Substation static flood prevention risk grading method, system and equipment based on combined weighting and medium

Abstract

The invention belongs to the technical field of transformer substation flood prevention risk assessment, and particularly relates to a transformer substation static flood prevention risk grading method and system based on combined weighting, equipment and a medium. The method comprises the following steps: firstly, establishing a substation static flood prevention capability evaluation index system, and carrying out quantitative processing on each index in the index system; performing combination weighting on the quantized indexes by using an order relation analysis method, an anti-entropy method and a Lagrangian multiplier method; and finally, according to the combination weight of each index, performing substation static flood prevention risk grade division through a fuzzy comprehensive evaluation method, and determining the substation static flood prevention capability. According to the method, the problems of complicated flood prevention evaluation indexes and strong subjectivity and fuzziness of flood prevention data are effectively solved, and the static flood prevention capacity of the transformer substation can be reasonably and effectively evaluated.

Description

Technical Field

[0001] The present invention belongs to the technical field of substation flood control risk assessment, and specifically relates to a substation static flood control risk level classification method, system, equipment and medium based on combined weighting. Background Art

[0002] At present, flood prevention in substations mainly adopts engineering measures, such as preparing sufficient flood prevention materials and equipment, ensuring a certain self-generating capacity of substations, and setting up drainage ditches in open excavation pits to ensure the safety of substations during flood seasons. Construction plans are usually based on masonry walls, water pumping and drainage, and new technologies such as new water retaining plates, prefabricated panels, and high-performance water-swelling reagents are not used enough. For non-engineering measures, including management measures such as flood prevention capacity assessment and flood prevention prediction, not enough attention is paid, and there is currently no clear assessment method, but the experience of other flood disaster prevention and control effect assessments can be used for flood prevention evaluation of substations.

[0003] In today's highly information-based, digital, and networked world, the flood prevention situation and traditional defense standards of substations will change. The focus of substation flood prevention is to evaluate whether various facilities meet the flood disasters that may occur in today's natural environment, and then take appropriate measures. From the perspective of substation management, the static flood prevention level of the substation itself is evaluated based on the actual site environment and facilities in the substation, so as to dispatch flood prevention materials in advance and make adequate flood prevention preparations. Considering the particularity of substation flood prevention assessment, it is necessary to combine the specific requirements of substation flood prevention, analyze the specific indicators of each project, use a combination of qualitative and quantitative methods to evaluate the situation of specific indicators, assign weights to each indicator, and finally comprehensively evaluate the flood prevention risk level of the substation, which is of great significance.

[0004] Due to the different scales, functions and flood control requirements of different substations, and the complex indicators of the flood control system, most of the data is collected manually, which is highly subjective and ambiguous, making it difficult to evaluate. The evaluation index weighting in the existing technology is usually performed using one of the subjective weighting methods or the objective weighting methods, such as the ordinal relationship analysis method, the hierarchical analysis method, the anti-entropy method, and the entropy weight method. The former can reasonably determine the weights of each indicator according to different scenarios, but it is highly subjective; the latter can eliminate the influence of human subjectivity and obtain the weights through calculation, but it is highly dependent on data. Summary of the invention

[0005] In view of the defects of the prior art, the present invention proposes a method, system, equipment and medium for classifying the static flood control risk level of substations based on combined weighting. Firstly, the flood control evaluation indicators are combined and weighted using the order relationship analysis method, the anti-entropy method and the Lagrange multiplier method. Then, the fuzzy comprehensive evaluation method is used to evaluate the static flood control capability of the substation. This effectively solves the problems of complicated flood control evaluation indicators, subjectivity and strong ambiguity of flood control data, and can reasonably and effectively evaluate the static flood control capability of the substation.

