Communication base station energy efficiency evaluation method and device, terminal and storage medium

Abstract

This application relates to the field of energy efficiency management technology, and discloses a method, device, terminal, and storage medium for evaluating the energy efficiency of communication base stations. The method includes: determining the values ​​of various evaluation indicators characterizing the energy efficiency level of the base station based on multi-source operational data of the target communication base station; determining the objective weight coefficients of each evaluation indicator using a contrast and conflict coupling analysis method based on a sample set covering a pre-defined deployment scenario; obtaining a comprehensive energy efficiency score for the target communication base station based on the values ​​of each evaluation indicator and their corresponding objective weight coefficients; and determining the energy efficiency level of the target communication base station based on its comprehensive energy efficiency score, combined with the geographical climate zone, DC power supply load level, and building structure type of the equipment room. This application can achieve accurate evaluation of base station energy efficiency.

Description

Technical Field

[0001] This application relates to the field of energy efficiency management technology, and in particular to a method, apparatus, terminal and storage medium for evaluating the energy efficiency of a communication base station. Background Technology

[0002] Currently, the large-scale deployment of communication base stations has brought significant energy consumption pressure, with an average power consumption of 3000W and an annual total power consumption exceeding 100 billion kWh. To support energy-saving renovations, the industry generally uses single indicators such as Power Usage Effectiveness (PUE) for energy efficiency assessment. However, existing methods are mostly based on static test scenarios, focusing only on the energy consumption ratio of supporting systems, and do not cover key dimensions such as the annual operating efficiency of temperature control equipment, the depth of business resource utilization, and the actual level of clean energy consumption; the determination of weights often relies on expert experience or simple statistical methods, which are difficult to reflect the discriminative value of indicators in real operating data; the grading standards also mostly use fixed thresholds, without considering the coupled impact of climate differences, load characteristics, and data center structure on energy efficiency, resulting in the same score representing inconsistent actual energy efficiency levels in different environments. Summary of the Invention

[0003] In view of this, embodiments of this application provide a method, apparatus, terminal and storage medium for evaluating the energy efficiency of communication base stations, which can achieve accurate energy efficiency evaluation of communication base stations.

[0004] In a first aspect, embodiments of this application provide a method for evaluating the energy efficiency of a communication base station, including: Based on multi-source operational data of the target communication base station, the values ​​of various evaluation indicators characterizing the energy efficiency level of the base station are determined. Based on a sample set covering the preset deployment scenarios, the objective weight coefficients of each evaluation index are determined using the contrast and conflict coupling analysis method. Based on the values ​​of each evaluation indicator and the corresponding objective weighting coefficient, the comprehensive energy efficiency score of the target communication base station is obtained; Based on the comprehensive energy efficiency score of the target communication base station, and in conjunction with the geographical climate zone to which the target communication base station belongs, the DC power supply load level, and the building structure type of the equipment room, the energy efficiency level of the target communication base station is determined.

[0005] In an optional implementation, after determining the energy efficiency level of the target communication base station, the method further includes: Identify the weakest link among the various evaluation indicators of the target communication base station that is below a preset reference level; Based on the energy efficiency rating, the bottleneck indicators, and the objective weighting coefficients of each of the evaluation indicators, energy-saving renovation recommendations are generated.

[0006] In an optional implementation, the objective weighting coefficients of each evaluation index are determined using a contrast and conflict coupling analysis method based on a sample set covering a preset deployment scenario, including: The original values ​​of each evaluation index in each sample of the sample set are normalized respectively; wherein, the index of base station energy efficiency level increases with the increase of the value is processed by positive normalization, and the index of base station energy efficiency level increases with the decrease of the value is processed by negative normalization. Calculate the standard deviation of the standardized values ​​of each evaluation index across all the samples, and use it as the contrast quantification value of the corresponding evaluation index; Calculate the correlation coefficients between all pairs of the evaluation indicators, and determine the conflict quantification value for each evaluation indicator based on the correlation coefficients; Multiply the contrast quantification value of each evaluation indicator by the conflict quantification value to obtain the information contribution degree of the corresponding evaluation indicator; The information contribution of each evaluation indicator is normalized to obtain the objective weight coefficient corresponding to each evaluation indicator.

[0007] In an optional implementation, determining the energy efficiency level of the target communication base station based on its comprehensive energy efficiency score, combined with the geographical climate zone, DC power supply load level, and building structure type of the equipment room, includes: Based on the comprehensive energy efficiency score of the target communication base station, and in conjunction with the geographical climate zone to which the target communication base station belongs, the DC power supply load level, and the building structure type of the equipment room, a preset multi-dimensional coupling classification threshold table is retrieved to determine the energy efficiency level of the target communication base station.

[0008] In an optional implementation, the step of obtaining the multidimensional coupling hierarchical threshold table includes: A predetermined number of comprehensive energy efficiency scores from communication base stations are collected to form an initial sample set; Using the principle of minimizing the sum of squared deviations within classes and maximizing the sum of squared deviations between classes, the energy efficiency comprehensive score samples in the initial sample set are divided into natural clusters to obtain the initial three-level classification thresholds. Based on the characteristics of geographical and climatic zones, DC power supply load levels, and computer room building structure types, the initial three-level classification thresholds are adaptively offset and corrected. The modified three-level classification thresholds are used to construct the multi-dimensional coupled classification threshold table according to the combination relationship of the geographical climate zone, the DC power supply load level, and the computer room building structure type.

[0009] In an optional implementation, the evaluation indicators include basic energy efficiency indicators characterizing the efficiency of electricity use, equipment energy efficiency indicators characterizing the operating efficiency of the temperature control system, business energy efficiency indicators characterizing the efficiency of business carrying capacity, and low-carbon energy efficiency indicators characterizing the level of energy utilization.

[0010] In an optional implementation, the multi-source operational data includes: Basic energy efficiency support data, including total input energy consumption of the base station and energy consumption of the main equipment; Equipment energy efficiency support data, including energy consumption of temperature control equipment and cumulative heat generation of heating equipment; Business efficiency support data includes total base station information traffic, number of logical base stations configured, and number of operating days; Low-carbon energy efficiency support data includes clean energy power supply and total input energy consumption of base stations.

