="notranslate">\; j</span>}}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; x</span>}<span\; class="notranslate">\; )</span>}^{\mathrm{<span\; class="notranslate">\; 2</span>}}}{\mathrm{<span\; class="notranslate">\; 2</span>}{<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; d</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; j</span>}<span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; )</span>}<span\; class="notranslate">\; -</span>{\mathrm{<span\; class="notranslate">\; d</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}\mathrm{<span\; class="notranslate">\; j</span>}}<span\; class="notranslate">\; )</span>}^{\mathrm{<span\; class="notranslate">\; 2</span>}}}<span\; class="notranslate">\; )</span>& {\mathrm{<span\; class="notranslate">\; d</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}\mathrm{<span\; class="notranslate">\; j</span>}}<span\; class="notranslate">\; <</span>\mathrm{<span\; class="notranslate">\; x</span>}<span\; class="notranslate">\; <</span>{\mathrm{<span\; class="notranslate">\; d</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; j</span>}<span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; )</span>}\\ \mathrm{<span\; class="notranslate">\; 0</span>}& \mathrm{<span\; class="notranslate">\; x</span>}<span\; class="notranslate">\; \&le;</span>{\mathrm{<span\; class="notranslate">\; d</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; j</span>}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; )</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; x</span>}<span\; class="notranslate">\; \&Greater\; Equal;</span>{\mathrm{<span\; class="notranslate">\; d</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; j</span>}<span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; )</span>}\end{array}\right.$

In the formula: r _ij represents the membership degree of the i-th factor to the j-level evaluation standard;

x _i represents the actual occurrence value of the i factor;

d _ij represents the standard value of the i-th factor at the j-level evaluation standard;

Wherein i=1, 2, ..., m; j = 2, 3, ..., n-1.

(6) Fuzzy comprehensive evaluation:

1) One-level fuzzy comprehensive evaluation of the present invention:

The evaluation fuzzy relationship R _i of factor U _i is obtained from the membership degree function, and r _kj(i) represents the membership degree of the kth factor of the second level in the factor U _i to the jth level evaluation standard. A first-level fuzzy comprehensive evaluation is carried out for each factor, and the evaluation result is recorded as B _i , namely:

where b _i1 =a _i1 ·r _11(i) +a _i2 ·r _21(i) + . . . + a _im ·r _m1(i) .

Then normalize B _i to get B' _i , record B' _i =[b' _i1 b' _i2 …b' _in ], where

2) Secondary fuzzy comprehensive evaluation of the present invention:

On the basis of the first-level fuzzy comprehensive evaluation, the evaluation vector B' _i is synthesized into R, where R is the fuzzy relationship matrix from the factor set U to the evaluation set Y, that is, the comprehensive evaluation transformation matrix. Carry out a second-level fuzzy comprehensive evaluation, denoted as B, namely:

where b _i =A ₁ ·r _1i +A ₂ ·r _2i + . . . +A _m ·r _mi .

Then B is normalized to get the final fuzzy evaluation vector B', B' is recorded as B'=[b' ₁ b' ₂ ... b' _n ], where $b_i^{\&prime;}=b_i/\underset{j=1}{\overset{\mathrm{no}}{\&Sigma;}}{b}_j,(i=1,2,...,\mathrm{no}).$

The evaluation grade is obtained by using the principle of maximum membership degree, and its corresponding value is α. The ideal breaking force T _p of the tower material is obtained by looking up the table, and the comprehensive breaking force of the tower material is usually taken as 95% of the ideal breaking force, that is, 95% T _p . Therefore, the comprehensive breaking force of the tower material strength T' _p = α·95% T _p .

Under complex working conditions, the terminal tension is calculated as T _t , and T _t is compared with T' _p to obtain the final result of the tower's structural safety. If T _t >T' _p , it means that the tower is unsafe in this case.

Example:

For the tension-resistant iron tower of a certain electric power company, the detailed meteorological data of the past five years and the detailed operation data of the iron tower put into operation were retrieved. After re-inversion and simulation by this system, the system safety evaluation is in a dangerous state of failure. In addition, for some other serious defect situations, taking the actual tower material breakage as an example, the system part (<10%) gives a faulty dangerous state of disconnection, and the system safety evaluation is slightly conservative. The line strength and evaluation results are shown in Figure 2-3.

The above is only a preferred embodiment of the present invention, and is not intended to limit the present invention in any other form, and any modification or equivalent change made according to the technical essence of the present invention still belongs to the scope of protection required by the present invention .

