Power grid equipment asset wall prediction method and system based on IAHP method and value chain theory

Abstract

The invention belongs to the technical field of power grid equipment asset wall prediction, and particularly relates to a power grid equipment asset wall prediction method and system based on an IAHP method and a value chain theory, and the method comprises the steps: constructing a power grid equipment risk assessment index system, and calculating the weight of each index; calculating and obtaining a risk assessment result of the power grid equipment based on the index data and the weight; performing risk grade division on the power grid equipment according to a risk assessment result; and fitting the scrap probability distribution functions under different risk levels, selecting the scrap probability distribution function corresponding to the risk level of the power grid equipment, performing asset wall prediction on the power grid equipment, and finally obtaining the investment scale of the power grid equipment in the investment planning period. According to the method, the influence of various factors on the power grid equipment is reflected by calculating the risk levels of the power grid equipment, and the scrapping probability distribution functions corresponding to different risk levels are fitted, so that the retirement life of the power grid equipment better conforms to the actual situation, and the accuracy of the final investment scale prediction result is improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of power grid equipment asset wall prediction, and in particular relates to a power grid equipment asset wall prediction method and system based on IAHP method and value chain theory. Background Art

[0003] In existing technologies, the document "Research on the Analysis Method of the 'Asset Wall' of Physical Power Grid Assets" utilizes classic asset wall theory to predict future funding pressures for technical upgrades based on different scenarios. The document "Research on the Prediction of Asset Operation, Maintenance, and Repair Resources for Power Grid Enterprises Based on the Asset Wall" focuses on the resource cost prediction of the asset wall during the asset operation, maintenance, and repair phase of power supply enterprises to optimize asset management. The document "Research on the Prediction of Technical Upgrades Based on the Physical Power Grid 'Asset Wall'" utilizes the asset wall to predict technical upgrades based on three scenarios: financial depreciation years, equipment service life, and equipment design life. These documents only shift the asset wall based on the specified lifespan of the equipment, ignoring the uncertainty of the actual operating lifespan of power grid equipment.

[0004] To better reflect the actual operational and obsolescence of power grid equipment, the paper "Research on Predicting the Scale of Technical Renovation of Power Grid Equipment Based on the 'Asset Wall'" uses a survival probability model to optimize the "asset wall" prediction method and establishes a power grid investment strategy and technical renovation investment demand model that takes into account the "asset wall" risk. The paper "Research on Predicting the Scale of Technical Renovation of Old Equipment Based on the 'Asset Wall'" uses the Weibull model to characterize the reliability changes of equipment during operation based on actual equipment obsolescence, converting the expected life of the equipment into the probability of equipment obsolescence, calculating the probability of equipment obsolescence under different service years, and introducing the asset wall prediction model to improve the accuracy of forecasting the demand for retrofitting of old equipment. However, these papers apply the same Weibull model to all power grid equipment and fail to consider the differences in the obsolescence of power grid equipment under different factors (including equipment health, operating environment, power load, and operating conditions), resulting in low accuracy in the final prediction results. Summary of the Invention

[0005] The purpose of the present invention is to address the above-mentioned problems existing in the prior art and to provide a power grid equipment asset wall prediction method and system based on the IAHP method and value chain theory, which considers the impact of different factors on the scrapping of power grid equipment to improve the accuracy of the prediction results.

[0006] To achieve the above objectives, the technical solutions of the present invention are as follows:

[0007] In a first aspect, the present invention provides a method for predicting the asset wall of power grid equipment based on the IAHP method and the value chain theory, the method comprising:

[0008] Construct a risk assessment index system for power grid equipment and use the improved analytic hierarchy process to obtain the weight of each index;

[0009] The risk assessment results of the power grid equipment to be predicted are obtained based on the data and weights of each indicator;

[0010] Classifying the risk level of the power grid equipment to be predicted according to the risk assessment results to obtain the risk level of the power grid equipment to be predicted;

[0011] The scrap probability distribution function under different risk levels is fitted, and the scrap probability distribution function corresponding to the risk level of the power grid equipment to be predicted is selected to perform asset wall prediction on the power grid equipment, and finally the investment scale of the power grid equipment in the investment planning period is obtained.

[0012] The improved analytic hierarchy process comprises:

[0013] The 1-9 scale method is used to determine the importance of each indicator, and the following judgment matrix A is constructed:

[0014]

[0015] In the above formula, a ij is the scale value of indicator i compared with indicator j, and n is the total number of indicators; a ji is the scale value of index j compared with index i;

[0016] The judgment matrix is ​​subjected to consistency check, and the steps of the consistency check are as follows:

[0017] Step 1: Calculate the consistency ratio CR of the judgment matrix A according to the following formula:

[0018]

[0019] In the above formula, CI represents the consistency index, λ max is the maximum eigenvalue of the judgment matrix A; RI represents the average random consistency index corresponding to the order of the judgment matrix, and the order of the judgment matrix is ​​equal to the total number of indicators;

[0020] Step 2: Determine whether the consistency ratio CR is less than a preset consistency ratio threshold. If so, consider the judgment matrix A as the corrected judgment matrix A' and proceed to step 4. Otherwise, proceed to step 3 to correct the judgment matrix A to obtain the corrected judgment matrix A'.

[0021] Step 3: Modify the judgment matrix A according to the following steps:

[0022] First calculate the eigenvalue matrix Q of the judgment matrix A:

[0023] Q=(q ij) n×n ;

[0024] In the above formula, q ij is the element in the i-th row and j-th column of the eigenvalue matrix Q; i ,q j are the i-th and j-th eigenvalues ​​of the judgment matrix A respectively;

[0025] Then use the eigenvalue matrix Q and the judgment matrix A to construct the deviation matrix D:

[0026] D=(d ij ) n×n ;d ij =a ij ·q ji -1;

[0027] In the above formula, d ij is the element in row i and column j of the deviation matrix D; q ji is the element in the jth row and ith column of the eigenvalue matrix Q;

[0028] Then select the largest element in the deviation matrix D, use the position of this element in the deviation matrix D as the row and column index (i*, j*) of the judgment matrix A, and modify the judgment matrix A according to the preset modification rules to obtain the modified judgment matrix A';

[0029] Finally, the consistency ratio CR(A') of the corrected judgment matrix A' is calculated to determine whether the consistency ratio CR(A') is less than the preset consistency ratio threshold. If so, proceed to step 4. Otherwise, the corrected judgment matrix A' is used as the input of step 3 to continue the calculation.