[0006] To achieve the above object, the present invention adopts the following technical solution:

[0007] A method for classifying static flood risk levels of substations based on combined weighting includes the following steps:

[0008] Step 1: Collect substation flood control data, build a static flood control capacity evaluation index system for substations, and quantify each index in the index system;

[0009] Step 2: Use the combined weighting method to combine and weight each indicator in the indicator system after quantification;

[0010] Step 3: According to the combined weights of each indicator, the static flood control risk level of the substation is divided through the fuzzy comprehensive evaluation method to determine the static flood control capacity of the substation.

[0011] Specifically, the combined weighting method described in step 2 is used to combine and weight each indicator after quantification in the indicator system, including three parts: weighting by order relationship analysis method, weighting by anti-entropy method and weight combination by Lagrange multiplier method. The specific steps are as follows:

[0012] Step 2-1: Use the ordinal relationship analysis method to subjectively weight each indicator after quantification in the indicator system to obtain the subjective weight value w of each indicator i ';

[0013] Step 2-2: Use the anti-entropy method to objectively weight each indicator after quantification in the indicator system to obtain the objective weight value w of each indicator i ”;

[0014] Step 2-3: Use the Lagrange multiplier method to convert w i ' and w i "Combined to get the combined weight of each indicator

[0015] Specifically, the order relationship analysis method described in step 2-1 is used to subjectively weight each indicator after quantification in the indicator system to obtain the subjective weight value w of each indicator. i ', the specific steps are:

[0016] Step 2-1-1: Determine the order relationship between evaluation indicators

[0017] If the rating index {X1,X2,...,X s} for the evaluation object {A1,A2,...,A n The influence degree of} is shown in formula (1), so the evaluation index {X1,X2,...,X s} establishes a sequence relationship in the form of “>”:

[0018] X1,X2,...,X s (1);

[0019] Step 2-1-2: Calculate the relative importance ratio of each evaluation index to the evaluation object

[0020] X i-1 With X i The relative importance ratio of each evaluation index is shown in formula (2), where r i Represents the evaluation index X i-1 The importance of the evaluation object is the evaluation index X i R i Times:

[0021] X i-1 / X i =r i (i=s,s-1,...,3,2)(2);

[0022] Step 2-1-3: Calculate the subjective weight value of each evaluation indicator

[0023] Based on steps 2-1-1 and 2-1-2, the subjective weight value of each indicator can be obtained according to formula (3):

[0024]

[0025] Specifically, the anti-entropy method described in step 2-2 is used to objectively weight each indicator after quantification in the indicator system to obtain the objective weight value w of each indicator. i ", the specific steps are:

[0026] Assume that the information anti-entropy of evaluation index i is as shown in formula (4), where like but And y ji represents the standard value of evaluation object j on evaluation index i, and y ji The value of is between 0 and 1:

[0027]

[0028] According to the anti-entropy value principle, the objective weight vector W'=(w'1',w'2',...w' s ') T , where w i " can be obtained by formula (5):

[0029]

[0030] Specifically, the game theory described in step 2-3 is used to i ' and w i "Combined to get the combined weight of each indicator The specific steps are:

[0031] Step 2-3-1: According to the principle of minimum information entropy, the combination weight can be known The following conditions must be met:

[0032]

[0033] Step 2-3-2: Solve the optimization problem in equation (6) using the Lagrange multiplier method, and we can get for:

[0034]

[0035] That is, calculate the combined weights of the final substation static flood control capacity indicators

[0036] Specifically, in step 3, according to the combined weights of various indicators, the static flood prevention risk level of the substation is divided by the fuzzy comprehensive evaluation method to determine the static flood prevention capacity of the substation. The specific steps are:

[0037] Step 3-1: Construct a substation static flood prevention capacity evaluation index set. The substation static flood prevention capacity evaluation index system is P. Taking the secondary index as an example, establish the primary index P1 and the secondary index P2, and construct the substation static flood prevention capacity evaluation set Q;

[0038] Step 3-2: Use the secondary indicators to rate the primary indicators, and then make a comprehensive evaluation of each secondary indicator P2 using formula (8):

[0039] K P2 =W P2 ·R P2 (8)