[0011] Secondly, embodiments of this application provide a communication base station energy efficiency evaluation device, comprising: The evaluation index determination module is used to determine the values ​​of various evaluation indicators that characterize the energy efficiency level of the base station based on multi-source operational data of the target communication base station. The objective weight coefficient determination module is used to determine the objective weight coefficient of each evaluation index based on a sample set covering the preset deployment scenario, using the contrast and conflict coupling analysis method. The energy efficiency comprehensive score acquisition module is used to obtain the energy efficiency comprehensive score of the target communication base station based on the values ​​of each of the evaluation indicators and the corresponding objective weight coefficients. The energy efficiency rating determination module is used to determine the energy efficiency rating of the target communication base station based on its comprehensive energy efficiency score, combined with the geographical climate zone to which the target communication base station belongs, the DC power supply load level, and the building structure type of the equipment room.

[0012] Thirdly, embodiments of this application provide a terminal device, the terminal device including a processor and a memory, the memory storing a computer program, and the processor executing the computer program to implement the above-described communication base station energy efficiency evaluation method.

[0013] Fourthly, embodiments of this application provide a computer-readable storage medium storing a computer program, which, when executed on a processor, implements the above-described communication base station energy efficiency evaluation method.

[0014] The embodiments of this application have the following beneficial effects: By combining base station energy efficiency assessment with the actual deployment environment, this application makes the assessment result no longer an abstract value detached from the operational context, but a quantitative representation that can truly map the actual energy efficiency performance of the base station under specific climatic conditions, power supply load status, and building physical characteristics. Since the energy consumption composition of communication base stations is highly dependent on external temperature control requirements, internal equipment load distribution, and the thermal performance of the building envelope, simply using uniform indicators or fixed thresholds is insufficient to reflect the essential differences between different scenarios. This method introduces geographical climate zones, DC power supply load levels, and the type of building structure in the equipment room as necessary constraints in the classification and judgment process, ensuring that the same comprehensive score corresponds to different energy efficiency levels in different environments, thereby avoiding misjudgments due to environmental differences. Simultaneously, the weight determination process abandons subjective experience intervention and objectively assigns weights based on the distinguishing ability and information independence of the indicators in real base station data, ensuring that the contribution of each indicator to the final score matches its actual impact. Therefore, the energy efficiency level output by this method has clear physical meaning and a comparable basis, providing reliable support for operators to carry out precise and differentiated energy-saving management. Attached Figure Description

[0015] To more clearly illustrate the technical solutions of the embodiments of this application, the accompanying drawings used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of this application and should not be regarded as a limitation of the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0016] Figure 1 This paper illustrates a first flowchart of a communication base station energy efficiency evaluation method according to an embodiment of this application. Figure 2 This paper illustrates a second flowchart of the communication base station energy efficiency evaluation method according to an embodiment of this application. Figure 3 A schematic diagram of the third process of the communication base station energy efficiency evaluation method according to an embodiment of this application is shown; Figure 4 A schematic diagram of the fourth process of the communication base station energy efficiency evaluation method according to an embodiment of this application is shown; Figure 5 A schematic diagram of a communication base station energy efficiency evaluation device according to an embodiment of this application is shown. Detailed Implementation

[0017] The technical solutions in the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments.

[0018] The components of the embodiments of this application described and illustrated in the accompanying drawings can be arranged and designed in a variety of different configurations. Therefore, the following detailed description of the embodiments of this application provided in the drawings is not intended to limit the scope of the claimed application, but merely to illustrate selected embodiments of the application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without inventive effort are within the scope of protection of this application.

[0019] In the following text, the terms "comprising," "having," and their cognates, which may be used in various embodiments of this application, are intended only to indicate a particular feature, number, step, operation, element, component, or combination thereof, and should not be construed as primarily excluding the presence of one or more other features, numbers, steps, operations, elements, components, or combinations thereof, or adding the possibility of one or more combinations thereof. Furthermore, the terms "first," "second," "third," etc., are used only for distinguishing descriptions and should not be construed as indicating or implying relative importance.

[0020] Unless otherwise specified, all terms used herein (including technical and scientific terms) shall have the same meaning as commonly understood by one of ordinary skill in the art to which the various embodiments of this application pertain. Terms (such as those defined in commonly used dictionaries) shall be interpreted as having the same meaning as in their contextual meaning in the relevant technical field and shall not be construed as having an idealized or overly formal meaning, unless clearly defined in the various embodiments of this application.

[0021] The following detailed description of some embodiments of this application is provided in conjunction with the accompanying drawings. Unless otherwise specified, the following embodiments and features can be combined with each other.

[0022] The energy efficiency evaluation method for this communication base station will be described below with reference to some specific embodiments. The communication base station in this embodiment can be a 5G base station, a 6G base station, or a 4G base station, etc. In subsequent detailed embodiments, the data descriptions of the communication base station will all use a 5G base station as an example; the same applies to other types of communication base stations.

[0023] Figure 1 A schematic flowchart of a communication base station energy efficiency assessment method according to an embodiment of this application is shown. Exemplarily, the communication base station energy efficiency assessment method includes steps S100-S400: Step S100: Based on the multi-source operation data of the target communication base station, determine the values ​​of each evaluation index characterizing the energy efficiency level of the base station.

[0024] Among them, multi-source operational data includes at least: basic energy efficiency support data, equipment energy efficiency support data, business energy efficiency support data, and low-carbon energy efficiency support data.

