A power transmission line audible noise evaluation method and system based on a two-dimensional cloud model

Abstract

The application belongs to the technical field of electromagnetic environment of power transmission line, and provides a power transmission line audible noise evaluation method and system based on a two-dimensional cloud model; a two-dimensional cloud model is established according to relevant parameter data and environmental noise data; the audible noise evaluation result of the power transmission line is obtained by comprehensively evaluating the two-dimensional cloud model; wherein the comprehensive evaluation of the two-dimensional cloud model comprises: the subjective weight, the objective weight and the independence weight of the evaluation index are respectively calculated, the combination weight matrix is obtained based on the weight relative change rate criterion self-learning weight model; the failure consequence and the failure probability of the index are taken as two groups of basic variables of the two-dimensional cloud, the combination weight matrix is combined, the risk cloud and the comprehensive cloud of each level are calculated; the risk level of the audible noise evaluation is determined by the proximity calculation according to the risk cloud and the comprehensive cloud; the relationship between the power transmission line parameters and the environmental noise can be more accurately described by using the two-dimensional cloud model evaluation system, and the accuracy of the evaluation result is improved.

Description

Technical Field

[0001] This invention belongs to the field of electromagnetic environment technology for power transmission lines, and particularly relates to a method and system for evaluating audible noise of power transmission lines based on a two-dimensional cloud model. Background Technology

[0002] The assessment of audible noise in power transmission lines is an important research area in the power industry.

[0003] The inventors discovered that traditional evaluation methods are mainly based on statistical analysis and subjective judgment, which have problems such as inaccurate evaluation results and strong subjectivity. In some evaluation methods, subjective and objective weights are combined to obtain combined weights before evaluating the relevant objects. However, if this method is used to evaluate the audible noise of transmission lines, the existing methods for obtaining combined weights have poor dynamic adaptability because the subjective and objective weights have a certain degree of dynamism, which affects the accuracy of the final evaluation results. Summary of the Invention

[0004] To address the aforementioned issues, this invention proposes a method and system for evaluating audible noise in transmission lines based on a two-dimensional cloud model. This invention employs a two-dimensional cloud model evaluation system, which can more accurately describe the relationship between transmission line parameters and environmental noise, thus improving the accuracy of the evaluation results. Furthermore, by using a self-learning weight model based on the relative rate of change of weights as a criterion, a combined weight matrix is ​​obtained, avoiding the one-sidedness of combined weight calculation and enhancing the dynamic adaptability of the evaluation method.

[0005] To achieve the above objectives, the present invention is implemented through the following technical solution:

[0006] In a first aspect, the present invention provides a method for evaluating audible noise in transmission lines based on a two-dimensional cloud model, comprising:

[0007] Obtain relevant parameter data of transmission lines, as well as environmental noise data;

[0008] A two-dimensional cloud model is established based on relevant parameter data and environmental noise data.

[0009] Fuzzy mathematics theory is used to comprehensively evaluate a two-dimensional cloud model to obtain the audible noise assessment results for transmission lines. The comprehensive evaluation of the two-dimensional cloud model includes: calculating the subjective weight, objective weight, and independence weight of the evaluation indicators; obtaining a combined weight matrix based on a self-learning weight model using the relative rate of change of weights criterion; obtaining standard clouds for each level according to the level quantification interval; using the failure consequences and failure probabilities of the indicators as two sets of basic variables for the two-dimensional cloud; and combining the combined weight matrix to calculate the risk clouds and comprehensive clouds at each level; and determining the risk level of the audible noise assessment based on the risk clouds and comprehensive clouds through similarity calculation.

[0010] Furthermore, the relevant parameter data for the transmission line includes the line structure and current load; the environmental noise data includes the noise spectrum and noise intensity.

[0011] Furthermore, the subjective weight, objective weight, and independence weight of the evaluation indicators are calculated using the interval estimation AHP method, entropy weight method, and independence weight method, respectively; and the weights of the indicators from different weighting methods are combined using a single weighting method.