[0030] The preset modification rules are:

[0031] First, get the sequence by 1-9 scaling method Based on the row and column index (i*, j*), the sequence O is obtained according to the following formula s Modified judgment matrix A l =(a l ij )n×n:

[0032]

[0033] In the above formula, a l ij For sequence O s Modified judgment matrix A l The element in row i and column j in O s (1) is sequence O s The lth value in, l = 1, 2, 3...17;

[0034] Then calculate the sequence O s Modified judgment matrix A l The consistency ratio CR(A l ), all the l )|≤CR ​​sequence O s Modified judgment matrix A l As a sequence O s Corrected judgment matrix collection;

[0035] Then get sequence O s Each sequence O in the modified judgment matrix collection s The row and column indexes of the modified judgment matrix are obtained to obtain the row and column index set If the row and column index set Represents an empty set, then the optimal row and column index is obtained To satisfy The row and column index of , otherwise the optimal row and column index is obtained To satisfy The row and column indices of ; where Represents element a in the judgment matrix A ij With sequence O s Modified judgment matrix A l Elements in The distance between them is calculated as follows:

[0036]

[0037] In the above formula, dp(a ij ,a mn ) represents element a ij With elements The distance between ij is the element in row i and column j of the judgment matrix A; element For sequence O s Modified judgment matrix A l The element in row i and column j in the sequence O; pos(·) means · in the sequence O s Position in

[0038] Finally, use the optimal row and column index According to the following formula, the corrected judgment matrix A'=(a ij ') n×n :

[0039]

[0040] Step 4: Calculate the weight of each indicator using the geometric mean method:

[0041]

[0042] In the above formula, ω i is the weight of indicator i; a kj ' is the element in the kth row and jth column of the modified judgment matrix A'.

[0043] The risk level classification of the power grid equipment to be predicted is carried out according to the risk assessment result, specifically: judging the risk assessment result E of the power grid equipment to be predicted c Whether 0<E c ≤0.6, if yes, it is judged as low risk level, otherwise it is judged as high risk level.

[0044] The asset wall of power grid equipment is predicted according to the following formula to obtain the investment scale of power grid equipment during the investment planning period:

[0045] f(t)=Q(t)+g(tT-1)-αg(t-1);

[0046] In the above formula, f(t) is the investment scale of power grid equipment in the investment planning period of year t after taking into account the LCW asset value; α is the regression factor; g(tT-1) represents the LCW asset value of power grid equipment in the year T+1 before the investment planning period of year t; T value represents an investment cycle; g(t-1) represents the LCW asset value of power grid equipment in the year before the investment planning period of year t; g(tT-1) and g(t-1) are both calculated from the equipment LCW asset value function; Q(t) represents the total asset wall scale of power grid equipment in year t, which is calculated from the total asset wall scale function of power grid equipment;

[0047] The expression of the equipment LCW asset value function is:

[0048]

[0049] In the above formula, g(t) is the LCW asset value of the power grid equipment in year t; V(t) is the annual total output value of the power grid equipment in year t; ε is the contribution ratio of the power grid equipment to the annual total output value; Y(t) is the operation and maintenance expenditure of the power grid equipment in year t; J(t) is the value of the retired equipment in year t; R j (t) is the net value of the jth device in the power grid equipment in year t;

[0050] The expression of the total asset wall scale function of the power grid equipment is:

[0051] Q(t+1)=λ 1 (t)×QW 1 (t)+λ 2 (t)×QW 2 (t);

[0052] In the above formula, Q(t+1) is the total asset size of power grid equipment in year t+1; 1 (t), λ 2 (t) are the scrap probability distribution functions corresponding to the high and low risk levels of power grid equipment; QW 1 (t), QW 2 (t) are the asset wall sizes of high-risk and low-risk grid equipment in year t, respectively.

[0053] The expression of the scrap probability distribution function is:

[0054]

[0055] In the above formula, is the probability of power grid equipment being scrapped in year t; β and η are fitting parameters;

[0056] The parameter fitting of the scrap probability distribution function is completed by minimizing the objective function, which is:

[0057]

[0058] In the above formula, is the probability of power grid equipment being scrapped in year t calculated by the scrapping probability distribution function; t is the actual probability of power grid equipment being scrapped in year t.

[0059] In a second aspect, the present invention provides a power grid equipment asset wall prediction system based on the IAHP method and value chain theory, the power grid equipment asset wall prediction system comprising:

[0060] The index system and weight calculation module is used to build a risk assessment index system for power grid equipment and obtain the weight of each index using the improved analytic hierarchy process;

[0061] A risk assessment module is used to calculate the risk assessment results of the power grid equipment asset wall to be predicted based on the data and weights of each indicator;

[0062] A risk level classification module is used to classify the risk level of the power grid equipment asset wall to be predicted according to the risk assessment results, and obtain the risk level of the power grid equipment asset wall to be predicted;

[0063] The asset wall prediction module is used to fit the scrap probability distribution function under different risk levels, select the scrap probability distribution function corresponding to the risk level of the power grid equipment asset wall to be predicted, perform power grid equipment asset wall prediction, and finally obtain the investment scale of power grid equipment during the investment planning period.

[0064] The indicator system and weight calculation module is used to obtain the weight of each indicator according to the following steps:

[0065] The 1-9 scale method is used to determine the importance of each indicator, and the following judgment matrix A is constructed:

[0066]

[0067] In the above formula, a ij is the scale value of indicator i compared with indicator j, and n is the total number of indicators; a ji is the scale value of index j compared with index i;

[0068] The judgment matrix is ​​subjected to consistency check, and the steps of the consistency check are as follows:

[0069] Step 1: Calculate the consistency ratio CR of the judgment matrix A according to the following formula:

[0070]

[0071] In the above formula, CI represents the consistency index, λ max is the maximum eigenvalue of the judgment matrix A; RI represents the average random consistency index corresponding to the order of the judgment matrix, and the order of the judgment matrix is ​​equal to the total number of indicators;

[0072] Step 2: Determine whether the consistency ratio CR is less than a preset consistency ratio threshold. If so, consider the judgment matrix A as the corrected judgment matrix A' and proceed to step 4. Otherwise, proceed to step 3 to correct the judgment matrix A to obtain the corrected judgment matrix A'.

[0073] Step 3: Modify the judgment matrix A according to the following steps:

[0074] First calculate the eigenvalue matrix Q of the judgment matrix A:

[0075] Q=(q ij ) n×n ;

[0076] In the above formula, q ij is the element in the i-th row and j-th column of the eigenvalue matrix Q; i ,q j are the i-th and j-th eigenvalues ​​of the judgment matrix A respectively;

[0077] Then use the eigenvalue matrix Q and the judgment matrix A to construct the deviation matrix D:

[0078] D=(d ij ) n×n ;d ij =a ij ·q ji -1;

[0079] In the above formula, d ij is the element in row i and column j of the deviation matrix D; q ji is the element in the jth row and ith column of the eigenvalue matrix Q;

[0080] Then select the largest element in the deviation matrix D, use the position of this element in the deviation matrix D as the row and column index (i*, j*) of the judgment matrix A, and modify the judgment matrix A according to the preset modification rules to obtain the modified judgment matrix A';

[0081] Finally, the consistency ratio CR(A') of the corrected judgment matrix A' is calculated to determine whether the consistency ratio CR(A') is less than the preset consistency ratio threshold. If so, proceed to step 4. Otherwise, the corrected judgment matrix A' is used as the input of step 3 to continue the calculation.

