A hierarchical indicator system-based method for evaluating energy consumption in mobile communication networks

Abstract

This invention proposes a method for assessing the energy consumption of a mobile communication network system using a hierarchical indicator system. The implementation steps are: initializing the regional communication network system; calculating Level II energy consumption evaluation indicators; obtaining Level II energy consumption evaluation indicators; calculating Level I energy consumption evaluation indicators; and obtaining regional network energy consumption evaluation results. This invention utilizes a hierarchical evaluation indicator system comprising Level II energy consumption evaluation indicators, Level I energy consumption evaluation indicators, and comprehensive energy consumption evaluation indicators. This system shifts the evaluation focus to the user end, ensuring a better match between the evaluation indicators and the communication network system, enhancing the evaluation representation capability, and combining post-deployment evaluations to analyze and verify the actual network energy consumption, forming a closed loop. This avoids the drawbacks of existing technologies that focus on single-bit energy efficiency indicators and improves the accuracy of the evaluation.

Description

Technical Field

[0001] The present invention belongs to the field of wireless communication technology networks and relates to a method for evaluating energy consumption of a mobile communication network, and more specifically to a method for evaluating energy consumption of a regional mobile communication network using a hierarchical indicator system. Background Art

[0002] With the development of mobile communication systems, the next generation of wireless networks will feature the convergence of telemetry and computing, as well as ground-to-space interconnection. Mobile communication services and traffic are rapidly increasing, and the communications industry is demanding network infrastructure deployment on a larger spatial and temporal scale, with a further increase in service types, diversified service demands, and differentiated energy supply and demand structures. Currently, 5G mobile communication systems continue to evolve with increasing numbers of antennas, bandwidth, and base station density, leading to larger, faster growth. This will significantly increase network energy consumption, impacting both network resource consumption and economic costs.

[0003] The number of antennas, bandwidth, and base station density of 5G mobile communication systems continue to evolve towards more, larger, and denser, which will cause a significant increase in network energy consumption. In order to reduce the impact of large energy consumption on communication network resource consumption and economic costs, reduce the carbon emission level of communication networks, and achieve sustainable development of the communication network ecology and economy, hardware and software technical means and optimization of network topology are usually used to achieve the purpose of reducing network energy consumption. However, the premise of adopting these methods is the need to collect comprehensive, clear, and accurate network energy consumption information, so as to establish an energy consumption assessment index system to accurately assess the energy consumption level of regional networks, and then evaluate whether the network settings, planning, and deployment are reasonable from the perspective of energy consumption.

[0004] Current wireless communication network energy consumption assessments often center on the per-bit energy efficiency metric, which measures the ratio of average data throughput over a period of time to energy consumption during that period. Existing methods typically extend traditional single-station energy efficiency assessments to network energy efficiency. These methods calculate network energy efficiency by equating regional network node throughput with average energy consumption, while maintaining quality of service (QoS). This method only reflects the relationship between network data transmission volume and network energy consumption within QoS constraints, and fails to address other network requirements.

[0005] The technical approach to implementing wireless network energy consumption assessment is to collect network information to establish network energy consumption indicators, use the energy consumption indicators to make qualitative or quantitative assessments of the current network energy consumption level and the improvement effect after network improvement, 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., in its patent application, "Network Energy Efficiency Evaluation Method and System Based on a New Generation Communication System" (Application Number: 202310361429.2, Application Publication Number: CN116367196A), discloses a network energy efficiency evaluation method based on a new generation communication system. The implementation steps are: selecting and marking target network nodes as evaluation targets, which include network elements and base stations in the core network; obtaining the actual average node throughput, target average node throughput, and average node energy consumption of each target network node within a preset time interval; calculating the basic energy efficiency based on the actual average node throughput and average node energy consumption; and calculating the adjustment parameters based on the actual average node throughput and target average node throughput. This invention takes into account the quality of communication service requirements and can provide certain feedback on network parameters and structure based on the evaluation results. However, with the increasing density of network deployment, the performance and power requirements of network node components are highly diverse. Setting indicators only to meet service quality requirements does not form a comprehensive evaluation system, and the evaluation accuracy cannot be guaranteed. In practical applications, this invention is difficult to ensure that the theoretical design scenario is highly consistent with the actual network scenario, and the evaluation accuracy cannot be guaranteed. Summary of the Invention

[0007] The purpose of the present invention is to address the deficiencies of the above-mentioned prior art and to propose a method for evaluating the energy consumption of a regional mobile communication network system based on a hierarchical indicator system, so as to solve the technical problem of low evaluation accuracy in the prior art.