[0012] Furthermore, the relative change rate refers to the slope generated by the relative proportion between two adjacent indicators. The relative change rate of weights under different weighting methods is used as a reference benchmark for the relative change rate of combined weights of indicators, which is used to limit the degree of change of combined weights of each indicator. The least squares method is used to guide the relative change rate of combined weights of indicators to approach the relative change rate of weights under different weighting methods, in order to find the optimal combined weight value.

[0013] The self-learning weights of the indicators are:

[0014]

[0015] in, Let g be the self-learning weight of the g-th indicator; The weight of the g-th index for the d-th weighting method; β gd denoted as , where is the combination coefficient of the g-th indicator in the d-th weighting method; u is the total number of weighting methods; n is the total number of indicators; and D is the distance between indicators.

[0016] Furthermore, by comparing the weights of each indicator using different weighting methods, the indicators are divided into increasing and decreasing types.

[0017] Furthermore, by comparing the weights of different weighting methods for each indicator, the indicators are divided into increasing and uncertain types.

[0018] Furthermore, by comparing the weights of each indicator using different weighting methods, the indicators are divided into decreasing and uncertain types.

[0019] Furthermore, by comparing the weights of each indicator using different weighting methods, the indicators are categorized into increasing, decreasing, and uncertain types.

[0020] Furthermore, the probability of accidents and the consequences of failure at each level of indicators are quantified, thereby obtaining the failure probability risk cloud and failure consequence risk cloud for each indicator; the two-dimensional cloud model is as follows:

[0021]

[0022] Among them, (x i ,y i ) and (P xi ,Pyi ) represents the output value, where i and j are constants; Ex x and Ex y Expected value; En x and En y He is the standard deviation; x and He y For the hyperentropy of the risk cloud; μ i is the membership function of the two-dimensional cloud model; F is a two-dimensional random function that follows a normal distribution.

[0023] Furthermore, based on the weights of each level of indicators obtained by the difference coefficient method, the risk cloud of secondary indicators is transformed into the risk cloud of primary indicators, thereby obtaining the comprehensive risk cloud of the evaluation object:

[0024]

[0025] Where C represents the digital characteristics of the comprehensive risk cloud; w represents the elements in the combined weight matrix; Ex, En, and He represent the expected value, entropy, and hyperentropy of the comprehensive cloud, respectively; and the risk level is determined using a two-dimensional cloud similarity calculation method.

[0026]

[0027] Where L represents the similarity. Ex, Ex' and These are the expected values ​​of the standard cloud of failure probability, the actual cloud of failure probability, the standard cloud of failure consequences, and the actual cloud of failure consequences, respectively.

[0028] Secondly, the present invention also provides a transmission line audible noise assessment system based on a two-dimensional cloud model, comprising:

[0029] The data acquisition module is configured to acquire relevant parameter data of the transmission line and environmental noise data;

[0030] The model building module is configured to: build a two-dimensional cloud model based on relevant parameter data and environmental noise data;

[0031] The evaluation module is configured to: comprehensively evaluate the two-dimensional cloud model using fuzzy mathematics theory to obtain the audible noise evaluation results of the transmission line; the comprehensive evaluation of the two-dimensional cloud model includes: calculating the subjective weight, objective weight, and independence weight of the evaluation indicators respectively, and obtaining a combined weight matrix based on the weight relative change rate criterion self-learning weight model; obtaining the standard cloud for each level according to the level quantification interval, using the failure consequences and failure probability of the indicators as two sets of basic variables of the two-dimensional cloud, and combining the combined weight matrix to calculate the risk cloud and comprehensive cloud at each level; and determining the risk level of the audible noise evaluation based on the risk cloud and comprehensive cloud through similarity calculation.

[0032] Thirdly, the present invention also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the transmission line audible noise evaluation method based on a two-dimensional cloud model as described in the first aspect.