[0025] Basic energy efficiency support data includes total input energy consumption and main equipment energy consumption of base stations, used to calculate basic energy efficiency indicators characterizing power usage efficiency; equipment energy efficiency support data includes energy consumption of temperature control equipment and cumulative energy consumption of heat-generating equipment, used to calculate equipment energy efficiency indicators characterizing the operating efficiency of temperature control systems; service energy efficiency support data includes total information traffic of base stations, number of logical base stations configured, and number of operating days, used to calculate service energy efficiency indicators characterizing service carrying efficiency; low-carbon energy efficiency support data includes clean energy power supply and total input energy consumption of base stations, used to calculate low-carbon energy efficiency indicators characterizing energy utilization levels. Among them, heat-generating equipment refers to active communication equipment in the base station that generates the main heat, including radio frequency unit (AAU), baseband processing unit (BBU), and transmission equipment; clean energy power supply comes from one or more combinations of the discharge of distributed photovoltaic power generation system, wind power generation device, or energy storage system; the total input energy consumption of the base station, the energy consumption of the main equipment, the energy consumption of the temperature control equipment, and the clean energy power supply can all be collected by smart energy meters with an accuracy level of not less than 1. The sampling interval can be set to not more than 1 hour, the continuous collection period is not less than 7 days, and all collected data are accompanied by a timestamp calibrated by a legal time service agency and a uniquely verifiable data source identifier to ensure the authenticity and integrity of the data.

[0026] The evaluation indicators are a comprehensive system covering four primary dimensions: basic energy efficiency, equipment energy efficiency, service energy efficiency, and low-carbon energy efficiency. Specifically, basic energy efficiency indicators reflect the additional energy consumption of the supporting power supply and cooling system relative to the main equipment's energy consumption, including electrical energy utilization efficiency; equipment energy efficiency indicators reflect the long-term matching relationship between heat generation from heat-generating equipment and energy consumption from temperature control equipment, including the comprehensive energy efficiency ratio and clean energy utilization rate (PER); service energy efficiency indicators reflect the energy consumption level corresponding to a unit of effective communication traffic, including power consumption per unit of information traffic and power consumption per logic base station; and low-carbon energy efficiency indicators reflect the contribution ratio of clean energy to the total energy consumption of the base station and the actual application effectiveness of energy-saving functions, including clean energy utilization rate and energy efficiency.

[0027] Specifically, energy efficiency This data can be continuously and synchronously collected by a Class 1 precision smart energy meter installed at the mains power inlet and an energy meter at the switching power supply end. It is defined as the ratio of the total input power of the base station to the power consumption of the main equipment, and is calculated as follows: ;in, Total energy consumption of the base station, in kWh. This represents the energy consumption of the base station's main equipment, measured in kWh. This energy efficiency index is dimensionless and is used to characterize the energy consumption ratio of supporting systems such as air conditioning and power supply. The smaller the value, the higher the energy efficiency of the supporting systems.

[0028] The overall energy efficiency ratio (AEER) of temperature control equipment can be obtained through the temperature control system monitoring platform. It is defined as the ratio of the sum of the cumulative energy consumption of all heat-generating devices to the cumulative energy consumption of the temperature control equipment. The calculation formula is as follows: In the formula, Let be the energy consumption of the j-th heating device, in kWh. The energy consumption of the temperature control equipment is expressed in kWh. The comprehensive energy efficiency ratio of the temperature control equipment is also a dimensionless quantity, used to quantify the overall energy efficiency performance of the temperature control system during the annual operation cycle. The higher the value, the higher the operating efficiency of the temperature control system.

[0029] PRB utilization rate This can be obtained in real time by collecting and retrieving the daily average value through the OMC network management platform. It is defined as the ratio of the number of physical resource blocks actually occupied by the base station to the total number of physical resource blocks configured in the system. The calculation formula is as follows: The actual PRB quantity and the total PRB quantity are both integers. This indicator is expressed as a percentage and is used to identify inefficient equipment operation. The data collection period for this indicator should be no less than 7 days to eliminate random deviations caused by daily business fluctuations.

[0030] Power consumption per unit information flow ( The total service traffic of the base station can be obtained through the OMC network management platform and calculated by combining it with the main equipment energy consumption collected by the information equipment energy consumption meter. It is defined as the ratio of the main equipment energy consumption to the effective information traffic output by the base station, and its calculation formula is as follows: ;in, Energy consumption of base station main equipment, in kilowatt-hours. This represents the total service traffic of the base station. The unit of this indicator is kWh / TB, and it is used to measure the energy consumed per unit of service traffic.

[0031] Power consumption of a single logic base station ( The number of logical base stations configured and their operation logs can be obtained through the OMC network management platform. This data, combined with the main equipment's energy consumption meter data, is used to calculate the ratio of the main equipment's energy consumption to the calculated number of operating days per logical base station. The calculation formula is as follows: ;in, The energy consumption of the base station main equipment is expressed in kWh. A represents the converted number of logical base stations, and T represents the number of operating days in days. This indicator is expressed in kWh / d and is used to adapt to the basic operational energy efficiency assessment in multi-system hybrid networking scenarios.

[0032] Clean Energy Utilization Rate (RER) can be obtained by combining distributed photovoltaic or wind power generation monitoring devices with the total energy consumption meter of the mains. It is defined as the proportion of renewable energy power supply to the total input energy consumption of the base station, and its calculation formula is as follows: In the formula, Electricity generated from renewable energy sources, measured in kWh. The total energy consumption of the base station is expressed as a percentage in kWh and is used to directly characterize the level of green energy application.

[0033] Energy efficiency ( This data can be continuously collected by a Class 1 precision smart energy meter at the mains power input under both the on and off states of the base station's energy-saving function. It is defined as the proportion of energy consumption reduced when the energy-saving function is enabled to the total energy consumption when it is disabled. The calculation formula is as follows: In the formula, The energy consumption of the base station after enabling energy-saving function, in kWh. This represents the base station's energy consumption when energy-saving functions are not enabled, expressed as a percentage. It is used to evaluate the actual power-saving effect of base station energy-saving technologies.

[0034] Among the above evaluation indicators, power usage efficiency, power consumption per unit information flow, and power consumption per single logic base station are negative indicators, and the smaller the value, the higher the energy efficiency level; the comprehensive energy efficiency ratio of temperature control equipment, PRB utilization rate, clean energy utilization rate, and energy saving efficiency are positive indicators, and the larger the value, the higher the energy efficiency level.

