A mineral insulated cable conductivity detection method and system

Abstract

The present invention provides a mineral insulated cable conductivity detection method and system, which relates to the field of power engineering application technology. The mineral insulated cable conductivity detection method includes the following steps: calculating the resistance value of the measuring section under different DC voltages by collecting the current and temperature of the measuring section; establishing candidate evaluation indicators, using the niche theory to analyze the influence of each candidate evaluation indicator on the resistance value, and screening the final evaluation indicator according to the analysis result; determining the weight of each final evaluation indicator by the anti-entropy weight method, and evaluating the conductivity of the mineral insulated cable by the multi-level fuzzy comprehensive evaluation method to obtain the performance evaluation test result. The present invention uses the niche theory to analyze the influence of the candidate evaluation indicators on the resistance value, can scientifically screen out the evaluation indicators with the greatest influence on the conductivity performance evaluation, and determines the weight of each final evaluation indicator by the anti-entropy weight method, which can objectively reflect the importance of each indicator in the conductivity performance evaluation.

Description

Technical Field

[0001] The present invention relates to the technical field of power engineering applications, and in particular to a method and system for detecting the conductivity of a mineral insulated cable. Background Art

[0002] Power cables are widely used in power systems, especially in cities and suburban areas. The use of cables is particularly important due to the limited space of overhead line corridors. However, the impact of power cable failures on power systems and even the economy and society is gradually increasing. Most cable failures are caused by the burning of the outer semi-conductive layer of the cable due to discharge. This type of failure is often caused by problems with the insulation layer of the cable, resulting in discharge.

[0003] The integrity of the insulation layer is critical to the safe operation of the cable. Cracks or moisture penetration may lead to current leakage and serious failure. Traditional cable testing methods may not be able to accurately measure electrical performance under complex or extreme conditions, and the results are inaccurate because they do not fully consider small changes in voltage and current measurements. In addition, existing technologies often rely on a single or limited indicator to evaluate cable performance, ignoring a variety of influencing factors and failing to fully reflect the true status and potential problems of the cable.

[0004] Currently, no effective solution has been proposed for the problems in the related technologies. Summary of the invention

[0005] In view of this, the present invention provides a mineral insulated cable conductivity detection method and system to solve the above-mentioned problems.

[0006] In order to solve the above problems, the specific technical solutions adopted by the present invention are as follows:

[0007] According to one aspect of the present invention, a method for detecting the conductivity of a mineral insulated cable is provided, the method comprising the following steps:

[0008] S1. Pre-configure the mineral insulated cable measuring section; introduce different DC voltages between the circumferential surface of the measuring section and the end cell, and calculate the resistance value of the measuring section under different DC voltages by collecting the current and temperature of the measuring section;

[0009] S2. Based on the resistance value of the measurement section under different DC voltages, candidate evaluation indicators are established according to the characteristics of the mineral insulated cable, and the influence of each candidate evaluation indicator on the resistance value is analyzed using the niche theory, and the final evaluation indicator is selected according to the analysis results;

[0010] S3. The weights of the final evaluation indicators are determined by the anti-entropy weight method, and the conductive performance of the mineral insulated cable is evaluated by the multi-level fuzzy comprehensive evaluation method to obtain the performance evaluation test results.

[0011] Preferably, introducing different DC voltages between the circumferential surface of the measuring section and the end cell, and calculating the resistance value of the measuring section under different DC voltages by collecting the current and temperature of the measuring section comprises the following steps:

[0012] S11, based on the voltage introduced at both ends of the measuring segment, using Ohm's law to calculate the preliminary resistance value of the measuring segment;

[0013] S12, determining a temperature coefficient of the measuring section after voltage is introduced according to the temperature of the measuring section and based on material properties of the measuring section;

[0014] S13. Based on the temperature coefficient and the preliminary resistance value of the measuring segment, the preliminary resistance value is corrected using a temperature correction formula to obtain the resistance value of the measuring segment under different DC voltages.

[0015] Preferably, based on the resistance value of the measuring section under different DC voltages, candidate evaluation indicators are established according to the characteristics of the mineral insulated cable, and the influence of each candidate evaluation indicator on the resistance value is analyzed by using the niche theory, and the final evaluation indicator is selected according to the analysis results, including the following steps:

[0016] S21, collecting characteristic parameters of the mineral insulated cable according to the resistance value of the measurement section, and determining candidate evaluation indicators related to the resistance value to obtain a candidate evaluation indicator set;

[0017] S22, using a normalization processing method to perform normalization processing on the candidate evaluation indicator data in the candidate evaluation indicator set to obtain a normalized indicator;

[0018] S23, analyze the changing trend of standardized indicators and calculate the niche width of each standardized indicator using niche theory;

[0019] S24, calculating the niche overlap between different standardized indicators based on the niche width of each standardized indicator;

[0020] S25. Select the final evaluation indicators that meet the preset conditions according to the niche width of each standardized indicator and the niche overlap between different standardized indicators.

[0021] Preferably, performing a change trend analysis on the standardized indicators and calculating the niche width of each standardized indicator using the niche theory comprises the following steps:

[0022] S231. Perform time series analysis on each standardized indicator and fit the trend line of each standardized indicator by linear regression method;

[0023] S232, calculating the slope of the trend line of each standard indicator, and using the slope as the potential value of the standard evaluation indicator, and using the indicator data of the standardized indicator as the state value;

[0024] S233. According to the potential value and state value, the niche width of each standardized indicator is calculated using the niche width calculation formula.