[0033] Fourthly, the present invention also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the steps of the transmission line audible noise evaluation method based on a two-dimensional cloud model as described in the first aspect.

[0034] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0035] This invention establishes a two-dimensional cloud model based on relevant parameter data and environmental noise data. Fuzzy mathematics theory is used to comprehensively evaluate the two-dimensional cloud model, yielding the audible noise assessment results for transmission lines. The comprehensive evaluation of the two-dimensional cloud model includes: calculating the subjective, objective, and independence weights of the evaluation indicators, and obtaining a combined weight matrix based on a self-learning weight model using the relative rate of change of weights criterion; obtaining standard clouds for each level based on the level quantification intervals, and using the failure consequences and failure probabilities of the indicators as two sets of basic variables for the two-dimensional cloud, combined with the combined weight matrix, calculating the risk clouds and comprehensive clouds at each level; determining the risk level of the audible noise assessment based on the risk clouds and comprehensive clouds through similarity calculations; using the two-dimensional cloud model evaluation system can more accurately describe the relationship between transmission line parameters and environmental noise, improving the accuracy of the evaluation results; obtaining the combined weight matrix based on the relative rate of change of weights criterion avoids the one-sidedness of combined weight calculations and enhances the dynamic adaptability of the evaluation method.

[0036] Non-adjacent indicators use the weight value of the intermediate indicator as a reference, which leads to inaccurate weight determination. To address this issue, this invention incorporates a distance parameter between indicators when calculating self-learning weights, taking into account the mutual influence between indicators. This allows the weights to be determined based on the correlation between indicators, thus improving the accuracy of the weights.

[0037] This invention combines a self-learning weight model based on the relative rate of change of weights, which can comprehensively evaluate various evaluation indicators and take into account the influence of multiple factors. The evaluation method is simple, effective, easy to implement, and can be widely applied in the field of audible noise evaluation of transmission lines.

[0038] This invention uses nonparametric probability density estimation, which can range the predicted values ​​and improve the reliability of the predicted values. Attached Figure Description

[0039] The accompanying drawings, which form part of this embodiment, are used to provide a further understanding of this embodiment. The illustrative embodiments and their descriptions are used to explain this embodiment and do not constitute an improper limitation of this embodiment.

[0040] Figure 1 This is the overall evaluation process for Embodiment 1 of the present invention;

[0041] Figure 2 This is the evaluation system of Embodiment 1 of the present invention;

[0042] Figure 3 This represents the relative change rate of the independence weights among the indicators in Embodiment 1 of the present invention. Detailed Implementation

[0043] The present invention will be further described below with reference to the accompanying drawings and embodiments.

[0044] It should be noted that the following detailed descriptions are exemplary and intended to provide further explanation of this application. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains.

[0045] Example 1:

[0046] Traditional evaluation methods are mainly based on statistical analysis and subjective judgment, which have problems such as inaccurate evaluation results and strong subjectivity. In some evaluation methods, subjective and objective weights are combined to obtain combined weights before evaluating the relevant objects. However, if this method is used to evaluate the audible noise of transmission lines, the existing methods for obtaining combined weights have poor dynamic adaptability because the subjective and objective weights have a certain degree of dynamism, which affects the accuracy of the final evaluation results.

[0047] To address the above problems, this embodiment provides a method for evaluating audible noise in transmission lines based on a two-dimensional cloud model, including:

[0048] Collect relevant parameter data of transmission lines, including, optionally, line structure and current load;

[0049] Collect environmental noise data, which may include noise spectrum and noise intensity, etc.

[0050] Establish a two-dimensional cloud model evaluation system to transform transmission line parameters and environmental noise data into a mathematical description of the two-dimensional cloud model;

[0051] The audible noise evaluation results of the transmission line were obtained by using fuzzy mathematics theory to comprehensively evaluate the two-dimensional cloud model.

