Mobile communication network energy consumption assessment method of hierarchical index system

Abstract

The invention provides an energy consumption evaluation method for a mobile communication network system of a hierarchical index system. The method comprises the following implementation steps: initializing a regional communication network system; iI-level energy consumption evaluation indexes are calculated; obtaining a grade-II energy consumption evaluation index; calculating an I-level energy consumption evaluation index; and obtaining a regional network energy consumption evaluation result. The hierarchical evaluation index system comprising the II-level energy consumption evaluation index, the I-level energy consumption evaluation index and the comprehensive energy consumption evaluation index is adopted, transfer from the evaluation center to the service user side is achieved, the evaluation indexes are well matched with the communication network system, the evaluation characterization capacity is higher, and the evaluation efficiency is improved. And the actual energy consumption of the network is analyzed and verified in combination with post-deployment evaluation to form a closed loop, so that the defect of evaluation by taking a single-bit energy efficiency index as a center in the prior art is avoided, and the evaluation accuracy is improved.

Description

Technical Field

[0001] This invention belongs to the field of wireless communication technology networks and relates to a method for assessing the energy consumption of mobile communication networks, specifically a hierarchical index system method for assessing the energy consumption of regional mobile communication networks. Background Technology

[0002] With the development of mobile communication systems, the next generation of wireless networks will possess characteristics such as convergence of communication, sensing, and computing, as well as interconnection between terrestrial and satellite networks. Mobile communication services and traffic volumes are rapidly increasing, leading to greater spatial and temporal scales in network infrastructure deployment, a further increase in service types, diversification of service demands, and differentiation in energy supply and demand structures. Currently, 5G mobile communication systems are evolving towards more, larger, and faster antennas, bandwidth, and base station density. This will result in a significant increase in network energy consumption, impacting network resource consumption and economic costs.

[0003] As 5G mobile communication systems continue to evolve towards more, larger, and denser antennas, bandwidth, and base station density, network energy consumption will increase significantly. To reduce the impact of this substantial energy consumption on communication network resource consumption and economic costs, lower carbon emissions, and achieve sustainable development of the communication network ecosystem and economy, hardware and software technologies and optimization of network topology are commonly used to reduce network energy consumption. However, these methods require the collection of comprehensive, clear, and accurate network energy consumption information to establish an energy consumption assessment index system for accurate evaluation of regional network energy consumption levels. This allows for an assessment of the rationality of network setup, planning, and deployment from an energy consumption perspective.

[0004] Current energy consumption assessments of wireless communication networks often focus on bit-level energy efficiency, which is the ratio of average data throughput to energy consumption over a given period. Existing methods typically extend traditional single-site energy efficiency assessments to network energy efficiency assessments. This means that, while meeting communication service quality requirements, network energy efficiency is calculated by equating network node throughput to average energy consumption per node. However, this approach only reflects the relationship between the amount of data transmitted and the energy consumed by the network under communication service quality constraints, failing to reflect other aspects of the network's needs.

[0005] The technical approach to wireless network energy consumption assessment is to collect network information to establish network energy consumption indicators, use these indicators to make qualitative or quantitative assessments of the current network energy consumption level and the improvement effect after network improvements, take improvement measures immediately after the assessment, and follow up and verify the accuracy of the assessment.

[0006] For example, Guangzhou Aipulu Network Technology Co., Ltd. disclosed a method for evaluating network energy efficiency based on a new generation of communication systems in its patent application "Network Energy Efficiency Evaluation Method and System Based on New Generation Communication Systems" (Application No.: 202310361429.2, Publication No.: CN116367196A). The implementation steps are as follows: select and mark target network nodes as evaluation targets, including network elements and base stations in the core network; obtain the actual average throughput, target average throughput, and average energy consumption of each target network node within a preset time interval; calculate the basic energy efficiency based on the actual average throughput and average energy consumption; and calculate the adjustment parameters based on the actual average throughput and target average throughput. This invention considers the quality of service requirements of communication and can provide certain feedback on network parameters and structure based on the evaluation results. However, with the densification of network deployment, the performance and power requirements of network node components are highly diversified. Setting indicators only meets the quality of service requirements, and the evaluation does not form a comprehensive system, which cannot guarantee the accuracy of the evaluation. Moreover, in practical applications, this invention cannot guarantee a high degree of matching between the theoretically designed scenario and the actual network scenario, and cannot guarantee the accuracy of the evaluation. Summary of the Invention

[0007] The purpose of this invention is to address the shortcomings of the prior art by proposing a hierarchical index system for evaluating the energy consumption of regional mobile communication network systems, thereby solving the technical problem of low evaluation accuracy in the prior art.