[0082] The preset modification rules are:

[0083] First, get the sequence by 1-9 scaling method Based on the row and column index (i*, j*), the sequence O is obtained according to the following formula s Modified judgment matrix A l =(a l ij )n×n:

[0084]

[0085] In the above formula, a l ij For sequence O s Modified judgment matrix A l The element in row i and column j in O s (1) is sequence O s The lth value in, l = 1, 2, 3...17;

[0086] Then calculate the sequence O s Modified judgment matrix A l The consistency ratio CR(A l ), all the l )|≤CR ​​sequence O s Modified judgment matrix A l As a sequence O s Corrected judgment matrix collection;

[0087] Then get sequence O s Each sequence O in the modified judgment matrix collection s The row and column indexes of the modified judgment matrix are obtained to obtain the row and column index set If the row and column index set Represents an empty set, then the optimal row and column index is obtained To satisfy The row and column index of , otherwise the optimal row and column index is obtained To satisfy The row and column indices of ; where Represents element a in the judgment matrix A ij With sequence O s Modified judgment matrix A l Elements in The distance between them is calculated as follows:

[0088]

[0089] In the above formula, dp(a ij ,a mn ) represents element a ij With elements The distance between ij is the element in row i and column j of the judgment matrix A; element For sequence O s Modified judgment matrix A l The element in row i and column j in the sequence O; pos(·) means · in the sequence O s Position in

[0090] Finally, use the optimal row and column index According to the following formula, the corrected judgment matrix A'=(a ij ') n×n :

[0091]

[0092] Step 4: Calculate the weight of each indicator using the geometric mean method:

[0093]

[0094] In the above formula, ω i is the weight of indicator i; a kj ' is the element in the kth row and jth column of the modified judgment matrix A'.

[0095] The risk level classification module is used to perform risk level classification according to the following steps: determining the risk assessment result E of the power grid equipment asset wall to be predicted c Whether 0<E c ≤0.6, if yes, it is judged as low risk level, otherwise it is judged as high risk level.

[0096] The asset wall prediction is used to predict the asset wall of power grid equipment according to the following formula to obtain the investment scale of power grid equipment during the investment planning period:

[0097] f(t)=Q(t)+g(tT-1)-αg(t-1);

[0098] In the above formula, f(t) is the investment scale of power grid equipment in the investment planning period of year t after taking into account the LCW asset value; α is the regression factor; g(tT-1) represents the LCW asset value of power grid equipment in the year T+1 before the investment planning period of year t; T value represents an investment cycle; g(t-1) represents the LCW asset value of power grid equipment in the year before the investment planning period of year t; g(tT-1) and g(t-1) are both calculated from the equipment LCW asset value function; Q(t) represents the total asset wall scale of power grid equipment in year t, which is calculated from the total asset wall scale function of power grid equipment;

[0099] The expression of the equipment LCW asset value function is:

[0100]

[0101] In the above formula, g(t) is the LCW asset value of the power grid equipment in year t; V(t) is the annual total output value of the power grid equipment in year t; ε is the contribution ratio of the power grid equipment to the annual total output value; Y(t) is the operation and maintenance expenditure of the power grid equipment in year t; J(t) is the value of the retired equipment in year t; R j (t) is the net value of the jth device in the power grid equipment in year t;

[0102] The expression of the total asset wall scale function of the power grid equipment is:

[0103] Q(t+1)=λ 1 (t)×QW 1 (t)+λ 2 (t)×QW 2 (t);

[0104] In the above formula, Q(t+1) is the total asset size of power grid equipment in year t+1; 1 (t), λ 2 (t) are the scrap probability distribution functions corresponding to the high and low risk levels of power grid equipment; QW 1 (t), QW 2 (t) are the asset wall sizes of high-risk and low-risk grid equipment in year t, respectively.

[0105] The expression of the scrap probability distribution function is:

[0106]

[0107] In the above formula, is the probability of power grid equipment being scrapped in year t; β and η are fitting parameters;

[0108] The parameter fitting of the scrap probability distribution function is completed by minimizing the objective function, which is:

[0109]

[0110] In the above formula, is the probability of power grid equipment being scrapped in year t calculated by the scrapping probability distribution function; t is the actual probability of power grid equipment being scrapped in year t.

[0111] In a third aspect, the present invention provides a power grid equipment asset wall prediction device based on the IAHP method and value chain theory, the power grid equipment asset wall prediction device comprising a memory and a processor; the memory is used to store computer program code and transmit the computer program code to the processor; the processor is used to execute the aforementioned power grid equipment asset wall prediction method based on the IAHP method and value chain theory according to the instructions in the computer program code.

[0112] In a fourth aspect, the present invention provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the aforementioned power grid equipment asset wall prediction method based on the IAHP method and the value chain theory.

[0113] Compared with the prior art, the present invention has the following beneficial effects:

[0114] 1. The power grid equipment asset wall prediction method based on the IAHP method and value chain theory described in the present invention constructs a power grid equipment risk assessment index system and uses the improved hierarchical analysis method to obtain the weight of each indicator; the risk assessment result of the power grid equipment to be predicted is calculated based on the data and weight of each indicator; the risk level of the power grid equipment to be predicted is divided according to the risk assessment result to obtain the risk level of the power grid equipment to be predicted; the scrap probability distribution function under different risk levels is fitted, and the scrap probability distribution function corresponding to the risk level of the power grid equipment to be predicted is selected to predict the asset wall of the power grid equipment, and finally the investment scale of the power grid equipment in the investment planning period is obtained; the above method first divides the risk level of the power grid equipment and then fits the scrap probability distribution function under different risk levels for power grid equipment asset wall prediction, which can reflect the influence of various factors on the power grid equipment, so that the retirement life of the power grid equipment is more in line with the actual situation, thereby improving the accuracy of the final investment scale prediction result.

[0115] 2. The grid equipment asset wall prediction method based on the IAHP method and value chain theory described in the present invention introduces a full life cycle management equipment asset value function when calculating the investment scale of grid equipment during the investment planning period, avoiding the drawbacks of simple translation of the classic asset wall, thereby reducing the error in the final investment scale prediction results. BRIEF DESCRIPTION OF THE DRAWINGS

[0116] Figure 1 Schematic diagram of the process of the present invention.

[0117] Figure 2 This is the power grid equipment risk assessment index system constructed by the present invention.

[0118] Figure 3 This is a structural block diagram of the system of the present invention.

[0119] Figure 4 This is a structural block diagram of the device described in the present invention. DETAILED DESCRIPTION

[0120] The present invention will be further described in detail below with reference to specific embodiments and the accompanying drawings.