[0025] Preferably, the niche width calculation formula is:

[0026] ;

[0027] In the formula, G i Indicates i The niche breadth value of the standard evaluation index;

[0028] R i Indicates i The state value of the ecological niche of the standard evaluation indicators;

[0029] R j Indicates j The state value of the ecological niche of the standard evaluation indicators;

[0030] S i Indicates i The potential value of the ecological niche of the standard evaluation indicators;

[0031] V j Indicates j The potential value of the ecological niche of the standard evaluation indicators;

[0032] V i and V j All represent dimension conversion coefficients;

[0033] m Indicates the number of standard evaluation indicators.

[0034] Preferably, according to the niche width of each standardized indicator, the calculation formula for calculating the niche overlap between different standardized indicators is:

[0035] ;

[0036] In the formula, H ij Indicates i Standard evaluation indicators and j The degree of niche overlap between the standard evaluation indicators;

[0037] G ix Indicates i The standard evaluation index isx The niche breadth value based on the indicator data;

[0038] G jx Indicates j The standard evaluation index is x The niche breadth value based on the indicator data;

[0039] n Indicates the number of indicator data in the standard evaluation indicator.

[0040] Preferably, the weights of the final evaluation indicators are determined by the anti-entropy weight method, and the conductive performance of the mineral insulated cable is evaluated by the multi-level fuzzy comprehensive evaluation method to obtain the performance evaluation result, which includes the following steps:

[0041] S31, collecting subjective scores of the final evaluation indicators, and constructing an evaluation indicator matrix according to the final evaluation indicators, and calculating the anti-entropy value of each final evaluation indicator by an anti-entropy calculation formula;

[0042] S32, obtaining the objective weight of each final evaluation indicator by normalizing the anti-entropy value of each final evaluation indicator;

[0043] S33, establishing a multi-level fuzzy comprehensive evaluation model, including determining an evaluation factor set, an evaluation weight set, a comment set and a value set and constructing a membership degree subset;

[0044] S34. Based on the membership subset, by constructing a fuzzy relationship matrix and using the weight vector for calculation, an evaluation result vector is obtained, and the evaluation result is analyzed according to the maximum membership principle to obtain the final evaluation result.

[0045] Preferably, the anti-entropy calculation formula is:

[0046] ;

[0047] In the formula, M i Indicates i The final evaluation index anti-entropy value;

[0048] t Indicates the number of resistance values ​​of the measurement segment at different DC voltages;

[0049] f iu Indicates u The resistance value is i The final evaluation index is subjectively scored.

[0050] Preferably, based on the membership subset, by constructing a fuzzy relationship matrix and calculating using a weight vector, an evaluation result vector is obtained, and the evaluation result is analyzed according to the maximum membership principle to obtain the final evaluation result, which includes the following steps:

[0051] S341, constructing a fuzzy relationship matrix according to the membership degree subset, and weighting the fuzzy relationship matrix through a weight vector to obtain a weighted fuzzy relationship matrix;

[0052] S342, summing each row of the weighted fuzzy relationship matrix to obtain the comprehensive membership of each evaluation object corresponding to different comment levels, and taking the comprehensive membership as the evaluation result vector;

[0053] S343. Analyze the evaluation result vector according to the maximum membership principle, find out the largest element, and use the comment level corresponding to the element as the final performance evaluation test result.

[0054] According to another aspect of the present invention, a mineral insulated cable conductivity detection system is provided, the mineral insulated cable conductivity detection system comprising: a resistance measurement module, an index screening module and an evaluation detection module;

[0055] The resistance measurement module is used to pre-configure the measurement section of the mineral insulated cable; introduce different DC voltages between the circumferential surface of the measurement section and the end cell, and calculate the resistance value of the measurement section under different DC voltages by collecting the current and temperature of the measurement section;

[0056] An index screening module is used to establish candidate evaluation indicators based on the resistance value of the measurement section under different DC voltages and the characteristics of the mineral insulated cable, and to analyze the influence of each candidate evaluation indicator on the resistance value using the niche theory, and to screen the final evaluation indicator based on the analysis results;

[0057] The evaluation and detection module is used to determine the weights of each final evaluation index through the anti-entropy weight method, and use the multi-level fuzzy comprehensive evaluation method to evaluate the conductive performance of the mineral insulated cable to obtain the performance evaluation test results.

[0058] The beneficial effects of the present invention are:

[0059] 1. The present invention can accurately calculate the resistance value of the measuring section under different conditions by introducing different DC voltages and collecting current and temperature data, and provide accurate basic data for subsequent conductive performance evaluation. The niche theory is used to analyze the influence of candidate evaluation indicators on the resistance value, and the most influential evaluation indicators for conductive performance evaluation can be scientifically screened out. The weights of each final evaluation indicator are determined by the anti-entropy weight method, which can objectively reflect the importance of each indicator in the conductive performance evaluation, avoiding the subjectivity and inaccuracy that may be caused by artificial weighting. The conductive performance of the mineral insulated cable is evaluated by a multi-level fuzzy comprehensive evaluation method. This method can comprehensively consider multiple evaluation indicators and weights to obtain a comprehensive and accurate performance evaluation test result.