[0008] To achieve the above objectives, the technical solution adopted by the present invention includes the following steps:

[0009] (1) Initialize the regional communication network system:

[0010] The initialization includes a regional communication network system with M macro base stations, each macro base station covering an area W containing N micro base stations and R user terminals. The effective information transmission duration of the macro base stations includes I time periods, each time period containing T moments. The evaluation set is V = {low energy consumption, medium energy consumption, high energy consumption}. The hierarchical index system includes Level II energy consumption evaluation index, Level I energy consumption evaluation index, and comprehensive energy consumption evaluation index, where M≥1, N≥0, and R≥1.

[0011] (2) Calculate the Level II energy consumption evaluation indicators:

[0012] Calculate the Level II energy consumption X for each macro base station coverage area in each time period. i , We obtain the set of Level II energy consumption evaluation indicators X = {X1, X2, ..., X...} for I time periods. i ,...,X I},in, Let j represent the j-th energy consumption, where j∈[1,2,3,4,5,6,7]. and These represent the traffic energy consumption, coverage energy consumption, bit energy consumption, connection energy consumption, latency energy consumption, computing energy consumption, and sensing energy consumption of each macro base station coverage area during the i-th time period, respectively.

[0013] (3) Construct a classification matrix of Level II energy consumption evaluation indicators:

[0014] The Level II energy consumption evaluation index X for I time periods is classified into three categories: communication energy consumption index I1, computing energy consumption index I2, and sensing energy consumption index I3. Based on the classification results, a data matrix X of communication energy consumption index with dimension I1×7 is constructed. L1 A computational energy consumption index data matrix X with dimensions I2×7 L2 And a sensory energy consumption index data matrix X with dimensions I3×7 L3 Then X L1 X L2 and X L3 The classification matrix set X that constitutes the Level II energy consumption evaluation index L ={X L1 ,X L2 ,X L3};

[0015] (4) Calculate the Level I energy consumption evaluation index:

[0016] Based on the Level II energy consumption evaluation index data set X L Calculate the communication service energy consumption Y1, sensing service energy consumption Y2, and computing service energy consumption Y3 for each macro base station coverage area, and combine Y1, Y2, and Y3 into a Level I energy consumption evaluation index data set Y = {Y1, Y2, Y3};

[0017] (5) Obtain the energy consumption assessment results of the regional network:

[0018] Based on the Level I energy consumption evaluation index data set Y = {Y1, Y2, Y3}, a 3*3 judgment matrix A is constructed. The index weight vector B is calculated through A, and the regional network comprehensive energy consumption evaluation index F and the membership degree vector S of the network energy consumption level are calculated through B. Then, the comment corresponding to the largest membership degree value in the membership degree vector S from the comment set V is selected. The comprehensive evaluation result of the regional network energy consumption level includes the comprehensive energy consumption evaluation index F and the selected comment.

[0019] Compared with the prior art, the present invention has the following advantages:

[0020] This invention adopts a hierarchical evaluation index system including Level II energy consumption evaluation index, Level I energy consumption evaluation index, and comprehensive energy consumption evaluation index. It realizes the transfer of the evaluation center to the service user end, which makes the evaluation index better matched with the communication network system and the evaluation characterization capability stronger. Combined with the post-deployment evaluation, it analyzes and verifies the actual energy consumption of the network to form a closed loop, avoiding the defects of the existing technology that uses a single bit energy efficiency index as the center of evaluation and improving the accuracy of evaluation. Attached Figure Description

[0021] Figure 1 This is a flowchart illustrating the implementation of the present invention.

[0022] Figure 2 This is a schematic diagram of the structure of the hierarchical evaluation index of this invention. Detailed Implementation

[0023] The present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0024] Reference Figure 1 The present invention includes the following steps:

[0025] Step 1) Initialize the regional communication network system:

[0026] The initialization includes a regional communication network system with M macro base stations, each macro base station covering an area W containing N micro base stations and R user terminals. The effective information transmission duration of the macro base stations includes I time periods, each time period containing T moments. The evaluation set is V = {low energy consumption, medium energy consumption, high energy consumption}. The hierarchical index system includes Level II energy consumption evaluation index, Level I energy consumption evaluation index, and comprehensive energy consumption evaluation index, where M≥1, N≥0, and R≥1.