[0035] It is understandable that this indicator system is divided into four categories at the primary level: basic energy efficiency, equipment energy efficiency, business energy efficiency, and low-carbon energy efficiency. At the secondary level, it includes seven core indicators, namely, power usage efficiency (belonging to the basic energy efficiency dimension), comprehensive energy efficiency ratio and PRB utilization rate (belonging to the equipment energy efficiency dimension), power consumption per unit information flow and power consumption per single logic base station (belonging to the business energy efficiency dimension), and clean energy utilization rate and energy saving efficiency (belonging to the low-carbon energy efficiency dimension).

[0036] It should be noted that the design of this four-dimensional, seven-indicator system extends energy efficiency assessment beyond the static perspective of the power usage effectiveness (PUE) of supporting systems. It considers factors such as the annual operating efficiency (AEER) of temperature control equipment, the depth of business resource utilization, the actual energy consumption level (RER) of clean energy, and the real power-saving effect of energy-saving functions. In a real-world test of a typical base station in a hot-summer, warm-winter region, the PUE score was 1.5, classifying it as medium energy efficiency. However, after introducing AEER and RER, it was found that the station's air conditioning operated for over 2200 hours annually with zero clean energy consumption, resulting in a comprehensive score of level 3. Subsequent upgrades verified annual power savings of 15,000 kWh, demonstrating that the four-dimensional system effectively identified energy efficiency shortcomings that traditional methods could not capture.

[0037] Step S200: Based on the sample set covering the preset deployment scenarios, the objective weight coefficients of each evaluation index are determined using the contrast and conflict coupling analysis method.

[0038] The preset deployment scenarios include, but are not limited to, 5 geographical and climatic zones (i.e., severe cold regions, cold regions, hot summer and cold winter regions, hot summer and warm winter regions, and temperate regions), 2 types of computer room building structures (brick-concrete structure and color steel plate structure), and 2 DC power supply load levels (low load level below 50 amps (50A) and high load level not less than 50 amps). In addition, to enhance the sample's coverage of mainstream hardware configurations, the sample set can also cover three active antenna unit (AAU) channel configurations, namely 64TR, 32TR, and 8TR.

[0039] In this step, operational data from a predetermined number (e.g., 120) of typical communication base stations are selected to form a sample set. This sample set ensures coverage of all geographical and climatic zones, DC power supply load levels, and combinations of equipment room building structures, with at least five samples corresponding to each combination. This guarantees that the determined objective weighting coefficients have sufficient statistical representativeness and engineering applicability. It should be noted that the three AAU channel configurations are enhanced coverage dimensions that improve sample diversity; their existence does not affect the core calculation logic and result validity of the contrast and conflict coupling analysis method.

[0040] In some implementations, such as Figure 2 As shown, step S200 specifically includes steps S210-S250: Step S210: Normalize the original values ​​of each evaluation index in each sample of the sample set.

[0041] In this step, it is first necessary to statistically analyze the original values ​​of each evaluation indicator (7 evaluation indicators for 120 samples in this example) for each sample in the sample set, and then calculate the maximum, minimum, and mean values ​​for each indicator; the statistical results are shown in Table 1:

[0042] Furthermore, since the physical meanings and dimensions of the various evaluation indicators differ significantly, this embodiment uses the extreme value method to normalize the original values ​​of each evaluation indicator in order to eliminate the influence of dimensions and make the different indicators comparable. Among them, the indicators that characterize the improvement of energy efficiency level with the increase of value, i.e., positive indicators, include the comprehensive energy efficiency ratio of temperature control equipment, PRB utilization rate, clean energy utilization rate, and energy saving efficiency; the indicators that characterize the improvement of energy efficiency level with the decrease of value, i.e., negative indicators, include power usage efficiency, power consumption per unit information traffic, and power consumption per single logic base station.

[0043] Specifically, positive indicators are calculated according to the formula. Normalization is performed, and negative indicators are processed according to the formula. Normalization was performed; among them, This represents the original value of the i-th sample on the j-th secondary indicator. and Let represent the maximum and minimum values ​​of the j-th secondary indicator in all samples, respectively. This represents the standardized numerical value. This normalization process ensures that all index values ​​are mapped to the range of zero to one, while maintaining their original magnitude relationships.

[0044] Step S220: Calculate the standard deviation of the standardized values ​​of each evaluation indicator across all samples, and use it as the contrast quantification value of the corresponding evaluation indicator.

[0045] In this step, for the j-th secondary indicator, we first calculate the arithmetic mean of its standardized values ​​across all 120 samples. Then according to the formula Calculate its standard deviation, where n is the total number of samples, i.e., 120, and Σ represents the summation over all samples. This is the contrast quantization value of the j-th secondary indicator. This value reflects the degree of data dispersion of this indicator in the sample set. The greater the dispersion, the richer the discriminative information provided by this indicator in distinguishing the energy efficiency levels of different base stations.

[0046] Step S230: Calculate the correlation coefficients between all pairs of evaluation indicators, and determine the conflict quantification value for each evaluation indicator based on the correlation coefficients.

[0047] In this step, we first construct a correlation coefficient matrix. (e.g. 7) A matrix with 7 columns), where The Pearson correlation coefficient between the j-th and k-th secondary indicators is expressed by the following formula: ;in, and are the standardized values ​​of the i-th sample on the j-th and k-th secondary indicators, respectively. Let be the arithmetic mean of the standardized values ​​of the j-th secondary indicator in all n samples; It is the arithmetic mean of the standardized values ​​of the k-th secondary indicator in all n samples.

[0048] It should be noted that, due to the correlation coefficient The range of values ​​for is [ [1,1], where the positive or negative sign only indicates the direction of the linear relationship, and the degree of information overlap between indicators depends on the magnitude of their absolute values. Therefore, in order to accurately quantify the information independence of indicator j from other indicators, in this embodiment, the conflict quantification value is... The calculation is based on the absolute value of the correlation coefficient, and its formula is as follows: Where m=7 and k j), this formula shows that when index j is completely uncorrelated with index k ( When the correlation is 0), its contribution is 1; when it is perfectly correlated ( When =1), its contribution is 0. The larger the value, the more unique information index j contains, and the higher its discriminative value in the energy efficiency evaluation system.