[0052] Make necessary adjustments and improvements based on the evaluation results;

[0053] The comprehensive evaluation of the two-dimensional cloud model includes: calculating the subjective weight, objective weight, and independence weight of the evaluation indicators respectively; obtaining a combined weight matrix based on the weight relative change rate criterion of the self-learning weight model; obtaining the standard cloud of each level according to the level quantification interval; using the failure consequences and failure probability of the indicators as two sets of basic variables of the two-dimensional cloud; and calculating the risk cloud and comprehensive cloud at each level by combining the combined weight matrix; and determining the risk level of audible noise assessment by calculating the similarity between the risk cloud and the comprehensive cloud.

[0054] Specifically, the two-dimensional cloud model evaluation system can more accurately describe the relationship between transmission line parameters and environmental noise, thus improving the accuracy of the evaluation results. Based on the weight relative change rate criterion, a self-learning weight model is used to obtain the combined weight matrix, which avoids the one-sidedness of the combined weight calculation and enhances the dynamic adaptability of the evaluation method.

[0055] In this embodiment, as Figure 1 As shown, firstly, subjective and objective weights are determined using self-learning weights. Specifically, the subjective, objective, and independence weights of the evaluation indicators are calculated using the AHP (Analytic Hierarchy Process) method based on interval estimation, the entropy weight method, and the independence weight method, respectively. A self-learning weight model based on the relative rate of change of weights is then proposed to obtain combined weights, enhancing the dynamic adaptability of the evaluation method. Secondly, standard clouds for each level are obtained based on the level quantification intervals. The failure consequences and failure probabilities of the indicators are used as two sets of basic variables in the two-dimensional cloud. Combined with the previously obtained weight matrix, risk clouds and comprehensive clouds at each level are calculated. Finally, optionally, the risk level is determined by plotting cloud maps and calculating similarity using Matlab.

[0056] In this embodiment, as Figure 2 As shown, the determination of risk assessment indicators includes: based on on-site maintenance experience, the transmission line noise assessment indicators are divided into three primary indicators and eight secondary indicators.

[0057] In this embodiment, based on the objective function of the relative rate of change of weights, the following single weighting method can be used to combine the index weights of different weighting methods.

[0058]

[0059] in, Let g be the self-learning weight of the g-th indicator; The weight of the g-th index for the d-th weighting method; β gd Let be the combination coefficient of the g-th indicator in the d-th weighting method; u be the total number of weighting methods; n be the total number of indicators; D be the distance between indicators, D = aA, where A is the number of indicators between two non-adjacent indicators, and a is an adjustment parameter that makes D equal to aA. The sum of the two indicators corresponds to the weights. As you can understand, the greater the gap between the two indicators and the greater their correlation, the greater the distance between them and the smaller the calculated self-learning weight.

[0060] The relative rate of change refers to the slope generated by the relative proportions of two adjacent indicators. This embodiment defines the relative rate of change of weights for different weighting methods for the following indicators.

[0061]

[0062] Where, k dg This represents the relative slope value of the weights between the g-th and g+1-th indicators in the d-th weighting method. The specific effect of the relative rate of change is illustrated using the independence weighting method as an example, as shown in the appendix. Figure 3 As shown.

[0063] Depend on Figure 3 As can be seen, the relative change rate of each pair of adjacent indicators is obtained based on the weight value. The relative change rate reflects the size relationship between the proportions of adjacent indicators. Various weighting methods reflect the characteristics contained in the indicator data from their own perspectives. Based on the defined weight relative change rate criterion, the change trend of indicator data is indirectly incorporated into the indicator weight.

[0064] For non-adjacent indicators, the weight value of the intermediate indicator is used as a reference to indirectly reflect the size relationship between them. This pattern is used to outline the relative distribution of the weight values ​​of each indicator in the complete indicator system. However, the reference value cannot accurately determine the weight. To address this issue, in this embodiment, a distance parameter between indicators is added when calculating the self-learning weight, so that the weight can be determined based on the correlation between indicators, thereby improving the accuracy of the weight.