[0040] Calculate the comprehensive evaluation vector K of the secondary index P2 , where R P2 represents the rating matrix of P2, W P2 represents the weight vector of P2;

[0041] Step 3-3: Make a comprehensive evaluation of each first-level indicator P1 using formula (9):

[0042] R P1 =K P2 (9)

[0043] Get the rating matrix R of P1 P1 , using formula (10):

[0044] K=W P1 ·R P1 =[k1,k2,k3,...,k m ](10)

[0045] Calculate the static flood control capacity evaluation vector K of the substation, where R P1 represents the rating matrix of P1, W P1 represents the weight vector of P1, k m Indicates the comprehensive evaluation value;

[0046] Step 3-4: According to the maximum membership principle, the level corresponding to the largest membership in K is selected as the final evaluation result, and the static flood control capacity of the substation is finally determined.

[0047] A substation static flood risk classification system based on combined weighting, including:

[0048] The indicator system construction module is used to collect substation flood control data, build a substation static flood control capacity evaluation indicator system, and quantify the substation flood control data;

[0049] The weighting module for indicators at all levels is used to combine and weight the indicators in the indicator system after quantification using the combined weighting method;

[0050] The flood prevention capacity assessment module is used to classify the static flood prevention risk level of the substation according to the combined weight of each indicator through the fuzzy comprehensive evaluation method to determine the static flood prevention capacity of the substation.

[0051] An electronic device comprises a processor and a memory, wherein the processor is used to execute a computer program stored in the memory to implement the substation static flood risk level classification method based on combined weighting.

[0052] A computer-readable storage medium stores computer-executable instructions, wherein the computer-executable instructions are used to enable a computer to execute the substation static flood risk level classification method based on combined weighting.

[0053] The present invention has positive beneficial effects:

[0054] The present invention proposes a method for classifying the static flood risk level of a substation based on combined weighting. First, a static flood prevention capacity evaluation index system of a substation is constructed, and the flood prevention evaluation index is quantified. Then, the flood prevention evaluation index is combined and weighted using the order relationship analysis method, the anti-entropy method and the Lagrange multiplier method. Finally, the static flood prevention risk level of the substation is classified by the fuzzy comprehensive evaluation method to determine the static flood prevention capacity of the substation. In the combined weighting of the present invention, the order relationship analysis method and the anti-entropy method are used to weight the evaluation indexes respectively. The order relationship analysis method is subjectively weighted, and the anti-entropy method is objectively weighted. The weights are combined by the Lagrange multiplier method to obtain the weights of the flood prevention evaluation indexes that contain both the historical case experience of substation flood prevention and the actual flood prevention data weighting. A reasonable and scientific static flood prevention capacity evaluation index system of a substation is established, and the weights of the flood prevention evaluation indexes are more reasonably and effectively allocated. At the same time, the static flood prevention risk level of the substation is classified based on the fuzzy comprehensive evaluation method. The fuzzy comprehensive evaluation method deeply mines the internal connection between the flood prevention evaluation index and the flood prevention level, and uses fuzzy mathematics to quantify and calculate all flood prevention indicators. In summary, the present invention effectively solves the problems of complicated flood control evaluation indicators, subjectivity and strong ambiguity of flood control data, and can reasonably and effectively evaluate the static flood control capacity of substations. DETAILED DESCRIPTION

[0055] To make the purpose, technical solution and advantages of the embodiments of the present invention more clear, the present invention is further described in detail below in conjunction with the embodiments. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of them.