[0049] Step S240: Multiply the contrast quantification value and the conflict quantification value of each evaluation indicator to obtain the information contribution of the corresponding evaluation indicator.

[0050] In this step, for each secondary indicator (such as the j-th secondary indicator), its information contribution degree is... You can use the formula The calculated value comprehensively represents the total amount of effective discriminative information carried by the indicator in the evaluation system, taking into account both its own data distinguishing ability (contrast) and its complementarity (conflict) with other indicators. The larger the value, the greater the role of indicator j in the evaluation system. Step S250: Normalize the information contribution of each evaluation indicator to obtain the objective weight coefficient corresponding to each evaluation indicator.

[0051] In this step, the information contribution of each secondary indicator needs to be normalized, that is, the information contribution of all seven secondary indicators needs to be normalized. to Add them together to get the total information contribution. Then for each Dividing by the sum, the quotient is the objective weighting coefficient of the secondary indicator. The calculation formula is as follows: The set of weight coefficients satisfies the condition that the sum is 1 and all of them are non-negative real numbers.

[0052] Table 2 provides an example of the calculation results for the objective weight coefficients based on 120 samples.

[0053] Table 2:

[0054] It is understandable that the weight distribution shown in Table 2 (PUE weight 0.275 is the highest, RER weight 0.0785 is the lowest) is not arbitrarily set, but rather stems from the actual dispersion and independence of each indicator in the 120 base station samples. For example, PUE has the largest standard deviation in the sample (0.2852) and a relatively weak correlation with other indicators (C). j=5.1238), thus having the highest information contribution; while RER, although currently low in adoption rate resulting in a smaller standard deviation (0.1451), still has a conflict factor of 2.876 because it is almost unrelated to core indicators such as PUE and AEER, ensuring that its strategic value is not underestimated. This weight generation method based on the intrinsic characteristics of data avoids the bias that may be brought about by expert scoring and also overcomes the systematic suppression of low adoption rate indicators by the entropy weight method.

[0055] Step S300: Based on the values ​​of each evaluation indicator and the corresponding objective weight coefficients, obtain the comprehensive energy efficiency score of the target communication base station.

[0056] The comprehensive energy efficiency score is a quantitative representation of the energy efficiency level of the target base station. Its value is between 0 and 1, and the higher the value, the higher the comprehensive energy efficiency level.

[0057] The formula for calculating the comprehensive energy efficiency score is as follows: ;in, Let be the standardized value of the target communication base station on the j-th secondary index; is the objective weight coefficient of the j-th secondary indicator obtained in step S250; S is the comprehensive energy efficiency score.

[0058] It is understandable that if S 0 indicates that all indicators are in their worst state (e.g., PUE is at its maximum, AEER is at its minimum, etc.). A value of 1 indicates that all indicators are in their optimal state (e.g., PUE is at its minimum, AEER is at its maximum, etc.).

[0059] Step S400: Based on the comprehensive energy efficiency score of the target communication base station, and in combination with the geographical climate zone to which the target communication base station belongs, the DC power supply load level and the building structure type of the equipment room, determine the energy efficiency level of the target communication base station.

[0060] The specific steps are as follows: First, confirm the combination category of the geographical climate zone, DC power supply load level, and computer room building structure type to which the target communication base station belongs; then, according to the combination category, look up the energy efficiency level corresponding to the comprehensive energy efficiency score S in the preset multidimensional coupling grading threshold table; wherein, in the multidimensional coupling grading threshold table, the energy efficiency level mapped by the same comprehensive energy efficiency score S is different under different combinations of geographical climate zones, different DC power supply load levels, and different computer room building structure types, thereby achieving differentiated and accurate determination of complex deployment scenarios.

[0061] In some implementations, such as Figure 3 As shown, the steps for obtaining the multidimensional coupled hierarchical threshold table include steps S410-S440: Step S410: Collect a preset number of comprehensive energy efficiency score samples from communication base stations to form an initial sample set.

[0062] In this step, the initial sample set is still composed of the comprehensive energy efficiency scores of 120 typical communication base stations. This sample set covers the combination relationships of all five types of geographical climate zones, two types of DC power supply load levels, and two types of computer room building structure types. The number of samples corresponding to each combination is no less than five, so as to ensure that the subsequent clustering results are statistically representative.

[0063] Step S420: Using the principle of minimizing the sum of squared deviations within a class and maximizing the sum of squared deviations between classes, natural clustering is performed on each energy efficiency comprehensive score sample in the initial sample set to obtain the initial three-level classification threshold.

[0064] In this embodiment, the clustering method used can be Jenks' natural breakpoint method, whose objective function is to minimize the sum of internal variances of the categories, which can be expressed as: The first layer of summation is performed for k categories (k 3) The second layer targets all samples i in the k-th class. The overall energy efficiency score for the i-th sample is... Let be the average score of the k-th class of samples. This method iteratively optimizes the breakpoint location using dynamic programming or heuristic algorithms to maximize the ratio of inter-class variance to population variance, i.e., the goodness of fit (GVF), which is calculated using the following formula: ;in, The total sum of squared deviations (GVF) was calculated to be 0.891, higher than the industry-recognized excellent standard of 0.85, indicating excellent clustering performance. Further statistical validation using analysis of variance (ANOVA) showed that the between-group variance was 0.092, the within-group variance was 0.011, the F-statistic was 8.36, and the significance level was p=0.0004. A value of 0.05 indicates that the grading results are statistically significant and conform to the natural distribution of the data.

[0065] Step S430: Based on the characteristics of geographical climate zones, DC power supply load levels, and computer room building structure types, adaptive offset correction is performed on the initial three-level classification threshold.

[0066] The correction in this step can be based on the following three physical laws: Geographic and climatic zone characteristics: temperate regions have moderate temperatures throughout the year and low operating loads for temperature control equipment, and should be assigned a higher energy efficiency rating under the same comprehensive energy efficiency score; hot-summer and warm-winter regions have high cooling energy consumption and weaker actual energy efficiency performance under the same score, and should be assigned a lower energy efficiency rating.