[0065] The weight value of each indicator is a quantification of its relative importance and proportion in the entire indicator system. The relative rate of change can intuitively and accurately reflect the relative distribution of the overall indicator weights under different weighting methods. Therefore, in order to effectively integrate the indicator weight information of different weighting methods, this embodiment uses the relative rate of change k of the weights under different weighting methods. dg As a reference benchmark for the relative change rate of the combined weights of the indicators, it is used to limit the degree of change of the combined weights of each indicator, and guides the relative change rate of the combined weights of the indicators to approach the direction of the relative change rate of the weights of different weighting methods using the least squares method, so as to find the optimal combined weight value and thus avoid the one-sidedness of the combined weight calculation. In summary, this embodiment constructs an objective function based on the relative change rate of weights as shown in equation (3).

[0066]

[0067] Currently, the subjective range of the weight self-learning method is the weight range of each indicator obtained through subjective evaluation. However, subjective evaluation involves significant subjective bias, and restricting the weights of each indicator to the subjective range obtained through subjective evaluation weakens the role of objective weights to some extent. Therefore, this embodiment determines a reasonable range for the combined weights of indicators based on the relationship between the subjective and objective weights of different indicators, ensuring that the combined weights of each indicator consider not only the constraints of subjective weights but also the role of objective weights, as detailed below:

[0068] Suppose there are u weighting methods to assign weights to n indicators, and construct a weight matrix A.

[0069]

[0070] in, Let A be the weighting value assigned to the g-th indicator by the d-th weighting method. The range of the combined weights can be determined from the weight matrix A. in:

[0071]

[0072] The subjective weight range of n indicators is obtained by formula (5).

[0073] The relative weights of the various indicators differ. To intuitively distinguish the information content of each indicator and guide the combined weights towards maximizing information, the information content of each indicator should be categorized. Therefore, this embodiment proposes comparing the weights of each indicator using different weighting methods, classifying the indicators into three categories: increasing, decreasing, and uncertain. The specific classification process is as follows:

[0074] Assume there are n index sets X Σ ={X1,X2,…,X n The weight sets corresponding to different weighting methods are: (d = 1, 2, ..., u). W d Each selected weight value is compared with the remaining unselected weight values.

[0075]

[0076] Among them, add g In the u-type weighting method, the weight value of the g-th indicator Xg is greater than X. Σ The number of times the weight value of the remaining indicator is used; jian g In the u-type weighting method, the weight value of the g-th index Xg is less than X. Σ The number of times the weight values ​​of the remaining indicators are displayed; buq g In the u-type weighting method, the weight value of the g-th indicator Xg is equal to X. ΣThe number of times the weight values ​​of the remaining indicators are expressed, where (g = 1, 2, ..., n and g ≠ k).

[0077] via add g , jian g and buq g Each parameter determines the type of the indicator (g = 1, 2, ..., n):

[0078]

[0079] Among them, h g Let {1, 2, 3} be the category label corresponding to the g-th indicator, and {1, 2, 3} correspond to the increasing, decreasing, and uncertain types, respectively.

[0080] The category label set H = {h1, h2, ..., hn} of n indicators is obtained through equation (7). Based on the category label set H, the following constraints are constructed in this embodiment:

[0081]

[0082] in, These are the upper and lower limits of the weight range for indicator g, respectively (g = 1, 2, ..., n).

[0083] In some other embodiments, the indicators may be classified as increasing and uncertain; or as decreasing and uncertain; or as increasing, decreasing and uncertain.

[0084] In this embodiment, three different weighting methods are selected for combined weight calculation. The AHP method based on interval estimation is chosen to calculate the subjective weight W1=[ω 11 ,…,ω n1 ] T Entropy weight method W2=[ω 12 ,…,ω n2 ] T Calculate the objective weight W3 = [ω] separately from the independence weight method. 13 ,…,ω n3 ] T .