[0060] 2. The present invention first collects the characteristic parameters of the mineral insulated cable and determines the candidate evaluation indicators related to the resistance value, thereby ensuring the comprehensiveness and systematicness of the evaluation. Through normalization processing, the indicators of different dimensions are made comparable. The candidate evaluation indicators are analyzed using the niche theory. By calculating the niche width and niche overlap, the influence of each indicator on the resistance value and the relationship between them are scientifically quantified. The trend line is fitted by the linear regression method, and the slope is calculated as the potential value. The changing trend of the indicator can be accurately captured, providing reliable data support for calculating the niche width.

[0061] 3. The present invention establishes a multi-level fuzzy comprehensive evaluation model, including the steps of determining an evaluation factor set, an evaluation weight set, a comment set and a numerical set and constructing a membership subset, thereby ensuring the scientificity and systematicity of the evaluation. By constructing a membership subset and a fuzzy relationship matrix, the influence of each evaluation index on the conductive performance can be quantified, thereby improving the accuracy and reliability of the evaluation. The evaluation results are analyzed by the maximum membership principle, and the comment level corresponding to the largest element is found as the final performance evaluation test result, so that the evaluation results are intuitive and easy to understand, which helps the evaluator to quickly understand the conductive performance of the mineral insulated cable and make corresponding decisions. BRIEF DESCRIPTION OF THE DRAWINGS

[0062] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work. In the drawings:

[0063] Figure 1 is a flow chart of a mineral insulated cable conductivity detection method according to an embodiment of the present invention;

[0064] Figure 2The invention is a principle block diagram of a mineral insulated cable conductivity detection system according to an embodiment of the invention.

[0065] In the figure:

[0066] 1. Resistance measurement module; 2. Index screening module; 3. Evaluation and detection module. DETAILED DESCRIPTION

[0067] In order to enable those skilled in the art to better understand the technical solutions in this application, the technical solutions in the embodiments of this application will be clearly and completely described below in conjunction with the drawings in the embodiments of this application. Obviously, the described embodiments are only part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of this application.

[0068] According to an embodiment of the present invention, a method and system for detecting conductivity of a mineral insulated cable are provided.

[0069] The present invention is further described with reference to the accompanying drawings and specific embodiments. Figure 1 As shown, according to one embodiment of the present invention, a method for detecting the conductivity of a mineral insulated cable is provided, and the method for detecting the conductivity of a mineral insulated cable comprises the following steps:

[0070] S1. Pre-configure the mineral insulated cable measuring section; introduce different DC voltages between the circumferential surface of the measuring section and the end cell, and calculate the resistance value of the measuring section under different DC voltages by collecting the current and temperature of the measuring section;

[0071] It should be noted that, in the production process of mineral insulated cables, a whole mineral insulated cable or a section of the whole mineral insulated cable is selected as the measuring section;

[0072] As a preferred embodiment, introducing different DC voltages between the circumferential surface of the measuring section and the end cell, and calculating the resistance value of the measuring section under different DC voltages by collecting the current and temperature of the measuring section includes the following steps:

[0073] S11, based on the DC voltage introduced at both ends of the measuring segment, using Ohm's law to calculate the preliminary resistance value of the measuring segment;

[0074] It should be noted that when measuring the conductive properties of mineral insulated cables, when different DC voltages are introduced between the circumferential surface of the measuring section and the end cell, high-precision ammeters and voltmeters are used to measure the current and applied voltage respectively, and the current values ​​passing through the insulation layer at different voltages are recorded. At the same time, the temperature of the measuring section is recorded, because the temperature will affect the resistance value of the insulation layer.

[0075] According to the production material of the battery cell in the mineral cable, the resistance value of the battery cell is obtained (obtained through previous measurement).

[0076] According to Ohm's law, calculate the resistance value of the entire measuring section.

[0077] S12, determining a temperature coefficient of the measuring section after voltage is introduced according to the temperature of the measuring section and based on material properties of the measuring section;

[0078] Specifically, use a temperature sensor (such as a platinum resistance Pt100 or a thermocouple) to measure the actual temperature T of the measuring section, and find or calculate its temperature coefficient α based on the material properties of the measuring section; for mineral insulated cables, the specific temperature coefficient needs to refer to the data sheet or standard provided by the manufacturer.

[0079] S13. Based on the temperature coefficient and the preliminary resistance value of the measuring segment, the preliminary resistance value is corrected using a temperature correction formula to obtain the resistance value of the measuring segment under different DC voltages.

[0080] Specifically, the preliminary resistance value is corrected using a temperature correction formula to account for the effect of temperature on resistance.

[0081] The correction formula is usually in the form of: R corrected =R initial ×(1+α×ΔT), where R corrected is the corrected resistance value, R initial is the preliminary resistance value, α is the temperature coefficient, and ΔT is the temperature change (i.e. the difference between the actual temperature T and the reference temperature).

[0082] Substitute the preliminary resistance value, temperature coefficient and temperature change into the correction formula to calculate the corrected resistance value.

[0083] For each voltage value, repeat the above temperature measurement, temperature coefficient determination and temperature correction steps to obtain the accurate resistance value at that voltage.