[0067] DC power supply load range characteristics: High load range ( Under 50A conditions, the main equipment accounts for a high proportion of energy consumption, while the marginal impact of the supporting system's energy consumption is weakened. Therefore, the actual energy efficiency level is higher under the same rating, and the threshold should be increased; at low load levels ( The energy consumption of supporting systems under 50A is becoming increasingly prominent, and the threshold should be lowered.

[0068] Characteristics of computer room building structure: The thermal conductivity coefficient of brick-concrete structure is higher than that of color steel plate structure. Therefore, brick-concrete structure has better thermal insulation performance than color steel plate structure. Thus, under the same climate and load, the energy efficiency threshold of brick-concrete computer room should be higher than that of color steel plate computer room.

[0069] Based on the above patterns, the initial three-level threshold obtained by the Jenks method (e.g., level 1: S) 0.84; Level 2: 0.64≤S<0.84; Level 3: S<0.64) are offset corrections to form a differentiated threshold system.

[0070] Step S440: The modified three-level classification thresholds are used to construct a multi-dimensional coupled classification threshold table based on the combination relationship between geographical climate zones, DC power supply load levels, and computer room building structure types.

[0071] The multidimensional coupling hierarchical threshold table is shown in Table 3:

[0072] It should be noted that all thresholds in Table 3 are based on the initial three-level classification thresholds obtained by Jenks' natural breakpoint method, and were determined after systematic offset corrections based on the characteristics of geographical climate zones, DC power supply load levels, and computer room building structure types. The correction rules are as follows: under the same DC power supply load level and the same computer room building structure type, the Level 1 threshold is highest in temperate regions and lowest in hot-summer and warm-winter regions; under the same geographical climate zone and the same DC power supply load level, the threshold for brick-concrete structure computer rooms is higher than that for color steel plate structures; under the same geographical climate zone and the same computer room building structure type, the threshold for high load levels is higher than that for low load levels.

[0073] In addition, the above-mentioned statistical significance (P) (0.05) confirms that the multi-dimensional coupling hierarchical threshold table constructed after the above correction can effectively distinguish energy efficiency differences in different deployment environments. For example, in hot summer and warm winter regions, DC loads... 50A. For base stations with a color steel plate structure in the equipment room, if its comprehensive energy efficiency score is 0.59, then the corresponding threshold (Level 1) in Table 3 shall apply. 0.78, Level 2 0.59, Level 3 The score of 0.59 falls precisely at the boundary between Level 2 and Level 3; this indicates that the score has clear significance in classifying levels within this specific scenario. A similar base station in Guangzhou (PUE=1.5) was classified as Level 3 according to Table 3, and achieved annual power savings of 15,000 kWh through the replacement of variable frequency air conditioners and the renovation of the equipment room insulation. In a test set consisting of 120 samples, the classification accuracy of this model reached 94.7%, while the classification accuracy of existing fixed threshold schemes is less than 70%.

[0074] In some implementations, such as Figure 4 As shown, after determining the energy efficiency level of the target communication base station, the process further includes: steps S500-S600: Step S500: Identify the weakest indicators among the various evaluation indicators of the target communication base station that are below the preset reference level. In this step, the preset reference level is the benchmark value obtained from statistics of 120 typical samples for each evaluation indicator. It can be determined by standardizing the value of each secondary indicator. The 90th percentile (P90) of the sample is used as the lower limit of the positive indicator, and the 50th percentile (P50) is used as the upper limit of the negative indicator.

[0075] Specifically, for positive indicators (overall energy efficiency ratio of temperature control equipment, PRB utilization rate, clean energy utilization rate, and energy saving efficiency), if their standardized values... If the value is lower than the P90 value of the indicator in the sample set, it is judged as a weak link indicator; for negative indicators (power consumption efficiency, power consumption per unit information traffic, power consumption per logic base station), if its standardized value is lower than the P90 value of the indicator in the sample set, it is considered a weak link indicator. If the value is higher than the P50 value of the indicator in the sample set, it is determined to be a weak link indicator.

[0076] This setting must ensure that the identified bottleneck indicators are statistically significant, i.e., within the worst 10% or 50% range of the sample performance, to avoid misjudging normal fluctuations as defects. It should be noted that P90 and P50 mentioned above are only exemplary, and specific settings can be made according to needs. In addition, the preset reference level can also be determined in other ways, which are not limited in this embodiment.

[0077] Step S600: Based on the energy efficiency level, the weak link indicators, and the objective weighting coefficients of each evaluation indicator, generate energy-saving renovation suggestions.

[0078] In this embodiment, the proposed generation logic is as follows: First, determine the current energy efficiency level; second, among the identified shortcomings, select the one with the largest objective weight coefficient as the target shortcomings indicator; finally, based on the physical meaning of the target shortcomings indicator and its dimensional affiliation in the energy efficiency system, match the corresponding engineering modification measures.

[0079] Specifically, when the energy efficiency rating is Level 1 (Excellent), it indicates that the overall energy efficiency of the target base station has reached an advanced level in the industry, and the focus of energy-saving renovation shifts to consolidating low-carbon capabilities and optimizing long-term operation. At this time, the energy-saving renovation recommendation is to promote the deployment of low-carbon facilities, including the deployment of distributed renewable energy power generation devices (such as rooftop photovoltaic arrays and small wind turbines) and supporting energy storage systems (such as lithium-ion battery packs), to improve the clean energy utilization rate (RER) and delay energy efficiency degradation caused by equipment aging or business growth.

[0080] When the energy efficiency level is Level 2 (benchmark), it indicates that the target base station meets the conventional energy-saving requirements, but there is still room for systematic optimization. At this time, the energy-saving renovation recommendation is to carry out equipment-level energy efficiency optimization, including replacing high-efficiency communication main equipment and enabling intelligent energy-saving control strategies (such as RF channel shutdown, symbol shutdown, and deep sleep functions based on service forecasting), so as to simultaneously improve PRB utilization and energy-saving efficiency, and promote the energy efficiency level from benchmark to excellent.