[0027] In this embodiment, M=19, N=4, and R=1500.

[0028] The hierarchical index system adopted in this invention is as follows: Figure 2 As shown, the Level I energy consumption evaluation indicators are the communication service energy consumption Y1, sensing service energy consumption Y2, and computing service energy consumption Y3 for each macro base station coverage area; the Level II energy consumption evaluation indicators include traffic energy consumption. Coverage energy consumption Bit power consumption Connection power consumption Delay and power consumption Calculate energy consumption and perceived energy consumption

[0029] Step 2) Calculate the Level II energy consumption evaluation indicators:

[0030] Calculate the Level II energy consumption X of each macro base station coverage area in the i-th time period. iIncluding flow rate and energy consumption Coverage energy consumption Bit power consumption Connection power consumption Delay and power consumption Calculate energy consumption and perceived energy consumption The energy consumption evaluation indexes of Group I, Level II, are obtained as follows: X = {X1, X2, ..., X...} i ,...,X I},

[0031]

[0032]

[0033] Among them, Q ma (t), P ma Q(t) represents the effective information transmission rate and input power of each macro base station at time t in each time period, respectively. mi (t), P mi (t) represents the effective information transmission rate and input power of each micro base station at time t, respectively, and the average power of the R user terminals in the system is P. EQ , D represents the average throughput of the regional network. is T represents the average transmission distance between the user terminal and the base station. r T represents the session duration of the r-th user within each time period in the region. E2E Let T be the end-to-end delay for the correct transmission of data packets from the sending side to the receiving side of the user terminal in the network, and let K and H be the number of types of computing tasks and sensing tasks executed by the communication system within T, respectively. ck Let p be the probability distribution of the k-th type of computational task being performed. sh Let p be the probability distribution of the h-th class of perceptual tasks being performed, and let p be the probability distribution vector of each class of tasks being performed. c ={p c1 ,p c2 ,…,p ck ,…,p cK}、p s ={p s1 ,p s2 ,…,p sh ,…,p sH};E ck E represents the typical energy consumption value for the k-th type of computing task. sh Let be the typical energy consumption value for the h-th type of sensing task, and ∑ be the summation operation.

[0034] The Level II energy consumption evaluation index X of Group I is divided into communication energy consumption (including indicators of Group I1), computing energy consumption (including indicators of Group I2), and sensing energy consumption (including indicators of Group I3). A Level II communication energy consumption index data matrix X of I1×7 is constructed. L1 The I2×7 calculation-type energy consumption index data matrix X L2 And the I3×7 sensing-type energy consumption index data matrix X L3 The data set X of Level II energy consumption evaluation indicators was obtained. L ={X L1 ,X L2 ,X L3}

[0035] Step 3) Construct a classification matrix for Level II energy consumption evaluation indicators. The steps are as follows:

[0036] (3a) Determine the energy consumption evaluation index X for each group of Level II energy consumption. i middle Compared with the standard value X s Does it meet the requirements? and If so, then X i If classified as communication-related energy consumption I1, otherwise proceed to step (3b);

[0037] (3b) Judgment Is it true? If so, then X i Classified as computational energy consumption I2, otherwise, X i Classified as sensor-based energy consumption I3;

[0038] Standard value X s Selection is usually made through expert judgment or subjective experience.

[0039] Step 4) Calculate the Level I energy consumption evaluation index:

[0040] Based on the Level II energy consumption evaluation index data set X L Calculate the communication service energy consumption Y1, sensing service energy consumption Y2, and computing service energy consumption Y3 in the coverage area of ​​each macro base station to obtain the Level I energy consumption evaluation index Y = {Y1, Y2, Y3}.

[0041] The energy consumption for communication services (Y1), sensing services (Y2), and computing services (Y3) for each macro base station's coverage area are calculated using the following formulas:

[0042] Y1 = C L1 ·X max

[0043] Y2 = C L2 ·X max

[0044] Y3 = C L3 ·Xmax

[0045] X max ={X max1 ,X max2 ,…,X maxj ,…,X max7}

[0046]

[0047]

[0048] Among them, X max For the negative ideal solution, X maxj For the j-th Level II indicator in the indicator data set X L The negative ideal value in C L1 C L2 C L3 For the weight vector, Let j be the information entropy of the j-th level II indicator for each level I indicator. The j-th level II indicator is located in the weight vector C. L1 C L2 C L3 The corresponding weights, · represents the dot product operation, and max() is the maximum value operation;

[0049] Negative ideal solutions indicate the worst-case scenario for energy consumption in communication networks.