[0056] Aiming at the complex flood control system of substations, the present invention proposes a method for classifying the static flood control risk level of substations based on combined weighting. First, a static flood control capacity evaluation index system of substations is established, and the flood control evaluation indexes are quantified. Then, the flood control evaluation indexes are combined and weighted using the order relationship analysis method, the anti-entropy method, and the Lagrange multiplier method. Then, the static flood control risk level of substations is classified by the fuzzy comprehensive evaluation method to determine the static flood control capacity of substations. Taking a substation in Nanyang, Henan as an example, the evaluation steps are as follows:

[0057] Step 1: Sort out and summarize the substation flood control data, build the substation static flood control capacity evaluation index system P according to the evaluation requirements, and quantify the substation flood control data. The processing results are shown in Table 1 below:

[0058] Table 1 Substation static flood control capability evaluation index system

[0059]

[0060] Step 2: Use the combined weighting method to combine and weight the indicators after quantification in the indicator system P to obtain the combined weights of each indicator. The combined weighting method includes three parts: subjective weighting by order relationship analysis method, objective weighting by anti-entropy method, and determination of combined weight by Lagrange multiplier method. The specific steps are as follows:

[0061] Step 2-1: Use the ordinal relationship analysis method to subjectively weight each indicator in the indicator system P and obtain the subjective weight value w of each indicator. i ', the order relationship analysis method is a new subjective weight assignment method proposed on the basis of improving the hierarchical analysis method. The use of the order relationship analysis method to determine the weight coefficient can omit the matrix construction link, so the calculation process is simpler. The specific steps are:

[0062] Step 2-1-1: Determine the order relationship between evaluation indicators

[0063] If the rating index {X1,X2,...,X s} for the evaluation object {A1,A2,...,A n The influence degree of} is shown in formula (1), so the evaluation index {X1,X2,...,X s} establishes a sequence relationship in the form of “>”:

[0064] X1,X2,...,X s (1)

[0065] Step 2-1-2: Calculate the relative importance ratio X of each evaluation index to the evaluation object i-1 With X i The relative importance ratio of each evaluation index is shown in formula (2), where r i Represents the evaluation index X i-1 The importance of the evaluation object is the evaluation index X i R i times, r i The value standard of is shown in Table 2:

[0066] X i-1 / X i =r i (i=s,s-1,...,3,2) (2)

[0067] Table 2r i Value Standard

[0068]

[0069] Step 2-1-3: Calculate the subjective weight value of each evaluation indicator

[0070] Based on steps 2-1-1 and 2-1-2, the subjective weight value of each indicator can be obtained according to formula (3):

[0071]

[0072] The subjective weight values ​​of each indicator are calculated as shown in Table 3:

[0073] Table 3. The subjective weight values ​​of each indicator obtained by ordinal relationship analysis method

[0074] index B1 B2 C1 C2 C3 C4 D1 D2 D3 D4 Weight value 0.667 0.333 0.152 0.117 0.290 0.441 0.161 0.100 0.252 0.487

[0075] Step 2-2: Use the anti-entropy method to objectively weight each indicator in the indicator system P and obtain the objective weight value w of each indicator. i ", Anti-entropy method is an objective weight assignment method that can better reflect the differences between evaluation indicators and effectively avoid the failure of evaluation indicators in extreme cases. Anti-entropy method has the advantages of being able to fully mine original data information under small sample capacity conditions, generating small contrast in the weight of evaluation indicators, and being able to avoid human interference to the greatest extent. The specific steps are:

[0076] Assume that the information anti-entropy of evaluation index i is as shown in formula (4), where like but And y ji represents the standard value of evaluation object j on evaluation index i, and y ji The value of is between 0 and 1:

[0077]

[0078] According to the anti-entropy value principle, the objective weight vector of each evaluation index can be determined: W”=(w1”,w'2',...w' s ') T , where w i " can be obtained by formula (5)

[0079]

[0080] The objective weight values ​​of each indicator are calculated as shown in Table 4:

[0081] Table 4 Objective weight values ​​of each indicator obtained by anti-entropy method

[0082] index B1 B2 C1 C2 C3 C4 D1 D2 D3 D4 Weight value 0.464 0.536 0.382 0.352 0.266 0 0 0.272 0.371 0.357

[0083] Step 2-3: Use the Lagrange multiplier method to transform the weight vector w i ' and w i "Combined to get the combined weight of each indicator