[0081] When the energy efficiency level is Level 3 (limited), it indicates that the target base station's energy efficiency does not meet the standard and targeted upgrades should be prioritized. In this case, the energy-saving upgrade recommendation is: select the shortcoming indicator with the largest objective weight coefficient from the shortcoming indicators identified in step S500 as the target shortcoming indicator, and directly upgrade the physical system represented by that indicator. The specific mapping relationship is as follows: If the target bottleneck indicator is power usage efficiency, it indicates that the energy consumption of supporting power supply and heat dissipation systems such as air conditioning and power supply is too high. The improvement measures include optimizing the supporting power supply system (such as installing high-efficiency modular UPS and mitigating harmonic current) and upgrading temperature control equipment (such as replacing fixed-frequency air conditioners with variable-frequency air conditioners or installing heat pipe heat exchange systems). If the target bottleneck indicator is clean energy utilization rate, it indicates insufficient renewable energy power supply capacity. The improvement measures include deploying distributed renewable energy power generation devices (such as installing monocrystalline silicon photovoltaic modules on the roof of the base station equipment room, with rated power covering more than 30% of the daily average load). If the target bottleneck indicator is the power consumption of a single logic base station or the power consumption per unit of information flow, and the building structure of its equipment room is a color steel plate structure, it indicates weak building envelope performance. The improvement measures include improving the performance of the building envelope structure (such as adding a 50mm thick rock wool insulation layer to the color steel plate exterior wall to improve the overall heat transfer coefficient). Down to The following approaches the level of brick-concrete structures.

[0082] This hierarchical, bottleneck-weighted suggestion generation method, as demonstrated in this embodiment, has been validated in practical applications by operators: the targeting of base station energy-saving upgrades has been improved by more than 50%, thus avoiding the waste of resources from blind upgrades; the payback period for upgrade investments has been shortened from the original 4 years to 2.8 years. This indicates that the entire process, from data collection standards to indicator normalization rules (using different extreme value normalization methods for positive and negative indicators), and then to the upgrade suggestion generation logic (based on energy efficiency levels, bottleneck indicators, and their objective weight coefficients), is feasible for deployment and reuse in large-scale base station networks.

[0083] This embodiment constructs a seven-core indicator system covering four primary dimensions: basic energy efficiency, equipment energy efficiency, service energy efficiency, and low-carbon energy efficiency. Using geographical climate zones, DC power supply load levels, and data center building structure types as coupling axes, it elevates energy efficiency assessment from static, single-ratio analysis to a dynamic, multi-dimensional, scenario-adaptive quantitative decision-making process. In the weight determination stage, it abandons subjective experience or reliance on single statistical features, instead employing the CRITIC method, which uses contrast and conflict co-representation, to ensure that the contribution of each indicator truly reflects its discriminative ability and information independence in real base station operation data. In the grading stage, it uses the Jenks natural breakpoint method to capture the inherent distribution pattern of the comprehensive energy efficiency score, and combines thermodynamic characteristics (climate), electrical characteristics (load), and building physical characteristics (data center structure) for systematic offset correction, so that the same comprehensive score corresponds to different energy efficiency levels in different deployment environments, thus more accurately reflecting the actual energy efficiency level. At the application level, it identifies weak indicators based on energy efficiency levels and determines priority targets for improvement based on the objective weights of each indicator.

[0084] Figure 5 A schematic diagram of a communication base station energy efficiency assessment device according to an embodiment of this application is shown. Exemplarily, the communication base station energy efficiency assessment device includes: The evaluation index determination module 100 is used to determine the values ​​of various evaluation indicators that characterize the energy efficiency level of the base station based on the multi-source operation data of the target communication base station.

[0085] The objective weight coefficient determination module 200 is used to determine the objective weight coefficients of each evaluation index based on a sample set covering the preset deployment scenario, using the contrast and conflict coupling analysis method.

[0086] The energy efficiency comprehensive score acquisition module 300 is used to obtain the comprehensive energy efficiency score of the target communication base station based on the values ​​of each evaluation indicator and the corresponding objective weight coefficients.

[0087] The energy efficiency rating determination module 400 is used to determine the energy efficiency rating of the target communication base station based on its comprehensive energy efficiency score, combined with the geographical climate zone to which the target communication base station belongs, the DC power supply load level, and the building structure type of the equipment room.

[0088] It is understood that the device in this embodiment corresponds to the communication base station energy efficiency evaluation method in the above embodiment, and the options in the above embodiment are also applicable to this embodiment, so they will not be described again here.

[0089] This application also provides a terminal device, exemplary of which includes a processor and a memory, wherein the memory stores a computer program, and the processor executes the computer program to enable the terminal device to perform the functions of the various modules in the above-described communication base station energy efficiency assessment method or the above-described communication base station energy efficiency assessment device.

[0090] The processor can be an integrated circuit chip with signal processing capabilities. The processor can be a general-purpose processor, including at least one of a Central Processing Unit (CPU), Graphics Processing Unit (GPU), Network Processor (NP), Digital Signal Processor (DSP), Application-Specific Integrated Circuit (ASIC), Field-Programmable Gate Array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components. The general-purpose processor can be a microprocessor or any conventional processor, capable of implementing or executing the methods, steps, and logic block diagrams disclosed in the embodiments of this application.

[0091] The memory can be, but is not limited to, Random Access Memory (RAM), Read Only Memory (ROM), Programmable Read-Only Memory (PROM), Erasable Programmable Read-Only Memory (EPROM), Electrically Erasable Programmable Read-Only Memory (EEPROM), etc. The memory is used to store computer programs, and the processor can execute the computer programs accordingly after receiving execution instructions.

[0092] This application also provides a computer-readable storage medium for storing the computer program used in the aforementioned terminal device. For example, the computer-readable storage medium may include, but is not limited to, various media capable of storing program code, such as a USB flash drive, a portable hard drive, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk.

[0093] In the several embodiments provided in this application, it should be understood that the disclosed apparatus and methods can also be implemented in other ways. The apparatus embodiments described above are merely illustrative. For example, the flowcharts and block diagrams in the accompanying drawings show the architecture, functionality, and operation of possible implementations of apparatus, methods, and computer program products according to various embodiments of this application. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that, in alternative implementations, the functions marked in the blocks may occur in a different order than those marked in the drawings. For example, two consecutive blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagram and / or flowchart, and combinations of blocks in the block diagram and / or flowchart, can be implemented using a dedicated hardware-based system that performs the specified function or action, or using a combination of dedicated hardware and computer instructions.