[0084] S2. Based on the resistance value of the measurement section under different DC voltages, candidate evaluation indicators are established according to the characteristics of the mineral insulated cable, and the influence of each candidate evaluation indicator on the resistance value is analyzed using the niche theory, and the final evaluation indicator is selected according to the analysis results;

[0085] As a preferred implementation, based on the resistance value of the measuring section under different DC voltages, candidate evaluation indicators are established according to the characteristics of the mineral insulated cable, and the influence of each candidate evaluation indicator on the resistance value is analyzed using the niche theory. The final evaluation indicator is selected according to the analysis results, including the following steps:

[0086] S21, collecting characteristic parameters of the mineral insulated cable according to the resistance value of the measurement section, and determining candidate evaluation indicators related to the resistance value to obtain a candidate evaluation indicator set;

[0087] Specifically, according to the resistance value of the measurement section, characteristic parameters of the mineral insulated cable are collected, and candidate evaluation indicators related to the resistance value are determined, and obtaining the candidate evaluation indicator set includes the following steps:

[0088] Analyze the stability of resistance value with voltage change. If the resistance value is relatively stable, it indicates that the cable has good insulation performance and physical integrity. The electrical characteristics of the cable can be evaluated by the resistance value change trend under different voltages. The resistance value change curve can provide information on the behavior of the cable under different working conditions.

[0089] Collect characteristic parameters, including: resistivity, temperature coefficient, insulation resistance, temperature stability, voltage resistance, mechanical strength, thermal stability, aging resistance, etc.

[0090] Based on the variation trend and characteristic parameters of the resistance value, candidate evaluation indicators related to the resistance value are determined: resistance stability, temperature influence, insulation performance, voltage resistance, mechanical strength, thermal stability, aging resistance, etc., forming a set of multiple candidate evaluation indicators.

[0091] S22, using a normalization processing method to perform normalization processing on the candidate evaluation indicator data in the candidate evaluation indicator set to obtain a normalized indicator;

[0092] Specifically, normalization is a data preprocessing technique that scales numerical attributes to a specific range, usually between 0 and 1, or between -1 and 1, so that the dimensional effects between different indicators can be eliminated and the data can be compared on the same scale.

[0093] Commonly used normalization methods include Min-Max Normalization and Z-Score normalization.

[0094] S23, analyze the changing trend of standardized indicators and calculate the niche width of each standardized indicator using niche theory;

[0095] As a preferred implementation, analyzing the change trend of the standardized indicators and calculating the niche width of each standardized indicator using the niche theory includes the following steps:

[0096] S231. Perform time series analysis on each standardized indicator and fit the trend line of each standardized indicator by linear regression method;

[0097] Specifically, the time series analysis of each standardized indicator and the trend line fitting of each standardized indicator by linear regression method include the following steps:

[0098] Analyze the time series data of each standardized indicator and observe its changing trend over time.

[0099] Use linear regression method to fit the trend line of each standardized indicator. The linear regression model can be expressed as: y=a+bx;

[0100] Where y is the value of the standardized indicator; x is the time (or measurement point); a is the intercept; and b is the slope.

[0101] S232, calculating the slope of the trend line of each standard indicator, and using the slope as the potential value of the standard evaluation indicator, and using the indicator data of the standardized indicator as the state value;

[0102] Specifically, the slope b is extracted from the linear regression model, which reflects the changing trend of the indicator over time.

[0103] S233. According to the potential value and state value, the niche width of each standardized indicator is calculated using the niche width calculation formula.

[0104] It should be noted that niche breadth is a concept in ecology, which is used to describe the size of the niche space occupied by a species or individual in an ecosystem. In this case, each standardized indicator can be regarded as a "species" and its niche breadth in the "ecosystem" (that is, the set of all standardized indicators) can be calculated.

[0105] As a preferred implementation, the niche width calculation formula is:

[0106] ;

[0107] In the formula, G i Indicates i The niche breadth value of the standard evaluation index;

[0108] R i Indicates i The state value of the ecological niche of the standard evaluation indicators;

[0109] R j Indicates j The state value of the ecological niche of the standard evaluation indicators;

[0110] S i Indicates i The potential value of the ecological niche of the standard evaluation indicators;

[0111] V j Indicates j The potential value of the ecological niche of the standard evaluation indicators;

[0112] V i and V j All represent dimension conversion coefficients;

[0113] m Indicates the number of standard evaluation indicators.

[0114] S24, calculating the niche overlap between different standardized indicators based on the niche width of each standardized indicator;

[0115] It should be noted that niche overlap is an important concept in ecology. It is used to describe the extent to which two or more species occupy similar niche space in an ecosystem. In this case, each standardized indicator can be regarded as a "species", and niche overlap reflects the extent to which these indicators occupy similar space in the "ecosystem" (that is, the set of all standardized indicators). The value of niche overlap ranges from 0 to 1. The closer the value is to 1, the higher the degree of niche overlap between the two standardized indicators, that is, the larger the similar space they occupy in the "ecosystem". The closer the value is to 0, the lower the degree of niche overlap between the two indicators, that is, the smaller the similar space they occupy in the "ecosystem".