[0084] The Lagrange multiplier method can be used to solve the optimal value problem of a function with constraints. The combined weight of the evaluation index is the coupling value of its subjective and objective weight values. When, requirements The distribution in space is as close to w as possible. i 'With w i ", the specific steps are as follows:

[0085] Step 2-3-1: According to the principle of minimum information entropy, the combination weight can be known The following conditions must be met:

[0086]

[0087] Step 2-3-2: Solve the optimization problem in equation (6) using the Lagrange multiplier method, and we can get for:

[0088]

[0089] Calculate the combined weights of the final substation static flood control capacity indicators As shown in Table 5:

[0090] Table 5 Combination weight results

[0091]

[0092]

[0093] Step 3: According to the combined weight of each indicator, the static flood control risk level of the substation is divided by the fuzzy comprehensive evaluation method to determine the static flood control capacity of the substation. The fuzzy comprehensive evaluation method uses fuzzy statistics and fuzzy mathematics. It uses the fuzzy transformation principle to standardize and quantify the indicators that are unclear or difficult to measure, and takes all the evaluation indicators in the system into consideration. Following the principle of maximum membership, the system can be evaluated reasonably and scientifically. The specific steps are as follows:

[0094] Step 3-1: Construct a set of static flood control capability evaluation indicators for substations: The static flood control capability evaluation indicator system for substations is P, with the first-level factor P2 = {B1, B2}, the second-level factor P3 = {C1, C2, C3, C4, D1, D2, D3, D4}, and construct a set of static flood control capability evaluation indicators for substations Q = {Q1, Q2, Q3, Q4} = {"fourth level", "threeth level", "secondary level", "first level"};

[0095] Step 3-2: Use the secondary indicators to rate the primary indicators, and then make a comprehensive evaluation of each secondary indicator P2 using the formula

[0096] K P2=W P2 ·R P2

[0097] In the formula, K P2 represents the comprehensive evaluation vector of the secondary indicators, R P2 represents the rating matrix of P2, W P2 represents the weight vector of P2;

[0098] Calculate the comprehensive evaluation vector K of the secondary index BC =[0.275,0.284,0.266,0.175],K BD =[0.202,0.215,0.369,0.214];

[0099] Step 3-3: Make a comprehensive evaluation of each first-level indicator P1 using the formula:

[0100] R P1 =K P2

[0101] Get the rating matrix R of P1 P1 , using the formula:

[0102] K=W P1 ·R P1 =[k1,k2,k3,...,k m ]

[0103] Where K represents the static flood control capability evaluation vector of the substation, R P1 represents the rating matrix of P1, W P1 represents the weight vector of P1, k m It represents the comprehensive evaluation value, that is, the comprehensive evaluation of the first-level indicators to which the second-level indicators belong is carried out using the second-level indicators and the corresponding rating matrix;

[0104] The static flood control capacity evaluation vector of the substation is calculated to be K = [0.242, 0.253, 0.312, 0.193];

[0105] Step 3-4: According to the above calculation results, the static flood control capacity evaluation set Q of the substation is Q = {"Level 4", "Level 3", "Level 2", "Level 1"}, and the membership degrees are 24.2%, 25.3%, 31.2% and 19.3% respectively. According to the maximum membership principle, the static flood control capacity of the substation is finally determined to be "Level 2".

[0106] A substation static flood risk classification system based on combined weighting, including:

[0107] The indicator system construction module is used to collect substation flood control data, build the substation static flood control capacity evaluation indicator system P, and quantify the substation flood control data;

[0108] The weighting module for indicators at all levels is used to combine and weight the indicators after quantification in the indicator system P using the combined weighting method;

[0109] The flood prevention capacity assessment module is used to classify the static flood prevention risk level of the substation according to the weight of each indicator through the fuzzy comprehensive evaluation method to determine the static flood prevention capacity of the substation.