[0094] In addition, the functional modules or units in the various embodiments of this application can be integrated together to form an independent part, or each module can exist independently, or two or more modules can be integrated to form an independent part.

[0095] If the aforementioned functions are implemented as software functional modules and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a smartphone, personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application.

[0096] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any changes or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application.

Claims

1. A method for evaluating energy efficiency of a communication base station, the method comprising: include: Based on multi-source operational data of the target communication base station, the values ​​of various evaluation indicators characterizing the energy efficiency level of the base station are determined. Based on a sample set covering the preset deployment scenarios, the objective weight coefficients of each evaluation index are determined using the contrast and conflict coupling analysis method. Based on the values ​​of each evaluation indicator and the corresponding objective weighting coefficient, the comprehensive energy efficiency score of the target communication base station is obtained; Based on the comprehensive energy efficiency score of the target communication base station, and in conjunction with the geographical climate zone to which the target communication base station belongs, the DC power supply load level, and the building structure type of the equipment room, the energy efficiency level of the target communication base station is determined.

2. The method for communication base station energy efficiency evaluation according to claim 1, c h a r a c t e r i z e d b y After determining the energy efficiency level of the target communication base station, the method further includes: Identify the weakest link among the various evaluation indicators of the target communication base station that is below a preset reference level; Based on the energy efficiency rating, the bottleneck indicators, and the objective weighting coefficients of each of the evaluation indicators, energy-saving renovation recommendations are generated.

3. The method for communication base station energy efficiency evaluation according to claim 1, c h a r a c t e r i z e d b y The sample set based on the pre-defined deployment scenario is used to determine the objective weight coefficients of each evaluation index using a contrast and conflict coupling analysis method, including: The original values ​​of each evaluation index in each sample of the sample set are normalized respectively; wherein, the index of base station energy efficiency level increases with the increase of the value is processed by positive normalization, and the index of base station energy efficiency level increases with the decrease of the value is processed by negative normalization. Calculate the standard deviation of the standardized values ​​of each evaluation index across all the samples, and use it as the contrast quantification value of the corresponding evaluation index; Calculate the correlation coefficients between all pairs of the evaluation indicators, and determine the conflict quantification value for each evaluation indicator based on the correlation coefficients; Multiply the contrast quantification value of each evaluation indicator by the conflict quantification value to obtain the information contribution degree of the corresponding evaluation indicator; The information contribution of each evaluation indicator is normalized to obtain the objective weight coefficient corresponding to each evaluation indicator.

4. The method for communication base station energy efficiency evaluation according to claim 1, characterized in that, The energy efficiency rating of the target communication base station is determined by combining its comprehensive energy efficiency score with the geographical climate zone, DC power supply load level, and building structure type of the equipment room. This includes: Based on the comprehensive energy efficiency score of the target communication base station, and in conjunction with the geographical climate zone to which the target communication base station belongs, the DC power supply load level, and the building structure type of the equipment room, a preset multi-dimensional coupling classification threshold table is retrieved to determine the energy efficiency level of the target communication base station.

5. The energy efficiency evaluation method for communication base stations according to claim 4, characterized in that, The steps for obtaining the multidimensional coupled hierarchical threshold table include: A predetermined number of comprehensive energy efficiency scores from communication base stations are collected to form an initial sample set; Using the principle of minimizing the sum of squared deviations within classes and maximizing the sum of squared deviations between classes, the energy efficiency comprehensive score samples in the initial sample set are divided into natural clusters to obtain the initial three-level classification thresholds. Based on the characteristics of geographical and climatic zones, DC power supply load levels, and computer room building structure types, the initial three-level classification thresholds are adaptively offset and corrected. The modified three-level classification thresholds are used to construct the multi-dimensional coupled classification threshold table according to the combination relationship of the geographical climate zone, the DC power supply load level, and the computer room building structure type.

6. The method for communication base station energy efficiency evaluation according to claim 1, characterized in that, The evaluation indicators include basic energy efficiency indicators that characterize the efficiency of electricity use, equipment energy efficiency indicators that characterize the operating efficiency of temperature control systems, business energy efficiency indicators that characterize the efficiency of business carrying capacity, and low-carbon energy efficiency indicators that characterize the level of energy utilization.

7. The method for communication base station energy efficiency evaluation according to claim 1, characterized in that, The multi-source operational data includes: Basic energy efficiency support data, including total input energy consumption of the base station and energy consumption of the main equipment; Equipment energy efficiency support data, including energy consumption of temperature control equipment and cumulative heat generation of heating equipment; Business efficiency support data includes total base station information traffic, number of logical base stations configured, and number of operating days; Low-carbon energy efficiency support data includes clean energy power supply and total input energy consumption of base stations.

8. A communication base station energy efficiency evaluation apparatus characterized by comprising: include: The evaluation index determination module is used to determine the values ​​of various evaluation indicators that characterize the energy efficiency level of the base station based on multi-source operational data of the target communication base station. The objective weight coefficient determination module is used to determine the objective weight coefficient of each evaluation index based on a sample set covering the preset deployment scenario, using the contrast and conflict coupling analysis method. The energy efficiency comprehensive score acquisition module is used to obtain the energy efficiency comprehensive score of the target communication base station based on the values ​​of each of the evaluation indicators and the corresponding objective weight coefficients. The energy efficiency rating determination module is used to determine the energy efficiency rating of the target communication base station based on its comprehensive energy efficiency score, combined with the geographical climate zone to which the target communication base station belongs, the DC power supply load level, and the building structure type of the equipment room.

9. A terminal device, comprising: The terminal device includes a processor and a memory, the memory storing a computer program, and the processor executing the computer program to implement the communication base station energy efficiency evaluation method according to any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, It stores a computer program, which, when executed on a processor, implements the communication base station energy efficiency evaluation method according to any one of claims 1-7.

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