[0121] Example 1:

[0122] See also Figure 1 ,A method for predicting the asset wall of power grid equipment based on IAHP method and value chain theory,,is carried out in the following steps:

[0123] S1. Construct a risk assessment index system for power grid equipment and use the improved analytic hierarchy process to obtain the weight of each index;

[0124] like Figure 2As shown in the figure, in order to comprehensively evaluate the risk level of power grid equipment, the power grid equipment risk assessment index system includes 4 first-level indicators and 16 second-level indicators; the 4 first-level indicators include equipment health risk, equipment operating environment risk, power load and operating condition risk, and equipment protection and safety risk; the equipment health risk is used to reflect the current health status of the equipment to assess whether the power grid equipment has potential fault hazards or has aged, and specifically includes the following four second-level indicators: equipment aging degree, maintenance history, fault history, and operating status monitoring data; equipment aging degree: the remaining life of the equipment and the degree of material aging. Equipment aging will increase the probability of failure; maintenance history: indicates the maintenance record and maintenance quality of the equipment, that is, whether the equipment has been regularly inspected and maintained, and whether it has been left unused for a long time. Maintenance history; Fault history: historical fault records of the equipment, including fault type, frequency and repair time; Operation status monitoring data package: real-time monitoring of equipment operating parameters (such as transformer oil temperature, cooling status, oil level, oil color, etc.) to evaluate the health status of the equipment; The equipment operating environment risks include the following four secondary indicators: ambient temperature, ambient humidity, air quality, vibration and mechanical load; Ambient temperature: the temperature at the location where the power grid equipment is located, used to reflect the impact of high and low temperatures on the equipment; Ambient humidity: the humidity at the location where the power grid equipment is located, a high humidity environment may cause risks such as reduced insulation performance, corrosion or short circuit of the equipment, especially for cables, distribution boards and other equipment; Air quality: pollutant content (such as dust, smoke, etc.), air Corrosive gases (such as hydrogen sulfide) will accelerate the corrosion of power grid equipment; vibration and mechanical load: it means that the power grid equipment is affected by external mechanical force and vibration during operation; the power load and operating conditions include the following four secondary indicators: equipment load rate, load fluctuation, power factor, and current fluctuation; equipment load rate: the ratio of the load carried by the power grid equipment during operation to the rated load of the equipment. Long-term overload will increase the risk of equipment failure; load fluctuation: large load fluctuations, frequent start and stop operations may cause the equipment to work under extreme conditions, thereby increasing the risk of failure; power factor: low power factor may cause unbalanced electrical load of the equipment, thereby accelerating the aging of the power grid equipment; current fluctuation: large current fluctuations may cause the equipment to operate for a long time Undergo overload and increase the risk of equipment damage; the equipment protection and safety risks include the following four secondary indicators: relay protection function, overload protection function, automation control system, and response time of the protection system; relay protection function: whether the relay protection of the equipment is reasonably set, whether the protection equipment can detect and cut off the fault current in time to reduce equipment damage; overload protection function: whether the equipment has sufficient overload protection to prevent failures caused by overload operation; automation control system: automation equipment can detect failures in time and take measures, such as remote control equipment shutdown, switching and other operations to reduce delays in manual operations; response time of the protection system: when a device fails, the shorter the response time of the protection system, the more effective it can be in avoiding serious damage to the equipment;.

[0125] The improved analytic hierarchy process (IAHP) method specifically comprises the following steps:

[0126] Step 1: Use the 1-9 scaling method to determine the importance of each indicator and construct the following judgment matrix A:

[0127]

[0128] In the above formula, a ij is the scale value of indicator i compared with indicator j, and n is the total number of secondary indicators; a ji is the scale value of index j compared with index i;

[0129] Step 2: Perform consistency check on the judgment matrix. The steps of the consistency check are as follows:

[0130] Step 1: Calculate the consistency ratio CR of the judgment matrix A according to the following formula:

[0131]

[0132] In the above formula, CI represents the consistency index, λ max is the maximum eigenvalue of the judgment matrix A; RI represents the average random consistency index corresponding to the judgment matrix order, and the judgment matrix order is equal to the total number of indicators; the RI value can be obtained by querying the corresponding relationship table between the judgment matrix order and the RI value shown in Table 1;

[0133] Table 1 Correspondence between judgment matrix order and RI value

[0134] Order 1 2 3 4 5 6 7 8 9 RI 0 0 0.58 0.9 1.12 1.24 1.32 1.41 1.45

[0135] Step 2: Determine whether the consistency ratio CR is less than a preset consistency ratio threshold. If so, consider the judgment matrix A as the corrected judgment matrix A' and proceed to step 4. Otherwise, proceed to step 3 to correct the judgment matrix A to obtain the corrected judgment matrix A'.

[0136] Step 3: Modify the judgment matrix A according to the following steps:

[0137] First calculate the eigenvalue matrix Q of the judgment matrix A:

[0138] Q=(q ij ) n×n ;

[0139] In the above formula, q ij is the element in the i-th row and j-th column of the eigenvalue matrix Q; i ,q jare the i-th and j-th eigenvalues ​​of the judgment matrix A respectively;

[0140] Then use the eigenvalue matrix Q and the judgment matrix A to construct the deviation matrix D:

[0141] D=(d ij ) n×n ;d ij =a ij ·q ji -1;

[0142] In the above formula, d ij is the element in row i and column j of the deviation matrix D; q ji is the element in the jth row and ith column of the eigenvalue matrix Q;

[0143] Then select the largest element in the deviation matrix D, use the position of this element in the deviation matrix D as the row and column index (i*, j*) of the judgment matrix A, and modify the judgment matrix A according to the preset modification rules to obtain the modified judgment matrix A';

[0144] Finally, the consistency ratio CR(A') of the corrected judgment matrix A' is calculated to determine whether the consistency ratio CR(A') is less than the preset consistency ratio threshold. If so, proceed to step 4. Otherwise, the corrected judgment matrix A' is used as the input of step 3 to continue the calculation.

[0145] The process of modifying the judgment matrix A is an iterative process. Each iteration will ensure that the consistency of the obtained judgment matrix is ​​better. The final judgment matrix will not be output until the preset consistency ratio threshold is reached. Preferably, the consistency ratio threshold is set to 0.1.

[0146] The preset modification rules are:

[0147] First, the sequence is obtained by experts according to the 1-9 scaling method Based on the row and column index (i*, j*), the sequence O is obtained according to the following formula s Modified judgment matrix A l =(a l ij )n×n:

[0148]

[0149] In the above formula, a l ij For sequence O s Modified judgment matrix A l The element in row i and column j in O s (1) is sequence O s The lth value in, l = 1, 2, 3...17;

[0150] Then calculate the sequence O s Modified judgment matrix A l The consistency ratio CR(A l ), all the l )|≤CR ​​sequence O s Modified judgment matrix A l As a sequence O s Corrected judgment matrix collection;

[0151] Then get sequence O s Each sequence O in the modified judgment matrix collection s The row and column indexes of the modified judgment matrix are obtained to obtain the row and column index set If the row and column index set Represents an empty set, which means that the judgment matrix A can reach a predefined consistency level by selecting appropriate indexes, and the optimal row and column index is obtained. To satisfy The row and column indexes, if the row and column index set This means that it is impossible to select the appropriate index to make the judgment matrix A reach the predefined consistency level. At this time, the optimal row and column index is obtained. To satisfy The row and column indices of Represents element a in the judgment matrix A ij With sequence O s Modified judgment matrix A l Elements in The distance between them is calculated as follows:

[0152]

[0153] In the above formula, dp(a ij ,a mn ) represents element a ij With elements The distance between ij is the element in row i and column j of the judgment matrix A; element For sequence O s Modified judgment matrix A l The element in row i and column j in the sequence O; pos(·) means · in the sequence O s Position in, for example a ij for When pos(a ij ) represents element a ij In sequence O s The position in is 1; according to dp(a ij ,a mn) to replace elements, thereby ensuring that the original expert experience is retained as much as possible;

[0154] Finally, use the optimal row and column index According to the following formula, the corrected judgment matrix A'=(a ij ') n×n :

[0155]

[0156] Step 4: Calculate the weight of each indicator using the geometric mean method:

[0157]

[0158] In the above formula, ω i is the weight of indicator i; a kj ' is the element in the kth row and jth column of the modified judgment matrix A', k≠i;

[0159] S2. Obtain the data of each indicator and normalize the indicator data. Weight the normalized indicator data and the indicator weight to calculate the risk assessment result E of the power grid equipment to be predicted. c ;

[0160] S3, according to the risk assessment results, the risk level of the power grid equipment to be predicted is divided into risk levels, and the risk level of the power grid equipment to be predicted is obtained; the specific steps are: determining the risk assessment result E of the power grid equipment to be predicted c Whether 0<E c ≤0.6, if yes, it is judged as low risk level, otherwise it is judged as high risk level;

[0161] S4. First, obtain the annual scrapping probability of a certain type of power grid equipment in a high-risk level group and a low-risk level group after commissioning, fit the original scrapping probability distribution function, and obtain scrapping probability distribution models under different risk levels. Then, select the scrapping probability distribution model corresponding to the risk level of the power grid equipment to be predicted, perform asset wall prediction on the power grid equipment, and ultimately obtain the investment scale of the power grid equipment during the investment planning period.

[0162] Specifically, the asset wall of power grid equipment is predicted according to the following formula to obtain the investment scale of power grid equipment during the investment planning period:

[0163] f(t)=Q(t)+g(tT-1)-αg(t-1);

[0164] In the above formula, f(t) is the investment scale of power grid equipment in the investment planning period of year t after taking into account the LCW asset value; α is a regression factor used to represent the growth of power grid business; g(tT-1) is the LCW asset value of power grid equipment in the year T+1 before the investment planning period of year t; T represents an investment cycle and is a constant value; g(t-1) is the LCW asset value of power grid equipment in the year before the investment planning period of year t; g(tT-1) and g(t-1) are both calculated from the equipment LCW asset value function; Q(t) is the total asset wall scale of power grid equipment in year t, which is calculated from the total asset wall scale function of power grid equipment.

[0165] The expression of the equipment LCW asset value function is:

[0166]

[0167] In the above formula, g(t) is the LCW asset value of the power grid equipment in year t. An increase in the value of g(t) means that the power grid equipment contributes more to the profits of the power grid enterprise, while equipment defects, equipment retirement, and overinvestment will flatten or even decrease g(t); V(t) is the annual total output value of the power grid equipment in year t; ε is the contribution ratio of the power grid equipment to the annual total output value, which is used to reflect the overinvestment in power grid equipment. The smaller the ε value, the greater the overinvestment; Y(t) is the operation and maintenance expenditure of the power grid equipment in year t, which is used to reflect the equipment defects; J(t) is the value of retired equipment in the power grid equipment in year t, which is used to reflect the equipment retirement situation; R j (t) is the net value of the jth device in the power grid equipment in year t;

[0168] The expression of the total asset wall scale function of the power grid equipment is:

[0169] Q(t+1)=λ 1 (t)×QW 1 (t)+λ 2 (t)×QW 2 (t);

[0170] In the above formula, Q(t+1) is the total asset size of power grid equipment in year t+1; 1 (t), λ 2 (t) are the scrap probability distribution models corresponding to high and low risk level power grid equipment; QW 1 (t), QW 2 (t) are the asset wall sizes of power grid equipment with high and low risk levels in year t;

[0171] The expression of the scrap probability distribution function is:

[0172]

[0173] In the above formula, is the probability of power grid equipment being scrapped in year t; β and η are fitting parameters;

[0174] The parameter fitting of the scrap probability distribution function is completed by minimizing the objective function, which is:

[0175]

[0176] In the above formula, is the probability of power grid equipment being scrapped in year t calculated by the scrapping probability distribution function; t is the actual probability of power grid equipment being scrapped in year t;

[0177] Specifically, the nonlinear least squares method can be used for parameter fitting, and the trust region algorithm can be introduced to iteratively solve the objective function. The trust region algorithm adjusts the optimization step size by calculating the Hessian matrix or its approximate value to ensure that each iteration approaches the optimal solution.

[0178] Performance Verification:

[0179] In order to verify the effectiveness of the power grid equipment asset wall prediction method of the present invention, it is compared with the classic asset wall method and the machine learning method, and the prediction errors of different methods are shown in Table 2;

[0180] Table 2 Prediction errors of different methods

[0181] method The method of the present invention Classic asset wall method Machine Learning Forecast investment scale 23841 24500 21750 Investment scale 23000 23000 23000 error 3.6％ 6.5％ 5.4％

[0182] As can be seen from Table 2, the error of the method of the present invention is the lowest, which is 3.6%. This shows that the power grid equipment asset wall prediction method of the present invention can effectively improve the accuracy of the power grid equipment asset wall prediction.

[0183] Example 2:

[0184] See also Figure 3, a power grid equipment asset wall prediction system based on the IAHP method and value chain theory, including an index system and weight calculation module, a risk assessment module, a risk level classification module, a risk level classification module, and an asset wall prediction module; the index system and weight calculation module are used to construct a power grid equipment risk assessment index system and use an improved hierarchical analysis method to obtain the weight of each indicator; the risk assessment module is used to calculate the risk assessment result of the power grid equipment asset wall to be predicted based on the data and weight of each indicator; the risk level classification module is used to classify the risk level of the power grid equipment asset wall to be predicted according to the risk assessment result, and obtain the risk level of the power grid equipment asset wall to be predicted; the asset wall prediction module is used to fit the scrap probability distribution function under different risk levels, select the scrap probability distribution function corresponding to the risk level of the power grid equipment asset wall to be predicted, perform power grid equipment asset wall prediction, and finally obtain the investment scale of the power grid equipment in the investment planning period; specifically, the index system and weight calculation module is used to obtain the weight of each indicator according to the following steps:

[0185] Step 1: Use the 1-9 scaling method to determine the importance of each indicator and construct the following judgment matrix A:

[0186]

[0187] In the above formula, a ij is the scale value of indicator i compared with indicator j, and n is the total number of indicators; a ji is the scale value of index j compared with index i;

[0188] Step 2: Perform consistency check on the judgment matrix. The steps of the consistency check are as follows:

[0189] Step 1: Calculate the consistency ratio CR of the judgment matrix A according to the following formula:

[0190]

[0191] In the above formula, CI represents the consistency index, λ max is the maximum eigenvalue of the judgment matrix A; RI represents the average random consistency index corresponding to the order of the judgment matrix, and the order of the judgment matrix is ​​equal to the total number of indicators;

[0192] Step 2: Determine whether the consistency ratio CR is less than a preset consistency ratio threshold. If so, consider the judgment matrix A as the corrected judgment matrix A' and proceed to step 4. Otherwise, proceed to step 3 to correct the judgment matrix A to obtain the corrected judgment matrix A'.