[0110] It should be noted that the substation flood risk level classification system based on combined weighting in this embodiment may include a business processing module, an edge database, a server version information register, and a data synchronization module, and the processor implements the method as described when executing the computer program.

[0111] The system embodiments described above are merely illustrative, and the units described as separate components may or may not be physically separated, that is, they may be located in one place or distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the present embodiment.

[0112] Based on similar inventive concepts, the present invention also provides an electronic device, including a processor and a memory, wherein the processor is used to execute a computer program stored in the memory to implement the substation static flood risk level classification method based on combined weighting.

[0113] The processor and the memory may be connected via a bus or other means.

[0114] The memory, as a non-transient computer-readable storage medium, can be used to store non-transient software programs and non-transient computer executable programs. In addition, the memory may include a high-speed random access memory, and may also include a non-transient memory, such as at least one disk storage device, a flash memory device, or other non-transient solid-state storage device. In some embodiments, the memory may optionally include a memory remotely disposed relative to the processor, and these remote memories may be connected to the processor via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0115] Based on similar inventive concepts, the present invention also provides a computer-readable storage medium, which stores computer-executable instructions, and the computer-executable instructions are used to enable a computer to execute the substation static flood risk level classification method based on combined weighting.

[0116] It will be appreciated by those skilled in the art that all or some of the steps and systems in the disclosed method above may be implemented as software, firmware, hardware and appropriate combinations thereof. Some physical components or all physical components may be implemented as software executed by a processor, such as a central processing unit, a digital signal processor or a microprocessor, or may be implemented as hardware, or may be implemented as an integrated circuit, such as an application specific integrated circuit. Such software may be distributed on a computer-readable medium, which may include a computer storage medium (or a non-transitory medium) and a communication medium (or a temporary medium). As known to those skilled in the art, the term computer storage medium includes volatile and non-volatile, removable and non-removable media implemented in any method or technology for storing information (such as computer-readable instructions, data structures, program modules or other data). Computer storage media include, but are not limited to, RAM, ROM, EEPROM, flash memory or other memory technologies, CD-ROM, digital versatile disks (DVD) or other optical disk storage, magnetic cassettes, magnetic tapes, disk storage or other magnetic storage devices, or any other medium that may be used to store desired information and may be accessed by a computer. Furthermore, it is well known to those skilled in the art that communication media typically embodies computer readable instructions, data structures, program modules, or other data in a modulated data signal such as a carrier wave or other transport mechanism, and may include any information delivery media.

Claims

1. A method for classifying static flood risk levels of substations based on combined weighting, characterized in that: The steps include: Step 1: Collect substation flood control data, build a static flood control capacity evaluation index system for substations, and quantify each index in the index system; Step 2: Use the combined weighting method to combine and weight each indicator in the indicator system after quantification; Step 3: According to the combined weights of each indicator, the static flood control risk level of the substation is divided through the fuzzy comprehensive evaluation method to determine the static flood control capacity of the substation.

2. The method for classifying substation static flood risk levels based on combined weighting according to claim 1 is characterized in that: The combined weighting method described in step 2 is used to combine and weight the indicators in the indicator system after quantification, including: Step 2-1: Use the ordinal relationship analysis method to subjectively weight each indicator after quantification in the indicator system to obtain the subjective weight value w of each indicator i '; Step 2-2: Use the anti-entropy method to objectively weight each indicator after quantification in the indicator system to obtain the objective weight value w of each indicator i ”; Step 2-3: Use the Lagrange multiplier method to convert w i ' and w i "Combined to get the combined weight w of each indicator i * .