[0116] As a preferred implementation, according to the niche width of each standardized indicator, the calculation formula for calculating the niche overlap between different standardized indicators is:

[0117] ;

[0118] In the formula, H ij Indicates i Standard evaluation indicators and j The degree of niche overlap between the standard evaluation indicators;

[0119] G ix Indicates i The standard evaluation index is x The niche breadth value based on the indicator data;

[0120] G jx Indicates j The standard evaluation index is x The niche breadth value based on the indicator data;

[0121] n Indicates the number of indicator data in the standard evaluation indicator.

[0122] S25. Select the final evaluation indicators that meet the preset conditions according to the niche width of each standardized indicator and the niche overlap between different standardized indicators.

[0123] It should be noted that before screening the final evaluation indicators that meet the preset conditions according to the niche width of each standardized indicator and the niche overlap between different standardized indicators, it is necessary to set the screening conditions. For example, the screening conditions are set:

[0124] Niche width threshold: Set a minimum niche width threshold G min , to ensure that the selected indicators have sufficient range and importance.

[0125] Niche overlap threshold: Set a maximum niche overlap threshold H max , to ensure that the selected indicators have low similarity, thus ensuring the independence of the indicators.

[0126] Select niche width greater than or equal to G min indicators.

[0127] From the initial screening indicators, remove those indicators whose niche overlap with other indicators is greater than H. max indicators.

[0128] For example, to set the filter conditions:

[0129] Niche width threshold (G min ): Set G min is 0.5, which means that only indicators with niche breadth greater than or equal to 0.5 are selected to ensure that these indicators have sufficient variation range and importance.

[0130] Niche overlap threshold (H max ): Set H max is 0.3, which means that only indicators with niche overlap with other indicators less than or equal to 0.3 are selected to ensure the independence between indicators.

[0131] Assume that there are five standardized indicators (A, B, C, D and E), and their niche widths and niche overlaps have been calculated as follows:

[0132] Niche Breadth:

[0133] A:0.6;

[0134] B:0.4 (less than G min , will be excluded);

[0135] C: 0.7;

[0136] D: 0.8;

[0137] E:0.5 (equal to G min , can be considered);

[0138] Niche overlap (only overlaps greater than 0 are listed):

[0139] A and C: 0.2 (less than H max , can be retained);

[0140] A and D: 0.4 (greater than H max , which requires further consideration);

[0141] C and D: 0.3 (equal to H max , can be retained but with caution);

[0142] C and E: 0.1 (less than H max , can be retained);

[0143] D and E: 0.5 (greater than H max , D and E cannot be retained at the same time);

[0144] Screening based on niche breadth:

[0145] First, exclude the niche width less than G min Indicator B.

[0146] Remaining indicators: A, C, D and E.

[0147] Screening based on niche overlap:

[0148] The niche overlap between A and D is 0.4, which is greater than H max ,Due to the large niche breadth of D (0.8) and the high overlap with other indicators, it is considered to exclude one of A or D. In this case, D is chosen to be retained because of its larger niche breadth.

[0149] The niche overlap between D and E is 0.5, which is also greater than H. max , since it has been decided to keep D, E is ruled out.

[0150] Among the remaining indicators, the niche overlap between A and C, and between C and E is less than or equal to H. max , can be retained.

[0151] After comprehensive consideration of niche breadth and niche overlap, the evaluation indicators finally selected were: C and D (or A, C, and E, depending on the specific situation, if it was decided to keep A but not D).

[0152] S3. The weights of the final evaluation indicators are determined by the anti-entropy weight method, and the conductive performance of the mineral insulated cable is evaluated by the multi-level fuzzy comprehensive evaluation method to obtain the performance evaluation test results.

[0153] As a preferred implementation, the weights of the final evaluation indicators are determined by the anti-entropy weight method, and the conductive performance of the mineral insulated cable is evaluated by the multi-level fuzzy comprehensive evaluation method. The performance evaluation results are obtained by the following steps:

[0154] S31, collecting subjective scores of the final evaluation indicators, and constructing an evaluation indicator matrix according to the final evaluation indicators, and calculating the anti-entropy value of each final evaluation indicator by an anti-entropy calculation formula;

[0155] It should be noted that by selecting experts or team members with relevant professional knowledge and experience as scorers, a scoring form is designed for each final evaluation indicator, and the scoring criteria and range (such as 1-10 points or 1-5 points, etc.) are clearly defined. The experts are asked to score each evaluation indicator according to the scoring form and record the scoring results.

[0156] Among them, constructing the evaluation index matrix includes:

[0157] Determine the matrix structure: set a matrix with p rows and q columns, where p represents the number of final evaluation indicators and q represents the number of resistance values ​​under different DC voltages (or the number of scored samples).

[0158] Filling the matrix: Fill the collected scoring data into the matrix in the order of evaluation indicators and resistance values ​​(or samples) to form an evaluation indicator matrix.

[0159] As a preferred implementation, the anti-entropy calculation formula is:

[0160] ;

[0161] In the formula, M i Indicates i The final evaluation index anti-entropy value;

[0162] t Indicates the number of resistance values ​​of the measurement segment at different DC voltages;

[0163] f iu Indicates u The resistance value is i The final evaluation index is subjectively scored.