Claims (1)

1., for iron tower structure assessment provides tower material strength assessment and computing method for criterion, it is characterized in that: comprise the steps:

Step 1: index set yojan;

Step 2: set of factors weight is determined;

Step 3: subordinate function is determined;

Step 4: actual strength is assessed;

The concrete grammar of described step 2:

Adopt Evaluation formula, the mode that namely weight adopts rough set method and improved AHP method to combine is determined, makes evaluation result more close to actual;

1) rough set method:

An infosystem S can be expressed as a four-tuple S={U _s, R _s, V _s, f _s; Wherein, U _suniverse (set that object is formed, U _s={ x ₁, x ₂..., x _m); R _sit is property set; V _sit is the set of property value; f _sfrom U _s× R _sto V _sinformation function; If community set u _s/ IND (B)={ x ₁x ₂x _m, then the quantity of information of B is defined as:

$I\left(B\right)=\underset{i=1}{\overset{m}{\&Sigma;}}\frac{\left|X_i\right|}{\left|U_1\right|}\&lsqb;1-\frac{\left|X_i\right|}{\left|U_1\right|}\&rsqb;=1-\frac{1}{|U_1{|}^2}\underset{i=1}{\overset{m}{\&Sigma;}}|X_i{|}^2$

Wherein, | X _i| represent equivalence class set X _iradix;

Removing { b in B _ithe size definition of caused afterwards quantity of information change is { b _iattribute Significance, { b _iimportance degree can be expressed as:

${\mathrm{SIG}}_{B-\left\{b_i\right\}}\left(b_i\right)=I\left(B\right)-I(B-\{b_i\left\}\right)$

Attribute { b _iweight definition be:

$w_{b_i}=\frac{{\mathrm{SIG}}_{B-\left\{b_i\right\}}\left(b_i\right)}{\underset{j=1}{\overset{m}{\&Sigma;}}{\mathrm{SGF}}_{B-\left\{b_j\right\}}\left(b_j\right)}=\frac{I\left(B\right)-I\left(B-\left\{b_i\right\}\right)}{mI\left(B\right)-\underset{j=1}{\overset{m}{\&Sigma;}}I\left(B-\left\{b_j\right\}\right)}$

Attribute Significance yojan is carried out to index set above, then according to formula, just tried to achieve the weight of I and II index respectively;

2) improved AHP method of the present invention:

A. hierarchical structure is set up: assessment indicator system and opinion rating are set up all;

B. Judgement Matricies:

Judgment matrix P defining method: p _i, p _j(i, j=1,2 ..., n) represent same level evaluation index, two indices compared, p _ijrepresent p _ito p _jrelative importance numerical value, p _ijvalue rule:

For the two-level index of meteorologic district, set up judgment matrix $P=\left[\begin{array}{ccc}\frac{10}{10}& \frac{9}{11}& \frac{7}{13}\\ \frac{11}{9}& \frac{10}{10}& \frac{9}{11}\\ \frac{13}{7}& \frac{11}{9}& \frac{10}{10}\end{array}\right];$

C. the eigenvalue of maximum λ of judgment matrix P is obtained _maxand characteristic of correspondence vector W, W normalization is the weight w of this level factor, calculates eigenvalue of maximum λ by judgment matrix P _max=3.0053 and individual features vector W=(0.4185,0.5500,0.7228), the weight that normalization obtains each factor is w=(0.2474,0.3252,0.4274);

D. consistency desired result; The step of consistency desired result is as follows:

E. first coincident indicator CI is calculated, $CI=\frac{{\mathrm{\&lambda;}}_{max}-n}{n-1}=\frac{3.0053-3}{3-1}=0.00265;$

A) then foundation n=3 tables look-up and obtains Aver-age Random Consistency Index RI=0.58;

B) finally consistency ration is calculated if during CR<0.10, think that the consistance of judgment matrix is acceptable, otherwise reply judgment matrix does suitable correction;

F. the weight that above-mentioned steps obtains each first class index and two-level index is repeated;

The general computing formula providing weight and weight optimization combination is as follows:

w＝μw ₁+(1-μ)w ₂

Wherein, μ (0< μ <1) is weight factor, the significance level of RS weight and MAHP weight in reflected appraisal process, and w is the weighted value under RS and MAHP combination, w ₁for the weighted value under RS, w ₂for the weighted value under MAHP;

The weight of index