[0050] Information entropy measures the uncertainty of data. The greater the probability of a certain fluctuation in an indicator, the greater the amount of existing information it provides, and the greater its corresponding information entropy. If the information entropy of a certain Level II indicator is smaller, it indicates that the indicator provides more information and plays a greater role in the evaluation of the corresponding Level I indicator, and its weight is greater. If a certain Level II indicator has the same value for all time periods, then the indicator has no effect under the corresponding Level I indicator.

[0051] The information entropy The calculation formulas are as follows:

[0052]

[0053]

[0054] in, The data matrix X represents the Level II indicator. L1 The data in the i-th column j, the standardized value corresponding to the data, and the probability corresponding to the standardized value; The data matrix X represents the Level II indicator. L2 The data in the i-th and j-th columns, the standardized value of the data, and the probability corresponding to the standardized value; The data matrix X represents the Level II indicator. L3 The data in column i3j, the standardized value of the data, and the probability corresponding to the standardized value; max() is the maximum value operation, and min() is the minimum value operation.

[0055] This indicates the probability of variation of the indicator, representing the standardized value of the corresponding data in the Level II indicator data matrix X. L1 X L2 X L3 The proportion, and Considered as a probability used in information entropy calculation, the larger the value, the greater the probability of the corresponding data changing.

[0056] Step 5) Obtain the energy consumption assessment results of the regional network:

[0057] Based on the Level I energy consumption evaluation index data set Y = {Y1, Y2, Y3}, a 3*3 judgment matrix A is constructed. The index weight vector B is calculated through A, and the regional network comprehensive energy consumption evaluation index F and the membership degree vector S of the network energy consumption level are calculated through B. Then, the comment corresponding to the largest membership degree value in the membership degree vector S from the comment set V is selected. The comprehensive evaluation result of the regional network energy consumption level includes the comprehensive energy consumption evaluation index F and the selected comment.

[0058] The calculation formulas are as follows:

[0059] F = B·Y

[0060] S = B·U

[0061] B = {B1, B2, B3}

[0062]

[0063]

[0064] Where · represents dot product; U is the fuzzy evaluation matrix for the energy consumption level of the regional network, with 3 rows equal to the number of Level I indicators and 3 columns equal to the number of comments in the comment set V. Each element of the matrix is ​​the fuzzy evaluation value of a comment in V for a given indicator. These fuzzy evaluation values ​​are obtained using a membership function, which is usually selected through expert judgment or subjective experience; B α For the αth Level I indicator Y α The corresponding weight vector; To determine the normalized eigenvectors of matrix A corresponding to the largest eigenvalue; A αβTo determine the element in the α-th row and β-th column of matrix A, we need to consider that matrix A has 3 rows and 3 columns, and each element of A represents a scale comparing two Level I indicators, reflecting the importance of the indicators in the comprehensive evaluation. This scale is typically determined by expert judgment or subjective experience, and Saaty's 1-9 scale method is used. Referring to Table 1, the meaning of Saaty's 1-9 scale method is as follows:

[0065] Table 1

[0066]

[0067] Referring to Table 2, the rating is a quantitative evaluation of the Level I indicators. It has a functional relationship with the Level I indicators such that the score ranges from (0, 10], and its value is accurate to one decimal place. The scale of the judgment matrix A is given based on the score. The comment set V = {low energy consumption, medium energy consumption, high energy consumption} has a membership function relationship with Y = {Y1, Y2, Y3}; Y α Let i be an element in Y and i = 1, 2, 3. EC1, EC2, EC3, EC4, EC5, EC6, EC7, EC8, and EC9 are dimensionless values ​​used as a reference for giving scores and comments.

[0068] Table 2

[0069]

[0070]

[0071] To determine that there are no logical problems in the judgment matrix A, a consistency check is required. The details are as follows:

[0072]

[0073] judge If yes, then judgment matrix A passes the consistency check; otherwise, judgment matrix A should be reconstructed.

[0074] CI is the standard for measuring the consistency of the judgment matrix A, determining whether a deviation from consistency has occurred. The larger the CI value, the more severe the matrix inconsistency. If CI = 0, it indicates that the judgment matrix is ​​completely consistent.