[0164] S32, obtaining the objective weight of each final evaluation indicator by normalizing the anti-entropy value of each final evaluation indicator;

[0165] It should be noted that in order to obtain the objective weight of each final evaluation indicator, the anti-entropy value of each evaluation indicator needs to be normalized. Normalization is a commonly used data processing method, which can convert data of different magnitudes or ranges into a unified metric, thus facilitating subsequent analysis and comparison.

[0166] S33, establishing a multi-level fuzzy comprehensive evaluation model, including determining an evaluation factor set, an evaluation weight set, a comment set and a value set, and constructing a membership degree subset;

[0167] Specifically, in order to comprehensively evaluate the conductive performance of mineral insulated cables, a multi-level fuzzy comprehensive evaluation model will be constructed. The establishment of the model includes the following steps:

[0168] 1. Determine the set of evaluation factors

[0169] The evaluation factor set refers to a collection of various factors or indicators that affect the conductive properties of mineral insulated cables. For example, resistivity, conductor cross-sectional area, insulation material properties, etc. can all be used as evaluation factors.

[0170] If the evaluation factors are numerous or complex, they can be further decomposed into more specific sub-factors to form a multi-level evaluation factor set.

[0171] 2. Determine the evaluation weight set

[0172] According to the importance of each evaluation factor, a corresponding weight is assigned to it. These weights can be determined through expert scoring, hierarchical analysis method, anti-entropy weight method and other methods.

[0173] The evaluation weight set consists of the weights of all evaluation factors and is used to measure the importance of each factor in the comprehensive evaluation.

[0174] 3. Determine the comment set and value set

[0175] Set a comment set to describe the different levels or states of the conductive performance of mineral insulated cables. For example, it can be set as "excellent", "good", "average", "poor", etc.

[0176] Set a numerical value for each review level for use in quantitative evaluation. These numerical values ​​can be specific scores or percentages to indicate the relative advantages and disadvantages of different review levels.

[0177] 4. Constructing membership subsets

[0178] According to the evaluation factors and comment sets, a membership function is constructed to calculate the membership of each evaluation factor under different comment levels. The membership indicates the degree to which the evaluation factor belongs to a certain comment level.

[0179] For each evaluation factor, its membership degree at different comment levels is calculated to form membership degree subsets, which will be used for subsequent fuzzy comprehensive evaluation.

[0180] For example, assuming that resistivity is selected, the comment set is set to "excellent", "good", "average", and "poor", and the corresponding values ​​are set for each comment level, then the membership subset can be constructed by following the steps below:

[0181] Resistivity: Based on the actual measured value of resistivity, calculate its membership to the grades of "Excellent", "Good", "Fair", and "Poor". For example, if the resistivity is low, it may have a higher membership to the grade of "Excellent".

[0182] Insulation material performance: Based on the performance parameters of the insulation material, calculate its degree of membership to different comment levels. For example, the insulation material's temperature resistance and dielectric properties can be used as evaluation basis.

[0183] S34. Based on the membership subset, by constructing a fuzzy relationship matrix and using the weight vector for calculation, an evaluation result vector is obtained, and the evaluation result is analyzed according to the maximum membership principle to obtain the final evaluation result.

[0184] As a preferred implementation, based on the membership subset, by constructing a fuzzy relationship matrix and using a weight vector for calculation, an evaluation result vector is obtained, and the evaluation result is analyzed according to the maximum membership principle. The final evaluation result is obtained by the following steps:

[0185] S341, constructing a fuzzy relationship matrix according to the membership degree subset, and weighting the fuzzy relationship matrix through a weight vector to obtain a weighted fuzzy relationship matrix;

[0186] It should be noted that the fuzzy relationship matrix R is a p×q matrix, where p is the number of evaluation factors and q is the number of comment levels.

[0187] In order to more comprehensively reflect the importance of each evaluation factor, a weight vector w is introduced. The weight vector is combined with the fuzzy relationship matrix R, and the weighted fuzzy relationship matrix is ​​obtained through weighted processing. w The element r wij The calculation method is:

[0188] r wij =w i × ij ;

[0189] In the formula, r ij represents the membership between the i-th evaluation factor and the j-th comment level. These membership values ​​come from the membership subset constructed previously.i Represents the weight of the i-th evaluation factor.

[0190] S342, summing each row of the weighted fuzzy relationship matrix to obtain the comprehensive membership of each evaluation object corresponding to different comment levels, and taking the comprehensive membership as the evaluation result vector;

[0191] It should be noted that the weighted fuzzy relationship matrix R w The sum is taken for each row to obtain the comprehensive membership of each evaluation object (in this case, it refers to the different resistance values ​​of the measurement section in the mineral insulated cable) corresponding to different comment levels.

[0192] These comprehensive memberships constitute an n-dimensional vector evaluation result vector B, where B=(b1,b2,...,b n ), b n Indicates the comprehensive membership of the evaluation object to the nth comment level.

[0193] S343. Analyze the evaluation result vector according to the maximum membership principle, find out the largest element, and use the comment level corresponding to the element as the final performance evaluation test result.

[0194] It should be noted that in the evaluation result vector B, the largest element b is found by traversing all elements in the vector and comparing their sizes. max Once the largest element b is found max , we need to determine the corresponding review level. Since each element of the evaluation result vector B corresponds to a specific review level, we can find the corresponding review level by looking up b max The position in the vector determines the corresponding review level.