3. The method for classifying substation static flood risk levels based on combined weighting according to claim 2 is characterized in that: The order relationship analysis method described in step 2-1 is used to subjectively weight each indicator after quantification in the indicator system to obtain the subjective weight value w of each indicator. i ',include: Step 2-1-1: If the rating index {X1,X2,...,X s } for the evaluation object {A1,A2,...,A n The influence degree of} is shown in formula (1), so the evaluation index {X1,X2,...,X s } establishes a sequence relationship in the form of ">": X1,X2,...,X s (1); Step 2-1-2: Calculate the relative importance ratio of each evaluation index to the evaluation object as shown in formula (2), where r i Represents the evaluation index X i-1 The importance of the evaluation object is the evaluation index X i R i Times: X i-1 / X i =r i (i=s,s-1,...,3,2)(2); Step 2-1-3: Based on steps 2-1-1 and 2-1-2, the subjective weight value of each indicator can be obtained according to formula (3):

4. The method for classifying substation static flood risk levels based on combined weighting according to claim 2 is characterized in that: The anti-entropy method described in step 2-2 objectively weights each quantified indicator in the indicator system to obtain the objective weight value w of each indicator. i ",include: Assume that the information anti-entropy of evaluation index i is as shown in formula (4), where like but And y ji represents the standard value of evaluation object j on evaluation index i, and y ji The value of is between 0 and 1: According to the anti-entropy value principle, the objective weight vector of each evaluation index can be determined: W”=(w1”,w'2',...w' s ') T , where w i " can be obtained by formula (5):

5. The method for classifying substation static flood risk levels based on combined weighting according to claim 2 is characterized in that: The Lagrange multiplier method described in step 2-3 is used to convert w i ' and w i "Combined to get the combined weight of each indicator include: Step 2-3-1: According to the principle of minimum information entropy, the combination weight can be known The following conditions must be met: Step 2-3-2: Solve the optimization problem in equation (6) using the Lagrange multiplier method, and we can get for:

6. The method for classifying substation static flood risk levels based on combined weighting according to claim 1 is characterized in that: According to the combined weights of various indicators described in step 3, the static flood prevention risk level of the substation is divided by the fuzzy comprehensive evaluation method to determine the static flood prevention capacity of the substation, including: Step 3-1: Construct a substation static flood prevention capacity evaluation index set. The substation static flood prevention capacity evaluation index system is P. Taking the secondary index as an example, establish the primary index P1 and the secondary index P2, and construct the substation static flood prevention capacity evaluation set Q; Step 3-2: Use the secondary indicators to rate the primary indicators, and then make a comprehensive evaluation of each secondary indicator P2 using formula (8): K P2 =W P2 ·R P2 (8) Calculate the comprehensive evaluation vector K of the secondary index P2 , where R P2 represents the rating matrix of P2, W P2 represents the weight vector of P2; Step 3-3: Comprehensively evaluate each primary indicator P1 using formula (9) R P1 =K P2 (9) Get the rating matrix R of P1 P1 , using formula (10) K=W P1 ·R P1 =[k1,k2,k3,...,k m ] (10) Calculate the static flood control capacity evaluation vector K of the substation, where R P1 represents the rating matrix of P1, W P1 represents the weight vector of P1, k m Indicates the comprehensive evaluation value; Step 3-4: According to the maximum membership principle, the level corresponding to the largest membership in K is selected as the final evaluation result to determine the static flood control capacity of the substation.

7. A substation static flood risk classification system based on combined weighting according to claim 1, characterized in that: include: The indicator system construction module is used to collect substation flood control data, build a substation static flood control capacity evaluation indicator system, and quantify the substation flood control data; The weighting module for indicators at all levels is used to combine and weight the indicators in the indicator system after quantification using the combined weighting method; The flood prevention capacity evaluation module is used to classify the static flood prevention risk level of the substation according to the combined weight of each indicator through the fuzzy comprehensive price method to determine the static flood prevention capacity of the substation.

8. An electronic device comprising a processor and a memory, characterized in that: The processor is used to execute the computer program stored in the memory to implement the method for classifying static flood risk levels of substations based on combined weighting as described in any one of claims 1-6.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer-executable instructions, and the computer-executable instructions are used to enable a computer to execute the method for classifying static flood risk levels of substations based on combined weighting as described in any one of claims 1-6.

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