[0193] Step 3: Modify the judgment matrix A according to the following steps:

[0194] First calculate the eigenvalue matrix Q of the judgment matrix A:

[0195] Q=(q ij ) n×n ;

[0196] In the above formula, q ij is the element in the i-th row and j-th column of the eigenvalue matrix Q; i ,q j are the i-th and j-th eigenvalues ​​of the judgment matrix A respectively;

[0197] Then use the eigenvalue matrix Q and the judgment matrix A to construct the deviation matrix D:

[0198] D=(d ij ) n×n ;d ij =a ij ·q ji -1;

[0199] In the above formula, d ij is the element in row i and column j of the deviation matrix D; q ji is the element in the jth row and ith column of the eigenvalue matrix Q;

[0200] Then select the largest element in the deviation matrix D, use the position of this element in the deviation matrix D as the row and column index (i*, j*) of the judgment matrix A, and modify the judgment matrix A according to the preset modification rules to obtain the modified judgment matrix A';

[0201] Finally, the consistency ratio CR(A') of the corrected judgment matrix A' is calculated to determine whether the consistency ratio CR(A') is less than the preset consistency ratio threshold. If so, proceed to step 4. Otherwise, the corrected judgment matrix A' is used as the input of step 3 to continue the calculation.

[0202] The preset modification rules are:

[0203] First, get the sequence by 1-9 scaling method Based on the row and column index (i*, j*), the sequence O is obtained according to the following formula s Modified judgment matrix A l =(a l ij )n×n:

[0204]

[0205] In the above formula, a l ij For sequence O s Modified judgment matrix A l The element in row i and column j in Os (1) is sequence O s The lth value in, l = 1, 2, 3...17;

[0206] Then calculate the sequence O s Modified judgment matrix A l The consistency ratio CR(A l ), all the l )|≤CR ​​sequence O s Modified judgment matrix A l As a sequence O s Corrected judgment matrix collection;

[0207] Then get sequence O s Each sequence O in the modified judgment matrix collection s The row and column indexes of the modified judgment matrix are obtained to obtain the row and column index set If the row and column index set Represents an empty set, then the optimal row and column index is obtained To satisfy The row and column index of , otherwise the optimal row and column index is obtained To satisfy The row and column indices of ; where Represents element a in the judgment matrix A ij With sequence O s Modified judgment matrix A l Elements in The distance between them is calculated as follows:

[0208]

[0209] In the above formula, dp(a ij ,a mn ) represents element a ij With elements The distance between ij is the element in row i and column j of the judgment matrix A; element For sequence O s Modified judgment matrix A l The element in row i and column j in the sequence O; pos(·) means · in the sequence O s Position in

[0210] Finally, use the optimal row and column index According to the following formula, the corrected judgment matrix A'=(a ij ') n×n :

[0211]

[0212] Step 4: Calculate the weight of each indicator using the geometric mean method:

[0213]

[0214] In the above formula, ω i is the weight of indicator i; a kj ' is the element in the kth row and jth column of the modified judgment matrix A';

[0215] Specifically, the risk level classification module is used to perform risk level classification according to the following steps: determining the risk assessment result E of the power grid equipment asset wall to be predicted; c Whether 0<E c ≤0.6, if yes, it is judged as low risk level, otherwise it is judged as high risk level;

[0216] Specifically, the asset wall prediction is used to predict the asset wall of power grid equipment according to the following formula to obtain the investment scale of power grid equipment during the investment planning period:

[0217] f(t)=Q(t)+g(tT-1)-αg(t-1);

[0218] In the above formula, f(t) is the investment scale of power grid equipment in the investment planning period of year t after taking into account the LCW asset value; α is the regression factor; g(tT-1) represents the LCW asset value of power grid equipment in the year T+1 before the investment planning period of year t; T value represents an investment cycle; g(t-1) represents the LCW asset value of power grid equipment in the year before the investment planning period of year t; g(tT-1) and g(t-1) are both calculated from the equipment LCW asset value function; Q(t) represents the total asset wall scale of power grid equipment in year t, which is calculated from the total asset wall scale function of power grid equipment;

[0219] The expression of the equipment LCW asset value function is:

[0220]

[0221] In the above formula, g(t) is the LCW asset value of the power grid equipment in year t; V(t) is the annual total output value of the power grid equipment in year t; ε is the contribution ratio of the power grid equipment to the annual total output value; Y(t) is the operation and maintenance expenditure of the power grid equipment in year t; J(t) is the value of the retired equipment in year t; R j (t) is the net value of the jth device in the power grid equipment in year t;

[0222] The expression of the total asset wall scale function of the power grid equipment is:

[0223] Q(t+1)=λ1 (t)×QW 1 (t)+λ 2 (t)×QW 2 (t);

[0224] In the above formula, Q(t+1) is the total asset size of power grid equipment in year t+1; 1 (t), λ 2 (t) are the scrap probability distribution functions corresponding to the high and low risk levels of power grid equipment; QW 1 (t), QW 2 (t) are the asset wall sizes of power grid equipment with high and low risk levels in year t;

[0225] The expression of the scrap probability distribution function is:

[0226]

[0227] In the above formula, is the probability of power grid equipment being scrapped in year t; β and η are fitting parameters;

[0228] The parameter fitting of the scrap probability distribution function is completed by minimizing the objective function, which is:

[0229]

[0230] In the above formula, is the probability of power grid equipment being scrapped in year t calculated by the scrapping probability distribution function; t is the actual probability of power grid equipment being scrapped in year t.

[0231] Example 3:

[0232] See also Figure 4 , a power grid equipment asset wall prediction device based on the IAHP method and value chain theory, including a memory and a processor; the memory is used to store computer program code and transmit the computer program code to the processor; the processor is used to execute the power grid equipment asset wall prediction method based on the IAHP method and value chain theory as described in Example 1 according to the instructions in the computer program code.

[0233] Example 4:

[0234] A computer-readable storage medium having a computer program stored thereon, wherein when the computer program is executed by a processor, the method for predicting the asset wall of power grid equipment based on the IAHP method and the value chain theory as described in Example 1 is implemented.

Claims

1. A method for predicting the asset wall of power grid equipment based on the IAHP method and value chain theory, characterized by: The power grid equipment asset wall prediction method includes: Construct a risk assessment index system for power grid equipment and use the improved analytic hierarchy process to obtain the weight of each index; The risk assessment results of the power grid equipment to be predicted are obtained based on the data and weights of each indicator; Classifying the risk level of the power grid equipment to be predicted according to the risk assessment results to obtain the risk level of the power grid equipment to be predicted; The scrap probability distribution function under different risk levels is fitted, and the scrap probability distribution function corresponding to the risk level of the power grid equipment to be predicted is selected to perform asset wall prediction on the power grid equipment, and finally the investment scale of the power grid equipment in the investment planning period is obtained.