[0195] like Figure 2 As shown, according to another embodiment of the present invention, a mineral insulated cable conductivity detection system is provided, the mineral insulated cable conductivity detection system comprising: a resistance measurement module 1, an index screening module 2 and an evaluation detection module 3;

[0196] The resistance measurement module 1 is used to pre-configure the measurement section of the mineral insulated cable; introduce different DC voltages between the circumferential surface of the measurement section and the end cell, and calculate the resistance value of the measurement section under different DC voltages by collecting the current and temperature of the measurement section;

[0197] The index screening module 2 is used to establish candidate evaluation indicators based on the resistance value of the measurement section under different DC voltages and the characteristics of the mineral insulated cable, and to analyze the influence of each candidate evaluation indicator on the resistance value by using the niche theory, and to screen the final evaluation indicator according to the analysis results;

[0198] The evaluation and detection module 3 is used to determine the weight of each final evaluation index through the anti-entropy weight method, and use the multi-level fuzzy comprehensive evaluation method to evaluate the conductive performance of the mineral insulated cable to obtain the performance evaluation test result.

[0199] In summary, with the help of the above technical scheme of the present invention, the present invention can accurately calculate the resistance value of the measuring section under different conditions by introducing different DC voltages and collecting current and temperature data, and provide accurate basic data for the subsequent conductivity performance evaluation. The niche theory is used to analyze the influence of candidate evaluation indicators on the resistance value, and the evaluation indicators with the greatest influence on the conductivity performance evaluation can be scientifically screened out. The weights of each final evaluation indicator are determined by the anti-entropy weight method, which can objectively reflect the importance of each indicator in the conductivity performance evaluation and avoid the subjectivity and inaccuracy that may be caused by artificial weighting. The conductivity of the mineral insulated cable is evaluated by a multi-level fuzzy comprehensive evaluation method. This method can comprehensively consider multiple evaluation indicators and weights to obtain a comprehensive and accurate performance evaluation test result. The present invention first collects the characteristic parameters of the mineral insulated cable and determines the candidate evaluation indicators related to the resistance value, ensuring the comprehensiveness and systematicness of the evaluation. Through normalization processing, indicators of different dimensions are made to have Comparison, the niche theory is used to analyze the candidate evaluation indicators, and the influence of each indicator on the resistance value and the relationship between them are scientifically quantified by calculating the niche width and niche overlap. The trend line is fitted by linear regression method, and the slope is calculated as the potential value, which can accurately capture the changing trend of the indicator and provide reliable data support for calculating the niche width. A multi-level fuzzy comprehensive evaluation model is established in the present invention, including the steps of determining the evaluation factor set, the evaluation weight set, the comment set and the value set and constructing the membership subset, which ensures the scientificity and systematicness of the evaluation. By constructing the membership subset and the fuzzy relationship matrix, the influence of each evaluation indicator on the conductive performance can be quantified, and the accuracy and reliability of the evaluation are improved. The evaluation results are analyzed by the maximum membership principle, and the comment level corresponding to the largest element is found as the final performance evaluation test result, so that the evaluation results are intuitive and easy to understand, which helps the evaluators to quickly understand the conductive performance of the mineral insulated cable and make corresponding decisions.

[0200] It should be understood by those skilled in the art that the embodiments of the present invention may be provided as methods, systems or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment or an embodiment combining software and hardware aspects. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, optical storage, etc.) containing computer-usable program code.