[0085] Among them, the AHP method based on interval estimation reflects an expert's empirical understanding of the importance of each indicator, while the entropy weighting method objectively reflects the amount of information contained in each indicator. Furthermore, considering the potential information redundancy among indicators, the independence weighting method is used to eliminate the insufficient information redundancy. The optimal combined weight vector is obtained through a self-learning weight model based on the relative rate of change of weights using these three weighting methods.

[0086]

[0087] Risk clouds are a comprehensive representation based on the probability of accident failure and the consequences of failure. To accurately measure the risk level of an accident, the probability of accident occurrence and the consequences of failure for each level of indicators are quantified, thereby obtaining the failure probability risk cloud and failure consequence risk cloud for each indicator. The calculation method and formula are as follows:

[0088]

[0089]

[0090]

[0091] Where Ex is the expected value of the risk cloud; En is the entropy of the risk cloud; and He is the hyperentropy of the risk cloud. M1 and S 2 These are the sample mean, sample first absolute central moment, and sample variance of the scores given by each expert for each indicator.

[0092] To address the stochastic and fuzzy problem arising from the combined influence of failure probability and failure consequences, a two-dimensional cloud model is introduced, characterized by two sets of numerical features (Ex, En, He). A large number of two-dimensional cloud droplets condense into a two-dimensional normal cloud, the mathematical model of which is as follows:

[0093]

[0094] Among them, (x i ,y i ) and (P xi ,P yi ) represents the output value, where i and j are constants; Ex x and Ex y Expected value; En x and En y He is the standard deviation; x and He y For the hyperentropy of the risk cloud; μ i is the membership function of the two-dimensional cloud model; F is a two-dimensional random function that follows a normal distribution.

[0095] The risk level classification criteria divide the failure probability and consequences of each indicator into five levels, and quantifies the risk level ranges. Furthermore, the maximum value R of each risk level's quantified range is used. max and minimum value R min A standard cloud consisting of five two-dimensional clouds is established as a reference standard for the risk cloud. The numerical characteristics of the standard cloud are determined by equation (14), and the risk descriptions and numerical characteristics at each level are shown in Table 1.

[0096]

[0097] Table 1. Quantitative Criteria for Risk Level

[0098]

[0099] The comprehensive risk cloud reflects the overall risk level of the evaluated object, and is composed of the comprehensive failure probability level and the comprehensive failure consequence level. Based on the weights of each level of indicators obtained by the difference coefficient method, the risk cloud of secondary indicators is converted into the risk cloud of primary indicators, thus obtaining the comprehensive risk cloud of the evaluated object. The calculation formula is as follows.

[0100]

[0101] Where C represents the digital characteristics of the comprehensive risk cloud, and Ex, En, and He represent the expected value, entropy, and hyperentropy of the comprehensive cloud, respectively.

[0102] The risk level is determined using a two-dimensional cloud similarity calculation method, and the calculation formula is as follows:

[0103]

[0104] Where L represents the similarity; Ex, Ex' and These are the expected values ​​of the standard cloud of failure probability, the actual cloud of failure probability, the standard cloud of failure consequences, and the actual cloud of failure consequences, respectively.

[0105] Example 2:

[0106] This embodiment provides a transmission line audible noise assessment system based on a two-dimensional cloud model, including:

[0107] The data acquisition module is configured to acquire relevant parameter data of the transmission line and environmental noise data;

[0108] The model building module is configured to: build a two-dimensional cloud model based on relevant parameter data and environmental noise data;

[0109] The evaluation module is configured to: comprehensively evaluate the two-dimensional cloud model using fuzzy mathematics theory to obtain the audible noise evaluation results of the transmission line; the comprehensive evaluation of the two-dimensional cloud model includes: calculating the subjective weight, objective weight, and independence weight of the evaluation indicators respectively, and obtaining a combined weight matrix based on the weight relative change rate criterion self-learning weight model; obtaining the standard cloud for each level according to the level quantification interval, using the failure consequences and failure probability of the indicators as two sets of basic variables of the two-dimensional cloud, and combining the combined weight matrix to calculate the risk cloud and comprehensive cloud at each level; and determining the risk level of the audible noise evaluation based on the risk cloud and comprehensive cloud through similarity calculation.