2. The method for predicting the asset wall of power grid equipment based on the IAHP method and the value chain theory according to claim 1 is characterized by: The improved analytic hierarchy process comprises: The 1-9 scale method is used to determine the importance of each indicator, and the following judgment matrix A is constructed: In the above formula, a ij is the scale value of indicator i compared with indicator j, and n is the total number of indicators; a ji is the scale value of index j compared with index i; The judgment matrix is ​​subjected to consistency check, and the steps of the consistency check are as follows: Step 1: Calculate the consistency ratio CR of the judgment matrix A according to the following formula: In the above formula, CI represents the consistency index, λ max is the maximum eigenvalue of the judgment matrix A; RI represents the average random consistency index corresponding to the order of the judgment matrix, and the order of the judgment matrix is ​​equal to the total number of indicators; Step 2: Determine whether the consistency ratio CR is less than a preset consistency ratio threshold. If so, consider the judgment matrix A as the corrected judgment matrix A' and proceed to step 4. Otherwise, proceed to step 3 to correct the judgment matrix A to obtain the corrected judgment matrix A'. Step 3: Modify the judgment matrix A according to the following steps: First calculate the eigenvalue matrix Q of the judgment matrix A: In the above formula, q ij is the element in the i-th row and j-th column of the eigenvalue matrix Q; i ,q j are the i-th and j-th eigenvalues ​​of the judgment matrix A respectively; Then use the eigenvalue matrix Q and the judgment matrix A to construct the deviation matrix D: D=(d ij ) n×n ;d ij =a ij ·q ji -1; In the above formula, d ij is the element in row i and column j of the deviation matrix D; q ji is the element in the jth row and ith column of the eigenvalue matrix Q; Then select the largest element in the deviation matrix D, use the position of this element in the deviation matrix D as the row and column index (i*, j*) of the judgment matrix A, and modify the judgment matrix A according to the preset modification rules to obtain the modified judgment matrix A'; Finally, the consistency ratio CR(A') of the corrected judgment matrix A' is calculated to determine whether the consistency ratio CR(A') is less than the preset consistency ratio threshold. If so, proceed to step 4. Otherwise, the corrected judgment matrix A' is used as the input of step 3 to continue the calculation. The preset modification rules are: First, get the sequence by 1-9 scaling method Based on the row and column index (i*, j*), the sequence O is obtained according to the following formula s Modified judgment matrix A l =(a l ij )n×n: In the above formula, a l ij For sequence O s Modified judgment matrix A l The element in row i and column j in O s (1) is sequence O s The lth value in, l = 1, 2, 3...17; Then calculate the sequence O s Modified judgment matrix A l The consistency ratio CR(A l ), all the l )|≤CR ​​sequence O s Modified judgment matrix A l As a sequence O s Corrected judgment matrix collection; Then get sequence O s Each sequence O in the modified judgment matrix collection s The row and column indexes of the modified judgment matrix are obtained to obtain the row and column index set If the row and column index set Represents an empty set, then the optimal row and column index is obtained To satisfy The row and column index of , otherwise the optimal row and column index is obtained To satisfy The row and column indices of ; where Represents element a in the judgment matrix A ij With sequence O s Modified judgment matrix A l Elements in The distance between them is calculated as follows: In the above formula, dp(a ij ,a mn ) represents element a ij With elements The distance between ij is the element in row i and column j of the judgment matrix A; element For sequence O s Modified judgment matrix A l The element in row i and column j in the sequence O; pos(·) means · in the sequence O s Position in Finally, use the optimal row and column index According to the following formula, the corrected judgment matrix A'=(a ij ') n×n : Step 4: Calculate the weight of each indicator using the geometric mean method: In the above formula, ω i is the weight of indicator i; a kj ' is the element in the kth row and jth column of the modified judgment matrix A'.

3. The method for predicting the asset wall of power grid equipment based on the IAHP method and the value chain theory according to claim 1 or 2, characterized in that: The risk level classification of the power grid equipment to be predicted is carried out according to the risk assessment result, specifically: judging the risk assessment result E of the power grid equipment to be predicted c Whether 0<E c ≤0.6, if yes, it is judged as low risk level, otherwise it is judged as high risk level.

4. The method for predicting the asset wall of power grid equipment based on the IAHP method and the value chain theory according to claim 3 is characterized by: The asset wall of power grid equipment is predicted according to the following formula to obtain the investment scale of power grid equipment during the investment planning period: f(t)=Q(t)+g(tT-1)-αg(t-1); In the above formula, f(t) is the investment scale of power grid equipment in the investment planning period of year t after taking into account the LCW asset value; α is the regression factor; g(tT-1) represents the LCW asset value of power grid equipment in the year T+1 before the investment planning period of year t; The T value represents an investment cycle; g(t-1) represents the LCW asset value of the power grid equipment in the year before the investment planning period in year t; g(tT-1) and g(t-1) are both calculated using the equipment LCW asset value function; Q(t) represents the total asset wall size of the power grid equipment in year t, calculated using the total asset wall size function of the power grid equipment; The expression of the equipment LCW asset value function is: In the above formula, g(t) is the LCW asset value of the power grid equipment in year t; V(t) is the annual total output value of the power grid equipment in year t; ε is the contribution ratio of the power grid equipment to the annual total output value; Y(t) is the operation and maintenance expenditure of the power grid equipment in year t; J(t) is the value of the retired equipment in year t; R j (t) is the net value of the jth device in the power grid equipment in year t; The expression of the total asset wall scale function of the power grid equipment is: Q(t+1)=λ 1 (t)×QW 1 (t)+λ 2 (t)×QW 2 (t); In the above formula, Q(t+1) is the total asset wall size of power grid equipment in year t+1; λ 1 (t), λ 2 (t) are the fitted probability distribution functions of the power grid equipment at high and low risk levels, respectively; QW 1 (t), QW 2 (t) are the asset wall sizes of high-risk and low-risk grid equipment in year t, respectively.

5. The method for predicting the asset wall of power grid equipment based on the IAHP method and the value chain theory according to claim 1 or 2, characterized in that: The expression of the scrap probability distribution function is: In the above formula, is the probability of power grid equipment being scrapped in year t; β and η are fitting parameters; The parameter fitting of the scrap probability distribution function is completed by minimizing the objective function, which is: In the above formula, is the probability of power grid equipment being scrapped in year t calculated by the scrapping probability distribution function; t is the actual probability of power grid equipment being scrapped in year t.