[0075] Based on the comprehensive assessment results obtained, the actual energy consumption of the regional network was measured and evaluated after deployment to verify the accuracy of the assessment:

[0076] Test points were set up at the base station power supply equipment in the selected cell coverage area of ​​the regional network after deployment. Ammeters, voltmeters, and power meters were connected. The ambient temperature was maintained between 0 and 28°C. The base station was allowed to operate stably for 3 hours after reaching the specified temperature. The base station power consumption was observed; measurement could begin once the power consumption fluctuation was less than 5%. Actual 0.5-hour input power consumption measurements were performed on the site segment, and the average input power was recorded for the first 0.5 hours. The average total input power of the actual base stations within the regional network was then calculated. The Level I and II indicator data, as well as weight vectors B and C, were adjusted based on the test data. L1 C L2 C L3 To ensure the accuracy of the assessment.

[0077] The following descriptions will further illustrate the different indicators in the examples.

[0078] Level II indicators reflect the impact of communication elements on the energy consumption of Level I indicators. Traffic energy consumption is selected as an example, reflecting the impact of transmission traffic data on energy consumption. Its indicator weight... This indicates the degree to which changes in data transmission traffic affect changes in communication energy consumption, Y1.

[0079] Level I indicators reflect the impact of communication services on the energy consumption of communication systems. Communication service energy consumption is selected for explanation, which reflects the measurement of communication services in the overall energy consumption of communication systems. Its indicator weight B1 indicates the degree of attention paid to communication service energy consumption in overall energy consumption.

[0080] The comprehensive energy consumption evaluation index F is an overall evaluation of the communication system. In this embodiment, the process of calculating F using the weight vector B and Y1, Y2, and Y3 is as follows:

[0081] Referring to Table 3, construct judgment matrix A according to Saaty's 1-9 scale method based on different service ratings:

[0082] Table 3

[0083]

[0084]

[0085] Referring to Table 4, calculate the weight vector B from the judgment matrix A:

[0086] Table 4

[0087]

[0088] B = {0.20, 0.68, 0.12}

[0089] Using the weight vector B and Y1, Y2, and Y3, the comprehensive energy consumption evaluation index F is calculated as one of the evaluation results:

[0090] F = 0.20Y1 + 0.68Y2 + 0.12Y3

[0091] The evaluation is a qualitative assessment of the energy consumption of the communication system in this embodiment. In this embodiment, the process of obtaining the evaluation through the weight vector B and the fuzzy evaluation matrix U is as follows:

[0092] S = B·U

[0093]

[0094] The membership vector S = [0.435, 0.555, 0] is calculated. The comment "medium energy consumption" is obtained through the principle of maximum membership. The comment "medium energy consumption" is the second evaluation result.

[0095] While some embodiments of the invention have been described in detail, it should be noted that the invention is not limited to the disclosed embodiments, but can be rearranged, modified and replaced in many ways without departing from the invention as set forth and defined in the appended claims.

Claims

1. A method for evaluating mobile communication network energy consumption based on a hierarchical indicator system, characterized in that: The steps include: (1) Initialize the regional communication network system: Initialize a regional communication network system including M macro base stations, each macro base station covers an area W including N micro base stations and R user terminals, the duration of effective information transmission of the macro base station includes I time periods, each time period has T moments, the comment set is V = {low energy consumption, medium energy consumption, high energy consumption}, the hierarchical index system includes II-level energy consumption evaluation index, I-level energy consumption evaluation index and comprehensive energy consumption evaluation index, where M≥1, N≥0, R≥1; (2) Calculate the energy consumption evaluation index of Level II: Calculate the level II energy consumption X of each macro base station coverage area in each time period i , Get the II-level energy consumption evaluation index set X = {X1, X2, ..., X i ,...,X I },in, represents the jth energy consumption, j∈[1,2,3,4,5,6,7], and They represent the traffic energy consumption, coverage energy consumption, bit energy consumption, connection energy consumption, delay energy consumption, computing energy consumption and perception energy consumption of each macro base station coverage area in the i-th time period respectively; (3) Construct a classification matrix of level II energy consumption evaluation indicators: The II-level energy consumption evaluation index X of time period I is classified to obtain the communication energy consumption index I1, computing energy consumption index I2 and perception energy consumption index I3, and the communication energy consumption index data matrix X with dimension I1×7 is constructed through the classification results. L1 , the computing energy consumption index data matrix X with dimension I2×7 L2 and the perception-based energy consumption indicator data matrix X with dimension I3×7 L3 , then X L1 , X L2 and X L3 The classification matrix set X that constitutes the II-level energy consumption evaluation index L ={X L1 ,X L2 ,X L3 }; (4) Calculate the energy consumption evaluation index of level I: According to the II-level energy consumption evaluation index data set X L Calculate the communication service energy consumption Y1, perception service energy consumption Y2 and computing service energy consumption Y3 of each macro base station coverage area, and combine Y1, Y2 and Y3 to form a level I energy consumption evaluation index data set Y = {Y1, Y2, Y3}; (5) Obtain regional network energy consumption assessment results: According to the level I energy consumption evaluation index data set Y = {Y1, Y2, Y3}, a judgment matrix A with a dimension of 3*3 is constructed, and the index weight vector B is calculated through A. Then, the regional network comprehensive energy consumption evaluation index F and the membership vector S of the network energy consumption level are calculated through B. Then, the comment corresponding to the maximum membership value in the membership vector S in the comment set V is selected. The comprehensive evaluation result of the regional network energy consumption level includes the comprehensive energy consumption evaluation index F and the selected comments.