[0110] The working method of the system is the same as that of the audible noise evaluation method for transmission lines based on a two-dimensional cloud model in Embodiment 1, and will not be repeated here.

[0111] Example 3:

[0112] This embodiment provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps of the transmission line audible noise evaluation method based on a two-dimensional cloud model as described in Embodiment 1.

[0113] Example 4:

[0114] This embodiment provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, it implements the steps of the audible noise evaluation method for transmission lines based on a two-dimensional cloud model as described in Embodiment 1.

[0115] The above description is merely a preferred embodiment of this practice and is not intended to limit the scope of this practice. Various modifications and variations can be made to this practice by those skilled in the art. Any modifications, equivalent substitutions, or improvements made within the spirit and principles of this practice should be included within the protection scope of this practice.

Claims

1. A method for evaluating audible noise in transmission lines based on a two-dimensional cloud model, characterized in that, include: Obtain relevant parameter data of transmission lines, as well as environmental noise data; A two-dimensional cloud model is established based on relevant parameter data and environmental noise data. Fuzzy mathematics theory is used to comprehensively evaluate a two-dimensional cloud model to obtain the audible noise evaluation results of transmission lines. The comprehensive evaluation of the two-dimensional cloud model includes: calculating the subjective weight, objective weight, and independence weight of the evaluation indicators respectively, and obtaining the combined weight matrix based on the weight relative change rate criterion self-learning weight model. The relative rate of change refers to the slope generated by the relative proportions of two adjacent indicators: ,in, k dg For the first d The first of the empowerment laws g The and the first g +1 relative slope values ​​of weights among indicators; The relative change rate of weights under different weighting methods is used as a reference benchmark for the relative change rate of combined indicator weights, which limits the degree of change in the combined weights of each indicator. The least squares method is then used to guide the relative change rate of the combined indicator weights towards the direction of the relative change rate of weights under different weighting methods, thus finding the optimal combined weight value. An objective function is constructed as follows: ； Introducing distance parameters between indicators ,in A represents the number of indicators that are not adjacent to each other. a To adjust the parameters, the self-learning weights of the indicator are: ， in, For the first g Self-learning weights of each indicator; For the first d The first of the weighting methods g The weight of each indicator; For the first d The first of the weighting methods g The combination coefficient of each indicator; u The total number of weighting methods; n The total number of indicators; D The distance between indicators; Based on the quantification range of the levels, standard clouds for each level are obtained. The failure consequences and failure probabilities of the indicators are used as two sets of basic variables of the two-dimensional cloud. Combined with the combined weight matrix, risk clouds and comprehensive clouds at each level are calculated. Based on the risk clouds and comprehensive clouds, the risk level of audible noise assessment is determined by similarity calculation.

2. The method for evaluating audible noise of transmission lines based on a two-dimensional cloud model as described in claim 1, characterized in that, The relevant parameter data of the transmission line include the line structure and current load; the environmental noise data includes the noise spectrum and noise intensity.

3. The method for evaluating audible noise of transmission lines based on a two-dimensional cloud model as described in claim 1, characterized in that, The subjective weight, objective weight, and independence weight of the evaluation indicators were calculated using the interval estimation AHP method, entropy weight method, and independence weight method, respectively; and the weights of the indicators from different weighting methods were combined using a single weighting method.