6. The power grid equipment asset wall prediction system based on the IAHP method and value chain theory is characterized by: The power grid equipment asset wall prediction system includes: The index system and weight calculation module is used to build a risk assessment index system for power grid equipment and obtain the weight of each index using the improved analytic hierarchy process; A risk assessment module is used to calculate the risk assessment results of the power grid equipment asset wall to be predicted based on the data and weights of each indicator; A risk level classification module is used to classify the risk level of the power grid equipment asset wall to be predicted according to the risk assessment results, and obtain the risk level of the power grid equipment asset wall to be predicted; The asset wall prediction module is used to fit the scrap probability distribution function under different risk levels, select the scrap probability distribution function corresponding to the risk level of the power grid equipment asset wall to be predicted, perform power grid equipment asset wall prediction, and finally obtain the investment scale of power grid equipment during the investment planning period.

7. The power grid equipment asset wall prediction system based on the IAHP method and value chain theory according to claim 6 is characterized by: The indicator system and weight calculation module is used to obtain the weight of each indicator according to the following steps: The 1-9 scale method is used to determine the importance of each indicator, and the following judgment matrix A is constructed: In the above formula, a ij is the scale value of indicator i compared with indicator j, and n is the total number of indicators; a ji is the scale value of index j compared with index i; The judgment matrix is ​​subjected to consistency check, and the steps of the consistency check are as follows: Step 1: Calculate the consistency ratio CR of the judgment matrix A according to the following formula: In the above formula, CI represents the consistency index, λ max is the maximum eigenvalue of the judgment matrix A; RI represents the average random consistency index corresponding to the order of the judgment matrix, and the order of the judgment matrix is ​​equal to the total number of indicators; Step 2: Determine whether the consistency ratio CR is less than a preset consistency ratio threshold. If so, consider the judgment matrix A as the corrected judgment matrix A' and proceed to step 4. Otherwise, proceed to step 3 to correct the judgment matrix A to obtain the corrected judgment matrix A'. Step 3: Modify the judgment matrix A according to the following steps: First calculate the eigenvalue matrix Q of the judgment matrix A: Q=(q ij ) n×n ; In the above formula, q ij is the element in the i-th row and j-th column of the eigenvalue matrix Q; i ,q j are the i-th and j-th eigenvalues ​​of the judgment matrix A respectively; Then use the eigenvalue matrix Q and the judgment matrix A to construct the deviation matrix D: D=(d ij ) n×n ;d ij =a ij ·q ji -1; In the above formula, d ij is the element in row i and column j of the deviation matrix D; q ji is the element in the jth row and ith column of the eigenvalue matrix Q; Then select the largest element in the deviation matrix D, use the position of this element in the deviation matrix D as the row and column index (i*, j*) of the judgment matrix A, and modify the judgment matrix A according to the preset modification rules to obtain the modified judgment matrix A'; Finally, the consistency ratio CR(A') of the corrected judgment matrix A' is calculated to determine whether the consistency ratio CR(A') is less than the preset consistency ratio threshold. If so, proceed to step 4. Otherwise, the corrected judgment matrix A' is used as the input of step 3 to continue the calculation. The preset modification rules are: First, get the sequence by 1-9 scaling method Based on the row and column index (i*, j*), the sequence O is obtained according to the following formula s Modified judgment matrix A l =(a l ij )n×n: In the above formula, a l ij For sequence O s Modified judgment matrix A l The element in row i and column j in O s (1) is sequence O s The lth value in, l = 1, 2, 3...17; Then calculate the sequence O s Modified judgment matrix A l The consistency ratio CR(A l ), all the l )|≤CR ​​sequence O s Modified judgment matrix A l As a sequence O s Corrected judgment matrix collection; Then get sequence O s Each sequence O in the modified judgment matrix collection s The row and column indexes of the modified judgment matrix are obtained to obtain the row and column index set If the row and column index set Represents an empty set, then the optimal row and column index is obtained To satisfy The row and column index of , otherwise the optimal row and column index is obtained To satisfy The row and column indices of ; where Represents element a in the judgment matrix A ij With sequence O s Modified judgment matrix A l Elements in The distance between them is calculated as follows: In the above formula, dp(a ij ,a mn ) represents element a ij With elements The distance between ij is the element in row i and column j of the judgment matrix A; element For sequence O s Modified judgment matrix A l The element in row i and column j in the sequence O; pos(·) means · in the sequence O s Position in Finally, use the optimal row and column index According to the following formula, the corrected judgment matrix A'=(a ij ') n×n : Step 4: Calculate the weight of each indicator using the geometric mean method: In the above formula, ω i is the weight of indicator i; a kj ' is the element in the kth row and jth column of the modified judgment matrix A'.

8. The power grid equipment asset wall prediction system based on the IAHP method and value chain theory according to claim 6 or 7, characterized in that: The risk level classification module is used to perform risk level classification according to the following steps: determining the risk assessment result E of the power grid equipment asset wall to be predicted c Whether 0<E c ≤0.6, if yes, it is judged as low risk level, otherwise it is judged as high risk level.

9. The power grid equipment asset wall prediction system based on the IAHP method and value chain theory according to claim 8 is characterized by: The asset wall prediction is used to predict the asset wall of power grid equipment according to the following formula to obtain the investment scale of power grid equipment during the investment planning period: f(t)=Q(t)+g(tT-1)-αg(t-1); In the above formula, f(t) is the investment scale of power grid equipment in the investment planning period of year t after taking into account the LCW asset value; α is the regression factor; g(tT-1) represents the LCW asset value of power grid equipment in the year T+1 before the investment planning period of year t; The T value represents an investment cycle; g(t-1) represents the LCW asset value of the power grid equipment in the year before the investment planning period in year t; g(tT-1) and g(t-1) are both calculated using the equipment LCW asset value function; Q(t) represents the total asset wall size of the power grid equipment in year t, calculated using the total asset wall size function of the power grid equipment; The expression of the equipment LCW asset value function is: In the above formula, g(t) is the LCW asset value of the power grid equipment in year t; V(t) is the annual total output value of the power grid equipment in year t; ε is the contribution ratio of the power grid equipment to the annual total output value; Y(t) is the operation and maintenance expenditure of the power grid equipment in year t; J(t) is the value of the retired equipment in year t; R j (t) is the net value of the jth device in the power grid equipment in year t; The expression of the total asset wall scale function of the power grid equipment is: Q(t+1)=λ 1 (t)×QW 1 (t)+λ 2 (t)×QW 2 (t); In the above formula, Q(t+1) is the total asset wall size of power grid equipment in year t+1; λ 1 (t), λ 2 (t) are the scrap probability distribution functions corresponding to the high and low risk levels of power grid equipment; QW 1 (t), QW 2 (t) are the asset wall sizes of high-risk and low-risk grid equipment in year t, respectively.

10. The power grid equipment asset wall prediction system based on the IAHP method and value chain theory according to claim 6 or 7, characterized in that: The expression of the scrap probability distribution function is: In the above formula, is the probability of power grid equipment being scrapped in year t; β and η are fitting parameters; The parameter fitting of the scrap probability distribution function is completed by minimizing the objective function, which is: In the above formula, is the probability of power grid equipment being scrapped in year t calculated by the scrapping probability distribution function; t is the actual probability of power grid equipment being scrapped in year t.

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