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

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

[0010] Initialize a regional communication network system consisting of M macro base stations, each of which covers an area W including N micro base stations and R user terminals. The duration of effective information transmission by the macro base station consists of I time periods, each of which has T moments. The evaluation set is V = {low energy consumption, medium energy consumption, high energy consumption}. The hierarchical indicator system includes a level II energy consumption evaluation indicator, a level I energy consumption evaluation indicator, and a comprehensive energy consumption evaluation indicator, where M ≥ 1, N ≥ 0, and R ≥ 1.

[0011] (2) Calculate the energy consumption evaluation index of level II:

[0012] 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 j-th 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;

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

[0014] Classify the II-level energy consumption evaluation index X of time period I to obtain the communication energy consumption index I1, computing energy consumption index I2 and perception energy consumption index I3, and construct the communication energy consumption index data matrix X with a dimension of I1×7 based on the classification results. L1 , the computing energy consumption index data matrix X with a dimension of I2×7 L2 And the perception energy consumption index 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};

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

[0016] 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};

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

[0018] 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. The index weight vector B is calculated through A, and 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 comment.

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

[0020] The present invention adopts a hierarchical evaluation indicator system including level II energy consumption evaluation indicators, level I energy consumption evaluation indicators and comprehensive energy consumption evaluation indicators, realizing the transfer of the evaluation center to the service user end, so that the evaluation indicators are better matched with the communication network system, and the evaluation characterization capability is stronger. Combined with the post-deployment evaluation, the actual energy consumption of the network is analyzed and verified to form a closed loop, avoiding the defects of the existing technology that uses a single bit energy efficiency indicator as the center for evaluation, and improving the accuracy of the evaluation. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Figure 1 It is an implementation flow chart of the present invention.

[0022] Figure 2 It is a structural diagram of the hierarchical evaluation index of the present invention. DETAILED DESCRIPTION

[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 comprises the following steps:

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

[0026] Initialize a regional communication network system consisting of M macro base stations, each of which covers an area W including N micro base stations and R user terminals. The duration of effective information transmission by the macro base station consists of I time periods, each of which has T moments. The evaluation set is V = {low energy consumption, medium energy consumption, high energy consumption}. The hierarchical indicator system includes a level II energy consumption evaluation indicator, a level I energy consumption evaluation indicator, and a comprehensive energy consumption evaluation indicator, where M ≥ 1, N ≥ 0, and R ≥ 1.

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

[0028] The hierarchical index system used in the present invention is as follows Figure 2 As shown in the figure, the energy consumption evaluation indicators of level I are the communication service energy consumption Y1, perception service energy consumption Y2 and computing service energy consumption Y3 of each macro base station coverage area; the energy consumption evaluation indicators of level II include traffic energy consumption Covering energy consumption Energy per bit Connection energy consumption Delay energy consumption Calculating energy consumption and perceived energy consumption

[0029] Step 2) Calculate the energy consumption evaluation index of level II:

[0030] Calculate the level II energy consumption X of each macro base station coverage area in the i-th time period i, including flow energy consumption Covering energy consumption Energy per bit Connection energy consumption Delay energy consumption Calculating energy consumption and perceived energy consumption The energy consumption evaluation index of group I and level II is obtained as X={X1,X2,...,X i ,...,X I},

[0031]

[0032]

[0033] 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 time t, and the average power of R user terminals in the system is P EQ , 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 T is the duration of the session of the rth user in each time period in the region, 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 energy consumption of the h-th type of perception task, and ∑ is the summation operation.