[0201] The specific embodiments described above further illustrate the objectives, technical solutions and beneficial effects of the present invention in detail. It should be understood that the above description is only a specific embodiment of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A method for detecting conductivity of a mineral insulated cable, characterized in that: The mineral insulated cable conductivity detection method comprises the following steps: S1. Pre-configure the mineral insulated cable measuring section; introduce different DC voltages between the circumferential surface of the measuring section and the end cell, and calculate the resistance value of the measuring section under different DC voltages by collecting the current and temperature of the measuring section; S2. Based on the resistance value of the measurement section under different DC voltages, candidate evaluation indicators are established according to the characteristics of the mineral insulated cable, and the influence of each candidate evaluation indicator on the resistance value is analyzed using the niche theory, and the final evaluation indicator is selected according to the analysis results; S3. Determine the weight of each final evaluation index by anti-entropy weight method, and use multi-level fuzzy comprehensive evaluation method to evaluate the conductive performance of mineral insulated cables to obtain performance evaluation test results; The method of establishing candidate evaluation indicators based on the resistance value of the measuring section under different DC voltages according to the characteristics of the mineral insulated cable, and using the niche theory to analyze the influence of each candidate evaluation indicator on the resistance value, and selecting the final evaluation indicator according to the analysis results includes the following steps: S21, collecting characteristic parameters of the mineral insulated cable according to the resistance value of the measurement section, and determining candidate evaluation indicators related to the resistance value to obtain a candidate evaluation indicator set; S22, using a normalization processing method to perform normalization processing on the candidate evaluation indicator data in the candidate evaluation indicator set to obtain a normalized indicator; S23, analyze the changing trend of standardized indicators and calculate the niche width of each standardized indicator using niche theory; S24, calculating the niche overlap between different standardized indicators based on the niche width of each standardized indicator; S25, selecting the final evaluation indicators that meet the preset conditions according to the niche width of each standardized indicator and the niche overlap between different standardized indicators; The method of analyzing the change trend of the standardized indicators and calculating the niche width of each standardized indicator using the niche theory includes the following steps: S231. Perform time series analysis on each standardized indicator and fit the trend line of each standardized indicator by linear regression method; S232, calculating the slope of the trend line of each standard indicator, and using the slope as the potential value of the standard evaluation indicator, and using the indicator data of the standardized indicator as the state value; S233, according to the potential value and state value, the niche width of each standardized indicator is calculated using the niche width calculation formula; The niche width calculation formula is: ； In the formula, G i Indicates i The niche breadth value of the standard evaluation index; R i Indicates i The state value of the ecological niche of the standard evaluation indicator; R j Indicates j The state value of the ecological niche of the standard evaluation indicator; S i Indicates i The potential value of the ecological niche of the standard evaluation indicators; V j Indicates j The potential value of the ecological niche of the standard evaluation indicators; V i and V j All represent dimension conversion coefficients; m Indicates the number of standard evaluation indicators; The calculation formula for calculating the niche overlap between different standardized indicators based on the niche width of each standardized indicator is: ； In the formula, H ij Indicates i Standard evaluation indicators and j The degree of niche overlap between the standard evaluation indicators; G ix Indicates i The standard evaluation index is x The niche breadth value based on the indicator data; G jx Indicates j The standard evaluation index is x The niche breadth value based on the indicator data; n Indicates the number of indicator data in the standard evaluation indicator; The method of determining the weight of each final evaluation index by the anti-entropy weight method and evaluating the conductive performance of the mineral insulated cable by the multi-level fuzzy comprehensive evaluation method to obtain the performance evaluation result includes the following steps: S31, collecting subjective scores of the final evaluation indicators, and constructing an evaluation indicator matrix according to the final evaluation indicators, and calculating the anti-entropy value of each final evaluation indicator by an anti-entropy calculation formula; S32, obtaining the objective weight of each final evaluation indicator by normalizing the anti-entropy value of each final evaluation indicator; S33, establishing a multi-level fuzzy comprehensive evaluation model, including determining an evaluation factor set, an evaluation weight set, a comment set and a value set, and constructing a membership degree subset; S34, based on the membership subset, by constructing a fuzzy relationship matrix and using the weight vector for calculation, an evaluation result vector is obtained, and the evaluation result is analyzed according to the maximum membership principle to obtain the final evaluation result; The method of obtaining an evaluation result vector by constructing a fuzzy relationship matrix and calculating using a weight vector based on a membership subset, and analyzing the evaluation result according to the maximum membership principle to obtain the final evaluation result includes the following steps: S341, constructing a fuzzy relationship matrix according to the membership degree subset, and weighting the fuzzy relationship matrix through a weight vector to obtain a weighted fuzzy relationship matrix; S342, summing each row of the weighted fuzzy relationship matrix to obtain the comprehensive membership of each evaluation object corresponding to different comment levels, and taking the comprehensive membership as the evaluation result vector; S343. Analyze the evaluation result vector according to the maximum membership principle, find out the largest element, and use the comment level corresponding to the element as the final performance evaluation test result.

2. A mineral insulated cable conductivity detection method according to claim 1, characterized in that: The method of introducing different DC voltages between the circumferential surface of the measuring section and the end cell, and calculating the resistance value of the measuring section under different DC voltages by collecting the current and temperature of the measuring section comprises the following steps: S11, based on the voltage introduced at both ends of the measuring segment, using Ohm's law to calculate the preliminary resistance value of the measuring segment; S12, determining a temperature coefficient of the measuring section after voltage is introduced according to the temperature of the measuring section and based on material properties of the measuring section; S13. Based on the temperature coefficient and the preliminary resistance value of the measuring segment, the preliminary resistance value is corrected using a temperature correction formula to obtain the resistance value of the measuring segment under different DC voltages.

3. A mineral insulated cable conductivity detection method according to claim 2, characterized in that: The anti-entropy calculation formula is: ； In the formula, M i Indicates i The final evaluation index anti-entropy value; t Indicates the number of resistance values ​​of the measurement segment at different DC voltages; f iu Indicates u The resistance value is i The final evaluation index is subjectively scored.

4. A mineral insulated cable conductivity detection system, used to implement the mineral insulated cable conductivity detection method according to any one of claims 1 to 3, characterized in that: The mineral insulated cable conductivity detection system includes: a resistance measurement module, an index screening module and an evaluation detection module; The resistance measurement module is used to pre-configure the measurement section of the mineral insulated cable; introduce different DC voltages between the circumferential surface of the measurement section and the end cell, and calculate the resistance value of the measurement section under different DC voltages by collecting the current and temperature of the measurement section; The index screening module is used to establish candidate evaluation indicators based on the resistance value of the measurement section under different DC voltages and according to the characteristics of the mineral insulated cable, and to analyze the influence of each candidate evaluation indicator on the resistance value by using the niche theory, and to screen the final evaluation indicator according to the analysis results; The evaluation and detection module is used to determine the weight of each final evaluation index through the anti-entropy weight method, and use the multi-level fuzzy comprehensive evaluation method to evaluate the conductive performance of the mineral insulated cable to obtain the performance evaluation and detection result.

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