4. The method for evaluating audible noise of transmission lines based on a two-dimensional cloud model as described in claim 1, characterized in that, By comparing the weights of different weighting methods for each indicator, the indicators are divided into increasing and decreasing types. Alternatively, by comparing the weights of different weighting methods for each indicator, the indicators can be divided into increasing and uncertain types. Alternatively, by comparing the weights of each indicator using different weighting methods, the indicators can be categorized into decreasing and uncertain types. Alternatively, by comparing the weights of different weighting methods for each indicator, the indicators can be categorized into increasing, decreasing, and uncertain types.

5. The method for evaluating audible noise of transmission lines based on a two-dimensional cloud model as described in claim 1, characterized in that, The probability of accidents and the consequences of failure at each level of indicator are quantified to obtain the failure probability risk cloud and failure consequence risk cloud for each indicator; the two-dimensional cloud model is as follows: in, and For the output value, i and j It is a constant; and This is the expected value; and Standard deviation; and The hyperentropy of the risk cloud; is the membership function of the two-dimensional cloud model; F It is a two-dimensional random function that follows a normal distribution.

6. The method for evaluating audible noise of transmission lines based on a two-dimensional cloud model as described in claim 1, characterized in that, Based on the weights of each level of indicators obtained by the difference coefficient method, the risk cloud of secondary indicators is transformed into the risk cloud of primary indicators, thereby obtaining the comprehensive risk cloud of the evaluation object: in, C To integrate the digital characteristics of the risk cloud; These are the elements in the combined weight matrix; Ex , En and He These are the expected value, entropy, and hyperentropy of the integrated cloud; the risk level is determined using a two-dimensional cloud similarity calculation method. in, L For similarity, , , and These are the expected values ​​of the standard cloud of failure probability, the actual cloud of failure probability, the standard cloud of failure consequences, and the actual cloud of failure consequences, respectively.

7. A transmission line audible noise assessment system based on a two-dimensional cloud model, characterized in that, include: The data acquisition module is configured to acquire relevant parameter data of the transmission line and environmental noise data; The model building module is configured to: build a two-dimensional cloud model based on relevant parameter data and environmental noise data; The evaluation module is configured to: use fuzzy mathematics theory to comprehensively evaluate the two-dimensional cloud model and obtain the evaluation results of the audible noise of the transmission line; wherein, the comprehensive evaluation of the two-dimensional cloud model includes: calculating the subjective weight, objective weight and independence weight of the evaluation index respectively, and obtaining the combined weight matrix based on the weight relative change rate criterion self-learning weight model. The relative rate of change refers to the slope generated by the relative proportions of two adjacent indicators: ,in, k dg For the first d The first of the empowerment laws g The and the first g +1 relative slope values ​​of weights among indicators; The relative change rate of weights under different weighting methods is used as a reference benchmark for the relative change rate of combined indicator weights, which limits the degree of change in the combined weights of each indicator. The least squares method is then used to guide the relative change rate of the combined indicator weights towards the direction of the relative change rate of weights under different weighting methods, thus finding the optimal combined weight value. An objective function is constructed as follows: ； Introducing distance parameters between indicators ,in A represents the number of indicators that are not adjacent to each other. a To adjust the parameters, the self-learning weights of the indicator are: ， in, For the first g Self-learning weights of each indicator; For the first d The first of the weighting methods g The weight of each indicator; For the first d The first of the weighting methods g The combination coefficient of each indicator; u The total number of weighting methods; n The total number of indicators; D The distance between indicators; Based on the quantification range of the levels, standard clouds for each level are obtained. The failure consequences and failure probabilities of the indicators are used as two sets of basic variables of the two-dimensional cloud. Combined with the combined weight matrix, risk clouds and comprehensive clouds at each level are calculated. Based on the risk clouds and comprehensive clouds, the risk level of audible noise assessment is determined by similarity calculation.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, it implements the steps of the method for evaluating audible noise of transmission lines based on a two-dimensional cloud model as described in any one of claims 1-6.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the steps of the method for evaluating audible noise of transmission lines based on a two-dimensional cloud model as described in any one of claims 1-6.

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