[0034] The energy consumption evaluation index X of group I and II is divided into communication energy consumption including group I1, computing energy consumption including group I2, and perception energy consumption including group I3. Then, the I1×7 data matrix X of II communication energy consumption index is constructed. L1 , I2×7 computing energy consumption index data matrix X L2 and the I3×7 perception-type energy consumption indicator data matrix X L3 , and obtain the II-level energy consumption evaluation index data set X L ={X L1 ,X L2 ,X L3}.

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

[0036] (3a) Determine the energy consumption evaluation index X of each group II 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 go to step (3b);

[0037] (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;

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

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

[0040] 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 in the coverage area of ​​each macro base station, and obtain the level I energy consumption evaluation index Y = {Y1, Y2, Y3};

[0041] The communication service energy consumption Y1, perception service energy consumption Y2 and computing service energy consumption Y3 in the coverage area of ​​each macro base station are calculated as follows:

[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 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, The j-th level II index is 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;

[0049] The negative ideal solution indicates the worst case of energy consumption of the communication network.

[0050] Information entropy measures the uncertainty of data. The greater the probability of an indicator fluctuating in a certain way, the greater the amount of existing information provided, and the corresponding information entropy is greater. If the information entropy of a certain level II indicator is smaller, it means that the indicator provides more information, and the role it can play in the evaluation of the corresponding level I indicator is also greater, and its weight is greater. If a 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:

[0052]

[0053]

[0054] in, They are the level II indicator data matrix X L1 The data of the jth column of the i1th column, the normalized value corresponding to the data, and the probability corresponding to the normalized value; They are the level II indicator data matrix X L2 The data in 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 normalized value of the data, and the corresponding probability of the normalized value; max() is the maximum value operation, and min() is the minimum value operation.

[0055] Indicates the variation probability of the indicator, indicating that the corresponding data standardization value is in the level II indicator data matrix X L1 、X L2 、X L3 The proportion of Considered as the probability used in information entropy calculation, the larger the value, the greater the probability that the indicator will mutate the corresponding data;

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

[0057] 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. The index weight vector B is calculated through A, and 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 comment.

[0058] The calculation formulas are:

[0059] F=B·Y

[0060] S=B·U

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

[0062]

[0063]

[0064] Where, · is the dot multiplication operation; U is the fuzzy evaluation matrix of the regional network energy consumption level, the number of its rows is the number of level I indicators 3, the number of its columns is the number of comments in the comment set V 3, and each element of the matrix is ​​the fuzzy evaluation value of a comment in V for an indicator. These fuzzy evaluation values ​​are obtained using membership functions, which are usually selected through expert judgment or subjective experience; B α is the αth level I indicator Y α The corresponding weight vector; is the normalized eigenvector of the judgment matrix A; A αβThe element in row α and column β of the judgment matrix A is 3. Each element of A represents a scale comparing two Level I indicators, reflecting the importance of the indicator in the comprehensive assessment. This scale is typically determined by expert judgment or subjective experience using Saaty's 1-9 scale. Referring to Table 1, the meaning of Saaty's 1-9 scale is as follows:

[0065] Table 1

[0066]

[0067] Refer to Table 2, where the score is a quantitative evaluation of the Level I indicators. There is a functional relationship between the score and the Level I indicators so that the score range is (0,10], and its value is accurate to one decimal place. The scale of the judgment matrix A is given with reference to the score. The comment set V = {low energy consumption, medium energy consumption, high energy consumption} and Y = {Y1, Y2, Y3} have a membership function relationship; Y α are elements in Y and i = 1, 2, 3, EC1, EC2, EC3, EC4, EC5, EC6, EC7, EC8, EC9 are dimensionless values ​​used as a reference for giving scores and comments;

[0068] Table 2

[0069]

[0070]

[0071] In order to ensure that there are no logical problems in the judgment matrix A, a consistency test is required. The details are as follows:

[0072]

[0073] judge If so, the judgment matrix A passes the consistency test, otherwise the judgment matrix A should be rebuilt. max is the maximum eigenvalue of the judgment matrix A.