2. The method according to claim 1, characterized in that The calculation formulas for the Level II energy consumption index described in step (2) are: Among them, Q ma (t), P ma (t) are the effective information transmission rate and input power of each macro base station at the tth moment in each time period, Q mi (t), P mi (t) are the effective information transmission rate and input power of each micro base station at the tth moment, P EQ is the average power of R user terminals, is the average throughput of the regional network, D is is the average transmission distance between the user terminal and the base station, T r is the duration of the session of the rth user in each time period in the region, T E2E is the end-to-end delay of the correct transmission of the data packet from the sending side to the receiving side of the user terminal in the network, K and H are the number of categories of computing tasks and sensing tasks performed by the communication system within T, respectively, and p ck is the probability distribution of the k-th type of computing task being executed, p sh is the probability distribution of the h-th type of perception task being executed, and the probability distribution vectors of each type of task being executed are p c ={p c1 ,p c2 ,…,p ck ,…,p cK }、p s ={p s1 ,p s2 ,…,p sh ,…,p sH }; E ck is the typical energy consumption value of the k-th computing task, E sh is the typical value of energy consumption of the h-th type of sensing task, and ∑ is the summation operation.

3. The method according to claim 1, characterized in that The steps for classifying the level II energy consumption evaluation index X of the I time period described in step (3) are as follows: (3a) Determine the level II energy consumption evaluation index X for each time period i middle With the standard value X s Is it satisfied? and If so, then X i Classify it as communication energy consumption I1, otherwise execute step (3b); (3b) Judgment Is it true? If so, then X i Classified as computing energy consumption I2, otherwise, X i Classified as perception type energy consumption I3.

4. According to the method of claim 1, the communication service energy consumption Y1, the perception service energy consumption Y2 and the computing service energy consumption Y3 of each macro base station coverage area in step (5) are calculated by the following formulas: Y1=C L1 ·X max Y2=C L2 ·X max Y3=C L3 ·X max X max ={X max1 ,X max2 ,…,X maxj ,…,X max7 } in, X max is a negative ideal solution, X maxj is the jth level II indicator in the indicator data set X L The negative ideal value in C L1 , C L2 , C L3 is the weight vector, is the information entropy of the j-th level II indicator to each level I indicator, are the j-th level II index in the weight vector C L1 , C L2 , C L3 The corresponding weight of , · is the dot multiplication operation, and max() is the maximum value operation.

5. The method according to claim 4, characterized in that The information entropy described in step (5) The calculation formulas are: in, They are the level II indicator data matrix X L1 The data of the i1th jth column, the standardized value corresponding to the data, and the probability corresponding to the standardized value; They are the level II indicator data matrix X L2 The data of the i2th jth column, the normalized value of the data, and the probability corresponding to the normalized value; They are the level II indicator data matrix X L3 The data in the i3jth column, the data normalized value, and the corresponding probability of the normalized value; max() is the maximum value operation, and min() is the minimum value operation.

6. The method according to claim 1, characterized in that The calculation formulas for the regional network comprehensive energy consumption evaluation index F and the membership vector S of the network energy consumption level described in step (5) are: F=B·Y S=B·U B={B1,B2,B3} Where, · is the dot multiplication operation; U is the fuzzy evaluation matrix of the regional network energy consumption level, with 3 levels of indicators in level I as rows and 3 comments in the comment set V as columns, B α is the αth level I indicator Y α The corresponding weight vector, is the normalized eigenvector of the judgment matrix A corresponding to the largest eigenroot, A αβ is the element in the αth row and βth column of A.

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