[0074] The CI is the metric used to measure the consistency of the judgment matrix A, determining whether it deviates from consistency. The larger the CI value, the more serious the matrix inconsistency. If the CI = 0, it means that the judgment matrix is ​​completely consistent.

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

[0076] After deployment, set up test points at the base station power supply equipment in the selected cell coverage area of ​​the regional network, connect ammeters, voltmeters, and power meters, maintain the test environment temperature at 0-28°C, and ensure the base station operates stably. After reaching the specified temperature for 3 hours, observe the changes in base station power consumption. Measurement can be started after the power consumption fluctuation is less than 5%. Perform actual measurement of the site segment input energy consumption for 0.5 hours, record the average input power for 0.5 hours after the start of the measurement, and obtain the actual average total input power of the base stations in the regional network; refer to the test data to adjust the level I and II indicator data and weight vectors B and C L1 、C L2 、C L3 , to ensure accurate assessment.

[0077] The following further describes the different indicators in the embodiments.

[0078] Level II indicators reflect the impact of communication elements on the energy consumption of level I indicators. Traffic energy consumption is selected to illustrate this. It reflects the measurement of energy consumption by transmission traffic data. Its indicator weight is It shows the degree of influence of changes in transmission data traffic on changes in communication energy consumption for communication service 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 illustration. It reflects the measurement of communication services in the comprehensive energy consumption of communication systems. Its indicator weight B1 shows the degree of attention paid to communication service energy consumption in the comprehensive 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, based on different service ratings, the judgment matrix A is constructed according to Saaty's 1-9 scale method:

[0082] Table 3

[0083]

[0084]

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

[0086] Table 4

[0087]

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

[0089] Through the weight vector B and Y1, Y2, 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 comment is a qualitative evaluation of the energy consumption of the communication system in this embodiment. In this embodiment, the process of obtaining the comment using 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, and the comment "medium energy consumption" is obtained through the maximum membership principle. The comment "medium energy consumption" is used as the second evaluation result.

[0095] While some embodiments of the present invention have been described in detail, it should be noted that the invention is not limited to the disclosed embodiments but is capable of many rearrangements, modifications, and substitutions 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 consisting of M macro base stations, each of which covers an area W including N micro base stations and R user terminals. The duration of effective information transmission by the macro base station consists of I time periods, each of which has T moments. The evaluation set is V = {low energy consumption, medium energy consumption, high energy consumption}. The hierarchical indicator system includes a level II energy consumption evaluation indicator, a level I energy consumption evaluation indicator, and a comprehensive energy consumption evaluation indicator, where M ≥ 1, N ≥ 0, and 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 j-th 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: Classify the II-level energy consumption evaluation index X of time period I to obtain the communication energy consumption index I1, computing energy consumption index I2 and perception energy consumption index I3, and construct the communication energy consumption index data matrix X with a dimension of I1×7 based on the classification results. L1 , the computing energy consumption index data matrix X with a dimension of I2×7 L2 And the perception energy consumption index 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 }, where the steps for classifying the Level II energy consumption evaluation index X in time period I 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 go to 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) 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 comment.

2. The method according to claim 1, characterized in that The calculation formulas for the Level II energy consumption indicators 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 T is the duration of the session of the rth user in each time period in the region, 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 energy consumption of the h-th type of perception task, and ∑ is the summation operation.

3. 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, The j-th level II index is 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.

4. The method according to claim 3, 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 jth column of the i1th column, the normalized value corresponding to the data, and the probability corresponding to the normalized value; They are the level II indicator data matrix X L2 The data in 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 normalized value of the data, and the corresponding probability of the normalized value; max() is the maximum value operation, and min() is the minimum value operation.

5. The method according to claim 1, wherein The calculation formulas for the regional network comprehensive energy consumption evaluation index F and the network energy consumption level membership vector S 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 regional network energy consumption level, with the number of level I indicators 3 as rows and the number of comments in the comment set V 3 as columns, B α is the αth level I indicator Y α The corresponding weight vector, is the eigenvector of the normalized judgment matrix A, A αβ is the element in the αth row and βth column of A.

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